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小学下册英语第3单元全练全测英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.What is the name of the famous children's book character who travels to a chocolate factory?A. AliceB. CharlieC. PeterD. Matilda2. A _______ (蜥蜴) can be seen basking in the sun.3.ts can improve water ______ and soil quality. (某些植物可以改善土壤质量和水分保持能力。
) Some pla4. A solution is formed when a solute dissolves in a _____.5.What is the main ingredient in salad?A. RiceB. LettuceC. BreadD. Pasta6.My ________ (玩具名称) brings joy to my day.7.What is the currency used in the United States?A. EuroB. DollarC. PoundD. YenB8. A rabbit has big _______ to help it hear everything around it.9. A __________ is a large piece of rock that has broken away from a larger mass.10.The ______ (秋风) can cause leaves to fall.11.My dad is a __________ (警察) and helps keep us safe.12.The Earth's ______ is a dynamic and ever-changing environment.13.We can ___ the rainbow. (see)14.She wears a _____ dress. (red/quick/small)15.In winter, I like to go ______ (滑冰).16.The chemical symbol for silicon is ______.17.What is the boiling point of water?A. 0°CB. 50°CC. 100°CD. 200°CC18.My brother is my best _______ (我哥哥是我最好的_______).19.I have a _____ (question/answer) for you.20. A ______ is a geological feature that attracts scientists and researchers.21.What do you call the process of removing hair?A. ShavingB. WaxingC. PluckingD. All of the above22.What is the main ingredient in bread?A. SugarB. FlourC. RiceD. SaltB23.What do we call the act of encouraging community involvement?A. EngagementB. ParticipationC. VolunteerismD. All of the AboveD24.What do we call the study of living organisms?A. BiologyB. AstronomyC. ChemistryD. Physics25.The sloth hangs from branches with its _______ (爪子).26.I have a pet ______.27.What is the opposite of fast?A. SlowB. QuickC. RapidD. Swift28.The ______ (鸭子) quacks when it swims.29.The teacher is ______ (helping) us with homework.30.The mosquito bites _______ (人) for blood.31.Which planet is known as the Red Planet?A. EarthB. VenusC. MarsD. JupiterC Mars32.My favorite color is ________.33.__________ are used in the production of cosmetics.34.The _____ (花园) is full of colorful plants.35.ts can grow in _____ (沙土). Some pla36. A lion's mane indicates its ________________ (健康) and dominance.37.What is the term for a baby horse?A. CalfB. FoalC. KidD. LambB38.I have a toy _______ that can bounce high.39.The ______ teaches us about modern technology.40.Which of these is a vegetable?A. BananaB. TomatoC. GrapeD. MangoB41.Which animal is known as "man's best friend"?A. CatB. DogC. RabbitD. Fish42.What is the name of the famous wizarding school in Harry Potter?A. HogwartsB. BeauxbatonsC. DurmstrangD. IlvermornyA43.The main component of the atmosphere is _____.44.What is the name of the first manned moon mission?A. Apollo 11B. Gemini 12C. Mercury 7D. Voyager 1A Apollo 1145.What is the name of the currency used in Japan?A. YenB. YuanC. WonD. DollarA46.The chemical symbol for barium is ______.47.What is the name of the famous artist who painted the ceiling of the Sistine Chapel?A. RaphaelB. MichelangeloC. Leonardo da VinciD. Titian48.The ______ is a popular aquarium fish.49.What is the capital city of Malta?A. VallettaB. MdinaC. SliemaD. Birkirkara50.What is the capital of South Africa?A. JohannesburgB. PretoriaC. Cape TownD. DurbanB51.What do we call the process of creating a plan?A. OrganizingB. PlanningC. StrategizingD. Arranging52.The __________ (历史的交汇) enriches our understanding.53.The ________ (食物链) begins with plants.54.Reading books about _________ (玩具) can spark my _________ (想象力).55.What do we call the outer layer of the Earth?A. CoreB. MantleC. CrustD. ShellC56.What sound does a cow make?A. MeowB. BarkC. MooD. Quack57.The _______ (The Harlem Renaissance) was a cultural movement in the 1920s.58.The __________ can be very hot during July. (天气)59.The sun sets and the sky is ______. (beautiful)60. A mixture that has a uniform appearance is called a ______ mixture.61.What do you call a story that is not true?A. FactB. FictionC. RealityD. TruthB62.What is the opposite of ‘big’?A. LargeB. SmallC. HugeD. Tall63.The _______ can help create a welcoming atmosphere.64.What is the capital of Indonesia?A. ManilaB. JakartaC. HanoiD. Bangkok65.The study of landforms is an important part of ______ science.66.The ancient Romans were renowned for their ________ and public works.67. A ______ can develop into various forms.68.We bake _____ (bread/cake) for the festival.69.What do we call the time when the sun rises?A. SunsetB. SunriseC. NoonD. Midnight70.The cat loves to explore the _____ backyard.71. A rock can become a metamorphic rock through heat and ______.72.The __________ (历史的思维模式) inspires innovation.73.My dad inspires me to be __________ (勇敢的) in life.74.The pig loves to roll in ______ (泥).75.Chemical bonds can be ionic or ________.76.What do we call a large, slow-moving animal with a shell?A. TurtleB. TortoiseC. SnailD. SlugB77.The _____ (小猪) loves to roll in the mud. It is very funny! 小猪喜欢在泥里打滚。
软件设计师中级专业英语词汇Software Designer: Intermediate Professional English Vocabulary.In the realm of software development, proficiency in professional English is paramount for effective communication, documentation, and collaboration. For mid-level software designers, a robust command of industry-specific vocabulary is essential to navigate complex technical discussions, comprehend documentation, and convey design concepts with precision. This article aims to provide an expansive list of intermediate-level English vocabulary tailored specifically to software designers, enabling them to enhance their professional communication and elevate their design capabilities.Core Concepts.Algorithm: A set of well-defined instructions that solve a specific problem or perform a computation.Architecture: The overall structure and organization of a software system, including its components and their interactions.Data structure: A way of organizing and storing data in a computer system to facilitate efficient access and manipulation.Design pattern: A reusable solution to a commonly occurring problem in software design.Framework: A reusable set of software components and libraries that provide a foundation for building specific applications.Methodology: A structured approach to software development, including processes, practices, and tools.Object: A data structure that encapsulates data and behavior, representing real-world entities.Requirement: A documented need or capability that a software system must fulfill.Source code: The human-readable text form of a computer program, written in a specific programming language.Testing: The process of evaluating the correctness and functionality of a software system.Components and Technologies.Application Programming Interface (API): A set of routines, protocols, and tools that define how two applications interact.Cloud computing: A model for delivering computing resources over the Internet.Database: A collection of organized data, typically stored electronically.Front-end: The part of a software application that interacts directly with the user.Middleware: Software that connects and facilitates communication between different parts of a software system.Operating system: The software that manages computer hardware and provides common services to applications.Server: A computer that provides services to other computers or devices over a network.Web service: A software system that allowsapplications to communicate over the Internet using standardized protocols.Development and Design.Agile: A software development methodology that emphasizes flexibility, adaptation, and customer collaboration.Design thinking: A human-centered approach to design that focuses on understanding user needs and preferences.Iterative development: A software development approach where the system is developed and refined incrementally.Kanban: A visual project management system that uses cards to represent tasks and their progress.Mockup: A low-fidelity representation of a software interface, used for design review and feedback.Prototype: A working model of a software system, used to test concepts and gather user feedback.Scrum: An agile software development framework that emphasizes collaboration, transparency, and iterative delivery.Technical debt: Code or design decisions that may compromise future development or maintenance.User experience (UX): The overall experience of a user when interacting with a software system.Documentation and Communication.Documentation: Written or visual information that explains the design, implementation, and use of a software system.Formal specification: A precise and unambiguous description of a software system's behavior.Issue tracker: A system for tracking and managing bugs or other issues in a software project.Knowledge