Simplifying Deterministic Parsing

American Airlines' Next Top ModelSara J. BeckSpencer D. K'BurgAlex B. TwistUniversity of Puget SoundTacoma, WAAdvisor: Michael Z. SpiveySummaryWe design a simulation that replicates the behavior of passengers boarding airplanes of different sizes according to procedures currently implemented, as well as a plan not currently in use. Variables in our model are deterministic or stochastic and include walking time, stowage time, and seating time. Boarding delays are measured as the sum of these variables. We physically model and observe common interactions to accurately reflect boarding time.We run 500 simulations for various combinations of airplane sizes and boarding plans. We analyze the sensitivity of each boarding algorithm, as well as the passenger movement algorithm, for a wide range of plane sizes and configurations. We use the simulation results to compare the effectiveness of the boarding plans. We find that for all plane sizes, the novel boarding plan Roller Coaster is the most efficient. The Roller Coaster algorithm essentially modifies the outside-in boarding method. The passengers line up before they board the plane and then board the plane by letter group. This allows most interferences to be avoided. It loads a small plane 67% faster than the next best option, a midsize plane 37% faster than the next best option, and a large plane 35% faster than the next best option.IntroductionThe objectives in our study are:To board (and deboard) various sizes of plane as quickly as possible."* To find a boarding plan that is both efficient (fast) and simple for the passengers.With this in mind:"* We investigate the time for a passenger to stow their luggage and clear the aisle."* We investigate the time for a passenger to clear the aisle when another passenger is seated between them and their seat.* We review the current boarding techniques used by airlines.* We study the floor layout of planes of three different sizes to compare any difference between the efficiency of a given boarding plan as plane size increases and layouts vary."* We construct a simulator that mimics typical passenger behavior during the boarding processes under different techniques."* We realize that there is not very much time savings possible in deboarding while maintaining customer satisfaction."* We calculate the time elapsed for a given plane to load under a given boarding plan by tracking and penalizing the different types of interferences that occur during the simulations."* As an alternative to the boarding techniques currently employed, we suggest an alternative plan andassess it using our simulator."* We make recommendations regarding the algorithms that proved most efficient for small, midsize, and large planes.Interferences and Delays for BoardingThere are two basic causes for interference-someone blocking a passenger,in an aisle and someone blocking a passenger in a row. Aisle interference is caused when the passenger ahead of you has stopped moving and is preventing you from continuing down the aisle towards the row with your seat. Row interference is caused when you have reached the correct row but already-seated passengers between the aisle and your seat are preventing you from immediately taking your seat. A major cause of aisle interference is a passenger experiencing rowinterference.We conducted experiments, using lined-up rows of chairs to simulate rows in an airplane and a team member with outstretched arms to act as an overhead compartment, to estimate parameters for the delays cause by these actions. The times that we found through our experimentation are given in Table 1.We use these times in our simulation to model the speed at which a plane can be boarded. We model separately the delays caused by aisle interference and row interference. Both are simulated using a mixed distribution definedas follows:Y = min{2, X},where X is a normally distributed random variable whose mean and standard deviation are fixed in our experiments. We opt for the distribution being partially normal with a minimum of 2 after reasoning that other alternative and common distributions (such as the exponential) are too prone to throw a small value, which is unrealistic. We find that the average row interference time is approximately 4 s with a standard deviation of 2 s, while the average aisle interference time is approximately 7 s with a standard deviation of 4 s. These values are slightly adjusted based on our team's cumulative experience on airplanes.Typical Plane ConfigurationsEssential to our model are industry standards regarding common layouts of passenger aircraft of varied sizes. We use an Airbus 320 plane to model a small plane (85-210 passengers) and the Boeing 747 for a midsize plane (210-330 passengers). Because of the lack of large planes available on the market, we modify the Boeing 747 by eliminating the first-class section and extending the coach section to fill the entire plane. This puts the Boeing 747 close to its maximum capacity. This modified Boeing 747 has 55 rows, all with the same dimensions as the coach section in the standard Boeing 747. Airbus is in the process of designing planes that can hold up to 800 passengers. The Airbus A380 is a double-decker with occupancy of 555 people in three different classes; but we exclude double-decker models from our simulation because it is the larger, bottom deck that is the limiting factor, not the smaller upper deck.Current Boarding TechniquesWe examine the following industry boarding procedures:* random-order* outside-in* back-to-front (for several group sizes)Additionally, we explore this innovative technique not currently used by airlines:* "Roller Coaster" boarding: Passengers are put in order before they board the plane in a style much like those used by theme parks in filling roller coasters.Passengers are ordered from back of the plane to front, and they board in seatletter groups. This is a modified outside-in technique, the difference being that passengers in the same group are ordered before boarding. Figure 1 shows how this ordering could take place. By doing this, most interferencesare avoided.Current Deboarding TechniquesPlanes are currently deboarded in an aisle-to-window and front-to-back order. This deboarding method comes out of the passengers' desire to be off the plane as quickly as possible. Any modification of this technique could leadto customer dissatisfaction, since passengers may be forced to wait while others seated behind them on theplane are deboarding.Boarding SimulationWe search for the optimal boarding technique by designing a simulation that models the boarding process and running the simulation under different plane configurations and sizes along with different boarding algorithms. We then compare which algorithms yielded the most efficient boarding process.AssumptionsThe environment within a plane during the boarding process is far too unpredictable to be modeled accurately. To make our model more tractable,we make the following simplifying assumptions:"* There is no first-class or special-needs seating. Because the standard industry practice is to board these passengers first, and because they generally make up a small portion of the overall plane capacity, any changes in the overall boarding technique will not apply to these passengers."* All passengers board when their boarding group is called. No passengers arrive late or try to board the plane early."* Passengers do not pass each other in the aisles; the aisles are too narrow."* There are no gaps between boarding groups. Airline staff call a new boarding group before the previous boarding group has finished boarding the plane."* Passengers do not travel in groups. Often, airlines allow passengers boarding with groups, especially with younger children, to board in a manner convenient for them rather than in accordance with the boarding plan. These events are too unpredictable to model precisely."* The plane is full. A full plane would typically cause the most passenger interferences, allowing us to view the worst-case scenario in our model."* Every row contains the same number of seats. In reality, the number of seats in a row varies due to engineering reasons or to accommodate luxury-class passengers.ImplementationWe formulate the boarding process as follows:"* The layout of a plane is represented by a matrix, with the rows representing rows of seats, and each column describing whether a row is next to the window, aisle, etc. The specific dimensions vary with each plane type. Integer parameters track which columns are aisles."* The line of passengers waiting to board is represented by an ordered array of integers that shrinks appropriately as they board the plane."* The boarding technique is modeled in a matrix identical in size to the matrix representing the layout of the plane. This matrix is full of positive integers, one for each passenger, assigned to a specific submatrix, representing each passenger's boarding group location. Within each of these submatrices, seating is assigned randomly torepresent the random order in which passengers line up when their boarding groups are called."* Interferences are counted in every location where they occur within the matrix representing the plane layout. These interferences are then cast into our probability distribution defined above, which gives ameasurement of time delay."* Passengers wait for interferences around them before moving closer to their assigned seats; if an interference is found, the passenger will wait until the time delay has finished counting down to 0."* The simulation ends when all delays caused by interferences have counted down to 0 and all passengers have taken their assigned seats.Strengths and Weaknesses of the ModelStrengths"* It is robust for all plane configurations and sizes. The boarding algorithms that we design can be implemented on a wide variety of planes with minimal effort. Furthermore, the model yields reasonable results as we adjust theparameters of the plane; for example, larger planes require more time to board, while planes with more aisles can load more quickly than similarlysized planes with fewer aisles."* It allows for reasonable amounts of variance in passenger behavior. While with more thorough experimentation a superior stochastic distribution describing the delays associated with interferences could be found, our simulationcan be readily altered to incorporate such advances."* It is simple. We made an effort to minimize the complexity of our simulation, allowing us to run more simulations during a greater time period and mini mizing the risk of exceptions and errors occurring."* It is fairly realistic. Watching the model execute, we can observe passengers boarding the plane, bumping into each other, taking time to load their baggage, and waiting around as passengers in front of them move out of theway. Its ability to incorporate such complex behavior and reduce it are key to completing our objective. Weaknesses"* It does not account for passengers other than economy-class passengers."* It cannot simulate structural differences in the boarding gates which couldpossibly speed up the boarding process. For instance, some airlines in Europeboard planes from two different entrances at once."* It cannot account for people being late to the boarding gate."* It does not account for passenger preferences or satisfaction.Results and Data AnalysisFor each plane layout and boarding algorithm, we ran 500 boarding simulations,calculating mean time and standard deviation. The latter is important because the reliability of plane loading is important for scheduling flights.We simulated the back-to-front method for several possible group sizes.Because of the difference in thenumber of rows in the planes, not all group size possibilities could be implemented on all planes.Small PlaneFor the small plane, Figure 2 shows that all boarding techniques except for the Roller Coaster slowed the boarding process compared to the random boarding process. As more and more structure is added to the boarding process, while passenger seat assignments continue to be random within each of the boarding groups, passenger interference backs up more and more. When passengers board randomly, gaps are created between passengers as some move to the back while others seat themselves immediately upon entering the plane, preventing any more from stepping off of the gate and onto the plane. These gaps prevent passengers who board early and must travel to the back of the plane from causing interference with many passengers behind them. However, when we implement the Roller Coaster algorithm, seat interference is eliminated, with the only passenger causing aisle interference being the very last one to boardfrom each group.Interestingly, the small plane's boarding times for all algorithms are greater than their respective boarding time for the midsize plane! This is because the number of seats per row per aisle is greater in the small plane than in the midsize plane.Midsize PlaneThe results experienced from the simulations of the mid-sized plane areshown in Figure 3 and are comparable to those experienced by the small plane.Again, the Roller Coaster method proved the most effective.Large PlaneFigure 4 shows that the boarding time for a large aircraft, unlike the other plane configurations, drops off when moving from the random boarding algorithm to the outside-in boarding algorithm. Observing the movements by the passengers in the simulation, it is clear that because of the greater number of passengers in this plane, gaps are more likely to form between passengers in the aisles, allowing passengers to move unimpeded by those already on board.However, both instances of back-to-front boarding created too much structure to allow these gaps to form again. Again, because of the elimination of row interference it provides for, Roller Coaster proved to be the most effective boarding method.OverallThe Roller Coaster boarding algorithm is the fastest algorithm for any plane pared to the next fastest boarding procedure, it is 35% faster for a large plane, 37% faster for a midsize plane, and 67% faster for a small plane. The Roller Coaster boarding procedure also has the added benefit of very low standard deviation, thus allowing airlines a more reliable boarding time. The boarding time for the back-to-front algorithms increases with the number of boarding groups and is always slower than a random boarding procedure.The idea behind a back-to-front boarding algorithm is that interference at the front of the plane is avoided until passengers in the back sections are already on the plane. A flaw in this procedure is that having everyone line up in the plane can cause a bottleneck that actually increases the loading time. The outside-in ("Wilma," or window, middle, aisle) algorithm performs better than the random boarding procedure only for the large plane. The benefit of the random procedure is that it evenly distributes interferences throughout theplane, so that they are less likely to impact very many passengers.Validation and Sensitivity AnalysisWe developed a test plane configuration with the sole purpose of implementing our boarding algorithms on planes of all sizes, varying from 24 to 600 passengers with both one or two aisles.We also examined capacities as low as 70%; the trends that we see at full capacity are reflected at these lower capacities. The back-to-front and outside-in algorithms do start to perform better; but this increase inperformance is relatively small, and the Roller Coaster algorithm still substantially outperforms them. Underall circumstances, the algorithms we test are robust. That is, they assign passenger to seats in accordance with the intention of the boarding plans used by airlines and move passengers in a realistic manner.RecommendationsWe recommend that the Roller Coaster boarding plan be implemented for planes of all sizes and configurations for boarding non-luxury-class and nonspecial needs passengers. As planes increase in size, its margin of success in comparison to the next best method decreases; but we are confident that the Roller Coaster method will prove robust. We recommend boarding groups that are traveling together before boarding the rest of the plane, as such groups would cause interferences that slow the boarding. Ideally, such groups would be ordered before boarding.Future WorkIt is inevitable that some passengers will arrive late and not board the plane at their scheduled time. Additionally, we believe that the amount of carry-on baggage permitted would have a larger effect on the boarding time than the specific boarding plan implemented-modeling this would prove insightful.We also recommend modifying the simulation to reflect groups of people traveling (and boarding) together; this is especially important to the Roller Coaster boarding procedure, and why we recommend boarding groups before boarding the rest of the plane.。

Data-Oriented TranslationArjen Poutsma Department of Computational Linguistics University of Amsterdamthe Netherlandspoutsma@wins.uva.nlAbstractIn this article,we present a statistical approach to machine translation that is based on Data-Oriented Parsing:Data-Oriented Translation(DOT).In DOT, we use linked subtree pairs for creating a derivation of a source sentence.Each linked subtree pair has a certain probability,and consists of two trees:one in the source language and one in the target lan-guage.When a derivation has been formed with these subtree pairs,we can create a translation from this derivation.Since there are typically many dif-ferent derivations of the same sentence in the source language,there can be as many different translations for it.The probability of a translation can be calcu-lated as the total probability of all the derivations that form this translation.We give the computa-tional aspects for this model,show that we can con-vert each subtree pair into a productive rewrite rule, and that the most probable translation can be com-puted by means of Monte Carlo disambiguation.Fi-nally,we discuss some pilot experiments with the Verbmobil corpus.1IntroductionThe Data-Oriented Parsing model has been pre-sented as a promising paradigm for natural language processing(Scha,1990;Bod,1995;Bod,1998). It has been shown that DOP has the ability to lo-cate syntactic and semantic dependencies,both of which are quite important for machine translation. We hope that,by basing our model on DOP,we can inherit these advantages,thus obtaining a new and interesting way to perform machine translation.In section2,we describe this novel model by identifying its parameters.In section3,we describe its computational aspects;in section4,we discuss some pilot experiments with this model;andfinally, in section5,we give some issues open for future research.2The Data-Oriented Translation Model In this section,we will give the instantiation of a model that uses DOP for MT purposes,which we will call Data-Oriented Translation(DOT).1This model is largely based on DOP1(Bod,1998,chapt.2).In DOT,we use linked subtree pairs as combi-national fragments.2Each linked subtree pair has a certain probability,and consists of a tree in the source language and a tree in the target language. By combining these fragments to form an an analy-sis of the source sentence,we automatically gener-ate a translation,i.e.we form a derivation of both source sentence and target sentence.Since there are typically many different derivations which con-tain the same source sentence,there can be equally many different translations for it.The probability of a translation can be calculated as the total probabil-ity of all the derivations that form this translation. The model presented here is capable of translat-ing between two languages only.This limitation is by no means a property of the model itself,but is chosen for simplicity and readability reasons only. The following parameters should be specified fora DOP-like approach to MT:1.the representations of sentences that are as-sumed,2.the fragments of these representations that canbe used for generating