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International Conference on Industrial Engineering and Systems Management (IESM)

作者： Xin Chun-Lin Liang Shuo Shen Feng-wu Beijing Univ Chem Technol Beijing Peoples R China Beijing Environm Sanitat Engn Grp Beijing Peoples R China

ISBN: (纸本)9781728115665

With the development of the urban, many small urban areas are integrated into big ones, which leads to redundancy of the domestic waste disposal facilities between the original urban areas. How to optimize these facilities is the major decision-making problems faced by urban administrative departments, hi this paper, three scenarios, which are considered. We present three strategies to deal with this problem, which are redundant deletion, garbage bin redistribution and system reconstruction respectively. And then, according to the redundant quantity of the regional integration, waste collection centers, three corresponding mathematical models are firstly designed. Furthermore, the Voronoi technology and the clustering algorithm are applied to handle these models. Finally, a simulation case based on the regional integration of the Old Eastern District, Old West District, Chongwen and Xuanwu District in Beijing is studied, the research results find that the efficiency of the system has increased from 74.9% to 85.9%, to 95.6% and to 100%.

关键词： Location problem Domestic waste collection and transportation network Voronoi graph theory clustering algorithm Restructure strategy

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CUCS: A Web Page Classification algorithm for Large Training Set

CUCS: A Web Page Classification Algorithm for Large Training...

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IFIP International Conference on Network and Parallel Computing

作者： Wang, Jing Cai, Hongming Xu, Boyi Jiang, Lihong Shanghai Jiao Tong Univ Sch Software Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econom & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780769533544

This paper presents a new algorithm of web page classification, CUCS(Combined UC and SVM), for large training set. CUCS combines the advantages of SVM (Support Vector Machine) and UC (Unsupervised clustering), achieving high precision and fast speed. In the training stage, CUCS gets clustering centers, which include positive example centers and negative ones, by means of UC. Then CUCS prunes training set to produce classifier by SVM. In the classifying stage, the minimum distance from a web page to the positive centers, as well as to the negative centers, is calculated. If the difference between the two distances is large enough, the web page will be classified by UC. Otherwise, the web page will be classified by pruned SVM. Through experiments. CUCS manifests precision that is much higher than UC and a little higher than SVM. As to time consumed, CUCS costs more time than UC and far less than SVM.

关键词： clustering algorithm Large training set SVM Web page classification

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Fleet Planning in the Car Rental Business

Fleet Planning in the Car Rental Business

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作者： WU, GEN-HAN PennState University Libraries

学位级别：Doctor of Philosophy

In the United States, consumption capacity in tourism is poised to grow significantly, and transportation is one of the essential elements in tourism. Airplanes and car rentals are the most typical modes of public travel. While air transport has earned significant scholarly attention and there exist abundant studies, fleet planning in the car rental business is discussed only minimally in operations research. Therefore, the purpose of this research is not only to build a thorough analytical framework for car rental fleet planning in different time phases, but also to develop practical algorithmic procedures. In long-term planning, pool segmentation and hub selection are studied. All rental locations are split into different pools and a hub is selected within each pool. The proposed clustering-based iterative algorithm offers a reliable clustering method to quickly find an initial solution with a small solution gap and an iterative method to gradually approach a near-optimum. In mid-term planning, inter-pool moves and asset replacement are distributed among different pools based on the change of seasonal demand. Numerical results have shown that the best-improvement descent local search with the structure of better neighbors has very good performance and can obtain a satisfactory solution in an extremely short time. In short-term planning, vehicle imbalance at different locations forces empty vehicles to be redistributed. Daily planning of demand allocation and empty flow redistribution is addressed in the same pool. Car upgrade policy and service level are also considered. A first-improvement descent local search is developed. Computing results demonstrate that the first-improvement descent local search not only obtains relatively good solutions in quite a short time but also solves very large scale integer programming problems easily.

