Multi-objective optimization of sensor array using genetic algorithm

A multi-objective optimization method using genetic algorithm was proposed for sensor array optimization. Based on information theory, selectivity and diversity were used as the criteria for constructing two objective functions. A statistic measurement of resolving power, general resolution factor, and visual inspection were used to evaluate the optimization results with the aid of principal component analysis. In each Pareto set, most nondominated solutions had better statistics than the combination using all potential sensors. Also the principal component plots showed that different vapor classes were generally better separated after optimization. The experiment results indicated that the proposed method could successfully identify a set of Pareto optimal solutions of small size; and most optimized sensor arrays provided input with improved quality, i.e. better separation of target analytes. The running time for implementing the multi-objective optimization was satisfactory.     

A sensor array optimization method for electronic noses with sub-arrays

Sensor array configuration needs to be optimized in developing an application-specific instrument of electronic noses. Currently, sensor array optimization is commonly done by feature selection techniques. These methods could solve how to optimize a sensor array. However, they could not figure out what are the unique functions that each sensor plays in the optimized sensor array. The method proposed in this paper could solve this problem by sensor clustering and dividing the whole sample classification mission into several small recognition tasks. A measurement with a six Taguchi Gas Sensors (TGS sensor hereinafter) sensors array to classify 11 gas sorts was used in the data validation. The sensor array was optimized to three sensors with the proposed method. Each sensor in the optimized array had unique functions to solve different recognition tasks. TGS2600 had the unique functions to discriminate butanone and acetaldehyde. TGS2602 had the unique functions to discriminate benzene and cyclohexane, methanol and ethanol. TGS813 had the unique functions to discriminate cyclohexane and pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate acetone and butanone, acetone and acetaldehyde. The proposed method might be a new generation of sensor array optimization methods.     

Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks

Typical packet traffic in a sensor network reveals pronounced patterns that allow an adversary analyzing packet traffic to deduce the location of a base station. Once discovered, the base station can be destroyed, rendering the entire sensor network inoperative, since a base station is a central point of data collection and hence failure. This paper investigates a suite of decorrelation countermeasures aimed at disguising the location of a base station against traffic analysis attacks. A set of basic countermeasures is described, including hop-by-hop reencryption of the packet to change its appearance, imposition of a uniform packet sending rate, and removal of correlation between a packet’s receipt time and its forwarding time. More sophisticated countermeasures are described that introduce randomness into the path taken by a packet. Packets may also fork into multiple fake paths to further confuse an adversary. A technique is introduced to create multiple random areas of high communication activity called hot spots to deceive an adversary as to the true location of the base station. The effectiveness of these countermeasures against traffic analysis attacks is demonstrated analytically and via simulation using three evaluation criteria: total entropy of the network, total overhead/energy consumed, and the ability to frustrate heuristic-based search techniques to locate a base station.     

水下成像的稀疏阵列乘性处理

针对阵列传感器布阵设计中的冗余问题,提供了一种水下声学成像稀疏阵列的处理方法。采用稀疏阵列乘性处理方法可合成一个收发一体的虚拟阵列且虚拟阵元数目等于实际阵列发射阵元与接收阵元的乘积。依据相位中心近似的原理,对虚拟阵元的布阵位置进行了数学的解析表示,使用几种不同结构的稀疏阵列进行比较。实验结果得出,使用上述方法建立的稀疏阵列性能指标在三维声场、波束、导向矢量、主瓣宽度和峰值旁瓣级等方面与同等数量的收发一体Mills cross稀疏阵列一致。表明了乘性处理方法能有效解决阵元冗余问题,利用上述方法可得到一种优于Mills cross的新型稀疏阵列。     

Phase‐spacing optimization of linear microstrip antenna arrays using simulation‐based surrogate superposition models

A technique for simulation‐driven optimization of the phase excitation tapers and spacings for linear arrays of microstrip patch antennas is presented. Our technique exploits two models of the array under optimization: an analytical model which is based on the array factor, as well as an electromagnetic (EM) simulation‐based surrogate model of the entire array. The former is used to provide initial designs which meet the design requirements imposed on the radiation response. The latter is used for tuning of the array radiation response while controlling the array reflection response as well as for validation of the final design. Furthermore, the simulation‐based surrogate model allows for subsequent evaluation of the array responses in the beam scanning operation at negligible computational costs. The simulation‐based surrogate model is constructed with a superposition of simulated radiation and reflection responses of the array under design with only one radiator active at a time. Low computational cost of the surrogate model is ensured by the EM‐simulation data computed with coarse meshes. Reliability of the model is achieved by means of suitable correction carried out with respect to the high‐fidelity array model. The correction is performed iteratively in the optimization process. Performance, numerical efficiency, and accuracy of the technique is demonstrated with radiation pattern synthesis of linear arrays comprising 32 microstrip patch antennas by phase‐spacing optimization. Properties of the optimal designs in the beam scanning operation are then studied using the superposition models and compared to suitably selected reference designs. The proposed technique is versatile as it also can be applied for simulation‐based optimization of antenna arrays comprising other types of individually fed elements. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:536–547, 2015.     

