Improvement of Stochastic Competitive Learning for Social Network

As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the F1 score of the improved algorithm is 9.07% higher than that of original algorithm.     

Rethinking Nano‐TiO2 Safety: Overview of Toxic Effects in Humans and Aquatic Animals

Titanium dioxide nanoparticles (nano‐TiO₂) are widely used in consumer products, raising environmental and health concerns. An overview of the toxic effects of nano‐TiO₂ on human and environmental health is provided. A meta‐analysis is conducted to analyze the toxicity of nano‐TiO₂ to the liver, circulatory system, and DNA in humans. To assess the environmental impacts of nano‐TiO₂, aquatic environments that receive high nano‐TiO₂ inputs are focused on, and the toxicity of nano‐TiO₂ to aquatic organisms is discussed with regard to the present and predicted environmental concentrations. Genotoxicity, damage to membranes, inflammation and oxidative stress emerge as the main mechanisms of nano‐TiO₂ toxicity. Furthermore, nano‐TiO₂ can bind with free radicals and signal molecules, and interfere with the biochemical reactions on plasmalemma. At the higher organizational level, nano‐TiO₂ toxicity is manifested as the negative effects on fitness‐related organismal traits including feeding, reproduction and immunity in aquatic organisms. Bibliometric analysis reveals two major research hot spots including the molecular mechanisms of toxicity of nano‐TiO₂ and the combined effects of nano‐TiO₂ and other environmental factors such as light and pH. The possible measures to reduce the harmful effects of nano‐TiO₂ on humans and non‐target organisms has emerged as an underexplored topic requiring further investigation.     

基于密度聚类与SVM的非法广播信号识别研究

针对非法广播信号的危害,以及传统人工检测效率低的问题,提出一种基于密度聚类与SVM的信号识别模型。首先,采用标准欧式距离对特征信号进行提取;其次,以聚类样本为基础,采用SVM分类器对信号分类;最后,以青海广播电视局中波台整点时刻前后300帧的数据为样本,以静音信号作为评价指标,对信号进行识别。结果表明,在正常信号中加入非法信号后,频谱中有少量的静音信号,且SVM训练时间和识别正确率都要优于传统算法。     

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.     

基于改进NSGA-Ⅱ算法的乙烯裂解炉操作优化

现有的乙烯裂解炉优化通常只针对两个目标函数即产物乙烯和丙烯的收率，并且采用遗传算法的收敛效果一般，故提出一种基于改进NSGA-Ⅱ算法来研究一个多目标运行的解决方案，以此来解决乙烯裂解炉的固定周期操作优化问题，即在增大产物乙烯和丙烯收率的同时减少原料以及蒸汽流量来提高整体运行状况。把具体问题量化为数学模型，分析了原料气烃比、原料流量、出口温度对乙烯和丙烯收率的影响。实验结果表明，相较于原有操作条件，提出的优化方案具有良好的可行性。     

Multi-objective optimization for efficient modeling and improvement of the high temperature PEM fuel cell based Micro-CHP system

Fuel cells due to different useful features such as high efficiency, low pollution, noiselessness, lack of moving parts, variety of fuels used and wide range of capacity of these sources can be the main reasons for their tendency to use them in different applications. In this study, the application of a high temperature proton exchange membrane fuel cell (HT-PEMFC) in a combined heat and power (CHP) plant has been analyzed. This study presents a multi-objective optimization method to provide an optimal design parameters for the HT-PEMFC based micro-CHP during a 14,000 h lifetime by considering the effect of degradation. The purpose is to optimize the net electrical efficiency and the electrical power generation. For the optimization process, different design parameters including auxiliary to process fuel ratio, anodic stoichiometric ratio, steam to carbon ratio, and fuel partialization level have been employed. For optimization, A new technique based on Tent mapping and Lévy flight mechanism, called improved collective animal behavior (ICAB) algorithm has been employed to solve the algorithm premature convergence shortcoming. Experimental results of the proposed method has been applied to the data of a practical plant (Sidera30) for analyzing the efficiency of the proposed ICAB based system, it is compared with normal condition and another genetic algorithm based method for this purpose. Final results showed that the difference between the maximum electrical power production under normal condition and ICAB based condition changes from 2.5 kW when it starts and reaches to its maximum value, 3.0 kW, after 14,000 h lifetime. It is also concluded that the cumulative average for the normal and the ICAB based algorithm are 24.01 kW and 27.04 kW, respectively which showed about 3.03 kW cumulative differences.     

基于GA-MVFOSM法的DSA250型受电弓运动可靠性分析

为分析运动副磨损与杆件尺寸不确定性2种因素对受电弓运动可靠性的影响，提出一种基于遗传一次二阶矩法(GA-MVFOSM)的受电弓运动可靠性计算方法。首先，构建受电弓运动学方程，应用Archard磨损理论建立其运动副磨损可靠性模型。在此基础上，考虑运动副磨损与杆件尺寸的不确定性，结合可靠性理论建立受电弓运动可靠性模型。其次，利用遗传算法优化一次二阶矩法，提出GA-MVFOSM算法，用以求解受电弓的运动可靠度。最后，以DSA250型受电弓为研究对象，利用所提方法分析其运动可靠性，并与传统方法进行对比。分析结果表明:各运动副磨损量分别在0.230 0 mm、0.136 6 mm、0.006 6 mm内时，运动副磨损可靠度处于较高水平，2种因素耦合情况下弓头运动可靠度最小值为0.964 7；基于遗传一次二阶矩的计算方法可以有效提高传统方法计算精度，为提高受电弓工作可靠性提供参考依据。     

高速冲压生产线上下料冗余机械臂的运动学分析

在应用于高速冲压生产线冗余机械臂姿态规划和轨迹优化过程中,以兼顾上下料效率与轨迹的平稳性为目的,对冗余机器臂的加加速度进行约束,基于旋量理论建立运动学模型,得到正运动学方程,采用遗传算法对冗余机械臂进行逆运动学分析,结合五次B样条插值方法,对末端执行器运动轨迹的加加速度进行优化。结果表明,该方案可有效提高上下料效率,保证冗余机械臂上下料过程运行平稳。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Profit maximization of TSP through a hybrid algorithm

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company’s product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one’s result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented.