Improving Interoperability in Collaborative Modelling
S. Roser1 and B. Bauer1
1
Programming of Distributed Systems, Institute of Computer Science, University of Augsburg, D-86135 Augsburg, Germany [roser|bauer]@informatik.uni-augsburg.de
Abstract. The application of model-driven development facilitates faster and more flexible integration by separating system descriptions to different levels of abstraction. In crossorganisational development new challenges arise to enable enterprise models sharing knowledge independent of language and tools. However, interoperability problems in modelling can be hardly overcome by solutions operating essentially at syntactical level. This paper presents an approach using the capabilities of semantic technologies in modeldriven development and discusses its improvements for collaborative modelling.
1 Introduction
In its vision for 2010 [12] the IDEAS network stated, that for enabling enterprises to seamlessly interoperate with others it will be necessary to integrate and adapt ontologies in architectures and infrastructures to the layers of enterprise architecture and to operational models. Since it is necessary to have different methodologies, for different purpose and enterprise’s role, interoperability between enterprise models has to be achieved, where two different enterprise models can share the knowledge independent of language and tools. Therefore mappings between different existing enterprise modelling formalisms based on an enterprise modelling ontology as well as tools and services for translating models have to be developed (IDEAS analysis - gap 12 [11]). Still, solutions aiming to improve such kind of interoperability (like [3], [15], [21]) address the problems of different representation formats, modelling guidelines, modelling styles, modelling languages, and methodologies at syntactical level, focusing on metamodels’ abstract and concrete syntax. Approaches providing interoperability solutions based on ontologies and automated reasoning are lacking of key features for modelling [7], like storing trace information of transformation executions in order to enable transactions or incremental updates when executing transformations [17]. To support the business
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interoperability needs of an enterprise, in [5] a rather abstract interoperability framework for model-driven development of software systems is proposed. Mutual understanding on all levels of integration, conceptual, technical, and applicative, has to be achieved. The conceptual reference model is used to model interoperability, where metamodels and ontologies will be used to define model transformations and model mappings between the different views of an enterprise system. In this work we present how the concrete approach of ontology-based model transformations (ontMT) ([18]) can be applied for improving interoperability between enterprise models despite of different modelling languages and tools. OntMT integrates ontologies in modelling by utilising different technological spaces [14] (namely MDA and Ontology technological space) automating generation of model transformations and mappings between metamodels. Interoperability in modelling is fostered by employing automated reasoning technology from ontology engineering technological space to the generation of model transformations. The paper is organized as follows: After introducing background information to our work in section 2, section 3 discusses challenges of collaborative modelling in a motivational scenario before in section 4 core problems are identified. The approach of ontology-based model transformation is presented in section 5 and 6. Section 7 discusses how the ontMT approach contributes to the interoperability scenarios described in section 3. Finally we discuss related work (section 8) and conclude with a short summary.
2 Background
Model-driven Development: Model-driven development (MDD), as a generalization of OMG?’s Model-driven Architecture paradigm (MDA?), is an approach to software development based on modelling and automated transformation of models to implementations. In MDD models are more than abstract descriptions of systems, as they are used for model- and code refinement – they are the key part of the definition of a software system. Largely automated model transformations refine abstract models to more concrete models (vertical model transformations) or simply describe mappings between models of the same level of abstraction (horizontal model transformations). Beneath of commercial products facilitating MDA there exist open source projects dedicated to MDD. The Eclipse Generative Modeling Tools project (GMT) [8] provides a set of research tools illustrating operations applicable to abstract models. Those tools range from code generation (oAW, MOFScript) over model transformation and weaving (ATL, AMW) to model management (AM3). The MODELWARE project aims to close the gap between the end-users and solutions of currently used software development methods by using models for the construction of software. It contributes to the Eclipse Model Driven Development integration project (MDDi). MDDi is dedicated to offer a platform the integration facilities needed for applying a MDD approach. It aims to provide the ability to integrate modelling tools to create a customizable MDD environment.
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Models: The definition of the mega-model (a model about modelling) presented in [6] describes a model as a system that enables us to give answers about a system under study without the need to consider directly this system under study (SUS). In short a model is representationOf a system, where systems can be physically observable elements like models or, more abstract concepts like modelling languages. A modelling language is a set models. Models are elementsOf a modelling language, if they conformTo a model of the modelling language (i.e. a metamodel). For one modelling language multiple (meta)models can exist, which can again differ in the language they are described in.
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Fig. 1. Modelling languages, metamodels, models and their relationships
Model transformations: Model transformations (MTs) are specified between metamodels. By executing a model transformation models conforming to the source metamodels are transformed to models conformant to the target metamodel. Vertical model transformations refine abstract models to more concrete models while horizontal model transformations describe mappings between models of the same level of abstraction. As model transformations play a key role in MDD, it is important that transformations can be developed as efficiently as possible [7]. With the MOF 2.0 Query, Views, and Transformation (QVT) specification [17] the OMG provided a standard syntax and execution semantics for transformations used in a MDD tools chain. QVT model transformations are specified themselves as models. Beneath vertical and horizontal model transformations, one has to distinguish whether the source and target models are element of the same language and conform to the same metamodel. An in-place transformation (same source and target model) can be used refactoring models. Ontology: Ontologies are considered a key element for semantic interoperability and act as shared vocabularies for describing the relevant notions of a certain application area, whose semantics is specified in a (reasonably) unambiguous and machine-processable form [4]. According to [16] an ontology differs from existing methods and technologies in the following way: (i) the primary goal of ontologies is to enable agreement on the meaning of specific vocabulary terms and, thus, to facilitate information integration across individual languages; (ii) ontologies are formalized in logic-based representation languages. Their semantic are thus specified in an unambiguous way. (iii) The representation languages come with executable calculi enabling querying and reasoning at run time. Application ontologies contain the definitions specific to a particular application [10], while reference ontologies refer to ontological theories whose focus is to clarify the intended meaning of terms used in specific domains.
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Technological Spaces: Kurtev et al. [14] introduce the concept of technological spaces (TS) aiming to improve efficiency of work by using the best possibilities of different technologies. A technological space is in short a zone of established expertise and ongoing research. It is a working context with a set of associated concepts, body of knowledge, tools, required skills, and possibilities. Initially five technological spaces (MDA TS, XML TS, Abstract Syntax TS, Ontology TS, DBMS TS) have been presented in [14], of which the MDA TS and the Ontology TS are important for our work. In the MDA TS models are considered as first-class citizens, representing particular views on the system being built. The Ontology TS can be considered as a subfield of knowledge engineering, mainly dealing with representation and reasoning.
3 Motivational Scenario
Interoperability can broadly be characterized as ‘the ability of enterprises to cooperate seamlessly with each other’. Interoperability is not only an issue of information and communication systems (ICT-systems) collaborating at runtime. It is also a matter of communicating both with internal and external organisation units in order to develop new models for collaboration and supporting ICTSystems. Information and knowledge about enterprises, their organisational structure, processes, collaboration with external organisation but also ICT-systems is commonly captured in models. To enable collaboration in enterprise and systems modelling, enterprises have to be supported by interoperability solutions for model sharing and model exchange independent of modelling languages and tools.
