基于竞争神经网络的中医智能诊断推理新方法

文章通过融合模糊逻辑推理、神经网络技术及可拓学方法,研究了一种基于竞争神经网络的智能推理模型,实现了对中医专家诊断推理过程的模拟。在复杂的亚健康状态中医诊断推理过程中的应用结果表明,该文研究的智能推理模型可以较好地应用于中医辨证。     

Balanced Scheduling toward Loss-Free Packet Queuing and Delay Fairness

In current networks, packet losses can occur if routers do not provide sufficiently large buffers. This paper studies how many buffers should be provided in a router to eliminate packet losses. We assume a network router has m incoming queues, each corresponding to a single traffic stream, and must schedule at any time on-line from which queue to take the next packet to send out. To exclude packet losses with a small amount of buffers, the maximum queue length must be kept low over the entire scheduling period. We call this new on-line problem the balanced scheduling problem (BSP). By competitive analysis, we measure the power of on-line scheduling algorithms to prevent packet losses. We show that a simple greedy algorithm is Θ(log m)-competitive which is asymptotically optimal, while Round-Robin scheduling is not better than m-competitive, as actually is any deterministic on-line algorithm for BSP. We also give a polynomial time algorithm for solving off-line BSP optimally. We also study another on-line balancing problem that tries to balance the delay among the m traffic streams.     

Online deadline scheduling with preemption penalties

This paper presents a study of the problem of online deadline scheduling under the preemption penalty model of Zheng, Xu, and Zhang (2007). In that model, each preemption incurs a penalty of ρ times the weight of the preempted job, where ρ ? 0 is the preemption penalty parameter. The objective is to maximise the total weight of jobs completed on time minus the total penalty.     

竞技体育运动训练机能监控预警系统的研究与应用

运用计算机与数据库技术,根据竞技体育运动训练机能监控的需求,构建竞技体育运动训练机能监控预警系统。该系统主要包括运动员基本信息管理、训练计划与测试方案、测试数据录入与查询、机能评定与监控预警,通过该系统实现对运动员运动训练机能监控预警。     

The Complete Solution of the Competitive Rank Selection Problem

Two decision-makers A and B observe sequentially a given permutation of n uniquely rankable options. A and B have one choice each (without recall) and both must make a choice. At each step only the relative ranks are known, and A has the priority of choice. At the end the (absolute) ranks are compared and the winner is the one who has chosen the better rank. Extending results by Enns and Ferenstein [6] and Berry et al. [1] this article gives, for both A and B , the optimal strategy and the corresponding winning probabilities. We show in particular that the limiting winning probabilities for A and B do exist, which closes a most important gap in the work of previous authors. This also provides an algorithm for numerically computing the limiting value of these probabilities. Although our proof is analytic in a strong sense, it is interesting to see that it would have been very hard to assemble it without the help of computer algebra. The reason is that the functions we have to investigate display subranges of indices which contrast considerably with respect to error terms when certain terms are replaced by approximations, and that computations were very helpful to locate those ranges where a particularly fine tuning of error estimates turned out to be indispensable. Received October 1997; revised March 15, 1998.     

Online scheduling on two parallel-batching machines with limited restarts to minimize the makespan

We study online scheduling on two unbounded parallel-batching machines with limited restarts to minimize the makespan. In this system jobs arrive over time and a batch can be restarted if and only if all the jobs in it have never been restarted. To tackle this difficult problem, we make the second-restart assumption whereby we can only interrupt a running batch B at time t if both machines are busy at time t and batch B has a later starting time than the other running batch. For this case, we provide a best online algorithm with a competitive ratio . For the general problem, we show that no online algorithms can have a competitive ratio less than 1.298, leaving a gap from 1.298 to 1.366.     

