Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在聚甘氨酸修饰电极上的电化学行为及其同时测定

在优化的实验条件下,利用电化学方法制备了甘氨酸修饰电极,对修饰膜的电活性进行了表征.用循环伏安法研究了鸟嘌呤(G)和8一羟基脱氧鸟嘌呤核苷(8-OH-dG)在聚甘氨酸修饰电极上的电化学行为,并建立了对两者进行分别检测和同时检测的分析方法.实验结果表明,聚甘氨酸修饰电极可以增强鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在电极表面的吸附,并且可以加快鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在电极表面的电子传输,使两种电活性物质在聚甘氨酸修饰电极上的电化学信号明显增大,检测灵敏度大大提高,并且该修饰电极具有良好的稳定性和重现性.可用于鸟嘌呤和8-羟基脱氧鸟嘌呤核昔的分别和同时检测.     

改进的猴王遗传算法求解组合拍卖最优竞胜标

应用遗传算法求解第一价格封闭组合拍卖问题,编码及约束条件复杂,在交叉和变异的过程中容易产生大量不可行解,并且在选择和精英保留中,一些局部最优点(早熟点)对应的个体容易被大量复制,使群体多样性下降。该文就以上的问题,改进了原有遗传算法求解组合拍卖最优竞胜标的方法,并采用了猴王遗传算法的精英保留策略。实际计算证明本算法编码方式自然并且直观表达了个体和标的的关系。在交叉和变异算子中,产生不可行解的数量下降,更积极的保存了上一代进化的部分成果。而改进猴王遗传算法的精英保留策略,使算法能够以高概率跳出早熟,达到算法速度与准确的平衡。     

关于CJJ/T135-2009标准中胶凝材料用量确定方法的对比讨论

文章讨论了《透水水泥混凝土路面技术规程》(CJJ/T135-2009)标准中配合比设计部分关于胶凝材料用量确定方法与其他两种胶凝材料用量确定方法的准确性、便捷性、应变性和可操作性.通过对实验结果的分析发现,CJJ/T135-2009中的胶凝材料用量确定方法误将水胶比理解成每立方米混凝土用水量与所有胶凝材料用量的体积之比,从而导致计算结果偏差.而采用实际测定浆体密度再确定胶凝材料用量的方法更符合工程应用的实际情况,且可操作性更强.     

基于GPS与惯性组件的复合测姿系统设计

针对微小型载体空间有限而无法安装大型测姿系统,提出了一种低成本的微型复合测姿方案.复合测姿系统由单天线GPS和MEMS惯性组件构成.利用扩展卡尔曼滤波将单天线GPS和MEMS惯性组件信息融合,提高了姿态估计精度.室外车载实验结果表明,利用该复合测姿系统解算的滚角和俯仰角误差可控制在1°以内,航向角误差可控制在2°以内.     

基于扩展卡尔曼滤波的卫星组合姿态确定物理仿真技术研究

针对未来卫星应用的高精度、高可靠性定姿技术需求,设计了卫星单轴组合姿态确定物理仿真系统;采用太阳敏感器和光纤陀螺组合定姿模式,提出了一种扩展卡尔曼滤波组合姿态确定算法,用C语言进行了实验软件设计,系统采用DSP为处理器,还设计了多敏感器数据采集扩展接口;物理仿真实验结果:姿态确定精度优于0.5°;陀螺常值漂移估计的平均值为3.625758e-4°/s,证明了组合姿态确定算法的有效性和该项技术对工程应用的可行性。     

Terahertz spectroscopic uncertainty analysis for explosive mixture components determination using multi-objective micro-genetic algorithm

In practical applications, many suspicious samples may be a kind of mixture and consist of various chemical components that make the spectral analysis difficult. Various explosives and related compounds (ERC) in the mixture can be identified and the concentration of each component can be estimated based on the known spectral data of the pure explosive components. In this paper, the terahertz spectroscopic uncertainty analysis using a micro-GA has been proposed, in which the random assignment of alleles from parents to offspring is implied. An intelligent computation-based technical road-map is also provided for the analysis and optimisation of the terahertz spectroscopic combination analysis. A simulation with two given test cases for the ERC has been devised. The results of the simulation show that micro-GA and its derivatives have the potential applications in the fields of security, medicine and food industry to fast identify mixtures.     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

Electrocatalytic reduction of hydrogen peroxide and its determination in antiseptic and soft-glass cleaning solutions at phosphotungstate-doped-glutaraldehyde-cross-linked poly-l-lysine film electrodes

The present work describes the electrocatalytic behavior of phosphotungstate-doped glutaraldehyde-cross-linked poly-l-lysine (PLL-GA-PW) film electrode towards reduction of hydrogen peroxide (H₂O₂) in acidic medium. The modified electrode was prepared by means of electrostatically trapping the phosphotungstate anion into the cationic PLL-GA coating on glassy carbon electrode. The PLL-GA-PW film electrode showed excellent electrocatalytic activity towards H₂O₂ reduction in 0.1 M H₂SO₄. Under the optimized conditions, the electrochemical sensor exhibited a linear response for H₂O₂ concentration over the range 2.5 × 10⁻⁶ to 6.85 × 10⁻³ M with a sensitivity of 1.69 μA mM⁻¹. The curvature in the calibration curve at high concentration is explained in terms of Michaelis-Menten (MM) saturation kinetics, and the kinetics parameters calculated by three different methods were compared. The PLL-GA-PW film electrode did not respond to potential interferents such as dopamine, ascorbic acid and uric acid. This unique feature of PLL-GA-PW film electrode allowed selective determination of H₂O₂. Finally, the proposed electrochemical sensor was successfully applied to determine H₂O₂ in commercially available antiseptic solution and soft-contact lenses cleaning solution and the method has been validated using independent estimation by classical potassium permanganate titration method. Major advantages of the method are simple electrode fabrication, stability and high selectivity towards hydrogen peroxide.