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Fig. 2. Scenario realizing cross-organisational business process modelling and execution
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Figure 2 illustrates the application of MDD to cross-organisational business process development. The vertical dimension distinguishes the different layers of abstraction applied in MDD and the horizontal dimension represents the collaborative modelling between two enterprises A and B. Models of enterprise A and B have to be shared at different level of abstraction in order to agree on and develop cross-organisational business processes. A concrete scenario implementing cross-organisational business process modelling and execution like show in figure 2 has been developed in the ATHENA project (more details can be found in [9]). Enterprise A and B develop models for their process (privates processes (PPs), view processes (VPs) and crossorganisational business processes (CBPs)) at three levels of abstraction, i.e. business expert, IT expert, and IT-system level. Vertical transformations, like presented in [1], encode knowledge about architecture and platform in order to transform models from higher level to models of lower abstraction level. For example ARIS models (eEPCs [13]) are transformed to models conformant to PIM4SOA [2]. Enterprise A and B use different modelling tools and languages at the various abstraction levels. At business level enterprise A uses ARIS while enterprise B applies Integrated Enterprise Modelling and the MO2GO tool [20]. To develop cross-organisational business process both enterprises have to provide public parts of their models as basis for discussion for collaborative modelling. The same holds for other levels of abstraction where the enterprises have to agree on more detailed issues of the cross-organisational business processes. Unfortunately there are issues preventing a more smooth realisation of such an MDD scenario. Two prominent candidates are a) the further advancements in modelling applied by the enterprises (like e.g. the application of new modelling languages or styles) and b) the exchange of models between the enterprises. Thus we have to deal with maintenance of model transformations and sharing models across inter-organisational relationships. Over a period of time enterprises will apply new (versions) of modelling languages and modelling styles. Therefore existing transformations have to be adjusted or developed. This can be a time consuming and error-prone task, since the vertical transformations of the different enterprises have to be aligned. The result of a refinement step (vertical transformation) not only depends on the next refinement step, but also on the other enterprises’ models at the same level of abstraction. Secondly, mappings have to be developed between the enterprises’ modelling languages and tools in order to get shared understanding of cross-organisational business process and to enable collaborative model-driven developing.
4 Problem Statement
Despite the differences in modelling at these levels of abstraction (like granularity of the models or differences in modelling approaches) the core principles (.e. representing information about real world things in models, see background section 2) and problems remain the same. The core barriers to model exchange and maintenance of model transformations are multiple representation formats and different modelling styles, serving the purposes of the particular application.
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Different representation format: The trend towards more and more people using domain specific languages (DSLs) to create their own domain specific models (DSMs) naturally results in a variety of different languages and metamodels. To exchange models conformant to these various metamodels (abstract syntax) model transformations have to be developed. Often there are even multiple model transformations for the same modelling language. Also time and again new versions of metamodels, e.g. the metamodels for UML 1.x and UML 2.x, are released. New model transformations have to be developed and existing model transformations have to be adjusted, whenever new versions replace the old ones. Though visual representations (concrete syntax) should be decoupled from internal representation (abstract syntax), in many cases different concrete syntax is considered in model transformations providing e.g. views on models. ? Different semantics: Since the semantics of modelling languages’ concepts is rarely formally specified (in the UML specification this is plain English), different people and organisations can associate different semantics with the same concepts used in the metamodel. By applying special model styles and representation guidelines this is often done consciously, especially within the boundaries of one enterprise. Again, model transformations have to be specified enabling sensible exchange of models according to the respective interpretation of the involved partners. Enabling seamless inter-organisational collaboration and system development despite of this heterogeneity in modelling, automation is needed to share models amongst various organizations, maintain existing model transformations as well as to adjust existing model transformations.
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5 The Approach of Ontology-based Model Transformation
To overcome those problems, ontMT facilitates methods to generate model transformations despite of structural and semantic differences of metamodels by applying semantic technologies of Ontology TS. Different versions of metamodels are bound to a reference ontology of a certain domain (see figure 3). Bindings (sem. Annotation) specify the semantic mapping from metamodels to the semantics of their concepts, i.e. to the reference ontology. To generate model transformations for various modelling languages ontMT makes use of reasoning mechanisms. The metamodels and the reference ontology are given, while the bindings of the metamodels to the reference ontology have to be specified. Finally an initial model transformation is needed, which is either given or generated automatically. The initial model transformation is a model transformation (e.g. from metamodel v1.5 to metamodel v2.0) in which transformation rules (and especially the semantics of the model transformations) are encoded and can be generated automatically in a bootstrapping process in the case of mappings. If e.g. a new model transformation from metamodel v1.5 to metamodel v2.1 has to be generated; only the delta between metamodel v2.0 and v2.1 has to be considered. The new model transformation is generated by substituting the concepts of metamodel v2.0 with the concepts of metamodel v2.1 in the initial model transformation.
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Fig. 3. Ontology-based model transformation – overall approach
The ontology-based model transformation component consists of three main components: A model manipulator, an inference component and a Sem-MTComponent. The model manipulator and the inference component operate essentially in a single technological space, i.e. in the MDA and the Ontology TS respectively. The Sem-MT-Component establishes the link between the two TS, by using the reasoning results to trigger the modification of the model transformation’s model (for a more detailed description and examples see [18]). The reasoner of the inference component is triggered by a set of rules specific to the model transformation approach. The reasoning computes information about all relationships important for ontMT (the main relationships between ontology elements identified in [19]: equivalence, containment, and overlap) and adds them to a knowledge base. The knowledge base can be queried for these relationships. The model manipulator provides modification operations on model transformations (the model transformations are models) and the respective metamodels. It solely works on the abstract syntax of the (meta)models in the MDA TS. It also checks, whether the modifications proposed by the inference component can sensibly be applied, i.e. if the proposed substitution produces type conformance errors or connections between transformation rules are lost. The Sem-MT-Component implements the core part of the ontMT approach. It realizes the main functionality of ontology-based model transformation by using inference results of the Ontology TS to gain a queue of adjustment operators for the modification and generation of model transformations in the MDA TS. After the model manipulator has identified metamodel’s concepts, which have to be substituted in the model transformation, the Sem-MT-Component queries the inference component and computes the ‘best possible’ substitution of the metamodel’s concepts (as a queue of adjustment operators) using heuristics.
6 Automating Model Transformation Development
Model transformations between various modelling languages can be automatically derived and generated by the ontMT approach (see figure 3). In this section we
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describe the procedure to generate mappings (i.e. semantically identical model transformations) between a modelling language A and a modelling language B. For both languages exists abstract syntax NA/NB in various technological spaces: A has (like B) an abstract syntax in the MDA TS NA-mda and the Ontology TS NA-ont which are synchronized. Thus we can work with the syntax and the capability of that technological space better suited for solving a problem (see figure 4a). Semantics of the concepts is described by the means of the semantic domain SD and its notation (e.g. OWL) in a reference ontology NRO. Semantics of languages is defined by semantic mappings to the semantic domain MA: A → SD and MB: B → SD. The ontological grounding1 is a notation of the semantic mapping from NA-ont to NRO. The goal of the transformation to generate is to define ‘identity’ relationships between the concepts of A and B. The model transformation MTmapAB: A ? B between A and B has the following semantics MMTmapAB: MTmapAB → id, where id is the identical mapping.
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Fig. 4. a) Modelling language, semantic mapping, semantic domain and their representations; b) Procedure of automated mapping generation
The generation procedure works on the model of the model transformation and the models of the modelling languages, and exploits the ontological grounding to the reference ontology. On the basis of reasoning results gained in the Ontology TS, modification operations are called to obtain the new model transformation working solely on the model of the model transformation and the metamodels. To generate the model transformation MTmapAB the following steps are performed (see figure 4b): ? An initial model transformation MTmapAA: A ? A is automatically generated, mapping A on itself. This bootstrapping step is necessary to obtain a first model of the model transformation (transforming NA to NA') 2, which only has to be adjusted by modifications operators. Assuming the same ontological grounding for NA and NA', the bootstrap model transformation is an id: MMTmapAA: MTmapAA → id. ? The inference engine derives interrelationships in between NA' and NB in the Ontology TS. This is possible, since both NA' and NB are mapped to the
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The definition of the ontological grounding is a semantic annotation comprising static semantics of the metamodels, i.e. the semantics of the concepts, i.e. an ontology. 2 A simple version of such a mapping can easily be generated on basis of a metamodel in the MDA TS. By traversing the metamodel via its containment relationships the appropriate mapping rules can be generated.