关键词： facility location neighborhood search car rental fleet planning clustering algorithm

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Intelligent Pricing and Replenishment Strategy for Vegetables Based on ARIMA Prediction and BP Neural Networks

Intelligent Pricing and Replenishment Strategy for Vegetable...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Yang, Quyixiao Liu, Enyu Qiao, Yue Dalian University of Technology Dalian China

ISBN: (纸本)9798350309614

With the improvement of people's living standard, people pay more and more attention to nutritional balance, which brings huge commercial profits, because vegetable goods can only be sold on the same day, taking into account the special characteristics of vegetable goods, the use of past sales data for data mining, to help store managers to make the best decision from the single product, category, replenishment volume, pricing. Firstly, using the idea of clustering, the single items are clustered, and Spearman correlation analysis model is established for correlation analysis, then, the weekly sales volume, sales unit price, wholesale price are counted, and the cost-plus pricing relationship of each category is back-calculated daily through the cost-plus pricing formula, and the total amount of replenishment of the vegetable category for a week and pricing strategy are predicted using the BP neural network prediction model forecasting, and the total amount of replenishment of the vegetable category for a week is predicted. Finally, the ARIMA time series prediction model is built to solve the pricing reference strategy for a single item on a single day and determine the replenishment quantity of a single item on a single day by considering the unit price of the vegetable category, the wholesale unit price, and the net profit from the sale of the vegetable category. The proposed model has excellent forecasting ability and the results are evaluated more objectively. © 2023 IEEE.

关键词： BP Neural Network Prediction Models clustering algorithm Intelligent Decision Making for Goods Polynomial Regression Spearman Correlation

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A Multi-attribute Large Group Decision-making Method Based on Interval-valued Pythagorean Fuzzy Number

A Multi-attribute Large Group Decision-making Method Based o...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： You, Xinshang Zhou, Zhe School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China

Firstly, a large group clustering algorithm based on data similarity is proposed, which can set different thresholds to cluster the decision results of expert groups. Secondly, the interval-valued pythagorean fuzzy number (IVPFN) hesitancy accuracy function and hesitancy score function are proposed. And based on the consideration of the distance between centroids and rectangular area in the geometric meaning of IVPFN, a calculation formula is proposed to distinguish different IVPFNs. Thirdly, use the above formula to construct a weight calculation model for evaluation criteria with adjustment coefficients. The decision matrix is weighted and its relative distance from the positive and negative ideal solutions is calculated to produce the final ranking. Finally, the cultural tourism project decision-making problem is analyzed as an arithmetic example and compared with the methods in the literature of other related fields to illustrate the rationality and scientificity of this paper. © 2024 X. You & Z. Zhou.

关键词： clustering algorithm cultural tourism project interval-valued pythagorean fuzzy number multi-attribute large group decision-making

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Supervised Classification by RBF Networks with Self-Constructing clustering

Supervised Classification by RBF Networks with Self-Construc...

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International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Ching-Sheng Tu Deng-Yi Wu Shie-Jue Lee Yann-Yean Su National Sun Yat-Sen University Kaohsiung Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-Sen University Kaohsiung Taiwan Wenzao Ursuline University of Languages Kaohsiung Taiwan

ISBN: (纸本)9781538680988;9781538680971

Radial basis function (RBF) networks have been widely used in a variety of applications, including supervised classification. Two issues are often encountered in the applications. First, the number of hidden nodes has to be decided. Second, the settings of the basis functions have to be set. In this paper, we propose a novel RBF network approach for supervised classification applications. Given a set of training patterns, the number of hidden nodes in the hidden layer is determined by applying a self-constructing clustering algorithm on the patterns. Normalized Gaussian functions are taken to be basis functions, and their centers and deviations are set according to the clusters obtained from the clustering algorithm. The optimal values for the weights associated with the output layer are derived by adapting them to the training patterns. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

关键词： Supervised classification RBF networks clustering algorithm Basis function

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Frontiers of Chinese entrepreneurship education research based on data visualization analysis 6

Frontiers of Chinese entrepreneurship education research bas...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Shen, Yanhua Zhang, Yongzhong Gu, Zhenyu Dai, Yonghui Shanghai Xingjian Coll Shanghai Peoples R China Shanghai Open Univ Inst Sci & Technol Shanghai Peoples R China Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai Peoples R China Shanghai Univ Int Business & Econ Sch Management Shanghai Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In order to visualize the current frontiers of entrepreneurship education research in China, we selected 1196 relevant research literatures in the last 20 years from CNKI database, and used CiteSpace tool and clustering algorithm to analyze the authors and research institutions. It includes visual analysis of cooperation network map of author cooperation, innovation and entrepreneurship research institution, keyword co-occurrence map and keyword cluster map. In addition, we also discussed and analyzed the three stages of China's entrepreneurship education research. The results show that the current frontiers of entrepreneurship education research in China is integration and new engineering teaching, which helps to intuitively understand the frontiers of entrepreneurship education research in China.