new representations, 3.the operator that is used to combine the frag-ments to form a translation,andS NPlikes NP AnneVPV`aNP3Links cannot occur at the terminal level,since we map between semantic equivalent parts on the level of syntactic categories.2.2FragmentsLikewise,we will use linked subtrees as our frag-ments.Given a pair of linked trees T s T t,a linked subtree pair of T s T t consists of two connected and linked subgraphs t s t t of T s T t such that:1.for every pair of linked nodes in t s t t,it holdsthat:(a)both nodes in t s t t have either zerodaughter nodes,or(b)both nodes have all the daughter nodes ofthe corresponding nodes in T s T tand2.every non-linked node in either t s(or t t)has allthe daughter nodes of the corresponding node in T s(T t),and3.both t s and t t consist of more than one node. This definition has a number of consequences. First of all,it is more restrictive than the DOP1def-inition for subtrees,thus resulting in a smaller or equal amount of subtrees per tree.Secondly,it de-fines a possible pair of linked subtrees.Typically, there are many pairs of linked subtrees for each set of linked trees.Thirdly,the linked tree pair itself is also a valid linked subtree pair.Finally,accord-ing to this definition,all the linked subtree pairs are semantic equivalents,since the semantic daughter nodes of the original tree are removed or retained si-multaneously(clause1).The nodes for which a se-mantic equivalent does not exist are always retained (clause2).We can now define the bag of linked subtree pairs,which we will use as a grammar.Given a corpus of linked trees C,the bag of linked subtree pairs of C is the bag in which linked subtree pairs occur exactly as often as they can be identified in C.4Figure2show the bag of linked subtree pairs for the linked tree pair T s T t.2.3Composition operatorIn DOT,we use the leftmost substitution opera-tor for forming combinations of grammar rules. The composition of the linked tree pair t s t t andSNPlikesNPAnne VPV`a NP likesNP Anne VPV`a NP SNPlikesNP SNPVPV`a NP likesNP SNPVPV`a NP NPCharlesNPAnne Figure 2:The bag of linked subtree pairs of T s T tu s u t ,written as t s t t u s u t ,is defined iffthe label of the leftmost nonterminal linked frontier node and the label of its linked counterpart are iden-tical to the labels of the root nodes of u s u t .If this composition is defined,it yields a copy of t s t t ,in which a copy of u s has been substituted on t s ’s left-most nonterminal linked frontier node,and a copy of u t has been substituted on the node’s linked coun-terpart.The composition operation is illustrated in figure 3.Given a bag of linked subtree pairs B ,a se-quence of compositions t s 1t t 1t s N t t N ,with t s i t t i B yielding a tree pair T s T t without non-terminal leaves is called a derivation D of T s T t .2.4Probability calculationTo compute the probability of the target composi-tion,we make the same statistical assumptions as in DOP1with regard to independence and representa-tion of the subtrees (Bod,1998,p.16).The probability of selecting a subtree pair t s t t is calculated by dividing the frequency of the sub-tree pair in the bag by the number of subtrees that have the same root node labels in this bag.In other words,let t s t t be the number of times the sub-tree pair t s t t occurs in the bag of subtree pairs,and r t be the root node categories of t ,then the probability assigned to t s t t isP t s t tt s t tSNP VPVplaˆıtPPPAnneNPAnneVPVplaˆıtPPPAnne Figure3:The composition operationThe justification of this last equation is quite triv-ial.As in any statistical MT system,we wish tochoose the target sentence w t so as to maximizeP w t w s(Brown et al.,1990,p.79).If we take the sum over all possible derivations that were formedfrom w s and derive w t,we can rewrite this as equa-tion4,as seen below.Since both w s and w t arecontained in D ws w t ,we can remove them both andarrive at equation5,which—as we maximize over w t—is equivalent to equation3above.maxw tP w t w smax w t∑D w s wtP w t D w s w t w s(4)max w t∑D w s wtP D ws w t(5)3Computational AspectsWhen translating using the DOT model,we can dis-tinguish between three computational stages:1.parsing:the formation of a derivation forest,2.translation:the transfer of the derivation for-est from the source language to the target lan-guage,3.disambiguation:the selection of the mostprobable translation from the derivation forest.3.1ParsingIn DOT,every subtree pair t s t t can be seen as a productive rewrite rule:root t s root t t frontier t s frontier t t,where all linkage in the frontier nodes is retained.The linked non-terminals in the yield constitute the symbol pairs to which new rules(subtree pairs)are applied.For instance,the rightmost subtree pair infigure3can be rewritten as S S Anne likes NP NP plaˆıt`a Anne This rule can then be combined with rules that have the root pair NP NP,and so on.If we only consider the left-side part of this rule, we can use algorithms that exist for context-free grammars,so that we can parse a sentence of n words with a time complexity which is polynomial in n.These algorithms give as output a chart-like derivation forest(Sima’an et al.,1994),which con-tains the tree pairs of all the derivations that can be formed.3.2TranslationSince every tree pair in the derivation forest contains a tree for the target language,the translation of this forest is trivial.3.3DisambiguationIn order to select the most probable translation,it is not efficient to compare all translations,since there can be exponentially many of them.Furthermore, it has been shown that the Viterbi algorithm cannot be used to make the most probable selection from a DOP-like derivation forest(Sima’an,1996). Instead,we use a random selection method to generate derivations from the target derivation forest,otherwise known as Monte Carlo sam-pling(Bod,1998,p.46–49).In this method, the random choices of derivations are based on the probabilities of the underlying subderivations.If we generate a large number of samples,we can esti-mate the most probable translation as the translation which results most often.The most probable trans-lation can be estimated as accurately as desired by making the number of random samples sufficiently large.4Pilot ExperimentsIn order to test the DOT-model,we did some pi-lot experiments with a small part of the Verbmo-bil corpus.This corpus consists of transliterated spoken appointment dialogues in German,English,and Japanese.We only used the German and En-glish datasets,which were aligned at sentence level, and syntactically annotated using different annota-tion schemes.5Naturally,the tree pairs in the corpus did not con-tain any links,so—in order to make it useful for DOT—we had to analyze each tree pair,and place links where necessary.We also corrected tree pairs that were not aligned correctly.Figure4shows an example of a corrected and linked tree from our cor-rection of the Verbmobil corpus.We used a blind testing method,dividing the266 trees of our corpus into an85%training set of226 tree pairs,and a15%test set of40tree pairs.We carried out three experiments,in both directions, each using a different split of training and test set. The226training set tree pairs were converted into fragments(i.e.subtree pairs),and were enriched with their corpus probabilities.The40sentences from the test set served as input sentences:they were translated with the fragments from the train-ing set using a bottom-up chart parser,and disam-biguated by the Monte Carlo algorithm.The most probable translations were estimated from probabil-ity distributions of1500sampled derivations,which accounts for a standard deviationσ0013.Fi-nally,we compared the resulting translations with the original translation as given in the test set.We also fed the test sentences into another MT-system: AltaVista’s Babelfish,which is based on Systran.6 4.1EvaluationIn a manner similar to(Brown et al.,1990,p.83), we assigned each of the resulting sentences a cate-gory according to the following criteria.If the pro-duced sentence was exactly the same as the actual Verbmobil translation,we assigned it the exact cat-egory.If it was a legitimate translation of the source sentence but in different words,we assigned it the alternate category.If it made sense as a sentence, but could not be interpreted as a valid translation of the source sentence,we assigned it the wrong cat-egory.If the translation only yielded a part of the source sentence,we assigned it the partial category: either partial exact if it was a part of the actual Verb-mobil translation,or partial alternate if it was part of an alternate translation.Finally,if no translationS LKwir NXdannSMDweVPVBitADVPMax.Size IncorrectExact Alternate Exact Alternate 1126318.92%18.92%2273332.43% 5.41%3822832.43% 5.41%41419232.43% 5.41%52214732.43% 5.41%62703932.43% 5.41%∞3347932.43% 5.41%8.11%37.84%0%0%Table1:Results of English to German translation experimentsEnglish to German experiment were much higher than Systran’s(32%versus Systran’s19%);vice-versa it was much lower(13%versus Systran’s 21%).Since the German grammar is more complex than the English grammar,this result could be ex-pected.It is simpler to map a complex grammar to a simpler than vice-versa.The partial translations,which are quite useful for forming the basis of a post-edited,manual trans-lation,varied around38%in our English to Ger-man experiments,and around55%when translating from German to English.Systran is incapable of forming partial translations.As can be seen from the tables,we experimented with the maximum depth of the tree pairs used.We expected that the performance of the model would increase when we used deeper subtree pairs,since deeper structures allow for more complex struc-tures,and therefore better translations.Our exper-iments showed,however,that there was very little increase of performance as we increased the maxi-mum tree depth.A possible explanation is that the trees in our corpus contained a lot of lexical context (i.e.terminals)at very small tree depths.Instead of varying the maximum