base: A repository of information and resources related to software development.Meeting agenda: A document that outlines the topics and objectives of a meeting.Proposal: A document that outlines a plan or solutionfor a software project.Technical report: A document that describes theresults of a technical investigation or analysis.White paper: A technical document that provides in-depth information on a specific topic.Wireframe: A low-fidelity representation of a software interface, used for planning the layout and structure.Additional Vocabulary.Binary tree: A data structure that consists of nodes arranged in a hierarchical manner, with each node having at most two child nodes.Cache: A temporary storage area that stores frequently accessed data to improve performance.Cipher: A method of encrypting data to protect its confidentiality.Debugger: A tool that helps identify and fix errors in code.Heap: A dynamic data structure that stores data in a tree-like structure.Inheritance: A mechanism that allows a new class to inherit attributes and methods from an existing class.Polymorphism: A language feature that allows objects of different classes to respond to the same method call differently.Recursion: A technique where a function calls itself, typically to solve a problem by breaking it down into smaller subproblems.Virtual machine: A software layer that simulates a computer system, allowing multiple operating systems to run on a single physical machine.By incorporating these intermediate-level vocabulary terms into their professional communication, software designers can elevate their discourse, enhance their problem-solving abilities, and become more effective collaborators in the dynamic and challenging world of software development.。
为你的学校夏季运动会设计一个海报英语作文The Summer Sports Festival is an annual tradition at our school, a much-anticipated event that brings the entire community together in a celebration of athletic achievement and school spirit. As a member of the school's marketing committee, I have been tasked with designing a poster to promote this year's festivities. This is an exciting opportunity to showcase the vibrancy and energy of our school while encouraging widespread participation in the upcoming games.In conceptualizing the design, I wanted to capture the essence of the Summer Sports Festival – the thrill of competition, the camaraderie of teammates, and the pride of representing one's school. The poster should be eye-catching and dynamic, immediately conveying the athletic prowess and youthful enthusiasm that define this event.The central image of the poster will be a collage of action shots showcasing a diverse array of sports. From the grace and power of abasketball player soaring for a layup, to the determination etched on the face of a sprinter breaking through the finish line, these vivid snapshots will instantly ignite the viewer's excitement. By highlighting a range of athletic disciplines, from track and field to aquatics to team sports, I aim to reflect the inclusive and comprehensive nature of the Summer Sports Festival.Surrounding these dynamic sports images, I will incorporate vibrant splashes of color that evoke the festive atmosphere of the event. Bold, contrasting hues of red, yellow, and blue will create a sense of movement and energy, drawing the eye across the poster's design. These color choices also subtly reference the school colors, reinforcing a sense of school pride and community.To further cultivate this spirit of unity, I will prominently feature the school's logo and name at the top of the poster. This will serve as an instantly recognizable identifier, letting viewers know that this is an official school event. Additionally, I will include the date, time, and location of the Summer Sports Festival in a clean, easy-to-read typographic treatment. This will ensure that all the key logistical details are clearly communicated to potential attendees.In terms of the poster's overall layout, I envision a dynamic, asymmetrical composition that creates a sense of movement and energy. Rather than a static, symmetrical design, I will strategicallyposition the various elements to guide the viewer's eye across the poster. The sports imagery will be arranged in a diagonal pattern, with the vibrant color splashes emanating outward from these focal points. This will give the poster a sense of depth and dimension, making it visually engaging and compelling.To complement the bold, energetic aesthetic of the poster, I will utilize a modern, sans-serif typeface for the text elements. This clean, streamlined font will ensure readability while also aligning with the poster's youthful, athletic vibe. I may also incorporate a subtle graphic element, such as a stylized athlete icon or a dynamic line motif, to further unify the design and add a touch of sophistication.Throughout the design process, I will be mindful of ensuring that the poster is visually striking and immediately captivating, yet also conveys the essential information that potential attendees will need. The goal is to create a design that not only generates excitement for the Summer Sports Festival but also clearly communicates the key details, making it easy for the school community to get involved and participate.As I finalize the poster design, I will also consider ways to optimize it for various platforms and applications. In addition to the primary printed version that will be displayed around the school and in the local community, I will create digital versions that can be shared onthe school's website and social media channels. This will allow the poster to reach a wider audience and effectively promote the event across multiple touchpoints.Designing the poster for the Summer Sports Festival is a wonderful opportunity to showcase my creative skills while also contributing to the vibrant culture of our school. By crafting a visually striking and informative design, I hope to inspire students, faculty, and families alike to come together and celebrate the athletic achievements and school spirit that make our community so special. I am excited to bring this vision to life and play a role in making the upcoming Summer Sports Festival a resounding success.。
Doubly-Fed Induction Machine Models for Stability Assessment of Wind FarmsMarkus A.P¨o llerAbstract—The increasing size of wind farms requires power sys-tem stability analysis including dynamic models of the wind power generation.Nowadays,the most widely used generator type for units above1MW is the doubly-fed induction machine.Doubly-fed induction machines allow active and reactive power control through a rotor-side converter,while the stator is directly con-nected to the grid.Detailed models for doubly-fed induction ma-chines are well known but the efficient simulation of entire power systems with hundreds of generators requires reduced order mod-els.This paper presents a fundamental frequency doubly-fed in-duction machine model including a typical control system and dis-cusses the accuracy of reduced order models under various oper-ating conditions.Index Terms—doubly-fed induction machines,off-shore wind power generation,power system stability,wind power generation, variable speed drivesI.I NTRODUCTIONT HE totally installed wind power capacity is constantly increasing.End of June2002,there were wind turbines with a total rated power of9837,27MW installed in Germany[1].Not only the overall installed wind power capacity,but also the average rated power per wind mill is constantly increasing.In Germany,during thefirst six months of2002,the average rated power per wind turbine went up to 1314kW,which is an increase of8%compared to the same period in 2001[1].Especially for wind mills above2,0MW,the doubly-fed induction generator is the most widely used generator concept(e.g.GE Wind Energy,Vestas,RE Power,Nordex,NEG Micon).Thesefigures clearly show that there is a strong need for power sys-tem stability analysis,including dynamic models of on-and off-shore wind farms.For dynamic power system analysis,different models, from fully detailed to highly reduced order models are proposed in the literature(e.g[5],[4]),but standard doubly-fed induction machine models for modeling large power systems are still under investigation [2].This paper presents an approach for standard doubly-fed induction machine models for stability analysis.It includes models of all com-ponents,the induction generator,the rotor-side-and the grid-side con-verters and typical approaches for the control circuits and aerodynam-ics of the wind turbine.All models have been implemented and tested in the power system analysis program DIgSILENT PowerFactory[10].II.T HE D OUBLY-F ED I NDUCTION M ACHINE C ONCEPT Figure1shows the general concept of the doubly-fed induction gen-erator.The mechanical power generated by the wind turbine