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IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)

作者： You, Hao Zhao, Jiufen Shi, Shaokun Tang, Qinhong Rocket Force Univ Engn Xian 710025 Peoples R China

ISBN: (纸本)9781538611715

In this paper, a " single-to-single " deployment optimization model is established and a variable-size discrete method is proposed. The maximum number of interceptions of missiles is taken as an indicator to initialize the zone and the small-scale discretization of the possible optimal deployment area is obtained. In the small-scale grid, the pso algorithm is used to search the deployment location. In order to solve the problem of low local search capability and low optimization ability in the post-deployment optimization process, this paper analyzes the inertial weight, speed, location updating strategy improved, analyzed the impact of the height and speed of incoming targets on deployment locations. The simulation results show that the proposed model and the improved algorithm have higher simulation efficiency and can meet the actual needs.

关键词： variable-size discrete method pso algorithm simulation efficiency

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Non-Gradient Based PDF Approximation for Sensor Selection in Cognitive Sensor Networks

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INTERNATIONAL JOURNAL OF INTERDISCIPLINARY TELECOMMUNICATIONS AND NETWORKING 2019年第1期11卷 1-16页

作者： Aghdam, Mohammad Reza Ghavidel Abdolee, Reza Sahbari, S. K. Seyyedi Tazehkand, Behzad Mozaffari Univ Tabriz Tabriz Iran Calif State Univ Dept Comp & Elect & Comp Sci Bakersfield CA USA

Energy consumption in detection is a key objective for cognitive sensor network. Therefore, measuring the energy consumption is an important issue for efficient spectrum sensing. In order to compute the consumed energy at sensor nodes, their energy probability density function (PDF) is often required. In this article, the authors study the problem of spectrum sensing in cognitive networks and focus on strategies that can substantially affect the energy efficiency and complexity of such algorithms. In particular, they consider an energy detection mechanism in cooperative spectrum sensing where the knowledge of the energy PDF is the key. Since in practice the true value of such a PDF is unavailable, the authors propose to use non-gradient based optimization algorithms to find the parameters of approximated PDF function. In the proposed method, the corresponding PDF parameters are computed iteratively using Genetic and pso algorithms. The numerical results show that the proposed technique outperforms prior methods.

关键词： Cognitive Radio Genetic algorithms Non-Gradient Optimization Methods Probability of Detection Probability of False Alarm pso algorithm Sensor Networks Spectrum Sensing

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Application of Improved pso - BP Neural Network in Customer Churn Warning

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Procedia Computer Science 2018年 131卷 1238-1246页

作者： Jiafa Huang Lili He

In view of the customer turnover becoming the outstanding issues in the enterprise for maintaining and managing the customer relationship, combined with the massive customer data complex and huge, I propose a combination of optimized pso algorithm and BP neural network algorithm to establish the enterprise customer churn warning model, giving full play to the generalization of BP neural network. The simulation results show that the model has a good application prospect for the analysis of customer classification and loss.

关键词： Loss Warning pso algorithm BP Neural Network RFMT Model Information gain

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New Parameter Identification Method for the Fractional Order, State Space Model of Heat Transfer Process

New Parameter Identification Method for the Fractional Order...

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International Conference on Automation

作者： Oprzedkiewicz, Krzysztof Dziedzic, Klaudia AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9783319771793;9783319771786

In the paper parameter identification problem for model of heat transfer process is considered. The system under consideration is described by fractional order, state equation using Caputo operator. The parameters of the model are identified via optimization of the MSE cost function, describing the difference between step responses of real experimental plant and tested model. The proposed by authors optimization method consists in associating pso and simplex algorithms. Results of numerical tests point that the proposed method assures good accuracy with short duration of calculations.

关键词： Fractional order systems Heat transfer pso algorithm Simplex algorithm Identification

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Communication Network Planning with Dual Network Coupling Characteristics Under Active Distribution Network 1st

Communication Network Planning with Dual Network Coupling Ch...

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1st International Conference on Intelligent Manufacturing and Internet of Things (IMIOT) / 5th International Conference on Computing for Sustainable Energy and Environment (ICSEE)

作者： Fu, Zhiqiang Li, Xue Du, Dajun Xu, Sheng Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Nantong Vocat Univ Sch Elect & Informat Engn Nantong 226007 Peoples R China

ISBN: (纸本)9789811323812;9789811323805

A novel communication network planning method is proposed by considering the relationship between optical fiber communication network and power grid and the topological stability of communication networks. Firstly, the minimum spanning tree is used to generate the shortest power grid topology in term of the minimum expense of initial power grid. Then, for dual network (i.e., power grid and communication network) coupling characteristics, considering the constraints of network looping rate and invulnerability, an economic planning model is established and further solved by using the particle swarm optimization (pso) algorithm. Finally, a 29-node system is employed to confirm the effectiveness and feasibility of the proposed method.

关键词： Communication networks Power grid Active distribution network Topology stability pso algorithm

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Tweet Summarization: A New Approch 2

Tweet Summarization: A New Approch

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Naik, Siddhi Lade, Shruti Mamidipelli, Swati Save, Ashwini Viva Inst Technol Comp Engn Mumbai India Viva Inst Technol Dept Comp Engn Mumbai India

ISBN: (纸本)9781538619742

Social Media has become a crucial source for gaining information about current goings-on in the world. Twitter is considered as fastest and most popular means of communication today where thousands of people tweet everyday regarding an event or a news-story. Number of tweets are posted on twitter on daily basis, which are analyzed by sentiment analysis to draw summary of opinions expressed by the user and categorize them to know the views of the user on an issue. The problem with sentiment analysis is that there are millions of users with differences in opinion to test. There are as well practical difficulties that arise while performing sentiment analysis. It may happen that someone may tweet some irrelevant stuff about an event and in such cases summarization comes into picture and plays a significant role. For providing user with better results tweets are not only summarized but they are first segmented. Segmentation helps in conserving semantic meaning of tweet. For summarization of tweets there are various clustering algorithms available which different researchers have used in their systems like K-means, Ant Colony. One of the optimal algorithms is the Particle Swarm Optimization algorithm (pso). pso works in an analogous way as that of swarm of bees. And hence to provide user with better results the proposed system makes use of the pso algorithm for clustering of tweets for summarization.

关键词： twitter tweets summarization pso algorithm

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Mid-Term Load Power Forecasting Considering Environment Emission using A Hybrid Intelligent Approach 5

Mid-Term Load Power Forecasting Considering Environment Emis...

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5th International Symposium on Environment-Friendly Energies and Applications (EFEA)

作者： Heydari, Azim Garcia, Davide Astiaso Keynia, Farshid De Santoli, Livio Sapienza Univ Dept Astronaut Elect & Energet Engn DIAEE Via Eudossiana 18 I-00184 Rome Italy Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

ISBN: (纸本)9781538655177

The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load). The combined method of the neural network and the particle optimization algorithm were used to predict the load, and then the maximum amount of environmental pollution caused by the production of electricity required to supply the predicted load was calculated. The applied method was tested on the data of a North American electric company for four months (four seasons) and in comparison to the other methods presented in previous studies, it had an acceptable accuracy.

关键词： environment emission daily peak load neural network pso algorithm Mid-Term forecasting

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Analysis of Supply Chain Network Design Model with Quality Cost

Analysis of Supply Chain Network Design Model with Quality C...

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作者： Worrasete Tansurat Wichai Chattinnawat Department of Industrial Engineering Faculty of Engineering Chiang Mai University

Designing the supply chain network model that takes into account product quality could be one of the most key factors that significantly improve the performance of organizations and also affects the most customer satisfaction in a long-term ***,this model is based on a single product and a single production *** paper aims at designing a three-echelon supply chain model including multiple suppliers,multiple manufacturers with multi-stages inspection,and multiple *** mathematical problem is formulated with Cost of Quality(COQ) integrated into Supply Chain Network Design(SCND) to minimize the total supply chain cost involving transportation costs and production ***,the paper proposes a meta-heuristic called Particle Swarm Optimization algorithm used to solve the model for the supply chain lot size and sampling inspection *** the numerical data of the case study,the developed technique can determine the minimum of total supply chain cost at quality inspection 30.95%.

关键词： Supply Chain Network Design Quality Cost Cost of Quality COQ pso algorithm

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IInvestigation of physical models accuracy optimized by pso for determining low-velocity impact behavior of warp-knitted spacer fabrics

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JOURNAL OF THE TEXTILE INSTITUTE 2018年第3期109卷 312-321页

作者： Yousefpour, Masih Payvandy, Pedram Ahmadi, Mohammad Saleh Yazd Univ Dept Text Engn Yazd Iran

Nowadays, warp-knitted spacer fabrics (WKSFs) have been developed for many applications. Impact response and energy absorption are important properties of WKSFs in protective applications. In this study, low-velocity impact (LVI) behavior of WKSFs was investigated. In this regard, physical models with maximum adoption to the behavior of WKSFs in LVI were determined. In the first step, LVI behavior of WKSFs was investigated using a high-speed camera. Then, the LVI response of WKSFs was assessed using three common physical models, i.e. Kelvin-Voigt, Maxwell, and standard linear models. In order to achieve the best adoption with WKSFs behavior in LVI, particle swarm optimization was applied to optimize the parameters of each model. The results showed that Kelvin-Voigt model had the highest accuracy with a root mean square error (RMSE) of 2.3%.

关键词： Low-velocity impact warp-knitted spacer fabric physical model pso algorithm

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Non-invasive Analytics Based Smart System for Diabetes Monitoring 4th

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4th International Conference on IoT Technologies for Health Care (HealthyIoT)

作者： Saravanan, M. Shubha, R. Ericsson India Global Serv Pvt Ltd Ericsson Res Chennai Tamil Nadu India VIT Univ Sch Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9783319762135;9783319762128

Wearable devices have made it possible for health providers to monitor a patient's health remotely using actuators, sensors and other mobile communication devices. Internet of Things for Medical Devices is poised to revolutionize the functioning of the healthcare industry by providing an environment where the patient data is transmitted via a gateway onto a secure cloud based platforms for storage, aggregation and analytics. This paper proposes new set of wearable devices- a smart neck band, smart wrist band and a pair of smart socks- to continuously monitor the condition of diabetic patients. These devices consist of different sensors working in tandem form a network that reports food intake, heart rate, skin moisture, ambient temperature, walking patterns and weight gain/loss. The devices with the aid of controllers send all the sensor values as a packet via Bluetooth to the Mobile App. With the help of Machine Learning algorithm, we have predicted the change in patient status and alert them.

关键词： Diabetes monitoring Non-invasive method Sensors and devices pso algorithm Mobile app

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