Array calibration of angularly dependent gain and phase uncertainties with carry-on instrumental sensors

Array calibration with angularly dependent gain and phase uncertainties has long been a difficult problem. Although many array calibration methods have been reported extensively in the literature, they almost all assumed an angularly independent model for array uncertainties. Few calibration methods have been developed for the angularly dependent array uncertainties. A novel and efficient auto-calibration method for angularly dependent gain and phase uncertainties is proposed in this paper, which is called ISM (Instrumental Sensors Method). With the help of a few well-calibrated instrumental sensors, the ISM is able to achieve favorable and unambiguous direction-of-arrivals (DOAs) estimate and the corresponding angularly dependent gain and phase estimate simultaneously, even in the case of multiple non-disjoint sources. Since the mutual coupling and sensor position errors can all be described as angularly dependent gain/phase uncertainties, the ISM proposed still works in the presence of a combination of     

覆盖表生成的粒子群算法:参数优化和自适应算法

组合测试是一种能有效检测由参数间相互作用所引发错误的软件测试方法,覆盖表的生成是该研究领域的一个重要问题.目前,很多方法已被应用于覆盖表生成,基于演化搜索的粒子群算法尽管能得到较优的解,但其性能容易受到配置参数的影响.本文首先使用试验设计的方法,对不同覆盖表生成的算法参数进行优化,系统分析了参数对算法性能的影响.同时,考虑到对不同的覆盖表,最优的算法参数往往不同,因此进一步提出了一种适用于覆盖表生成的自适应粒子群算法.实验结果表明,在一定的参数取值范围内粒子群算法都能获得较好的结果,且不存在一组对任意覆盖表都能有最优性能的算法参数.通过参数调优,能使粒子群算法获得比已有结果规模更小的覆盖表,同时,与经过参数调优后的算法相比,自适应粒子群算法在大部分情况下有更好的性能.     

Self-organization of unattended wireless acoustic sensor networks for ground target tracking

A distributed, self-organization algorithm for ground target tracking using unattended acoustic sensor network is developed. Instead of using microphone arrays, each sensor node in the sensor network uses only a single microphone as its sensing device. This design can greatly reduce the size and cost of each sensor node and allow more flexible deployment of the sensor network. The self-organization algorithm presented in this paper can dynamically select proper sensor nodes to form the localization sensor groups that can work as a virtual microphone array to perform energy efficient target localization and tracking. To achieve this, we use a time-delay based bearing estimation plus triangulation for source localization in the sensor network. Major error sources of the localization method like time delay estimation, bearing calculation and triangulation are analyzed and sensor selection criteria are developed. Based on these criteria and neighborhood information of each sensor node, a distributed self-organization algorithm is developed. Simulation results show the effectiveness of the proposed algorithm.     

Optimization of circular antenna arrays using the cuckoo search algorithm

This article presents a study of circular antenna array design and optimization using the cuckoo search (CS) algorithm. The goal of optimization is to minimize the maximum sidelobe level with and without null steering. The CS algorithm is used to determine the parameters of the array elements that produce the desired radiation pattern. We illustrated the effectiveness of the CS in the design and optimization of circular antenna arrays by means of extensive numerical simulations. We compared our results with other methods from the literature whenever possible. We presented numerous examples that show the excellent performance and robustness of the CS algorithm and the results reveal that the design of circular antenna arrays using the CS algorithm provides acceptable enhancement compared with the uniform array or the design obtained using other optimization methods.     

Space exploration and global optimization for computationally intensive design problems: a rough set based approach

Modern engineering design problems often involve computation-intensive analysis and simulation processes. Design optimization based on such processes is desired to be efficient, informative and transparent. This work proposes a rough set based approach that can identify multiple sub-regions in a design space, within which all of the design points are expected to have a performance value equal to or less than a given level. The rough set method is applied iteratively on a growing sample set. A novel termination criterion is also developed to ensure a modest number of total expensive function evaluations to identify these sub-regions and search for the global optimum. The significance of the proposed method is twofold. First, it provides an intuitive method to establish the mapping from the performance space to the design space, i.e. given a performance level, its corresponding design region(s) can be identified. Such a mapping could be potentially used to explore and visualize the entire design space. Second, it can be naturally extended to a global optimization method. It also bears potential for more broad application to problems such as metamodeling-based design and robust design optimization. The proposed method was tested with a number of test problems and compared with a few well-known global optimization algorithms.     