is trans-formed into electrical power by an induction generator and is fed into the main grid through the stator and the rotor windings.The rotor winding is connected to the main grid by self commutated AC/DC converters allowing controlling the slip ring voltage of the induction machine in magnitude and phase angle.Markus P¨o ller is with DIgSILENT GmbH,Heinrich-Hertz-Str.9,72810Go-maringen,Germany(email:m.poller@digsilent.de)Rotor-Side Grid-SideFig.1.Doubly-fed induction generator systemIn contrast to a conventional,singly-fed induction generator,the electrical power of a doubly-fed induction machine is independent from the speed.Therefore,it is possible to realize a variable speed wind generator allowing adjusting the mechanical speed to the wind speed and hence operating the turbine at the aerodynamically optimal point for a certain wind speed range.III.I NDUCTION G ENERATORr x x rFig.2.Equivalent circuit of the doubly-fed induction generator Fig.2shows the equivalent circuit diagram of the doubly-fed induc-tion generator from which the model equations in a constantly,with rotating reference frame can be derived as follows:(1)(2) Theflux linkage can be expressed by the following equations:(4) The induction machine model is completed by the mechanical equa-tion:The electrical torque is calculated from the stator current and the stator flux:(6)All quantities are expressed in a stator-side per unit system.This induction machine model of fifth order is able to represent rotor and stator transients correctly.In stability studies however,transient phenomena of the electrical network are usually not considered [7].Applying the principle of neglecting stator transients to the doubly-fed induction machine model leads to the following third order model:(7)(8)The mechanical equation is the same as in case of the fifth order model.Neglecting rotor transients results in a first order induction machine model that consists of steady-state stator and rotor voltage equations.(9)(10)The only dynamic equation of the first order model is the mechanical equation according to (5).A.Rotor Current Protectionu r x x r c cFig.3.Doubly-fed induction generator with inserted crow-bar protection (andIn case of faults near to the generator,rotor currents are increasing and risk to damage the rotor-side converter.For avoiding any damages,the rotor-side converter is bypassed when the rotor currents exceed a certain limit (”crow-bar”protection,see Fig.3).While the rotor-side converter is bypassed,the generator operates as a normal induction generator.Since the speed can be considerably above synchronous speed before a fault occurs or the machine quickly accelerates during a fault,the stalling point of the machine is usually exceeded during a fault leading to very high reactive power consump-tion.Bypassing the rotor with an additional resistance and an addi-tional reactance (see and in Fig.3)shifts the stalling point to a higher speed value and reduces the machine’s reactive power con-sumption considerably .This mode of operation can be considered in the rotor-voltage (2)and the rotor flux-linkage equation (4)as follows:(11)(13)The AC-voltage phase angle is defined by the PWM converter.The pulse-width modulation factor is the control variableof the PWM converter.Equation (13)is valid for .For values larger than 1the converter starts saturating and the level of low order harmonics starts increasing.The complete characteristic of the PWM converter,including the saturated range is shown in Fig.5.Fig.5.PWM Converter fundamental frequency characteristicThe converter model is completed by the power conservation equa-tion:3A.Rotor-Side ConverterThe rotor-side converter operates in a stator-flux dq-reference frame that decomposes the rotor current into an active power(q-axis)and a reactive power(d-axis)component.A very fast inner control loop regulates the active-and the reactive component of the rotor current.The current-setpoints are defined by a slower outer control loop regulating active and reactive power(see Fig.6).Fig.6.Rotor-side converter controllerB.Grid-Side ConverterThe control concept of the grid-side converter is very similar to the rotor-side controller concept.The grid-side converter controller oper-ates in an AC-voltage dq-reference system.Active and reactive com-ponents of the grid-side converter currents are regulated by a fast inner control loop(see Fig.7).A slower outer control loop defining the q-current setpoint regulates the DC-voltage to a pre-defined value.The setpoint of the d-axis component can be used for optimum reactive power sharing between the generator and the grid-side converter or simply kept to a constantvalue.Fig.7.Grid-side converter controllerC.Reactive Power ControlReactive power control is possible through the d-axis component of the rotor-and the grid-side converters.Variable speed wind power gen-erators can be operated at a constant power factor,which is the stan-dard operation mode today.Alternatively,an AC-voltage controller defining the d-axis current setpoint can be used in Fig.6instead of the Q-controller,or secondary voltage control can be supported by adjust-ing the Q-reference value(e.g.[4]).D.Reduced Order Converter ModelFor longer term simulations,when calculation time becomes an im-portant issue,it might be desirable to neglect the very fast time con-stants associated with the DC intermediate circuit shown in Fig. 4. When neglecting the DC-capacitance,the DC currents of the rotor-and grid-side converters are forced to be equal.Consequently,the grid-side controller must be replaced by an ideal voltage controller,without the inner current control loops.On the AC side,the active power injection is defined by the power conservation between AC and DC according to (14).For defining the reactive power balance,the d-axis current com-ponent(reactive current)of the grid-side converter can directly be set by a reactive power controller or a constant reactive current component can be assumed.A further model reduction consists of completely neglecting the time constants of the DC voltage controller.The grid-side converter and it’s controls is then modeled by a steady-state device,keeping the DC voltage constant.With this simplification however,saturation ef-fects(see Fig.5)in the grid-side controller cannot be considered. E.Turbine ControlEquation(15)shows the aerodynamic equation of a wind turbine that relates mechanical power to wind speed and mechanical speed of the turbine(e.g.[5]):(16) with being the mechanical frequency of the wind turbine.Equation(15)defines the steady-state aerodynamic behaviour of a wind turbine;it cannot reflect dynamic stall effects correctly.An ap-proximate method for including dynamic stall effects is described in [9].Fig.8.Generic wind turbine modelFig.9.Generic model of the pitch-control systemA generic wind turbine model for stability studies based on a max-imum power tracking strategy[5]can be implemented according to Fig.8.4In case of rotor frequencies below,active power is regulated according to the maximum power tracking(MPT)characteristic that defines the maximum power depending on the shaft speed as power reference of the power controller.When the maximum shaft speed is reached,the active power setpoint remains constant and the pitch angle control system(see Fig.9)starts acting driving the shaft speed back to the maximum permitted value.An alternative control scheme is described in[4].Here,the speed reference is calculated from the actually generated electrical power (inverse MPT characteristic).As a result,the generator is driven into optimal speed.When is reached,the pitch angle control takes over and regulates the actual power to.F.Wind FluctuationsWindfluctuations can be modeled by varying the wind-speed-input of the aerodynamic block in Fig.8.Wind speed is usually modeled by superposing several deterministic and stochastic components.However,since the response of doubly-fed induction machines to system faults is in the center of interest of this paper and wind speed can be assumed to be constant in these cases, no concrete wind-speed models are described here.Generally,com-mon wind-speed models(e.g.[9])can be combined with the models presented in this paper.G.Torsional OscillationsWhen the simulated applications are limited to the impact of wind fluctuations,it is usually sufficient to consider just a single-mass shaft model because shaft oscillations of variable speed wind generators are not reflected to the electrical grid due to the fast active power control.[5].In stability analysis however,when the system response to heavy disturbances is analyzed,the shaft must be approximated by at least a two mass model.One mass represents the turbine inertia,the other mass is equivalent to the generator inertia.The equations describing the mechanical coupling of turbine and generator through the gear box by a two-mass model can be expressed as follows(see e.g.[8]):(18)The electrical torque is defined by(6).VI.C ASE S TUDIESFor validating the presented models,the results of simulating a heavy and a relatively weak disturbance in the system shown in Fig. 10are described.The system consists of an external infeed modeled by a source behind an impedance that is connected to a synchronous generator through a line.And wind farm consisting of three doubly-fed induction generators with a rating of5MW each is connected to the mid-point of the transmission line.Each doubly-fed induction generator is modeled according to Fig. 11,including the three winding transformer,the induction generator, the grid-side and the rotor-side converter and the intermediate DC-circuit.Fig.10.Power system used for model validationA.Strong DisturbanceThefirst case simulates a solid three phase short circuit(duration: 200ms)at the”Connection Point”(see Fig.11).The rotor-current-protection systems bypass the rotor-side converters immediately after the fault has been inserted(see Fig.12and Fig.13).After the fault was cleared,at t=200ms,the generators continue to operate with bypassed converters until t=800ms,when the”crow-bars”are removed.When the converters are back into operation,the system is driven back to the initial state.The speed shows a weakly damped torsional oscillation.Fig.12compares the results obtained with afifth order induction generator model according to(1)and a third order model according to (7).In the simulation of thefifth order model,not only stator transients of the induction machine but also the transient behaviour of transform-ers and lines was considered(EMT-compatible network model).Together with the third order machine model,a quasi-steady-state network model was used,corresponding to the classical”stability”-approach as described e.g.in[7].When comparing the results from both models,as shown in Fig. 12,it can be noted that the reduced order model represents the average of power and voltage very well.Higher frequency transients,as they can be observed in the results from the detailed model,are due to net-work transients and are therefore not represented by the reduced order model.The reduced order model can therefore be used for analyz-ing the influence of doubly-fed induction machines to the power sys-tem,but it is not possible to predict peaks in electrical power or torque correctly.Therefore,thefifth order model together with a”transient”network model is required.5Fig.11.Wind Generator with detailed grid-side converterThe”speed”variable highlights differences between the two mod-els:In the detailed model,the speed is initially reduced before the system starts accelerating.In the reduced order model however,the machine starts accelerating immediately after the fault was inserted.The initial speed reduction,as shown by the detailed model,is a consequence of decaying DC-components in the machine’s currents and is known as the”back-swing”effect of electrical machines.Since the third order model does not represent stator transients,the initial back-swing is not represented by the reduced order model leading to an immediate acceleration.Fig.13compares the results of further model reductions:1)Third order model with detailed grid-side converter,the same asin Fig.122)Third order model with simplified grid-side converter,as de-scribed in section V-D(complete reduction of the intermediate DC-circuit.)3)First order model with simplified grid-side converter.All results are in good agreement.Especially the reduction of the grid-side converter does not seem to have a big impact on the model accu-racy when system stability is studied.Thefirst order model however does not represent any rotorflux transients and differs therefore from the other curves during a short period after a heavy disturbance.With regard to computational efficiency,some information about the integration step-size might be of interest.All models were imple-mented and tested in DIgSILENT PowerFactory[10].PowerFactory uses a variable step size algorithm,in which the minimum step size can be specified by the user.During the simulation,the step size is auto-matically increased whenever the accuracy of the numerical algorithm allows for it.In case of the fully detailed,fifth order machine model in combi-nation with a transient network model(Fig.12),the step size varied between ms and ms.In case of the reduced order models together with the steady-state network model it varied between ms and ms. The calculation time of the third order andfirst order model was prac-tically the same.B.Weak DisturbanceIn a second case,a three phase short circuit at the synchronous generator terminal was simulated(”Gen-LV-Bus”).This short circuitparison offifth order and third order model in case of a three phase fault near to the wind generatorscauses the voltage at the wind park terminal to drop to about0.6p.u. (see Fig.14).Because the rotor current protection is not triggered,the disturbance can be classified as a fault remote from the wind park.As Fig.14shows,the three wind turbines are differently loaded in this case.While the fault is in the system,the active and reactive power con-trollers remain in operation why active and reactive power is fully con-trolled.Clearing the fault disturbs the system again,but active and reactive power are well regulated.The speed of all turbines shows a weakly damped torsional oscillation.These simulations were carried out with the third order model and the simplified grid-side converter model.The model represents very well the initial active and reactive power transients and the controller response following to the disturbance.VII.C ONCLUSIONSThis paper presented a variable speed wind generator model suited for stability analysis of large power systems with large on-shore and off-shore wind farms.The presented components were the doubly-fed induction generator,the grid-side converter,the rotor-side converter, the aerodynamic behaviour of the wind turbine and the pitch control system.For simulating powerfluctuations,the wind speed variable must be fed from a measurementfile,or stochastic wind models must be used (e.g.[9]).Possible model reductions making the model suitable for stability assessment in large power systems were presented and discussed.6parison of models of various order in case of a three phase fault near to the wind generatorsThe models were implemented and tested in the power system anal-ysis package DIgSILENT PowerFactory [10].Every reduced order model was validated against higher order models.The results of the test cases show that a third order induction ma-chine model including crow bar protection together with a simplified model of the grid-side converter provides sufficient accuracy and the necessary computational efficiency for carrying out stability studies in large power systems with several hundreds of machines.R EFERENCES[1] C.Ender,“Wind Energy Use in Germany -Status 30.06.2002,”DEWIMagazin Nr.21,August 2002[2]S.Stapelton and George Rizopoulos,“Dynamic Modelling of ModernWind Turbine Generators and Stability Assessment of On-and Off-Shore Wind Farms ”Proceedings of the 3rd MED POWER Conference ,2002[3] C.R.Kelber and W.Schumacher,“Adjustable Speed Constant FrequencyEnergy Generation with Doubly-Fed Induction Machines”Proceedings of the European Conference Variable Speed in Small Hydro,Grenoble,France ,2000[4]J.L.Rodriguez-Amenedo,“Automatic Generation Control of a WindFarm With Variable Speed Wind Turbines,”IEEE Transactions on En-ergy Conversion ,V ol 17,No.2,June 2002[5]J.G.Slootweg,S.W.H.de Haan,H.Polinder,W.L.Kling,“AggregatedModelling of Wind Parks with Variable Speed Wind Turbines in Power System Dynamics Simulations,”Proceedings of the 14th Power Systems Computation Conference ,Sevilla,2002[6]W.Hofmann and F.Okafor “Doubly-Fed Full-Controlled Induction WindGenerator for Optimal Power Utilisation,”Proceedings of the PEDS’01,2001Fig.14.Simulation of a weak disturbance (without crow-bar insertion)[7]P.Kundur “Power System Stability and Control,”McGraw-Hill,Inc.,1994[8]P.M.Anderson,B.L.Van Agrawal and J.E.Ness,“SubsynchronousResonance in Power Systems,”IEEE Press,New York ,1989[9]P.Soerensen, A.D.Hansen,L.Janosi,J.Bech and B.Bak-Jensen “Simulation of interaction between wind farm and power system”,Technical-Report,Risoe-R-128(EN),2001,http://www.risoe.dk/rispubl/VEA/veapdf/ris-r-1281.pdf[10]DIgSILENT GmbH “DIgSILENT PowerFactory V13-User Manual,”DIgSILENT GmbH ,2002Markus P¨o ller was born in Stuttgart,Germany on July 22,1968.In 1995,he received Dipl.-Ing.degrees from the Uni-versity of Stuttgart and Ecole Nationale Sup´e rieure des T´e l´e communications Paris.In 2000,he received the Dr.-Ing.degree from the University of Hannover.Since 1995he works with DIgSILENT GmbH,Ger-many,where he is responsible for the algorithms and models of the power system analysis software DIgSI-LENT PowerFactory .He is also involved in power system studies and he presents software-and powersystem analysis courses.His current research interests include wind power sys-tems,optimal power flow dispatch and probabilistic load flow analysis.。