tree depth,we should ex-periment with varying the maximum tree width.We plan to perform such experiments in the future.5Future workThough thefindings presented in this article cover the most important issues regarding DOT,there are still some topics open for future research.As we stated in the previous section,we wish to see whether DOT’s performance increases as we vary the maximum width of a tree.In the experiments it became clear that DOT lacks a large lexicon,thus resulting in less alternate trans-lations than Systran.By using an external lexicon, we can form a part-of-speech sequences from the source sentence,and use this sequence as input for DOT.The resulting target part-of-speech sequence can then be reformed into a target sentence.The experiments discussed in this article are pilot experiments,and do not account for much.In order tofind more about DOT and its(dis)abilities,more experiments on larger corpora are required.6ConclusionIn this article,we have presented a new approach to machine translation:the Data-Oriented Translation model.This method uses linked subtree pairs for creating a derivation of a sentence.Each subtree-pair consists of two trees:one in the source lan-guage and one in the target ing these subtree pairs,we can form a derivation of a given source sentence,which can then be used to form a target sentence.The probability of a translation canCorpus Categorical accuracyCorrect Partial Depth Ungr.Wrong15.38% 2.56%41.03%15.38%12.82%7.69%35.90%17.95%12.82%10.26%38.46%17.95%15.38%7.69%35.90%17.95%15.38% 5.13%35.90%17.95%15.38% 5.13%38.46%17.95%15.38%7.69%38.46%17.95%Systran20.51%41.03%。

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Easy and Intuitive Engineering EnvironmentControl Builder is the control engineering and maintenance tool for the Control Execution Environment, and improves the control engineer’s productivity by simplifying configuration with a graphical user interface and predefined function blocks ready for wiring into a specific control strategy. The control engineer can enable and change standard function block features without the need to build these from the ground up. The control strategy can be documented with embedded objects such as text, documents or web-links.Online Monitoring Is Available to the Engineer and OperatorOnce control strategies are created and loaded to the C300 controller, the engineer can monitor the strategy on-line using the same graphical interface. This is helpful for verifying a control strategy or for troubleshooting a process problem. The control or maintenance engineer can directly modify parameters from the engineering environment without needing an operator interface. Controller Based Model Predictive Tuning with ProfitLoopProfit Loop is Honeywell’s patented algorithm that provides a single input / single output model-predictive function block that is included in the standard C300 controller function block library. It has the operating simplicity and computational efficiency of a standard PID function block, yet provides tight, robust control, increasing process stability by up to 30 percent. Profit Loop creates a simple model of the process to predict the effect of control moves on the process (controlled) variable. Because Profit Loop can anticipate future process behavior, the controller knows exactly how much to move the process to meet the desired control objectives. Profit Loop incorporates the best elements of both traditional PID algorithms and the model-based control and optimization technologies of Profit Controller at the regulatory level.For More InformationTo learn more about how Honeywell’sPlantCruise by Experion C300 Controller can improve plant performance, visit our website or contact your Honeywell account manager. Honeywell Process Solutions Honeywell1250 West Sam Houston Parkway South Houston, TX 77042Honeywell House, Arlington Business Park Bracknell, Berkshire, England RG12 1EB Shanghai City Centre, 100 Junyi Road Shanghai, China 20051 Custom Algorithm Blocks Custom Algorithm Blocks (CABs) are similar in purpose and structure to native function blocks included with Control Builder. These blocks have predefined algorithms and data structures. By contrast, Custom Algorithm Blocks have user defined algorithms and data structures. CABs are developed using Visual Basic integrated into Control Builder.The C300 controller supports the execution of CABs in Experion LX. CABs can greatly reduce the effort required to create complex control strategies that require the robust control environment offered by the C300.Investment ProtectionHoneywell is committed to protecting customer investments by supporting and integrating previous control products. Consistent with this philosophy, the Control Execution Environment, which holds the user application, is platform- independent. This allows the user to make use of new, more powerful hardware platforms when they become available, while retaining the specific user application.PN-13-16-ENG February 2014© 2014 Honeywell International Inc.。