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Underwater scene is one of the most marvelous environments in the world. In this study, we present an efficient procedural modeling and rendering system to generate marine ecosystems for swim-through graphic applications. To produce realistic and natural underwater scenes, several techniques and algorithms have been presented and introduced. First, to distribute sealife naturally on a seabed, we employ an ecosystem simulation that considers the influence of the underwater environment. Second, we propose a two-level procedural modeling system to generate sealife with unique biological features. At the base level, a series of grammars are designed to roughly represent underwater sealife on a central processing unit (CPU). Then at the fine level, additional details of the sealife are created and rendered using graphic processing units (GPUs). Such a hybrid CPU-GPU framework best adopts sequential and parallel computation in modeling a marine ecosystem, and achieves a high level of performance. Third, the proposed system integrates dynamic simulations in the proposed procedural modeling process to support dynamic interactions between sealife and the underwater environment, where interactions and physical factors of the environment are formulated into parameters and control the geometric generation at the fine level. Results demonstrate that this system is capable of generating and rendering scenes with massive corals and sealife in real time.

To improve the knowledge management during product design process, a new model of knowledge composition and knowledge activity is constructed within or between organizations and persons. And based on the analysis of knowledge acquisition, transfer and innovation, the strategy for the implement of knowledge management project is proposed. At last the functions and structures of a software system are provided that supporting the knowledge management for product design in an manufacturing enterprise.

The correct inventory strategy is important to control reasonable inventory within manufacturing enterprises. And a new material inventory strategy is proposed based on the classification scheme according to the prosperities. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

A dynamic scheduling method for FJSP is put forward based on real-time adjustment of controllable margin, aiming at uncertain information and dynamic characteristic during practical job-shop scheduling process. Strategy and methods of dynamic scheduling adjustment are presented, which respond to real-time event. In view of practical operation condition and accuracy demands, two methods are proposed. One is rescheduling that condenses domain, the other is scheduling updating based on adjustment. Such strategy obtains dynamic robust scheduling of FJSP to ensure the real-time of algorithm, continuity and stability of scheduling schemes.

Combined with the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), a modified Adaptive Genetic Algorithm based on iterative generation and analysis of fitness values distribution is presented in this paper, which has both methods advantages. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using the modified AGA, which overcomes traditional GAs limitation that initial stage of evolution is early and random search of medium-late stage is slow. Such methods are verified to accelerate convergence process, enhance searching efficiency and solving precision as well as avoid low efficiency and local optimum.

Aiming at uncertain information and dynamic characteristic during flexible job-shop scheduling process, some kind of dynamic scheduling method for flexible job-shop scheduling problem (FJSP) is put forward based on real-time adjustment. A dynamic simulation solution mode framework is presented for FJSP. This framework is inspired by adaptive control, combined with the robust scheduling and foreseeing scheduling. It has both advantages of such two scheduling methods, and its stable and highly efficient. Preliminary scheme generation method based on foreseeing dynamics scheduling is introduced then. Foreseeing function is realized by fault-handling algorithm and dynamic simulation solver on the basis of Adaptive Genetic Algorithm (AGA).

A control system is developed based on PC, which could collect real-time data and monitor the equipments state for assembly line. The software platform is C#. The technology of sensors, PLC, computer are integrated, and OPC technology is used based on the idea of configuration software to achieve an automatic monitoring system. The data of PLC can be exchanged through the database system integrated in PC. The complex calculation, friendly man-machine interface are achieved in the system. Through monitoring the equipment and environment on the shop floor on-line, the causes of the problems in automatic assembly line can be accurately analyzed in time to make a quick deal. The result shows that the PC-based monitoring system is easy to control, convenient to operate and low-cost to develop and run.

The material tracking management system in the magnetic pump assembly process is studied to the machine shop and assembly shop of a pump company as an example. The system mainly includes production line management module, assembly process management module, material tracking management module and work in process (WIP) tracking management module. In this system, the workshop visual management, real-time status information collecting on-site, materials status monitoring, and product life cycle file establishing are implemented. The functions of product assembly processes tracking can be realized, so that the traceability of the parts, components, WIP and products in the process can be achieved. The system ensures the material supply timely and equipment operation normally in the production processes.

To improve product development decisions and to obtain a competitive advantage, an industry should effectively organize, store and retrieve such knowledge and experience. In this study, a knowledge model in product design domain named the Product Knowledge Model (PKM) is identified including four knowledge types, resource description knowledge, organization competence knowledge, product technology knowledge, and business process knowledge, and is expressed in ontology providing a formal conceptualization of a particular domain. A solution for interconnecting tacit and explicit knowledge resource to support designers making decisions on product design is proposed, considering the same domain ontology set for modeling the knowledge items annotations, the user competency profile, and the task context. A knowledge-intensive support prototype system (KSS) is designed and implemented based on the J2EE programming language and platform.

Aiming at the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), an Adaptive Genetic Algorithm (AGA) is presented in this paper, combined with existing GA problems for solving FJSP. In the solving process, AGA automatically adjusts selection rate P s , crossover rate P c , mutation rate P m and other operation parameters according to the genetic generation. Furthermore, it automatically changes scope of action of crossover operator and mutation operator on the chromosome. In the AGA solution scheme, linear interpolation method is used to realize automatic change of operation parameters from initial stage to middle stage; extended power function implements adaptive adjustment in the function scope of operators. Meanwhile, coding method is adjusted correspondingly for realizing operator adaptation. Instance simulation verifies that the FJSP's own characteristics are utilized in its solution by using AGA, which overcomes traditional GA's limitation. Such a method has a relatively high search power during the whole solution process, especially in the end of the process. And solving efficiency and precision are improved greatly.

In this paper, an ontology-based KM system (KMS) is presented, which provides users with a product knowledge model (PKM) that helps them to locate proper information, and supports reuse of product data among different application systems throughout the product life cycle. The model is divided into information resource ontology, management activity ontology, product semantic ontology, as well as organization competence ontology in order to realize semantic match for knowledge search, and enhance the performance of organization capability and knowledge sharing. Furthermore, prototype system architecture is proposed, using the Semantic Web technology, for seamless sharing domain-specific design knowledge among multidisciplinary organizations. Finally, the application of the prototype system is presented.