Evolutionary design of dynamic neural networks for evaporator control

This paper presents a novel neural network (NN) to control an ammonia refrigerant evaporator. Inspired by the latest findings on the biological neuron, a dynamic synaptic unit (DSU) is proposed to enhance the information processing capacity of artificial neurons. Treating the dynamic synaptic activity after the nonlinear somatic activity helps to capture the dynamics demarcated by the Gaussian activation pertaining to the input space. This practice leads to a remarkable reduction in curse of dimensionality. The proposed NN architecture has been compared with two other conventional architectures; one with dynamic neural units (DNUs) and the other with nonlinear static functions as perceptrons. The objective is to control evaporator heat flow rate and secondary fluid outlet temperature while keeping the degree of refrigerant superheat in the range 4–7 K at the evaporator outlet by manipulating refrigerant and evaporator secondary fluid flow rates. The drawbacks of conventional approaches to this problem are discussed, and how the novel method can overcome them are presented. An evolutionary approach is adopted to optimize the parameters of the NN controllers. Then evaporator process model is accomplished as a combination of governing equations and a sub NN resulting in a simple and sufficiently accurate model. The effectiveness of the proposed dynamic NN controller for the evaporator system model is validated using experimental data from the ammonia refrigeration plant.     

一种新型微带光子带隙结构的优化设计

本文对一种渐变尺寸的新型光子带隙结构进行了优化设计。根据优化后得出的最优结构参数 ,设计出PBG结构。实验结果证明 ,这种优化后的新型结构可以在保持阻带抑制幅度的同时减小通带内的波纹     

太原市城南地区汾河与东岸防洪隐患与出路

本文对当前城南地区汾河以东防洪存在的诸多隐患进行了阐述，并提出了相应的见解与措施。     

Development of an undergraduate course – Internet-based instrumentation and control

The objective, strategy, and implementation details of a new undergraduate course, Internet-based Instrumentation and Control, are presented. The course has a companion laboratory that is supported by the National Science Foundation and industry. The combination is offered to senior-level undergraduate engineering students interested in sensing, instrumentation, control, and web programming that want to learn more about the integration of these technologies for solving real-world engineering problems. The course will also be offered to gifted high school seniors with similar interests and can serve as a vehicle to attract them to engineering disciplines. Preliminary assessment of the first offering of the course is encouraging and has shown that the course has achieved success in helping students understand concepts and master basic technologies for developing Internet-based automatic systems.     

Hardware and software architecture for power quality analysis

This paper presents a novel concept for power quality hardware and software architecture. Software tools related to detection, classification and characterization of power quality events can be integrated into a Distribution Control Center in which the interconnection between different subsystems is being carried out through the Internet allowing flexibility to the system. This system is implemented using wavelet analysis applied in all proposed algorithms. Different power quality events are taken as examples to illustrate the capabilities of the proposed method. The obtained results reveal that the proposed architecture is feasible to be implemented as an integral part of a control center system.The proposed system has the property of fast and accurate detection and classification of any power quality disturbance event and introduces a new PQ index determination that allows characterizing any type of disturbance including the non-periodic signals.     

Sensitivity of generation reserve requirements in deregulated power systems

Since demand for power exhibits great variability the amount of firm energy to be purchased to meet real time demand based on forecasts is usually different from the realized demand for that period. The role of generation reserves is to meet the real time fluctuations of power demand. The predictable part of the demand is met through purchases of firm energy. In this paper a model is presented to determine optimal quantities of firm energy and generation reserves to meet random demands. The model is then parameterized introducing a set of factors to perform a sensitivity study. A full factorial experiment is designed to study the impact of five factors on the response variable (i.e., proportion of generation reserves on the total purchased quantity). An example consisting of 640 simulations corresponding to 2⁵ treatment combinations evaluated over 20 randomly generated mean demands is used to identify significant factors on the response variable. Results from the experiment suggest that generation reserve requirements should be adjusted considering changes in significant factors and in the mean demand over the dispatch interval.     

Economic Model to Optimize Underground Utility Protection

The need for better protecting our vital infrastructure from being damaged or destroyed has received increased attention since the terrorist attacks on September 11, 2001. The tragedy of having thousands of innocent people die before the eyes of an entire nation awakened people to the reality of “managed” attacks of unthinkable magnitudes. However, tragedies of a smaller scale are a daily occurrence but accepted as “collateral damage” of work in an unsafe environment. This paper presents a cost-benefit analysis to address the question of how much money should be spent in protecting underground utilities from damage. During the study of an actual incident it was found that the total costs of such accidents are vastly underreported because only costs for emergency responses and repair are tallied up. This paper makes the case that a comprehensive approach for assessing the total economic impact of such incidents on the public, business, and government is the critical stepping stone to a mathematical optimization of expenditure for damage prevention. In addition, the reader will quickly realize that the use of the presented optimization model provides theoretical underpinning for the engineering profession in its effort to better protect our critical infrastructure from terrorist attacks.     

我国印制电路板非ODS清洗技术现状引发的思考

我国印制电路板(PCB)的非ODS清洗技术已基本解决。当今需面对的是：(1)新型元器件的组装、(2)无铅焊工艺、(5)小产量生产的清洗技术，以及(4)优化方案的思考。     

An optimization algorithm based on stochastic sensitivity analysis for noisy objective landscapes

A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information.