A Model for Dynamic Shape and Its ApplicationsChe-Bin Liu and Narendra AhujaBeckman InstituteUniversity of Illinois at Urbana-ChampaignUrbana,IL61801,USAcbliu,ahuja@AbstractVariation in object shape is an important visual cue for de-formable object recognition and classification.In this pa-per,we present an approach to model gradual changes in the-D shape of an object.We represent-D region shape in terms of the spatial frequency content of the region con-tour using Fourier coefficients.The temporal changes in these coefficients are used as the temporal signatures of the shape changes.Specifically,we use autoregressive model of the coefficient series.We demonstrate the efficacy of the model on several applications.First,we use the model pa-rameters as discriminating features for object recognition and classification.Second,we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence.Third,we show that,with its capa-bility of predicting shape,the model can be used to predict contours of moving regions which can be used as initial es-timates for the contour based tracking methods.1.IntroductionChanges in the shape of a dynamic object offer important cues for object recognition.In this paper,we are concerned with models of gradual changes in the shape of a-D region. We present a simple model of shape variation which was seen limited use in the past work.This model models the changes in the-D shape of a region in terms of the changes in its contour representation.Specifically,an autoregressive time series model of the changes in the Fourier coefficients of the region contour is used.We use it to model,recognize, and synthesize-D dynamic shape.We present applications to(i)modelingfire motion and detectingfire in video se-quences,(ii)classification of objects based on motion pat-terns,(iii)synthesis of novel image sequences of evolving shapes,and(iv)object boundary prediction for use by con-tour tracking methods.The-D shape representation and its use has received much attention in computer vision.A survey of shape anal-ysis methods can be found in[7].Pavlidis[11]proposed the following three classifications for shape based methods us-ing different criteria.(i)Boundary(or External)or Global (or Internal):Algorithms that use region contour are classi-fied as external and boundary,such as Fourier transforms based approaches;Those that use interior region for the analysis are classified as internal and global,such as mo-ment based methods.(ii)Numeric or Non-numeric:This classification is based on the result of the analysis.For in-stance,medial axis transform generates a new image with a symmetric axis,and is categorized as non-numeric.In con-trast,Fourier and moment based methods produce scalar numbers,and thus are in numeric category.(iii)Informa-tion Preserving or Non-preserving:Approaches that allow users to reconstruct shapes from their shape descriptors are classified as information preserving.Otherwise,they are information non-preserving.We propose a dynamic shape model that describes shape at any given time using Fourier transform coefficients and an autoregressive(AR)model to capture the temporal changes in these coefficients.The Fourier description pos-sesses boundary,numeric,and information preserving prop-erties.The autoregressive model is a simple probabilistic model that has shown remarkable effectiveness in the map-ping and prediction of signals.As Srivastava[19]points out,the temporal change of Fourier representation may not be linear.However,a linear model is more manageable to approximate such a process,and requires a small number of observations to estimate parameters.The remainder of this paper is organized as follows.In Section2,we present our dynamic shape model and its pa-rameter estimation.In Section3,we apply the proposed ap-proach to modeling and detection offire in video sequences. In Section4,we classify several objects and visual phe-nomena based on their evolving region contours.In Sec-tion5,we apply the model to synthesis of evolving shape sequences.In Section6,we use our model to predict ob-ject shape in a video sequence for object contour tracking. Section7discusses the limitations of the proposed model. Section8summarizes the contribution of this work.2.Dynamic Shape ModelOur dynamic shape model includes two parts:a spatial rep-resentation of-D shape and a temporal representation of shape variation.The detailed model and its parameter es-timation are described in the following sections.We also compare our model to other relevant models.2.1.Spatial Representation of ShapeFourier Descriptors(FD),the Fourier Transform coeffi-cients of the shape boundary,represents a-D shape us-ing an-D function.There are several variations of Fourier based-D boundary representation in literature[9].In this paper,we use Persoon and Fu’s method[13]for its simplic-ity.Given an extracted region in an image,wefirst retrieve its boundary using eight-connected chain code.Assume that we have points from the chain code representation of the boundary.We express these points in complex form:where are the image coordi-nates of boundary points as the boundary is traversed clock-wise.The coefficients of the Discrete Fourier Transform (DFT)of are(1) If harmonics are used(),the coefficients are the Fourier Descriptors used to character-ize the shape.To reconstruct boundary pointsusing harmonics,we perform inverse DFT as:(2)Note that represents the center of grav-ity of the-D boundary,which does not carry shape infor-mation.We neglect this term to achieve translation invari-ance for recognition and classification.We keep this term for synthesis and shape prediction because it accounts for scale changes.Most related works in Fourier based shape description discuss about similarity measures that make FD invariant to relevant transformations,e.g.,rotation,translation and scal-ing.The requirement for each invariance depends on the applications.In this paper,we do not consider rotation in-variance because we need to reconstruct the boundary of shape.Since rotation invariance is not relevant,we can always choose the starting point as the topmost boundary pixel along the vertical axis through the center of gravity of the entire shape.Our representation approximates scale in-variance(if we drop term)since we have dense sampling of points along region boundary using chain code.Chain code expression discretizes the arc and Equation(1)nor-malizes the arc length.2.2.Temporal Representation of Shape Varia-tionThe stochastic characteristics of boundary motion are esti-mated by an autoregressive model of changes in Fourier co-efficients of the region boundary.The autoregressive(AR) model,also known as a linear dynamical system(LDS),is used based on the assumption that each term in the time se-ries depends linearly on several previous terms along with a noise term[8].In this work,the AR model is used to capture different levels of temporal variation in FDs.Suppose are the-dimensional random vectors ob-served at equal time intervals.The-variate AR model of order(denoted as AR()model)is defined as(3) The matrices are the coefficient matrices of the AR()model,and the-dimensional vectors are uncor-related random vectors with zero mean.The-dimensional parameter vector is a vector of intercept terms that is in-cluded to allow for a nonzero mean of the time series.Our dynamic shape model uses FDs to represent shape, so the random vector is in a form of FD at time. To select the optimum order of the AR model,we adopt Schwarz’s Bayesian Criterion[16]which chooses the order of the model so as to minimize the forecast mean-squared error.We estimate the parameters of our AR model using Neumaier and Schneider’s algorithm[10]which ensures the uniqueness of estimated AR parameters using a set of nor-malization conditions.parison to Other ModelsModels of active contour tracking that predict contour mo-tion and deformation[1,3,23]have been proposed to ac-count for dynamic object shape.For example,Terzopou-los and Szeliski[23]incorporate Kalmanfiltering with the original snake model[4].Blake et al.