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same reference ontology NRO. It is computed, how the concepts of NA' can be substituted by semantically identical concepts of NB (σ(MTmapAA)=MTmapAB). Those interrelationships can be transferred to the MDA TS as the modelling languages A and B have synchronous representations in both MDA TS and Ontology TS. ? Finally the concepts of NA' are substituted with the concepts of NB in the model of MTmapAA and we obtain a model of the model transformation MTmapAB with MMTmapAB: MTmapAB→ id. The substitution is performed via modification operations on the abstract syntax (model) of the model transformation MTmapAA in MDA TS. The first (bootstrapping) step helps to extend our approach to scenarios in which given model transformations have to be adjusted to modelling languages and metamodels for which they initially have not been designed. The bootstrap transformation is simply replaced by a given (already existing) transformation and step 2 and 3 can be performed like described above. Avoiding to derive model transformations directly from ontologies results in a more flexible and wellstructured architecture. OntMT can both generate new model transformations and easily reuse knowledge encoded in existing transformations. Issues concerning the model transformation, like checking if its model conforms to the QVT metamodel or considering the cardinality of associations’ ends, are all dealt within the MDA TS. The Sem-MT-Component invokes modifications operations on the basis of the reasoning results and the application of heuristics. More detailed technical description of the approach can be found in [18].
7 Application of our Contributions to Interoperability Scenarios
In this section we discuss how the ontology-based model transformation approach contributes in solving the challenges of collaborative modelling presented in section 3. The overview in figure 5 depicts two different types of model transformations in the horizontal domain, mapping and refinement, and the challenges for applying model transformations for collaborative modelling in the vertical domain. Mappings are model transformations on a certain level of abstraction where no information is lost and no additional information is added to the models. Refinements are model transformations adding additional information about e.g. architecture or platform to the generated model. Thus the target model of a refinement is more detailed than the source model. Previously in the motivation we have identified two main challenges for realising smooth cross-organisational MDD: model sharing and model transformation maintenance. In model sharing scenarios collaborating organisations exchange information either directly or via a shared modelling space by the means of their models and model transformations. New organisations join the information exchange, though often there exists no transformations for their information representation (i.e. their models). In maintenance scenarios model transformations for transforming certain models already exist. Due to the continuous evolution of modelling languages, metamodels and modelling styles used for modelling by the organisations existing model transformation have to be adjusted.
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The values of the table in figure 5 represent how the ontology-based model transformation approach supports interoperability in collaborative modelling for the various scenarios. For example for model sharing between different metamodels ontMT can most likely generate model transformations automatically for exchanging those models. The automated generation also includes automated generation of the necessary bootstrap model transformation. In the case of automated modification (autom.mod.) existing model transformations are e.g. adjusted to new source or target metamodels. If there exists no initial model transformation and it cannot be generated automatically, the initial model transformation has to be specified manually (man.).
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Fig. 5. Application of ontMT to model sharing and model transformation maintenance
OntMT supports the collaborative modelling challenges and model transformation types in different ways regarding the problems of different DSLs, metamodels and modelling styles described in section 4. In the case of dealing with different and new metamodels and modelling styles ontMT is able to automatically generate mappings for model sharing. OntMT supports maintenance of existing mappings in two different ways, either generating new mappings or modifying existing ones. The more individual features, which are different to the core structure of the metamodels, are encoded in existing mappings, the more preferable it is to modify this mappings. Generating new mappings will be preferred, if the new metamodel provides extensions to the old ones or new modelling styles specify fundamental different composition of modelling elements. For maintaining refinements ontMT provides the possibility of automated modification and adjustment. These model transformations cannot be totally automatically generated, since individual knowledge about software architecture or platform is encoded, e.g. the application of patterns like broker, model-view-controller, etc.. Model transformations between different DSLs are supported by ontMT in a similar way. Whether it is possible to generate the mapping totally automatically depends on how different the DSLs and their modelling approaches are. In ontMT knowledge about the concepts of DSLs is captured in bindings to the reference ontology. So, if two DSLs totally differ in e.g. their modelling approaches their bindings will be to mostly unconnected subsets of the reference ontology. In order to generate model transformations with ontMT additional transformation knowledge could be encoded in the bindings. However, in our opinion, the better solution is to encode this transformation knowledge in an initial model transformation. If the DSLs can be sensibly connected to similar sets of the RO, we obtain reasoning result which can be used for fully automated model transformation generation and adjustment.
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8 Related Work
In [21], the authors introduce model typing as extension of object-oriented typing and propose an algorithm for checking the conformance of model types. It is presented, how model typing permits more flexible reuse of model transformations across various metamodels while preserving type safety. This approach improves reuse of model transformations whenever small changes to metamodels occur, e.g. like altering the cardinality of an association. In case of major change in models’ representation formats or modelling guidelines model transformations still have to be modified manually. Furthermore automatic mapping generation is not provided. The ATLAS Model Weaver (AMW) tool implements the model weaving approach introduced in [3]. It enables the representation of correspondences between models, in so-called weaving models, from which model transformations can be generated. Nevertheless, though model weaving is to improve efficiency in the creation and maintenance of model transformations, creating weaving links is not automatic and weaving models have to be adjusted whenever changes DSLs, metamodels or model guidelines of source and target models are made. The Model-based Semantic Mapping Framework (Semaphore) [15] follows a similar approach as the ATLAS Model Weaver. By aiming to support mappings between domain models, it supports (graphically) specification of mappings between DSLs. These specifications are saved in mapping model, which for example can be used to generate code for transforming the models. Like in AMW mappings have to be specified manually and have to be adjusted when ever changes to the model representations or modelling guidelines occur. The ATHENA Semantic Suite provides tools for improving interoperability between organizations. In this approach e.g. XML Schemas can be annotated by a reference ontology and reasoning rules can be specified, so that reasoner can convert XML documents. The reasoner can be used as mediator transforming messages at runtime. This approach could be extended to modelling by transforming XML serialisations of models. The problem is that there would be no traceability of transformation executions between models. However this is a key feature for MDD [7] and also cross-organisational modelling. Since this is provided by model transformation languages it is also supported by our approach.
9 Summary
The approach of ontology-based model transformation provides interoperability technology for collaborative modelling. It integrates ontologies in MDD and makes use of the reasoning capabilities of the Ontology TS. By automated generation of mappings it offers new possibilities for the integration of domain specific languages and ‘legacy’ models in a plug&play manner, making it easier for new organisations to join collaborations. OntMT also supports organisations evolving their modelling techniques like using new and more advanced versions of modelling languages as well as developing their own modelling styles by supporting automated maintenance of existing model transformations. Furthermore we can think of generic initial model transformations encoding knowledge e.g.
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about software or platform architecture independent, which can be used as references by organisations intending to apply certain methodologies. Nevertheless our approach uses additional information, which has to be provided by the people developing metamodels and domain specific languages. Hopefully these ontological groundings can also be used by other tools using semantic technology. Problems also arise, when no appropriate reference ontology exists. In those cases techniques for matching and merging ontologies, like linguistic, schema-based or probabilistic approaches, combined with human intervention have to be applied to obtain a suitable reference ontology.
References
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常用整流二极管 型号VRM/Io IFSM/ VF /Ir 封装用途说明1A5 600V/1.0A 25A/1.1V/5uA[T25] D2.6X3.2d0.65 1A6 800V/1.0A 25A/1.1V/5uA[T25] D2.6X3.2d0.65 6A8 800V/6.0A 400A/1.1V/10uA[T60] D9.1X9.1d1.3 1N4002 100V/1.0A 30A/1.1V/5uA[T75] D2.7X5.2d0.9 1N4004 400V/1.0A 30A/1.1V/5uA[T75] D2.7X5.2d0.9 1N4006 800V/1.0A 30A/1.1V/5uA[T75] D2.7X5.2d0.9 1N4007 1000V/1.0A 30A/1.1V/5uA[T75] D2.7X5.2d0.9 1N5398 800V/1.5A 50A/1.4V/5uA[T70] D3.6X7.6d0.9 1N5399 1000V/1.5A 50A/1.4V/5uA[T70] D3.6X7.6d0.9 1N5402 200V/3.0A 200A/1.1V/5uA[T105] D5.6X9.5d1.3 1N5406 600V/3.0A 200A/1.1V/5uA[T105] D5.6X9.5d1.3 1N5407 800V/3.0A 200A/1.1V/5uA[T105] D5.6X9.5d1.3 1N5408 1000V/3.0A 200A/1.1V/5uA[T105] D5.6X9.5d1.3 RL153 200V/1.5A 60A/1.1V/5uA[T75] D3.6X7.6d0.9 RL155 600V/1.5A 60A/1.1V/5uA[T75] D3.6X7.6d0.9 RL156 800V/1.5A 60A/1.1V/5uA[T75] D3.6X7.6d0.9 RL203 200V/2.0A 70A/1.1V/5uA[T75] D3.6X7.6d0.9 RL205 600V/2.0A 70A/1.1V/5uA[T75] D3.6X7.6d0.9 RL206 800V/2.0A 70A/1.1V/5uA[T75] D3.6X7.6d0.9 RL207 1000V/2.0A 70A/1.1V/5uA[T75] D3.6X7.6d0.9 RM11C 1000V/1.2A 100A/0.92V/10uA D4.0X7.2d0.78 MR750 50V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 MR751 100V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 MR752 200V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 MR754 400V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 MR756 600V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 MR760 1000V/6.0A 400A/1.25V/25uA D8.7x6.3d1.35 常用整流二极管(全桥） 型号VRM/Io IFSM/ VF /Ir 封装用途说明RBV-406 600V/*4A 80A/1.10V/10uA 25X15X3.6 RBV-606 600V/*6A 150A/1.05V/10uA 30X20X3.6 RBV-1306 600V/*13A 80A/1.20V/10uA 30X20X3.6 RBV-1506 600V/*15A 200A/1.05V/50uA 30X20X3.6 RBV-2506 600V/*25A 350A/1.05V/50uA 30X20X3.6 常用肖特基整流二极管SBD 型号VRM/Io IFSM/ VF Trr1/Trr2 封装用途说明EK06 60V/0.7A 10A/0.62V 100nS D2.7X5.0d0.6 SK/高速 EK14 40V/1.5A 40A/0.55V 200nS D4.0X7.2d0.78 SK/低速 D3S6M 60V/3.0A 80A/0.58V 130p SB340 40V/3.0A 80A/0.74V 180p SB360 60V/3.0A 80A/0.74V 180p SR260 60V/2.0A 50A/0.70V 170p MBR1645 45V/16A 150A/0.65V <10nS TO220 超高速
常用二极管参数 2008-10-22 11:48 05Z6.2Y 硅稳压二极管 Vz=6~6.35V, Pzm=500mW, 05Z7.5Y 硅稳压二极管 Vz=7.34~7.70V, Pzm=500mW, 05Z13X 硅稳压二极管 Vz=12.4~13.1V, Pzm=500mW, 05Z15Y 硅稳压二极管 Vz=14.4~15.15V, Pzm=500mW, 05Z18Y 硅稳压二极管 Vz=17.55~18.45V, Pzm=500mW, 1N4001 硅整流二极管 50V, 1A,(Ir=5uA, Vf=1V, Ifs=50A) 1N4002 硅整流二极管 100V, 1A, 1N4003 硅整流二极管 200V, 1A, 1N4004 硅整流二极管 400V, 1A, 1N4005 硅整流二极管 600V, 1A, 1N4006 硅整流二极管 800V, 1A, 1N4007 硅整流二极管 1000V, 1A, 1N4148 二极管 75V, 4PF, Ir=25nA, Vf=1V, 1N5391 硅整流二极管 50V, 1.5A,(Ir=10uA, Vf=1.4V, Ifs=50A) 1N5392 硅整流二极管 100V, 1.5A, 1N5393 硅整流二极管 200V, 1.5A, 1N5394 硅整流二极管 300V, 1.5A, 1N5395 硅整流二极管 400V, 1.5A, 1N5396 硅整流二极管 500V, 1.5A, 1N5397 硅整流二极管 600V, 1.5A, 1N5398 硅整流二极管 800V, 1.5A, 1N5399 硅整流二极管 1000V, 1.5A, 1N5400 硅整流二极管 50V, 3A,(Ir=5uA, Vf=1V, Ifs=150A) 1N5401 硅整流二极管 100V, 3A, 1N5402 硅整流二极管 200V, 3A, 1N5403 硅整流二极管 300V, 3A, 1N5404 硅整流二极管 400V, 3A, 1N5405 硅整流二极管 500V, 3A, 1N5406 硅整流二极管 600V, 3A, 1N5407 硅整流二极管 800V, 3A, 1N5408 硅整流二极管 1000V, 3A, 1S1553 硅开关二极管 70V, 100mA, 300mW, 3.5PF, 300ma, 1S1554 硅开关二极管 55V, 100mA, 300mW, 3.5PF, 300ma, 1S1555 硅开关二极管 35V, 100mA, 300mW, 3.5PF, 300ma, 1S2076 硅开关二极管 35V, 150mA, 250mW, 8nS, 3PF, 450ma, Ir≤1uA, Vf≤0.8V,≤1.8PF, 1S2076A 硅开关二极管 70V, 150mA, 250mW, 8nS, 3PF, 450ma, 60V, Ir≤1uA, Vf≤0.8V,≤1.8PF, 1S2471 硅开关二极管80V, Ir≤0.5uA, Vf≤1.2V,≤2PF, 1S2471B 硅开关二极管 90V, 150mA, 250mW, 3nS, 3PF, 450ma, 1S2471V 硅开关二极管 90V, 130mA, 300mW, 4nS, 2PF, 400ma, 1S2472 硅开关二极管50V, Ir≤0.5uA, Vf≤1.2V,≤2PF, 1S2473 硅开关二极管35V, Ir≤0.5uA, Vf≤1.2V,≤3PF,
Join In剑桥小学英语(改编版)入门阶段Unit 1Hello,hello第1单元嗨,嗨 and mime. 1 听,模仿 Stand up Say 'hello' Slap hands Sit down 站起来说"嗨" 拍手坐下来 Good. Let's do up Say 'hello' Slap hands Sit down 好. 我们来再做一遍.站起来说"嗨"拍手坐下来 the pictures. 2 再听一遍给图画编号. up "hello" hands down 1 站起来 2 说"嗨" 3 拍手 4 坐下来说 3. A ,what's yourname 3 一首歌嗨,你叫什么名字 Hello. , what's yourname Hello. Hello. 嗨. 嗨. 嗨, 你叫什么名字嗨,嗨. Hello, what's yourname I'm Toby. I'm Toby. Hello,hello,hello.嗨, 你叫什么名字我叫托比. 我叫托比 . 嗨,嗨,嗨. I'm Toby. I'm Toby. Hello,hello, let's go! 我是托比. 我是托比. 嗨,嗨, 我们一起! Hello. , what's yourname I'm 'm Toby. 嗨.嗨.嗨, 你叫什么名字我叫托比.我叫托比. Hello,hello,hello. I'm 'm Toby. Hello,hello,let's go! 嗨,嗨,嗨.我是托比. 我是托比. 嗨,嗨,我们一起! 4 Listen and stick 4 听和指 What's your name I'm Bob. 你叫什么名字我叫鲍勃. What's your name I'm Rita. What's your name I'm Nick. 你叫什么名字我叫丽塔. 你叫什么名字我叫尼克. What's your name I'm Lisa. 你叫什么名字我叫利萨. 5. A story-Pit'sskateboard. 5 一个故事-彼德的滑板. Pa:Hello,Pit. Pa:好,彼德. Pi:Hello,:What's this Pi:嗨,帕特.Pa:这是什么 Pi:My new :Look!Pi:Goodbye,Pat! Pi:这是我的新滑板.Pi:看!Pi:再见,帕特! Pa:Bye-bye,Pit!Pi:Help!Help!pi:Bye-bye,skateboard! Pa:再见,彼德!Pi:救命!救命!Pi:再见,滑板! Unit 16. Let's learnand act 第1单元6 我们来边学边表演.
