提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题,满足GPS定位信号的精度和信号数据处理时间的要求,提出1种群调度滤波算法,通过实验仿真,分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间,具有较大优势,可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

基于人工神经网络的气体泄爆最大超压预测研究

为解决传统经验公式在预测气体泄爆中最大超压出现时的较大偏差或过于保守的问题,提出使用人工神经网络预测气体泄爆最大超压。基于124组实验数据,采用BP与RBF神经网络,通过优化算法计算与迭代循环对泄爆样本中的影响因素进行降维与选择,并确定2类神经网络本身在学习与计算气体泄爆样本时的相关参数。结果表明:PCA（主成分分析法）在当前样本条件下的降维效果较差,而通过迭代对比确认气体泄爆样本中的5类特征全部保留时神经网络的训练模拟效果最好;通过对124组实验数据进行随机挑选训练集与测试集的训练模拟结果发现,神经网络对气体泄爆中最大超压的预测效果较好;通过对比Molkov提出的和经Fakandu等改进的NFPA 68经验公式以及2类神经网络的预测结果表明,神经网络相比于传统气体泄爆经验公式具有明显优势。     

Location selection of emergency supplies repositories for emergency logistics management: A variable weighted algorithm

With the development of the city, the number of establishments that are proposed or under construction is increasing year by year, and if they are industries that handle flammable, explosive, toxic, harmful, and dangerous substances, the public safety will face great threats, which will bring great challenges to emergency rescue work. Therefore, providing reasonable solutions to the problem of location selection of emergency supplies repositories are necessary for improving the emergency response efficiency in chemical industrial parks. A mathematical model for location selection of emergency supplies repositories in emergency logistics management are presented considering more actual factors. The optimization objectives of the model are to minimize total transport length and cost. And then a Variable Weighted Algorithm is designed to solve the model, where an auxiliary function was constructed with different methods of building weighting factors based on the theory and method of solving multi-objective optimization problems in operational research. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.     

Research based on double coverage rate and reliability of gas detector layout optimization

The gas detector layout should be highly attuned to combustible gas leakage and attain a good reliability in avoiding detector malfunction, which is an important guarantee for the normal production of the chemical industry and other related enterprises. Herein, a gas detector layout optimization method based on double coverage and reliability is proposed. The key gas leakage monitoring area is determined through layout scene field investigation. To improve the detection probability and detection system reliability, the dual coverage target and voting mechanism are set, and the gas detector layout is determined with the ray-casting algorithm according to the coverage target. Combined with FLACS software to simulate a variety of typical leakage conditions under different layout scenarios, the relationship between the leaked gas concentration detected by gas detectors in each layout scheme and time is obtained, and the gas leakage detection probability in each layout scheme, number of detectors that can trigger the alarm, shortest time to trigger the alarm and reliability are comprehensively evaluated. The decision-maker selects the final gas detector layout plan according to the evaluation results and actual site needs. The study shows that the detection probability of each layout scheme set according to the double coverage is high, and multiple detectors can trigger the alarm (up to 100%), which ensures that the alarm can be triggered within 10 s under all applicable conditions. According to the evaluation results, the decision-maker can obtain a layout scheme that not only agrees with the actual site conditions but also attains a high detection probability, short detection time and strong reliability.     

面源模式的通用算法探讨

提出了矩形面源扩散模拟计算的数值积分法和中心虚点源法,以及二者结合的综合法,并用算例进行了计算精度、计算速度和结果连续性的对比.结果表明:与传统的后置虚点源法及其改进算法——谷清经验法相比,该算法与理论真值的相对误差可从20%以上降低到3%以下,并且可以准确地反映风向和面源的形状,但在计算量上要增大十几倍到上百倍.对于任意不规则面源,提出了正方形分割算法,并对分割正方形的取舍采用简便的重心判断法,以准确方便地模拟其形状.综合运用笔者提出的适于机算的通用面源算法,对不同的计算任务权衡速度和精度采用合适的算法,可以处理各种气象条件下任意形状面源的扩散计算,并可取得理想的计算精度.      

Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

基于MODIS地面温度数据的成都市热岛时空变化

研究利用2000、2005和2010年逐月的MODIS/Terra和MODIS/Aqua卫星的地表温度数据产品,通过等间距法进行分析,得出成都市建成区2000~2010年间昼夜热岛的时空变化以及相互间差异。研究结果表明:成都市昼夜热岛存在显著差异,日间热岛主要呈散点分布,夜间热岛集中在城市中心区域。日夜间热岛变化趋势有显著不同,日间热岛继续分散而夜间热岛范围则有所扩大。日间热岛分布与植被覆盖存在一定的负相关性,但道路也在一定程度上促使了地面温度的增加,夜间城市热岛范围及变化则与其下垫面组成存在关系。     

Methodology of industry scale analysis under the constraint of regional resource capacity using HSY algorithm

This paper develops a methodology of proper scale analysis for regional industry development, which can be used in industry planning with the consideration of regional resource capacities. In the face of different data sources and even data scarcity, alternative methods based on linear programming and quadratic programming algorithms for calculating the resource intensity factors are designed. Based on the empirical study of industrialization, initial scenarios of industry development were set. Using HSY algorithm, sensitive industries that may cause exceeding regional resource capacity can be identified, and the risk of exceeding can be predicted and expressed in probability. Furthermore, a proper scale range can be designed for these sensitive industries according to resource capacity. Taking the case of Dalian city in China, this paper estimated the regional urban development plan, various resources capacities were studied, and land resources were estimated to be the most critical resource for the city. The land resource depletion intensities of different industries are calculated by quadratic programming algorithm. Under the constraint of 427.56 km² available industrial land resources, the electronic and power industries have the most significant impact on total land use, if the scale of power industry exceeds 1.27 billion USD, the probability of land resource capacity breakthrough will be 50%.