Enhancements to the dynamic self-organising neural network

Class Directed Unsupervised Learning (CDUL) is a dynamic self-organising network which has been shown to overcome many of the problems associated with unsupervised learning, thereby yielding performance characteristics superior to similar networks such as counter-propagation and LVQ. In this paper, the CDUL algorithm is developed further, to a point where the original two-phase learning process is combined into a single system of dynamic parameter variation; a training cycle that can then be terminated automatically at a point of zero error over the training set. The ability to improve training times using a FastCDUL algorithm is also explored. The new algorithm, CDUL2, is subsequently applied to the benchmark problem of mine detection given sonar data, and shown to outperform both backpropagation and LVQ in terms of training speed and recall performance. Finally, a measure of computational cost is estimated for both CDUL2 and LVQ training periods, reinforcing the suggested efficiency of CDUL2 over its counterparts.     

Environmental effects on the coevolution of pursuit and evasion strategies

The game of tag is frequently used in the study of pursuit and evasion strategies that are discovered through competitive coevolution. The aim of coevolution is to create an arms race where opposing populations cyclically evolve in incremental improvements, driving the system towards better strategies. A coevolutionary simulation of the game of tag involving two populations of agents; pursuers and evaders, is developed to investigate the effects of a boundary and two obstacles. The evolution of strategies through Chemical Genetic Programming optimizes the mapping of genotypic strings to phenotypic trees. Four experiments were conducted, distinguished by speed differentials and environmental conditions. Designing experiments to evaluate the efficacy of emergent strategies often reveal necessary steps needed for coevolutionary progress. The experiments that excluded obstacles and boundaries provided design pointers to ensure coevolutionary progress as well as a deeper understanding of strategies that emerged when obstacles and boundaries were added. In the latter, we found that an awareness of the environment and the pursuer was not critical in an evader’s strategy to survive, instead heading to the edge of the boundary or behind an obstacle in a bid to ‘throw-off or hide from the pursuer’ or simply turn in circles was often sufficient, thereby revealing possible suboptimal strategies that were environment specific. We also observed that a condition for coevolutionary progress was that the problem complexity must be surmountable by at least one population; that is, some pursuer must be able to tag an opponent. Due to the use of amino-acid building blocks in our Chemical Genetic Program, our simulations were able to achieve significant complexity in a short period of time.

Internal structure of coalitions in competitive and altruistic graphical coalitional games

This paper introduces a certain graphical coalitional game where the internal topology of the coalition depends on a prescribed communication graph structure among the agents. The game Value Function is required to satisfy four Axioms of Value. These axioms make it possible to provide a refined study of coalition structures on graphs by defining a formal graphical game and by assigning a Positional Advantage, based on the Shapley value, to each agent in a coalition based on its connectivity properties within the graph. Using the Axioms of Value the graphical coalitional game can be shown to satisfy properties such as convexity, fairness, cohesiveness, and full cooperativeness. Three measures of the contributions of agents to a coalition are introduced: marginal contribution, competitive contribution, and altruistic contribution. The mathematical framework given here is used to establish results regarding the dependence of these three types of contributions on the graph topology, and changes in these contributions due to changes in graph topology. Based on these different contributions, three online sequential decision games are defined on top of the graphical coalitional game, and the stable graphs under each of these sequential decision games are studied. It is shown that the stable graphs under the objective of maximizing the marginal contribution are any connected graph. The stable graphs under the objective of maximizing the competitive contribution are the complete graph. The stable graphs under the objective of maximizing the altruistic contribution are any tree.     

Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency

We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by different users or user groups in large parallel systems, where both user-level fairness and system-wide efficiency are of important concerns. To achieve fairness, we use the well-known equi-partitioning algorithm to distribute the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler that periodically adjusts the processor allocations within each set by consciously exploiting the jobs’ execution history. We show that our algorithm achieves asymptotically competitive performance with respect to the set response time, which incorporates two widely used performance metrics, namely, total response time and makespan, as special cases. Both theoretical analysis and simulation results demonstrate that our algorithm improves upon an existing scheduler that provides only fairness but lacks efficiency. Furthermore, we provide a generalized framework for analyzing a family of scheduling algorithms based on feedback-driven policies with provable efficiency. Finally, we consider an extended multi-level hierarchical scheduling model and present a fair and efficient solution that effectively reduces the problem to the two-level model.