1.塑封整流二极管 序号型号IF VRRM VF Trr 外形 A V V μs 1 1A1-1A7 1A 50-1000V 1.1 R-1 2 1N4001-1N4007 1A 50-1000V 1.1 DO-41 3 1N5391-1N5399 1.5A 50-1000V 1.1 DO-15 4 2A01-2A07 2A 50-1000V 1.0 DO-15 5 1N5400-1N5408 3A 50-1000V 0.95 DO-201AD 6 6A05-6A10 6A 50-1000V 0.95 R-6 7 TS750-TS758 6A 50-800V 1.25 R-6 8 RL10-RL60 1A-6A 50-1000V 1.0 9 2CZ81-2CZ87 0.05A-3A 50-1000V 1.0 DO-41 10 2CP21-2CP29 0.3A 100-1000V 1.0 DO-41 11 2DZ14-2DZ15 0.5A-1A 200-1000V 1.0 DO-41 12 2DP3-2DP5 0.3A-1A 200-1000V 1.0 DO-41 13 BYW27 1A 200-1300V 1.0 DO-41 14 DR202-DR210 2A 200-1000V 1.0 DO-15 15 BY251-BY254 3A 200-800V 1.1 DO-201AD 16 BY550-200～1000 5A 200-1000V 1.1 R-5 17 PX10A02-PX10A13 10A 200-1300V 1.1 PX 18 PX12A02-PX12A13 12A 200-1300V 1.1 PX 19 PX15A02-PX15A13 15A 200-1300V 1.1 PX 20 ERA15-02～13 1A 200-1300V 1.0 R-1 21 ERB12-02～13 1A 200-1300V 1.0 DO-15 22 ERC05-02～13 1.2A 200-1300V 1.0 DO-15 23 ERC04-02～13 1.5A 200-1300V 1.0 DO-15 24 ERD03-02～13 3A 200-1300V 1.0 DO-201AD 25 EM1-EM2 1A-1.2A 200-1000V 0.97 DO-15 26 RM1Z-RM1C 1A 200-1000V 0.95 DO-15 27 RM2Z-RM2C 1.2A 200-1000V 0.95 DO-15 28 RM11Z-RM11C 1.5A 200-1000V 0.95 DO-15 29 RM3Z-RM3C 2.5A 200-1000V 0.97 DO-201AD 30 RM4Z-RM4C 3A 200-1000V 0.97 DO-201AD 2.快恢复塑封整流二极管 序号型号IF VRRM VF Trr 外形 A V V μs （1）快恢复塑封整流二极管 1 1F1-1F7 1A 50-1000V 1.3 0.15-0.5 R-1 2 FR10-FR60 1A-6A 50-1000V 1. 3 0.15-0.5 3 1N4933-1N4937 1A 50-600V 1.2 0.2 DO-41 4 1N4942-1N4948 1A 200-1000V 1.3 0.15-0. 5 DO-41 5 BA157-BA159 1A 400-1000V 1.3 0.15-0.25 DO-41 6 MR850-MR858 3A 100-800V 1.3 0.2 DO-201AD
常用稳压管型号对照——（朋友发的） 美标稳压二极管型号 1N4727 3V0 1N4728 3V3 1N4729 3V6 1N4730 3V9 1N4731 4V3 1N4732 4V7 1N4733 5V1 1N4734 5V6 1N4735 6V2 1N4736 6V8 1N4737 7V5 1N4738 8V2 1N4739 9V1 1N4740 10V 1N4741 11V 1N4742 12V 1N4743 13V 1N4744 15V 1N4745 16V 1N4746 18V 1N4747 20V 1N4748 22V 1N4749 24V 1N4750 27V 1N4751 30V 1N4752 33V 1N4753 36V 1N4754 39V 1N4755 43V 1N4756 47V 1N4757 51V 需要规格书请到以下地址下载， 经常看到很多板子上有M记的铁壳封装的稳压管，都是以美标的1N系列型号标识的，没有具体的电压值，刚才翻手册查了以下3V至51V的型号与电压的对 照值，希望对大家有用 1N4727 3V0 1N4728 3V3 1N4729 3V6 1N4730 3V9
1N4733 5V1 1N4734 5V6 1N4735 6V2 1N4736 6V8 1N4737 7V5 1N4738 8V2 1N4739 9V1 1N4740 10V 1N4741 11V 1N4742 12V 1N4743 13V 1N4744 15V 1N4745 16V 1N4746 18V 1N4747 20V 1N4748 22V 1N4749 24V 1N4750 27V 1N4751 30V 1N4752 33V 1N4753 36V 1N4754 39V 1N4755 43V 1N4756 47V 1N4757 51V DZ是稳压管的电器编号，是和1N4148和相近的，其实1N4148就是一个0.6V的稳压管，下面是稳压管上的编号对应的稳压值，有些小的稳压管也会在管体 上直接标稳压电压，如5V6就是5.6V的稳压管。 1N4728A 3.3 1N4729A 3.6 1N4730A 3.9 1N4731A 4.3 1N4732A 4.7 1N4733A 5.1 1N4734A 5.6 1N4735A 6.2 1N4736A 6.8 1N4737A 7.5 1N4738A 8.2 1N4739A 9.1 1N4740A 10 1N4741A 11 1N4742A 12 1N4743A 13
Join In剑桥小学英语(改编版)入门阶段 Unit 1Hello,hello第1单元嗨,嗨 1.Listen and mime. 1 听,模仿 Stand up Say 'hello' Slap hands Sit down 站起来说"嗨" 拍手坐下来 Good. Let's do itagain.Stand up Say 'hello' Slap hands Sit down 好. 我们来再做一遍.站起来说"嗨"拍手坐下来 2.listen again.Number the pictures. 2 再听一遍给图画编号. 1.Stand up 2.Say "hello" 3.Slap hands 4.Sit down 1 站起来 2 说"嗨" 3 拍手 4 坐下来说 3. A song.Hello,what's yourname? 3 一首歌嗨,你叫什么名字? Hello. Hello.Hello, what's yourname? Hello. Hello. 嗨. 嗨. 嗨, 你叫什么名字? 嗨,嗨. Hello, what's yourname? I'm Toby. I'm Toby. Hello,hello,hello. 嗨, 你叫什么名字? 我叫托比. 我叫托比 . 嗨,嗨,嗨. I'm Toby. I'm Toby. Hello,hello, let's go! 我是托比. 我是托比. 嗨,嗨, 我们一起! Hello. Hello.Hello, what's yourname? I'm Toby.I'm Toby. 嗨.嗨.嗨, 你叫什么名字? 我叫托比.我叫托比. Hello,hello,hello. I'm Toby.I'm Toby. Hello,hello,let's go! 嗨,嗨,嗨.我是托比. 我是托比. 嗨,嗨,我们一起! 4 Listen and stick 4 听和指 What's your name? I'm Bob. 你叫什么名字? 我叫鲍勃. What's your name ? I'm Rita. What's your name ? I'm Nick. 你叫什么名字? 我叫丽塔. 你叫什么名字? 我叫尼克. What's your name ? I'm Lisa. 你叫什么名字? 我叫利萨. 5. A story-Pit'sskateboard. 5 一个故事-彼德的滑板. Pa:Hello,Pit. Pa:好,彼德. Pi:Hello,Pat.Pa:What's this? Pi:嗨,帕特.Pa:这是什么? Pi:My new skateboard.Pi:Look!Pi:Goodbye,Pat! Pi:这是我的新滑板.Pi:看!Pi:再见,帕特! Pa:Bye-bye,Pit!Pi:Help!Help!pi:Bye-bye,skateboard! Pa:再见,彼德!Pi:救命!救命!Pi:再见,滑板! Unit 16. Let's learnand act 第1单元6 我们来边学边表演.
