An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.     

Profibus-DP数据显示从站设计

用于设备级控制系统与分散式I/O通信的Profibus-DP是市场占有率绝对领先的总线技术.设计了一种基于西门子的SPC3的Profibus-DP的数据显示智能从站,详细分析了从站的硬件及软件结构,研究并解决了RS485通信现场设备与Profibus-DP现场总线通信问题.测试实验采用基于PC加软PLC构成Profibus-DP 1类主站,数据显示单元做从站,通过在PC机上监控Profibus通信报文的方法,成功验证了此显示单元模块能和西门子的设备进行互连,符合Profibus-DP标准.     

Improving job scheduling performance with parallel access to replicas in Data Grid environment

Data Grid has evolved to be the solution for data-intensive applications, such as High Energy Physics (HEP), astrophysics, and computational genomics. These applications usually have large input of data to be analyzed and these input data are widely replicated across Data Grid to improve the performance. The job scheduling performance on traditional computing jobs can be studied using queuing theory. However, with the addition of data transfer, the job scheduling performance is too complex to be modeled. In this research, we study the impact of data transfer on the performance of job scheduling in the Data Grid environment. We have proposed a parallel downloading system that supports replicating data fragments and parallel downloading of replicated data fragments, to improve the job scheduling performance. The performance of the parallel downloading system is compared with non-parallel downloading system, using three scheduling heuristics: Shortest Turnaround Time (STT), Least Relative Load (LRL) and Data Present (DP). Our simulation results show that the proposed parallel download approach greatly improves the Data Grid performance for all three scheduling algorithms, in terms of the geometric mean of job turnaround time. The advantage of parallel downloading system is most evident when the Data Grid has relatively low network bandwidth and relatively high computing power.     

New robust stability analysis for genetic regulatory networks with random discrete delays and distributed delays

This paper investigates the delay-probability-distribution-dependent stability problem of stochastic genetic regulatory networks (GRNs) with random discrete time delays and distributed time delays which exist in both translation process and feedback regulation process. The information of the probability distribution of the discrete time delays is considered and transformed into parameter matrices of the GRN models. By introducing a new Lyapunov functional which takes into account the ranges of delays and employing some free-weighting matrices, some new delay-probability-distribution-dependent stability criteria are established in the form of linear matrix inequalities (LMIs) to guarantee the GRNs to be asymptotically stable in the mean square. In addition, when estimating the upper bounds of the derivative of Lyapunov functionals, we carefully handle the additional useful terms about the distributed delays, which may lead to the less conservative results. The new criteria are applicable to both slow and fast time delays. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results and less conservativeness of the proposed method.     

一种求解Ramsey数的DNA计算机算法

Ramsey理论是组合数学中一个庞大而又丰富的领域,在集合论、逻辑学、分析以及代数学上具有极重要的应用.Ramsey数的求解是非常困难的,迄今为止只求出9个Ramsey数的准确值.探讨了DNA生物分子超级计算在求解这一困难数学问题的可能性.将Adleman-Lipton模型生物操作与粘贴模型解空间相结合的DNA计算模型...     

Fragment-based responsive character motion for interactive games

Fragment-based character animation has become popular in recent years. By stringing appropriate motion capture fragments together, the system drives characters responding to the control signals of the user and generates realistic character motions. In this paper, we propose a novel, straightforward and fast method to build the control policy table, which selects the next motion fragment to play based on the current user’s input and the previous motion fragment. During the synthesis of the control policy table, we cluster similar fragments together to create several fragment classes. Dynamic programming is employed to generate the training samples based on the control signals of the user. Finally, we use a supervised learning routine to create the tabular control policy. We demonstrate the efficacy of our method by comparing the motions generated by our controller to the optimal controller and other previous controllers. The results indicate that although a reinforcement learning algorithm known as value iteration also creates the tabular control policy, it is more complex and requires more expensive space–time cost in synthesis of the control policy table. Our approach is simple but efficient, and is practical for interactive character games.     

Two-Dimensional Multilabel Active Learning with an Efficient Online Adaptation Model for Image Classification

Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site—Corbis.     

Design and implementation of an agent-based collaborative product design system

The evolution of computer science and technology has brought new opportunities for multidisciplinary designers and engineers to collaborate with each other in a concurrent and coordinated manner. The development of computational agents with unified data structures and software protocols contributes to the establishment of a new way of working in collaborative design, which is increasingly becoming an international practice. In this paper, based on the analysis of the dynamic nature of collaborative design process, a new framework for collaborative design is described. This framework adopts an agent-based approach and relocates designers, managers, systems, and the supporting agents in a unified knowledge representation scheme for product design. In order to model the constantly evolving design process and the rationales resulted from design collaboration, a Collaborative Product Data Model (CPDM) and a constraint-based Collaborative Design Process Model (CDPM) are proposed to facilitate the management and coordination of the collaborative design process as well as design knowledge management. A prototype system of the proposed framework is implemented and its feasibility is evaluated using a real design scenario whose objective is designing a set of dining table and chairs.