关键词： Entrepreneurship education data visualization map analysis clustering algorithm

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Social Choice in a Computer-Assisted Simulation

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SIMULATION & GAMING 2009年第4期40卷 488-512页

作者： Thavikulwat, Precha Towson Univ Dept Management Towson MD 21252 USA

Pursuing a line of inquiry suggested by Crookall, Martin, Saunders, and Coote, the author applied, within the framework of design science, an optimal-design approach to incorporate into a computer-assisted simulation two innovative social choice processes: the multiple period double auction and continuous voting. Expectations that the multiple- period-double-auction market would be bustling, that the continuous voting process would be adaptive, and that the simulation would be a suitable candidate for the assessment of learning were met in an administration of the simulation involving about 87 participants. The author suggests that the technology is ready for computer-assisted simulations to be much more widely used than they are today, but that progress may nevertheless be slow because a great deal of personal investment of time and energy is needed to do good work.

关键词： affinity propagation assessment of learning business bustling market clustering algorithm computer-assisted continuous voting GEO periodic double auction proposal voting social choice

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Detecting and Classifying Problematic Behavior: A Method Based on Multi-Dimensional Software Modeling Behavioral Characteristics

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ACM TRANSACTIONS ON COMPUTING EDUCATION 2024年第1期25卷 1-25页

作者： Wang, Yiting Li, Tong You, Jiahui Zhang, Xinran Geng, Congkai Liu, Yu Beijing Univ Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China NYU New York NY USA

Understanding software modelers' difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers' analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software modeling process, such as incorrect connections or misunderstandings, but neglects the behavioral patterns that can reveal underlying, unaddressed modeling problems. This oversight fails to identify deeper problems that do not manifest as obvious issues but still represent significant potential problems in the software modeling process. Our research concentrates on detecting and classifying problematic modeling behaviors from software modeling process data, revealing the potential problems hidden in the process for MDE education. Specifically, we first construct problematic modeling behavior patterns from three dimensions, including anomalous time intervals, repetitions, and frequencies, to further identify characteristics and priorities relevant to problematic modeling behaviors. Then, we design rules with characteristics and priorities to detect and classify problematic modeling behaviors from problematic patterns. To evaluate the effectiveness of our proposal, we apply it to a data-flow diagram modeling platform. This platform can record modelers' processes and has been practically used in software engineering courses for five years. We have conducted a case study with 12 participants. The macro F1 of detection and classification problematic modeling behaviors is 82.3%, which shows the effectiveness of our approach. Then, to evaluate the usefulness of our proposal for assisting modeling instructors in MDE education, we conducted another case study with 5 modeling instructors. The results show that our approach can help instructors uncover problems hidden in the software modeling process. The results of two case studies demonstr

关键词： Model-Driven Engineering Modeling behavior analysis Goal model Frequent pattern mining clustering algorithm

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Decentralized Multi-Target Tracking for Netted Radar Systems with Non-Overlapping Field of View

Decentralized Multi-Target Tracking for Netted Radar Systems...

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IEEE Radar Conference (RadarConf)

作者： Peng, Cong Mao, Haiyi Liu, Yue Chai, Lei Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665436694

In this paper, a robust and high-accuracy decentralized fusion strategy is proposed for multi-target tracking (MTT) in netted radar systems with non-overlapping field of view (FoV). Each radar in the network runs a local Probability Hypothetical Density (PHD) filter with the decentralized consensus protocol to reduce communication bandwidth and eliminate information inconsistency among nodes. In the above process, the most critical core is an effective fusion strategy. Our proposed method adopts the geometric covariance intersection (GCI) rule to improve fusion accuracy. However, the standard GCI fusion is not suitable for the netted radar systems with non-overlapping FoV because it only focuses on the targets within the intersection of radar FoVs. Consider that, we extend the weights in GCI fusion to be a set of state-dependent weights instead of scalars to perform GCI fusion in a more robust manner. Furthermore, the radar FoVs are always unknown and time-varying in practical scenarios. Towards addressing this case, we combine a clustering algorithm based on highest posterior density to maintain a good fusion performance. The Gaussian mixture implementation of the proposed method is provided. Numerical simulations are designed to verify the effectiveness of the proposed method.

关键词： decentralized multi-target tracking field of view consensus protocol state-dependent generalized covariance intersection fusion clustering algorithm

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