二极管封装大全 篇一：贴片二极管型号、参数 贴片二极管型号.参数查询 1、肖特基二极管SMA（DO214AC） 2010-2-2 16:39:35 标准封装： SMA 2010 SMB 2114 SMC 3220 SOD123 1206 SOD323 0805 SOD523 0603 IN4001的封装是1812 IN4148的封装是1206 篇二：常见贴片二极管三极管的封装 常见贴片二极管/三极管的封装 常见贴片二极管/三极管的封装 二极管： 名称尺寸及焊盘间距其他尺寸相近的封装名称 SMC SMB SMA SOD-106 SC-77A SC-76/SC-90A SC-79 三极管： LDPAK
DPAK SC-63 SOT-223 SC-73 TO-243/SC-62/UPAK/MPT3 SC-59A/SOT-346/MPAK/SMT3 SOT-323 SC-70/CMPAK/UMT3 SOT-523 SC-75A/EMT3 SOT-623 SC-89/MFPAK SOT-723 SOT-923 VMT3 篇三：常用二极管的识别及ic封装技术 常用晶体二极管的识别 晶体二极管在电路中常用“D”加数字表示，如： D5表示编号为5的二极管。 1、作用：二极管的主要特性是单向导电性，也就是在正向电压的作用下，导通电阻很小;而在反向电压作用下导通电阻极大或无穷大。正因为二极管具有上述特性，无绳电话机中常把它用在整流、隔离、稳压、极性保护、编码控制、调频调制和静噪等电路中。 电话机里使用的晶体二极管按作用可分为：整流二极管(如1N4004)、隔离二极管(如1N4148)、肖特基二极管(如BAT85)、发光二极管、稳压二极管等。 2、识别方法：二极管的识别很简单，小功率二极管的N极(负极)，在二极管外表大多采用一种色圈标出来，有些二极管也用二极管专用符号来表示P极(正极)或N极(负极)，也有采用符号标志为“P”、“N”来确定二极管极性的。发光二极管的正负极可从引脚长短来识别，长
1N 系列常用整流二极管的主要参数
反向工作 峰值电压 URM/V 额定正向 整流电流 整流电流 IF/A 正向不重 复浪涌峰 值电流 IFSM/A 正向 压降 UF/V 反向 电流 IR/uA 工作 频率 f/KHZ 外形 封装
型 号
1N4000 1N4001 1N4002 1N4003 1N4004 1N4005 1N4006 1N4007 1N5100 1N5101 1N5102 1N5103 1N5104 1N5105 1N5106 1N5107 1N5108 1N5200 1N5201 1N5202 1N5203 1N5204 1N5205 1N5206 1N5207 1N5208 1N5400 1N5401 1N5402 1N5403 1N5404 1N5405 1N5406 1N5407 1N5408
25 50 100 200 400 600 800 1000 50 100 200 300 400 500 600 800 1000 50 100 200 300 400 500 600 800 1000 50 100 200 300 400 500 600 800 1000
1
30
≤1
＜5
3
DO-41
1．5
75
≤1
＜5
3
DO-15
2
100
≤1
＜10
3
3
150
≤0.8
＜10
3
DO-27
常用二极管参数： 05Z6.2Y 硅稳压二极管 Vz=6~6.35V,Pzm=500mW,
公司登记（备案）申请书 注：请仔细阅读本申请书《填写说明》，按要求填写。 □基本信息 名称 名称预先核准文号/ 注册号/统一 社会信用代码 住所 省（市/自治区）市（地区/盟/自治州）县（自治县/旗/自治旗/市/区）乡（民族乡/镇/街道）村（路/社区）号 生产经营地 省（市/自治区）市（地区/盟/自治州）县（自治县/旗/自治旗/市/区）乡（民族乡/镇/街道）村（路/社区）号 联系电话邮政编码 □设立 法定代表人 姓名 职务□董事长□执行董事□经理注册资本万元公司类型 设立方式 （股份公司填写） □发起设立□募集设立经营范围 经营期限□年□长期申请执照副本数量个
□变更 变更项目原登记内容申请变更登记内容 □备案 分公司 □增设□注销名称 注册号/统一 社会信用代码登记机关登记日期 清算组 成员 负责人联系电话 其他□董事□监事□经理□章程□章程修正案□财务负责人□联络员 □申请人声明 本公司依照《公司法》、《公司登记管理条例》相关规定申请登记、备案，提交材料真实有效。通过联络员登录企业信用信息公示系统向登记机关报送、向社会公示的企业信息为本企业提供、发布的信息，信息真实、有效。 法定代表人签字：公司盖章（清算组负责人）签字：年月日
附表1 法定代表人信息 姓名固定电话 移动电话电子邮箱 身份证件类型身份证件号码 （身份证件复印件粘贴处） 法定代表人签字：年月日
附表2 董事、监事、经理信息 姓名职务身份证件类型身份证件号码_______________ （身份证件复印件粘贴处） 姓名职务身份证件类型身份证件号码_______________ （身份证件复印件粘贴处） 姓名职务身份证件类型身份证件号码_______________ （身份证件复印件粘贴处）
《剑桥小学英语Join In ——Book 3 下学期》教材教法分析2012-03-12 18:50:43| 分类：JOIN IN 教案| 标签：|字号大中小订阅. 一、学情分析： 作为毕业班的学生，六年级的孩子在英语学习上具有非常显著的特点： 1、因为教材的编排体系和课时不足，某些知识学生已遗忘，知识回生的现象很突出。 2、有的学生因受学习习惯及学习能力的制约，有些知识掌握较差，学生学习个体的差异性，学习情况参差不齐、两级分化的情况明显，对英语感兴趣的孩子很喜欢英语，不喜欢英语的孩子很难学进去了。 3、六年级的孩子已经进入青春前期，他们跟三、四、五年级的孩子相比已经有了很大的不同。他们自尊心强，好胜心强，集体荣誉感也强，有自己的评判标准和思想，对知识的学习趋于理性化，更有自主学习的欲望和探索的要求。 六年级学生在英语学习上的两极分化已经给教师的教学带来很大的挑战，在教学中教师要注意引导学生调整学习方式： 1、注重培养学生自主学习的态度 如何抓住学习课堂上的学习注意力，吸引他们的视线，保持他们高涨的学习激情，注重过程的趣味化和学习内容的简易化。 2、给予学生独立思考的空间。 3、鼓励学生坚持课前预习、课后复习的好习惯。 六年级教材中的单词、句子量比较多，难点也比较多，学生课前回家预习，不懂的地方查英汉词典或者其它资料，上课可以达到事半功倍的效果，课后复习也可以很好的消化课堂上的内容。 4、注意培养学生合作学习的习惯。 5、重在培养学生探究的能力：学习内容以问题的方式呈现、留给学生更多的发展空间。 二、教材分析： （一）.教材特点： 1．以学生为主体，全面提高学生的素质。
1N系列稳压管
快恢复整流二极管