[1]propose a con-tour tracking method that works particularly well for affine deformation of object shape.Snake based methods process the contour directly in the spatial domain and consider lo-cal deformations[4,12].In contrast,in our representation, shape information is distributed in each coefficient of FD. Thus,we consider global deformations.Only a few meth-ods,such as[1,22],consider both local and global deforma-tions.Local deformations of all contour points comprise too large a data set to be convenient for shape recognition and Note:scale invariance is achieved if the distances between a pixel and its eight neighbors are considered as equal.classification.In addition,models of active contour track-ing predict motion and deformation for one image frame. In contrast,we model global temporal characteristics of a whole image sequence.Most importantly,most work on deformable shape modeling is aimed at region contour iden-tification by using a deformable,evolving snake to converge on the desired contour.Instead,in our work,the evolving shape description is aimed at describing a temporal chang-ing shape.There is also some work using level sets to represent dy-namic shape such as[26].The advantage of the level set method is its ability to handle topology changes.However, as will be shown later,our model requires significantly less computation.3.Application I:RecognitionIn this section,we will show that using the temporal infor-mation of shape variation improves recognition results that use shape only.We choose the problem offire recognition in video sequences as an example.Fire has diverse,multispectral signatures,several of which have been utilized to devise different methods for its detection.Most of the methods can be categorized into smoke,heat,or radiation detection.However,there are only a few papers aboutfire detection in computer vision liter-ature.Healey et al.[2]use a purely color based model. Phillips et al.[14]use pixel color and its temporal variation, which does not capture the temporal property offire which is more complex and requires a region level representation.3.1.Fire Detection AlgorithmsOurfire detection algorithms include two main steps:(i) Extract potentialfire regions in each image;and(ii)Rep-resent each extracted region using FD and AR parameters, and then use a classifier to recognizefire regions.To extract potentialfire regions in each image,we use algorithms described in[6].For each potentialfire region, we represent it independently by taking the magnitude of its FDs.We thenfind matching regions in previous images of the sequence,and estimate parameters of the AR model for the correspondingfire regions.The FD and estimated AR parameters are both used as features of current region. We use a two-class Support Vector Machine(SVM)classi-fier[24]with RBF kernel forfire region recognition.3.2.Experimental ResultsThe video clips used in our experiments are taken from a random selection of commercial/training video tapes.They include different types offires such as residentialfire,ware-housefire,and wildlandfire.We use images captured at day time,dusk or night time to evaluate systemperformanceFigure1:Selectedfire images used in experiments. under different lighting conditions.We also use other im-age sequences containing objects withfire-like appearances such as sun and light bulbs as negative examples.The videoclips that we tested our algorithm on contain a total ofimage frames in sequences.Figure1shows some se-lectedfire images used in our experiments.The(red)con-tours depicted in the images are the detectedfire region con-tours.In our test data,the potential region extraction algorithmextracted a total offire-like region contours,of which were truefire region contours.For shape representa-tion in terms of Fourier Descriptors,wefind that using40coefficients(i.e.)is sufficient to approximate the relevant properties of thefire region contours.In this exper-iment,we assume that different FDs at any given time areindependent of each other,so we have diagonal coefficient matrices in our AR model,where if. Thus it can be viewed as modeling independent time series.We alsofind that the AR(1)model yields the mini-mal forecast mean-squared error.Therefore,we use ARcoefficients to represent the stochastic characteristics of the temporal changes in FDs.Table1:Recognition rate offire and non-fire contour recog-nition.Experiments Fire Non-Fire Use shape only(FD)0.9960.904 Use shape+evolution(FD+AR)0.999 1.0We tested our algorithms in two ways:Thefirst set of experiments with only spatial information of region con-tours(FD only as the feature vector),and the second set of experiments with spatial and temporal information of re-gion contour evolution(FD and AR parameters as the fea-ture vector).In the second set of experiments,we required that afire contour be seen in at least previous four frames. Note that three frames are the minimum requirement to es-timate parameters of our AR(1)model.For each set of ex-periments,we repeated the test ten times using one-tenth offire and non-fire region contours to train the SVM clas-sifier,and the other region contours for test.In this way, we used many morefire examples than counter examples on training.This was intended to tilt the detector in favor of false positives vs false negatives as corroborated by the experimental results.The average recognition rate is shown in Table1.It is clear that temporal information of shape evolution indeed improved the detection performance and reduced false alarm rate significantly.4.Application II:ClassificationIn this section,we demonstrate that the temporal informa-tion of shape variation alone is a good discriminant for clas-sifying several objects and visual phenomena.Under our proposed framework,we show that object shape variation is indeed an important visual cue for object classification.Follow the model presented in Section2.Assume that harmonics in the FDs are used to represent the region boundary of an object in each image of the sequence,and AR(1)model is used to describe boundary dynamics.We then have AR coefficients to represent the temporal characteristics of the evolving object shape in an image se-quence.Let and be AR coefficients modeling a dynamic shape and a dynamic shape,respectively. We define the distance between the two dynamic shape se-quences as(4)A simple nearest-neighbor classifier using metric(4)is used for classification.4.1.Experimental ResultsThe image sequences used in the experiments include two running human sequences,three wavingflag sequences,and twofire sequences.Thefire contours are extracted as de-scribed in[6];The region boundaries offlags and running human are semi-automatically extracted using active con-tour method[4]for each image frame.We use forty FDs to approximate each object boundary.The AR parameters are estimated using each whole sequence.Therefore,the esti-mated AR parameters represent the global dynamics of the object boundary in a sequence.The experiments are done using the cross-validation method.Only one out of seven image sequences is misclassified,where a running human sequence is classified as a wavingflag sequence.5.Application III:SynthesisIn this section,we apply our model to synthesis of dynamic shape.In particular,we synthesizefire boundary sequences, where the dynamic shape model is obtained from afire im-age sequence in as described Section3.We choosefire as an example becausefire region can be modeled as nested subregions,where each subregion shows temporal variation (see Figure2,leftmost image).Synthesis of dynamic shape is a novel topic in computer vision.The most relevant work are those of image based dy-namic/temporal texture synthesis.Some of them use only local image structures and ignore the underlying dynam-ics[25].Some other works that learn the underlying dy-namics in pixel level[21]or in image subspace[18]do not use region level image structures.Instead,they learn the global dynamics of the whole image.In our method,we learn the dynamics of regions using region boundaries.Many physics based methods have been proposed to pro-duce visual phenomena such asfire[15,17,20].How-ever,since these methods do not learn dynamics from im-ages,they are not capable of generating subsequent images based on a given image.Image based method,such as[18], generates an image sequence if given an initial image and the learned image dynamics.But the resulting images will show significant artifacts if the region of motion is notfixed. Our approach is image based,and it directly deals with tem-poral variation of regions.5.1.Synthesis ResultsOur synthesis of new sequence is based on Equation(3), after AR parameters have been estimated from the given image sequence.For a given initial image,we retrieve the object boundary in the image and represent it using FDs. We perform desired number of iterations of the AR model to estimate FDs for the entire synthesis sequence.The shape sequence are reconstructed using the estimated FDs(2).In this experiment,we use afire sequence as a training example.Afire region is modeled as a nested ring struc-ture where each ring is associated with a color spectrum. Although the changes in color is continuous,we threshold thefire region(by grayscale intensity)into three subregions. Each region boundary in the given image