图像识别的一个综合网络模型及其应用

综合集成技术是为解决开放的复杂巨系统问题而提出的一种方法论 ,强调开放的系统观和人 -机集成的重要性。提出一个多层的带有反馈信息的综合网络模型 ,在染色体图像识别中使用该模型 。

一种用于多类模式识别的综合感知器网络和学习算法

提出一种由感知器神经元组合成的综合网络模型及相应的学习算法 .网络由 3层感知器神经元组成 ,中间层到输出层的权值相应地为 +1或 - 1.输入层到中间层的权值通过学习获得 ,且中间层每个神经元的权值单独学习完成 .该学习算法在有限次迭代步骤内终止 .当算法终止时 ,对于可线性划分的多类模式识别问题总是能找到正确的解 .如果还有模式不能识别 ,则说明这是一个不可线性划分的多类模式识别问题 。

不同负荷模型对东北电网送电能力的影响分析

以2004年东北电网冬腰方式为例,对考虑配电网络的综合负荷模型(synthetic load model considering distribution network,SLM)以及各种常用的负荷模型进行了适应性分析。吉黑断面稳定极限和辽吉断面稳定极限的计算结果表明,采用考虑配电网络的综合负荷模型与II型马达、40%恒阻抗+60%恒功率的静态负荷模型以及30%恒阻抗+30%恒电流+40%恒功率的静态负荷模型计算结果比较接近。最后对考虑配电网络的综合负荷模型进行了参数灵敏度分析。

东平湖生态环境质量综合评价

针对南水北调东线工程对东平湖生态环境质量的要求,利用实地调查和监测数据资料,参考国内外有关研究成果,提出了湖泊生态环境质量综合评价的指标和标准,建立了相应的神经网络综合评价模型,并对东平湖生态环境质量现状进行了分析评价,可为东平湖生态环境有效的控制和治理以及南水北调东线工程的实施提供科学依据。

A NOVEL CALL ADMISSION CONTROL SCHEME IN CELLULAR/WLAN INTEGRATION AND PERFORMANCE ANALYSIS

In order to achieve the Quality of Service(QoS) provisioning and efficient resource utili-zation in cellular network and Wireless Local Area Network(WLAN) integration,an Integrated Ser-vice-Based Call Admission Control(ISB-CAC) scheme is proposed in this paper.The integrated network is modeled by using multi-dimensional Markov chains.The numerical analysis is presented to evaluate the important performance measures such as the blocking probability of originating calls,the dropping probability,and the average transfer time,etc.The steady-state probabilities of the multi-dimensional Markov chains are obtained by using an iterative approach,and the CAC pa-rameters are optimally designed.The analytical model is validated by the computer simulation.It is shown that compared with the conventional WLAN-First Call Admission Control(WF-CAC) scheme,the proposed ISB-CAC scheme not only provides better QoS for mobile users but also utilizes the bandwidth resources more efficiently.

Combined mass and heat exchange network synthesis based on stage-wise superstructure model

Integrating multiple systems into one has become an important trend in Process Systems Engineering research field since there is strong demand from the modern industries. In this study, a stage-wise superstructurebased method is proposed to synthesize a combined mass and heat exchange network(CM&HEN) which has two parts as the mass exchange network(MEN) and heat exchange network(HEN) involved. To express the possible heat exchange requirements resulted from mass exchange operations, a so called "indistinct HEN superstructure(IHS)", which can contain the all potential matches between streams, is constructed at first. Then, a non-linear programming(NLP) mathematical model is established for the simultaneous synthesis and optimization of networks. Therein, the interaction between mass exchange and heat exchange is modeling formulated.The NLP model has later been examined using an example from literature, and the effectiveness of the proposed method has been demonstrated with the results.

A Composite Service Provision Method Based on Novel Node Model in Mobile Ad Hoc Networks

Composite service provision in mobile ad hoc networks encounters great challenges and its success rate is not satisfactory because the nodes' locations are dynamic and the nodes maybe unavailable at any time.Composite service is built through the service composition.In mobile ad hoc networks,the factors influencing the success rate of service composition are mainly the number of nodes and the time spent for the composition.The node's failure probability is proportional to the length of time the node exist in the networks.In order to improve the success rate of service composition,we take several measures.First,we split the service requirement into several segments and cluster the nodes,so that the nodes' waiting time for service composition can be reduced.Second,we propose a new node model of "one node contains multiple services" in mobile ad hoc networks.Using this type of nodes model,the number of nodes required for service composition can be reduced.These means can increase the success rate of service composition.

Distribution of primary school based on spatial network comprehensive model in low-income mountainous cities： a case study in Wanyuan, China

Focusing on the peculiarities of urban construction resulting from specific mountain terrains, the purpose of this study is to find out a suitable method based on a Spatial Network Comprehensive Model(SNCM) to reasonably plan and distribute primary schools in low-income mountain cities. The construction principles and advantages of the SNCM method are proposed and the method tested in Wanyuan city of Qinba Mountain area(Southwest China) to verify its feasibility and optimization. Taking account of the mountain terrain and its influence on user behavior, we used the SNCM method to build a comprehensive model which integrates the road slope and the walking speed of pupils into the basic spatial model. The model is used to calculate a reasonable layout of the primary schools and to validate the rationale. The results show that the SNCM method can be effectively applied in low-income mountainous cities. It can not only improve the accessibility and service efficiency of primary schools using as little capital-investment as possible, but also help the city grow in an intensive and efficient way.

Hybrid tracking model and GSLM based neural network for crowd behavior recognition

Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.

Network-Based Synthesis Evaluation Model

With the explosion of information, people are receiving numerous of messages at any moment. How to explore the relationships between everything you know？ And how to make wise decisions while stuck in a huge information net？