常用整流二极管型号和参数 05Z6.2Y 硅稳压二极管 Vz=6~6.35V,Pzm=500mW, 05Z7.5Y 硅稳压二极管 Vz=7.34~7.70V,Pzm=500mW, 05Z13X硅稳压二极管 Vz=12.4~13.1V,Pzm=500mW, 05Z15Y硅稳压二极管 Vz=14.4~15.15V,Pzm=500mW, 05Z18Y硅稳压二极管 Vz=17.55~18.45V,Pzm=500mW, 1N4001硅整流二极管 50V, 1A,(Ir=5uA,Vf=1V,Ifs=50A) 1N4002硅整流二极管 100V, 1A, 1N4003硅整流二极管 200V, 1A, 1N4004硅整流二极管 400V, 1A, 1N4005硅整流二极管 600V, 1A, 1N4006硅整流二极管 800V, 1A, 1N4007硅整流二极管 1000V, 1A, 1N4148二极管 75V, 4PF,Ir=25nA,Vf=1V, 1N5391硅整流二极管 50V, 1.5A,(Ir=10uA,Vf=1.4V,Ifs=50A) 1N5392硅整流二极管 100V,1.5A, 1N5393硅整流二极管 200V,1.5A, 1N5394硅整流二极管 300V,1.5A, 1N5395硅整流二极管 400V,1.5A, 1N5396硅整流二极管 500V,1.5A, 1N5397硅整流二极管 600V,1.5A, 1N5398硅整流二极管 800V,1.5A, 1N5399硅整流二极管 1000V,1.5A, 1N5400硅整流二极管 50V, 3A,(Ir=5uA,Vf=1V,Ifs=150A) 1N5401硅整流二极管 100V,3A, 1N5402硅整流二极管 200V,3A, 1N5403硅整流二极管 300V,3A, 1N5404硅整流二极管 400V,3A,
有限合伙企业登记注册操作指南 风险控制部 20xx年x月xx日
目录 一、合伙企业的概念 (4) 二、有限合伙企业应具备的条件 (4) 三、有限合伙企业设立具备的条件 (4) 四、注册有限合伙企业程序 (5) 五、申请合伙企业登记注册应提交文件、证件 (6) （一）合伙企业设立登记应提交的文件、证件： (6) （二）合伙企业变更登记应提交的文件、证件： (7) （三）合伙企业注销登记应提交的文件、证件： (8) （四）合伙企业申请备案应提交的文件、证件： (9) （五）其他登记应提交的文件、证件： (9) 六、申请合伙企业分支机构登记注册应提交的文件、证件 (9) （一）合伙企业分支机构设立登记应提交的文件、证件 (10) （二）合伙企业分支机构变更登记应提交的文件、证件： (10) （三）合伙企业分支机构注销登记应提交的文件、证件： (11) （四）其他登记应提交的文件、证件： (12) 七、收费标准 (12) 八、办事流程图 (12) （一）有限合伙企业创办总体流程图（不含专业性前置审批） (12) （二）、工商局注册程序 (15)
（三）、工商局具体办理程序（引入网上预审核、电话预约方式） (16) 九、有限合伙企业与有限责任公司的区别 (16) （一）、设立依据 (16) （二）、出资人数 (16) （三）、出资方式 (17) （四）、注册资本 (17) （五）、组织机构 (18) （六）、出资流转 (18) （七）、对外投资 (19) （八）、税收缴纳 (20) （九）、利润分配 (20) （十）、债务承担 (21) 十、常见问题解答与指导 (21)
常用稳压二极管技术参数及老型号代换 型号最大功耗 (mW) 稳定电压(V) 电流(mA) 代换型号国产稳压管日立稳压管 HZ4B2 500 3.8 4.0 5 2CW102 2CW21 4B2 HZ4C1 500 4.0 4.2 5 2CW102 2CW21 4C1 HZ6 500 5.5 5.8 5 2CW103 2CW21A 6B1 HZ6A 500 5.2 5.7 5 2CW103 2CW21A HZ6C3 500 6 6.4 5 2CW104 2CW21B 6C3 HZ7 500 6.9 7.2 5 2CW105 2CW21C HZ7A 500 6.3 6.9 5 2CW105 2CW21C HZ7B 500 6.7 7.3 5 2CW105 2CW21C HZ9A 500 7.7 8.5 5 2CW106 2CW21D HZ9CTA 500 8.9 9.7 5 2CW107 2CW21E HZ11 500 9.5 11.9 5 2CW109 2CW21G HZ12 500 11.6 14.3 5 2CW111 2CW21H HZ12B 500 12.4 13.4 5 2CW111 2CW21H HZ12B2 500 12.6 13.1 5 2CW111 2CW21H 12B2 HZ18Y 500 16.5 18.5 5 2CW113 2CW21J HZ20-1 500 18.86 19.44 2 2CW114 2CW21K HZ27 500 27.2 28.6 2 2CW117 2CW21L 27-3 HZT33-02 400 31 33.5 5 2CW119 2CW21M RD2.0E(B) 500 1.88 2.12 20 2CW100 2CW21P 2B1 RD2.7E 400 2.5 2.93 20 2CW101 2CW21S RD3.9EL1 500 3.7 4 20 2CW102 2CW21 4B2 RD5.6EN1 500 5.2 5.5 20 2CW103 2CW21A 6A1 RD5.6EN3 500 5.6 5.9 20 2CW104 2CW21B 6B2 RD5.6EL2 500 5.5 5.7 20 2CW103 2CW21A 6B1 RD6.2E(B) 500 5.88 6.6 20 2CW104 2CW21B RD7.5E(B) 500 7.0 7.9 20 2CW105 2CW21C RD10EN3 500 9.7 10.0 20 2CW108 2CW21F 11A2 RD11E(B) 500 10.1 11.8 15 2CW109 2CW21G RD12E 500 11.74 12.35 10 2CW110 2CW21H 12A1 RD12F 1000 11.19 11.77 20 2CW109 2CW21G RD13EN1 500 12 12.7 10 2CW110 2CW21H 12A3 RD15EL2 500 13.8 14.6 15 2CW112 2CW21J 12C3 RD24E 400 22 25 10 2CW116 2CW21H 24-1
五 年 级 英 语 测 试 卷 学校 班级 姓名 听力部分（共20分） 一、Listen and colour . 听数字，涂颜色。（5分） 二、 Listen and tick . 听录音，在相应的格子里打“√”。 （6分） 三、Listen and number.听录音，标序号。（9分） pig fox lion cow snake duck
sheep 笔试部分（共80分） 一、Write the questions.将完整的句子写在下面的横线上。(10分) got it Has eyes on a farm it live sheep a it other animals eat it it Is 二、Look and choose.看看它们是谁，将字母填入括号内。(8分) A. B. C. D.
E. F. G. H. ( ) pig ( ) fox ( ) sheep ( ) cat ( ) snake ( ) lion ( ) mouse ( ) elephant 三、Look at the pictures and write the questions.看图片，根据答语写出相应的问题。(10分) No,it doesn’t. Yes,it is.
Yes,it does. Yes,it has. Yes,it does. 四、Choose the right answer.选择正确的答案。(18分) 1、it live on a farm？ 2. it fly？
3. it a cow？ 4. it eat chicken？ 5. you swim？ 6. you all right？ 五、Fill in the numbers.对话排序。(6分) Goodbye. Two apples , please. 45P , please. Thank you.