sequence are in-dependently modeled by our approach.The color spectra of each region are modeled as a mixture of Gaussian.Once the parameters of three AR models have been estimated, we use the mean boundaries in the given sequence as initial boundaries,and simulate the AR models to generate sub-sequent boundaries.An inner region boundary is confined to its outer region boundary so that we maintain the nested ring structure.To avoid spin-up effects,thefirst thousand time steps of the AR models are discarded.The pixel colors of each region are drawn from respective color models.Fig-ure2shows the nested ring model,an examplefire image of the input video and some selected synthesizedfire imageframes.Our method is capable of solving the following two prob-lems:Given afire image sequence,(i)generate a new se-quence offire shapes,where both shapes and dynamics are similar to the given image sequence;(ii)also given an initial fire shape,generate a new sequence offire shapes,where thedynamics is similar to the given image sequence.To achieve photo-realisticfire rendering,since we can solve problem (i),we need only a more sophisticated model that enforcesspectral gradient tofill colors in the synthesizedfire re-gion.For non-photo-realisticfire rendering,such as car-toon drawing,we ask artists to drawfire regions as nested rings and assign a color for each subregion.Our approach will automatically generate subsequent images based on thelearned dynamical model.The synthesized sequence can then be overlaid into other image sequences.6.Application IV:Shape Prediction The capability of predicting shape comes naturally in our dynamic shape model.In this section,we apply our method to tracking deformable objects.The contour based trackingmethods consist two parts:obtaining an initial contour and conforming the initial contour to object boundary.A good initial contour estimate provides a predicted contour closerto true object boundary in both geometry and position.Most works on contour tracking are based on the active contour model(or snake model)proposed by Kass et al.[4].Some works assume that the motion of the object is slow and its deformation is small[5].So the optimal contour estimate in the previous image frame is used as the initialcontour in the current frame.When the changes in shape are large,these methods are very likely to fail.Other worksthat estimate motion and deformation are compared to our method in Section2.3.Using our proposed framework,the contours are again represented by FDs.To account for large changes in shape,we estimate our AR model locally using a small number of previous image frames.Afirst-order AR model is esti-mated.Then the initial contour is predicted by Equation3 with.Note that the zeroth term of FDs has posi-tional information.So our dynamical model simultaneouslypredicts the position and shape for the current image frame. Any contour based tracking methods can then be used to conform the contour to object boundary.6.1.Experimental ResultsWe test our algorithms using a Bream sequence,where a fish initially swims to the right,makes a sharp turn,and then swims to the left.We choose this image sequence be-cause there are large changes in shape when thefishmakes Figure3:The green contour is predicted by our dynamic shape model,and the red contour is the optimal contour of the previous image frame with predicted translation.a sharp turn,which makes the tracking challenging.We compare our method to the method that predicts only shape translation but not shape deformation.Figure3shows the estimated initial contours of both methods.It is clear that our method accounts for scale change in horizontal dimension,but the other method does not.Thefin on the upper right side of thefish is partially occluded in the previous image frame.Both methods do not predict this discontinuous change in shape.But our method does move thefin upward according to its appearance in previous image frames.The quality of the converged con-tour by any snake model will benefit from a better initial shape prediction.7.LimitationsIn Section2.1,we approximate scale invariance for FD by densely sampling along the boundary to obtain the chain-code.However,for small regions,the spatial quantization is likely to introduce considerable noise to the FD.To avoid this problem,we eliminate regions smaller than a certain size.Consequently,our model does not detect small or far awayfires.Small regions are expected to increase misclas-sification rate and synthesis results are better for larger re-gions.The AR model is a linear dynamical system.There may be cases where linear model is not sufficient.In such cases, nonlinear dynamical model can be adopted under the pro-posed framework.Similarly,any other shape description method with boundary,numeric,and information preserv-ing properties may be used in place of FD.8.ConclusionIn this paper,we have proposed a novel model for dy-namic shape.Although both FD and AR model have beenFigure2:Leftmost image:A nested ring structure models thefire region.Second image:An examplefire image from the given video sequence.Others:Selected frames of the synthesizedfire image sequence.well established,using them together to analyze temporal shape variation is not discussed in literature.Traditional shape analysis focuses on spatial similarity,but not tempo-ral similarity.The autoregressive model has been applied mainly to model-D signals[8]and-D pixel interdepen-dences[18,21].We are not aware of any work on AR mod-eling of region shape changes.References[1] A.Blake,R.Curwen,and A.Zisserman.Affine-invariantcontour tracking with automatic control of spatiotemporal scale.In ICCV,pages66–75,1993.[2]G.Healey,D.Slater,T.Lin,B.Drda,and D.Goedeke.Asystem for real-timefire detection.In Computer Vision and Pattern Recognition,pages605–606,1993.[3]M.Isard and A.Blake.Contour tracking by stochastic prop-agation of conditional density.In European Conference on Computer Vision,volume1,pages343–356,1996.[4]M.Kass,A.Witkin,and D.Terzopoulos.Snakes:Activecontour models.International Journal of Computer Vision, pages321–331,1987.[5] F.Leymarie and M.Levine.Tracking deformable objectsin the plane using an active contour model.IEEE Trans.on PAMI,15(6):617–634,1993.[6] C.-B.Liu and N.Ahuja.Vision basedfire detection.In17thInternational Conference on Pattern Recognition,2004. 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[12] A.Pentland and S.Sclaroff.Closed-form solutions for phys-ically based shape modeling and recognition.IEEE Trans.on PAMI,13(7):715–729,1991.[13] E.Persoon and K.Fu.Shape discrimination using fourierdescriptors.IEEE Transactions on Systems,Man and Cy-bernetics,7(3):170–179,March1977.[14]W.Phillips,III,M.Shah,and N.da Vitoria Lobo.Flamerecognition in video.In Fifth IEEE Workshop on Applica-tions of Computer Vision,pages224–229,December2000.[15]W.T.Reeves.Particle systems–a technique for modelinga class of fuzzy objects.ACM Transactions on Graphics,2:91–108,April1983.[16]G.Schwarz.Estimating the dimension of a model.Annalsof Statistics,6:461–464,1978.[17]K.Sims.Particle animation and rendering using data parallelcomputation.ACM Computer Graphics(SIGGRAPH’90), 24(4):405–413,1990.[18]S.Soatto,G.Doretto,and Y.Wu.Dynamic textures.InIEEE International Conference on Computer Vision,pages 439–446,2001.[19] A.Srivastava,W.Mio,E.Klassen,and X.Liu.Geomet-ric analysis of constrained curves for image understanding.In Proc.Second IEEE Workshop on Variational,Geometric and Level Set Methods in Computer Vision,2003.[20]J.Stam and E.Fiume.Depictingfire and other gaseousphenomena using diffusion processes.Proceedings of ACM SIGGRAPH1995,pages129–136,1995.[21]M.Szummer and R.W.Picard.Temporal texture modeling.In IEEE International Conference on Image Processing,vol-ume3,pages823–826,1996.[22] D.Terzopoulos and D.Metaxas.Dynamic3d models withlocal and global deformations:deformable superquadrics.IEEE Trans.on PAMI,13(7):703–714,1991.[23] D.Terzopoulos and R.Szeliski.Tracking with kalmansnakes.In A.Blake and A.Yuille,editors,Active Vision, pages3–20.MIT Press,Cambridge,MA,1992.[24]V.N.Vapnik.The Nature of Statistical Learning Theory.Springer,second edition,1999.[25]L.-Y.Wei and M.Levoy.Fast texture synthesis using tree-structured vector quantization.In Proceedings of ACM SIG-GRAPH2000,pages479–488,2000.[26] A.J.Yezzi and S.Soatto.Deformotion:Deforming motion,shape average and the joint registration and approximation of structures in images.International Journal Comput.Vi-sion,53(2):153–167,2003.。
什么叫开挂模式英语作文标题，Unlocking the "God Mode": An Insight into the Phenomenon of Cheating in Video Games。
Introduction。
Cheating in video games, commonly referred to as "hacking" or entering "God mode," has become a prevalent issue in the gaming community. This unethical practice involves manipulating the game's code or using external software to gain unfair advantages over other players. In this essay, we will delve into the reasons behind this behavior, its impact on the gaming community, and potential solutions to combat it.Reasons for Cheating。