Fast simulation‐driven optimization of planar microstrip antenna arrays using surrogate superposition models

A technique for simulation‐driven design of excitation tapers for planar antenna arrays is presented. Our methodology exploits antenna array models constructed as a superposition of simulated radiation and reflection responses of the array under design, with only one radiator active at a time. Low computational costs of these models are ensured by using iteratively corrected electromagnetic‐simulation data computed with coarse meshes. Our technique allows for simultaneous control of the radiation pattern and the reflection coefficients of the array. Numerical efficiency as well as scalability of the technique is demonstrated using the design examples of various sizes and topologies, including a sixteen element and hundred element microstrip patch antenna arrays of the Cartesian lattice and a hundred element microstrip antenna array of the hexagonal lattice. The proposed technique is versatile as it also can be applied for simulation‐based optimization of antenna arrays comprising other types of individually fed elements, e.g., wires, strips, or dielectric resonator antennas. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:371–381, 2015.     

Simulation based optimization of stochastic systems with integer design variables by sequential multipoint linear approximation

Optimization problems are considered for which objective function and constraints are defined as expected values of stochastic functions that can only be evaluated at integer design variable levels via a computationally expensive computer simulation. Design sensitivities are assumed not to be available. An optimization approach is proposed based on a sequence of linear approximate optimization subproblems. Within each search subregion a linear approximate optimization subproblem is built using response surface model building. To this end, N simulation experiments are carried out in the search subregion according to a D-optimal experimental design. The linear approximate optimization problem is solved by integer linear programming using corrected constraint bounds to account for any uncertainty due to the stochasticity. Each approximate optimum is evaluated on the basis of M simulation replications with respect to objective function change and feasibility of the design. The performance of the optimization approach and the influence of parameters N and M is illustrated via two analytical test problems. A third example shows the application to a production flow line simulation model. Received April 28, 2000     

基于极化和复用的SAW传感器阵列

针对谐振型声表面波(SAW)温度传感器难以灵活构建较大规模传感器阵列的问题,结合天线技术,基于极化和复用的思想,通过频分、时分、空间角度分集和极化,研究实现较大规模的SAW传感器阵列的方法。并通过实际测试,实验结果表明:在略微增加发射天线数目的代价下,阵列规模迅速增大,阵列中的传感器元能得到有效辨识。实验结果验证了设计方法的可行性。     

基于虚拟阵列变换的单通道接收机测向新方法

提出了一种改进单通道接收机波达方向估计的超分辨解决方案。该方法通过对任意阵列实施变换,将实际的传感器阵列扩展成多个具有不同阵列孔径的虚拟阵列,有效地增加了阵元数目,提高了空间谱估计的分辨率,增加了可检测信号源数目以及阵列的解相干能力。通过理论分析和仿真实验验证了该方法的有效性和优良性。     

Selection of polymeric sensor arrays for quantitative analysis

A novel method has been developed to optimize the selection of polymeric materials to be used within a chemiresistor array for anticipated samples without preliminary experiments. It is based on the theoretical predicted responses of chemiresistors and the criterion of minimizing the mean square error (MSE) of the chemiresistor array. After the number of chemiresistor to be used in an array and anticipated sample chemistry are determined, the MSE values of all combinations of the candidate chemiresistors are calculated. The combination which has the minimum MSE value is the best choice. This can become computationally intensive for selection of polymers for large arrays from candidates in a large database. The number of combination can be reduced by using the branch and bound method to save computation time. This method is suitable for samples at low concentrations where thermodynamic multi-component interactions are linear.     

Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.     

Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm

We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level (SLL) and null depth level (NDL), and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array’s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.     

一种新的光学遥感器优化设计方法

针对高分辨率空间光学遥感器,既要实现轻小型化,又要获得高品质遥感图像的要求,本文提出一种基于成像链路仿真的遥感器优化设计方法。文章以MTF和噪声模型为基础分析成像链路中的各个环节对成像质量的影响,同时考虑末端图像复原处理,构建成像链路仿真模型,通过遥感器指标评价和仿真图像质量评价来优化遥感器的总体指标,从而在保证成像质量的同时降低遥感器研制难度。实验验证了优化设计方法的可行性和优势。     

Evaluation of multiple muscle loads through multi-objective optimization with prediction of subjective satisfaction level: Illustration by an application to handrail position for standing

Proposed here is an evaluation of multiple muscle loads and a procedure for determining optimum solutions to ergonomic design problems. The simultaneous muscle load evaluation is formulated as a multi-objective optimization problem, and optimum solutions are obtained for each participant. In addition, one optimum solution for all participants, which is defined as the compromise solution, is also obtained. Moreover, the proposed method provides both objective and subjective information to support the decision making of designers. The proposed method was applied to the problem of designing the handrail position for the sit-to-stand movement. The height and distance of the handrails were the design variables, and surface electromyograms of four muscles were measured. The optimization results suggest that the proposed evaluation represents the impressions of participants more completely than an independent use of muscle loads. In addition, the compromise solution is determined, and the benefits of the proposed method are examined.     

A new hybrid particle swarm and simulated annealing stochastic optimization method

A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.