A 24-GHz Patch Array with a Power Amplifier/Low-Noise Amplifier MMIC

This paper presents an active patch array designed at 24 GHz. It can be used as a front-end component for a phased array. A series resonant array structure is chosen which is compact and easy excite. With 5 elements, the array proved a 12-dB antenna gain. A power amplifier and a low noise amplifier are designed on a single GaAs chip (PALNA). Bias switch is used in the PALNA, which greatly reduces the switch loss in a transceiver and increases the efficiency. 20-dB small signal gain is achieved in both power amplifier and low noise amplifier. The active patch array is built by the combination of the patch array and PALNA. The measured active gain of this antenna is 35-dB for the PA mode and 31-dB for the LNA mode. This active patch array can obtain an EIRP of 34 dBm with a total radiated power of 22dBm and a maximum PAE of 32%. To check the noise performance, we applied sources at both normal temperature and 77K (liquid nitrogen) and extracted the noise figure (3.5 dB) of the active antenna by the Y factor method. The results proved that the active antenna is working efficiently as both a transmitting and receiving antenna.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Design and evaluation of an active electromechanical wheel suspension system

This paper presents an electromechanical wheel suspension, where the upper arm of the suspension has been provided with an electric levelling and a damper actuator, both are allowed to work in a fully active mode. A control structure for the proposed suspension is described. The complex design task involving the control of the electric damper and its machine parameters is tackled by genetic optimisation. During this process, these parameters are optimised to keep the power dissipation of the electric damper as low as possible, while maintaining acceptable comfort and road-holding capabilities. The results of the evaluations carried out demonstrate that the proposed suspension can easily adopt its control parameters to obtain a better compromise of performance than that offered by passive suspensions. If the vehicle is to maintain acceptable performance during severe driving conditions, the damper has to be unrealistically large. However, if the electric damper is combined with a hydraulic damper, the size of the electric damper is significantly reduced. In addition, the design of the electric damper with the suggested control structure, including how it regenerates energy, is discussed.     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.     

Active control and a simple estimation method for the onset of Rayleigh-Bénard convection in temperature-dependent-viscosity liquids

Active feedback control for the onset of Rayleigh-Bénard Convection in temperature-dependent-viscosity liquids is investigated. In this paper, three major problems are addressed: (1) The results of Tang-Bau control are improved by considering the effects of temperature-dependent viscosity; (2) A more efficient two-plate control strategy is presented. A phenomenon of coalescence of the unstable modes is observed as the controller gain is large enough; (3) A simple way to estimate the critical Rayleigh number under the effects of temperature-dependent viscosity is described. Numerical results show that the effects of temperature-dependent viscosity on the critical Rayleigh number should be taken into account in some cases and the onset of Rayleigh-Bénard convection can be effectively delayed or advanced by the active feedback control strategies studied here.     

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.     

TFT有源矩阵α－Si∶H薄膜PECVD淀积工艺研究

分析了α－Ｓｉ∶Ｈ薄膜的质量和厚度对α－Ｓｉ∶ＨＴＦＴ关键性指标的影响，深入、详细地讨论了其ＰＥＣＶＤ淀积工艺，并在实验的基础上确定了最佳淀积工艺参数，从而获得了高性能的７５ｍｍ３７２×２７６像素α－Ｓｉ∶ＨＴＦＴ有源矩阵     

多站远程无线控制信号系统通信软件的开发

多站远程无线控制系统是以计算机为中心控制 ,用多个信号源作为下位机通过无线模块进行通信 ,文中介绍了通过无线数传模块实现无线通讯以及ActiveX控件的使用方法 ,提出了使系统应用程序更安全可靠 ,效率更高 ,维护更加方便的几种措施。     

Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components     

Schedule-an expert-like system for machine scheduling

The construction of an expert-like system for machine scheduling called SCHEDULE is presented. Essential parts of SCHEDULE were developed by students in a laboratory course Operations Research on Microcomputers at the University of Karlsruhe, Germany. SCHEDULE consists of the components data base, knowledge base, inference engine, explanation facility, dialog component, and knowledge acquisition component. The knowledge base contains an algorithm base for solving different types of scheduling problems. To establish the rules of the knowledge base the well-known three-field classification of deterministic machine scheduling problems and the concept of the reduction digraph are exploited. Experiences gained during building and demonstrating SCHEDULE are reported.