Several factors contribute to the widespread adoption of cheating in video games. Firstly, some players resort to cheating due to a lack of skill or patience required toprogress through the game legitimately. By activating cheat codes or using hacks, they can bypass challenging levels and achieve instant gratification. Moreover, the competitive nature of online gaming fuels the desire to outperform others, leading some individuals to seek unfair advantages through cheating. Additionally, for a minority, cheating may stem from a desire to disrupt the gaming experience for others or gain a sense of power and superiority.Impact on the Gaming Community。
关于写霹雳舞的英语作文Breakdancing, often referred to as "breaking" or simply "b-boying/b-girling," is a dynamic and expressive form of street dance that originated in the 1970s within the African-American and Puerto Rican communities of New York City. It has since evolved into a global phenomenon, recognized not only as a competitive sport but also as a powerful form of artistic expression. In this essay, we will explore the history, key elements, and cultural significance of breakdancing.Origins and EvolutionBreakdancing emerged as part of the hip-hop culture, which also includes DJing, graffiti, and rap. It was a way for young people to express themselves in a time when they felt marginalized by society. The dance form was heavily influenced by James Brown's energetic dance moves, Capoeira, a Brazilian martial art, and the gymnastic moves of African and African-American athletes.Over the years, breakdancing has evolved from its initial stages, incorporating elements from various dance styles and cultures. It has also been featured in numerous films, music videos, and television shows, which has helped to popularize the art form worldwide.Key Elements of BreakdancingBreakdancing is characterized by its high-energy movements and acrobatic stunts. Some of the fundamental elements include:1. Top Rock: A series of up-tempo footwork that serves as a dancer's introduction to the dance floor.2. Down Rock: Lower body movements performed close to the ground, often involving intricate hand and arm gestures.3. Power Moves: High-impact, acrobatic moves such as spins, flips, and freezes that showcase a dancer's strength and agility.4. Freezes: Static positions where the dancer balances on one or more body parts, often in a complex or unusual manner.Cultural Impact and Competitive SceneBreakdancing has had a profound impact on global youth culture. It has been a catalyst for social change, providing a positive outlet for self-expression and community building. The competitive nature of breaking has led to the establishment of numerous dance battles and championships, such as the Red Bull BC One, where the best b-boys and b-girls from around the world compete for the title.The Future of BreakdancingAs breakdancing continues to gain recognition, it has been proposed for inclusion in the Olympics, which would further legitimize the art form and provide a platform for dancers to showcase their skills on an international stage.In conclusion, breakdancing is more than just a dance style; it is a cultural movement that has transcended geographical boundaries and social barriers. It represents the resilience and creativity of the human spirit, and its evolution continues to inspire new generations of dancers and artists around the world.。
**Dynamic Table Tennis**Table tennis, a sport that sizzles with energy and precision, is truly captivating.The action on the table tennis table is a flurry of movement. Players flick their wrists with lightning speed, sending the small white ball zipping across the table like a bullet. They lunge and reach for shots, their bodies in constant motion as they anticipate the ball's trajectory. Forehands and backhands are executed with finesse, the ball bouncing off the racket with a satisfying ping. Serves are launched with strategic intent, aiming to catch the opponent off guard.The³¡µØ for table tennis is a sturdy table with a smooth surface. The table is divided by a net that seems to act as a battleground divider. The surrounding area can be a lively sports hall or a cozy corner of a recreation room.In terms of×°±¸, players wield lightweight rackets with rubber surfaces that provide grip and spin. The table tennis ball is a tiny sphere that demands quick reflexes and accurate aim.The¸ÐÊÜ of playing table tennis is electrifying. There is a rush of adrenaline as you engage in a fast-paced rally, a sense of accomplishment when you win a point. It's a sport that challenges both the body and the mind.Table tennis offers numerousºÃ´¦. Physically, it is an excellent workout for hand-eye coordination, reflexes, and cardiovascular health. It helps improve balance and agility. Mentally, table tennis sharpens focus and concentration. It teaches strategic thinking and decision-making.Socially, table tennis brings people together. Doubles matches create a sense of teamwork and camaraderie. Tournaments and friendly games provide opportunities to meet new people and build friendships.As the famous quote by Muhammad Ali says, ¡°Float like a butterfly, sting like a bee.¡± In table tennis, players move gracefully around the table like butterflies, while theirshots pack a punch like a bee's sting.In conclusion, dynamic table tennis is a sport that offers a world of excitement, challenge, and growth. Whether you're a beginner or an experienced player, table tennis has the power to inspire and entertain. So, pick up your racket and get ready for some high-octane action.。
Geometric ModelingAs a seasoned writer, I am well-equipped to tackle intricate and lengthy writing tasks, ensuring that the content produced is always original. My expertise lies in crafting engaging and informative pieces that captivate readers anddeliver valuable insights. With a keen eye for detail and a passion for storytelling, I strive to create compelling narratives that resonate with audiences across various platforms. When it comes to delving into the realm of geometric modeling, there is a world of possibilities waiting to be explored. From intricate 3D designs to mathematical algorithms, the field of geometric modeling offers a rich tapestry of concepts and techniques that can be harnessed to bring ideas to life. By leveraging the power of geometric modeling software, designers and engineers can create stunning visualizations and simulations that push the boundaries of imagination. One of the key benefits of geometric modeling is its ability to streamline the design process and enhance collaboration among team members. By creating detailed digital representations of objects and structures, designers can iterate on their ideas more efficiently and communicate their vision with greater clarity. This not only saves time and resources but also ensures that the final product meets the desired specifications and requirements. Furthermore, geometric modeling plays a crucial role in a wide range of industries, from architecture and engineering to animation and virtual reality. By harnessing the power of geometric modeling techniques, professionals in these fields can create realistic simulations, interactive experiences, and innovative solutions that push the boundaries of what is possible. Whether it's designing a cutting-edge skyscraper or developing a lifelike character for a video game, geometric modeling offers endless opportunities for creativity and innovation. In addition to its practical applications, geometric modeling also holds a special allure for those who appreciate the beauty and elegance of mathematical forms. From the graceful curves of a parametric surface to the intricate patterns of a fractal geometry, geometric modeling allows us to explore the underlying principles that govern the natural world and uncover the hidden symmetries that shape our universe. By immersing ourselves in the world of geometric modeling, we can gain a deeper appreciation for the interconnectedness of mathematics and art, and unlock newways of seeing and understanding the world around us. In conclusion, geometric modeling is a powerful tool that empowers us to bring our ideas to life, collaborate more effectively, and push the boundaries of creativity and innovation. By embracing the principles of geometric modeling and harnessing the latest technologies and techniques, we can unlock new possibilities and create a brighter future for generations to come. So let us continue to explore, experiment, and create with passion and curiosity, knowing that the world of geometric modeling holds endless opportunities for discovery and growth.。