→客户提供：场所证明租赁协议身份证委托书三张一寸相片 →需准备材料：办理税务登记证时需要会计师资格证与财务人员劳动合同 →提交名称预审通知书→公司法定代表人签署的《公司设立登记申请书》→全体股东签署的《指定代表或者公共委托代理人的证明》（申请人填写股东姓名）→全体股东签署的公司章程（需得到工商局办事人员的认可）→股东身份证复印件→验资报告（需到计师事务所办理：需要材料有名称预审通知书复印件公司章程股东身份证复印件银行开具验资账户进账单原件银行开具询证函租赁合同及场所证明法人身份证原件公司开设临时存款账户的复印件）→任职文件（法人任职文件及股东董事会决议）→住所证明（房屋租赁合同）→工商局（办证大厅）提交所有材料→公司营业执照办理结束 →需带材料→公司营业执照正副本原件及复印件→法人身份证原件→代理人身份证→公章→办理人开具银行收据交款元工本费→填写申请书→组织机构代码证办
理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→公章→公司法定代表人签署的《公司设立登记申请书》→公司章程→股东注册资金情况表→验资报告书复印件→场所证明（租赁合同）→法人身份证复印件原件→会计师资格证（劳动合同）→税务登记证办理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→税务登记证原件及复印件→公章→法人身份证原件及复印件→代理人身份证原件及复印件→法人私章→公司验资账户→注以上复印件需四份→办理时间个工作日→办理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→公章→公司法定代表人签署的《公司设立登记申请书》→公司章程→股东注册资金情况表→验资报告书复印件→场所证明（租赁合同）→法人身份证复印件原件→会计师资格证（劳动合同）→会计制度→银行办理的开户许可证复印件→税务登记证备案办理结束
常用稳压管型号参数对照 3V到51V 1W稳压管型号对照表1N4727 3V0 1N4728 3V3 1N4729 3V6 1N4730 3V9 1N4731 4V3 1N4732 4V7 1N4733 5V1 1N4734 5V6 1N4735 6V2 1N4736 6V8 1N4737 7V5
1N4739 9V1 1N4740 10V 1N4741 11V 1N4742 12V 1N4743 13V 1N4744 15V 1N4745 16V 1N4746 18V 1N4747 20V 1N4748 22V 1N4749 24V 1N4750 27V 1N4751 30V
1N4753 36V 1N4754 39V 1N4755 43V 1N4756 47V 1N4757 51V 摩托罗拉IN47系列1W稳压管IN4728 3.3v IN4729 3.6v IN4730 3.9v IN4731 4.3 IN4732 4.7 IN4733 5.1
IN4735 6.2 IN4736 6.8 IN4737 7.5 IN4738 8.2 IN4739 9.1 IN4740 10 IN4741 11 IN4742 12 IN4743 13 IN4744 15 IN4745 16 IN4746 18 IN4747 20
IN4749 24 IN4750 27 IN4751 30 IN4752 33 IN4753 34 IN4754 35 IN4755 36 IN4756 47 IN4757 51 摩托罗拉IN52系列 0.5w精密稳压管IN5226 3.3v IN5227 3.6v
2011-2012 学年度上学期六年级复习题（Unit2-Unit3 ） 一、听力部分 1、听录音排序 ( ) () ()() () 2、听录音，找出与你所听到的单词属同一类的单词 （） 1. A. spaceman B. pond C . tiger （） 2. A.mascots B. potato C . jeans （） 3. A. door B. behind C . golden （） 4. A. sometimes B. shop C . prince （） 5. A. chair B. who C . sell 3、听录音，将下面人物与他的梦连线 Sarah Tim Juliet Jenny Peter 4、听短文，请在属于Mr. Brown的物品下面打√ ( ) ( ) ( ) ( ) ( ) ( ) ( ) 5、听问句选答句 （） 1. A. Yes, I am B. Yes, I have C . Yes, you do （） 2. A.Pink B. A friendship band C . Yes. （） 3. A. OK B. Bye-bye. C . Thanks, too. （） 4. A. Monday. B. Some juice. C . Kitty. （） 5. A. I ’ve got a shookbag. B. I ’m a student. C . It has got a round face. 6、听短文，选择正确答案 （） 1. Where is Xiaoqing from? She is from . A.Hebei B. Hubei C . Hunan （） 2. She goes to school at . A.7:00 B.7:30 C . 7:15 （） 3. How many classes in the afternoon? classes. A. four B. three C . two （） 4. Where is Xiaoqing at twelve o ’clock? She is . A. at home B. at school C .in the park （） 5. What does she do from seven to half past eight? She . A.watches TV B. reads the book C. does homework
G ENERAL PURPOSE RECTIFIERS – P LASTIC P ASSIVATED J UNCTION 1.0 M1 M2 M3 M4 M5 M6 M7 SMA/DO-214AC G ENERAL PURPOSE RECTIFIERS – G LASS P ASSIVATED J UNCTION S M 1.0 GS1A GS1B GS1D GS1G GS1J GS1K GS1M SMA/DO-214AC 1.0 S1A S1B S1D S1G S1J S1K S1M SMB/DO-214AA 2.0 S2A S2B S2D S2G S2J S2K S2M SMB/DO-214AA 3.0 S3A S3B S3D S3G S3J S3K S3M SMC/DO-214AB F AST RECOVERY RECTIFIERS – P LASTIC P ASSIVATED J UNCTION MERITEK ELECTRONICS CORPORATION
U LTRA FAST RECOVERY RECTIFIERS – G LASS P ASSIVATED J UNCTION
S CHOTTKY B ARRIER R ECTIFIERS
S WITCHING D IODES Power Dissipation Max Avg Rectified Current Peak Reverse Voltage Continuous Reverse Current Forward Voltage Reverse Recovery Time Package Part Number P a (mW) I o (mA) V RRM (V) I R @ V R (V) V F @ I F (mA) t rr (ns) Bulk Reel Outline 200mW 1N4148WS 200 150 100 2500 @ 75 1.0 @ 50 4 5000 SOD-323 1N4448WS 200 150 100 2500 @ 7 5 0.72/1.0 @ 5.0/100 4 5000 SOD-323 BAV16WS 200 250 100 1000 @ 7 5 0.8 6 @ 10 6 5000 SOD-323 BAV19WS 200 250 120 100 @ 100 1.0 @ 100 50 5000 SOD-323 BAV20WS 200 250 200 100 @ 150 1.0 @ 100 50 5000 SOD-323 BAV21WS 200 250 250 100 @ 200 1.0 @ 100 50 5000 SOD-323 MMBD4148W 200 150 100 2500 @ 75 1.0 @ 50 4 3000 SOT-323-1 MMBD4448W 200 150 100 2500 @ 7 5 0.72/1.0 @ 5.0/100 4 3000 SOT-323-1 BAS16W 200 250 100 1000 @ 7 5 0.8 6 @ 10 6 3000 SOT-323-1 BAS19W 200 250 120 100 @ 100 1.0 @ 100 50 3000 SOT-323-1 BAS20W 200 250 200 100 @ 150 1.0 @ 100 50 3000 SOT-323-1 BAS21W 200 250 250 100 @ 200 1.0 @ 100 50 3000 SOT-323-1 BAW56W 200 150 100 2500 @ 75 1.0 @ 50 4 3000 SOT-323-2 BAV70W 200 150 100 2500 @ 75 1.0 @ 50 4 3000 SOT-323-3 BAV99W 200 150 100 2500 @ 75 1.0 @ 50 4 3000 SOT-323-4 BAL99W 200 150 100 2500 @ 75 1.0 @ 50 4 3000 SOT-323- 5 350mW MMBD4148 350 200 100 5000 @ 75 1.0 @ 10 4 3000 SOT-23-1 MMBD4448 350 200 100 5000 @ 75 1.0 @ 10 4 3000 SOT-23-1 BAS16 350 200 100 1000 @ 75 1.0 @ 50 6 3000 SOT-23-1 BAS19 350 200 120 100 @ 120 1.0 @ 100 50 3000 SOT-23-1 BAS20 350 200 200 100 @ 150 1.0 @ 100 50 3000 SOT-23-1 BAS21 350 200 250 100 @ 200 1.0 @ 100 50 3000 SOT-23-1 BAW56 350 200 100 2500 @ 70 1.0 @ 50 4 3000 SOT-23-2 BAV70 350 200 100 5000 @ 70 1.0 @ 50 4 3000 SOT-23-3 BAV99 350 200 100 2500 @ 70 1.0 @ 50 4 3000 SOT-23-4 BAL99 350 200 100 2500 @ 70 1.0 @ 50 4 3000 SOT-23-5 BAV16W 350 200 100 1000 @ 75 0.86 @ 10 6 3000 SOD-123 410-500mW BAV19W 410 200 120 100 @ 100 1.0 @ 100 50 3000 SOD-123 BAV20W 410 200 200 100 @ 150 1.0 @ 100 50 3000 SOD-123 BAV21W 410 200 250 100 @ 200 1.0 @ 100 50 3000 SOD-123 1N4148W 410 150 100 2500 @ 75 1.0 @ 50 4 3000 SOD-123 1N4150W 410 200 50 100 @ 50 0.72/1.0 @ 5.0/100 4 3000 SOD-123 1N4448W 500 150 100 2500 @ 7 5 1.0 @ 200 4 3000 SOD-123 1N4151W 500 150 75 50 @ 50 1.0 @ 10 2 3000 SOD-123 1N914 500 200 100 25 @ 20 1.0 @ 10 4 1000 10000 DO-35 1N4148 500 200 100 25 @ 20 1.0 @ 10 4 1000 10000 DO-35 LL4148 500 150 100 25 @ 20 1.0 @ 10 4 2500 Mini-Melf SOT23-1 SOT23-2 SOT23-3 SOT23-4 SOT23-5 SOT323-1 SOT323-2 SOT323-3 SOT323-4 SOT323-5