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Optimizing risk budgets in portfolio selection problem: A bi-level model and an efficient gradient-based algorithm

IISE TRANSACTIONS 2024年第8期56卷 841-854页

作者： Bayat, Maryam Hooshmand, Farnaz MirHassani, Seyed Ali Amirkabir Univ Technol Tehran Polytech Dept Math & Comp Sci Tehran Iran

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time.

关键词： Risk budgeting portfolio bi-level optimization gradient descent method k-means clustering algorithm

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MOSS SOFTWARE: A NEW TOOL FOR MULTI-OBJECTIVE GREEN SUPPLIER SELECTION

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第2期29卷 244-266页

作者： Toktas-Palut, Peral Onan, kivanc Gurbuz, Mustafa Zahid Gulden-Ozdemir, Birsen Dogus Univ Dept Ind Engn Istanbul Turkey Dogus Univ Dept Comp Engn Istanbul Turkey

Competition between companies is getting more intense by the day. Corporations need to decrease costs while improving the quality and reliability of their deliveries. Meanwhile, the demand for environmentally sustainable products and operations is increasing. These issues are all compounded by the supplier relations between companies and their need to develop better economic and environmental supply chain contracts. This research thus aims to propose a tool, i.e., the MOSS Software, concerning the total cost, rate of defected materials, rate of late deliveries, and environmental improvement potential for suppliers. MOSS is a two-stage software. First, the objectives are included in a multi-objective evolutionary mathematical model, and the model is solved using the NSGA-III algorithm. In the second stage, as a post-Pareto analysis approach, the k means algorithm is used for selecting representative solutions among Pareto sets by comparing silhouette values for different k values. An application of the MOSS software is also presented in this paper.

关键词： Green supplier selection Lot-sizing Quantity discount Multi-objective optimization Genetic algorithm k-means clustering algorithm

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What are the critical barriers to the development of hydrogen refueling stations in China? A modified fuzzy DEMATEL approach

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ENERGY POLICY 2020年 142卷 111495-111495页

作者： Xu, Chuanbo Wu, Yunna Dai, Shuyu North China Elect Power Univ Sch Econ & Management Beijing Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing Peoples R China State Power Investment Corp Hydrogen Energy Co Ltd Beijing Peoples R China

Hydrogen fuel cell vehicle is one of the important modalities of the next generation of new energy vehicles. As the basic infrastructure of hydrogen fuel cell vehicles, hydrogen refueling stations (HRSs) have drawn attention from all social circles. However, hindered by various barriers, the development of HRSs is much slower than the Chinese government's expectation. Hence, this study aims to explore critical barriers of the HRS development in China based on a modified fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Firstly, a barrier list is identified through a literature survey and expert consultation, which consists of four categories (economic, technological, social and political-regulatory aspect) and 12 potential barriers. Later, considering the subjectivity of threshold setting in the traditional fuzzy DEMATEL method, the k-means clustering algorithm is utilized to optimize the threshold value, and thus a modified fuzzy DEMATEL approach is proposed to investigate the prominence of these barriers and the cause-effect relationships among them. The results show that six barriers are regarded as key barriers to China's HRS development, and corresponding policy measures are formulated to mitigate or eliminate these critical barriers.

关键词： Hydrogen refueling stations Critical barriers Modified fuzzy DEMATEL k-means clustering algorithm Policy measures

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A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems

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WATER 2018年第12期10卷 1734页

作者： Chang, Ming-Jui Chang, Hsiang-kuan Chen, Yun-Chun Lin, Gwo-Fong Chen, Peng-An Lai, Jihn-Sung Tan, Yih-Chi Natl Taiwan Univ Res Ctr Climate Change & Sustainable Dev Taipei 10617 Taiwan Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan Natl Taiwan Univ Hydrotech Res Inst Taipei 10617 Taiwan Natl Taiwan Univ Ctr Weather Climate & Disaster Res Taipei 10617 Taiwan

Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEk model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1-3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1-3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

关键词： early flood warning disaster risk k-means clustering algorithm support vector machine flood inundation forecasting flood inundation map

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Non-invasive method to analyse the risk of developing diabetic foot

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HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 109-113页

作者： Silva, Rebeca N. Ferreira, Ana C. B. H. Ferreira, Danton D. Barbosa, Bruno H. G. Univ Fed Lavras Dept Engn BR-37200000 Lavras MG Brazil Univ Fed Juiz de Fora Sch Nursing BR-36036900 Juiz De Fora MG Brazil

Foot complications (diabetic foot) are among the most serious and costly complications of diabetes mellitus. Amputation of all or part of a lower extremity is usually preceded by a foot ulcer. To prevent diabetic foot, an automatic non-invasive method to identify patients with diabetes who have a high risk of developing diabetic foot is proposed. To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the k-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. Both real and simulated data were used to evaluate the method in which promising results were achieved with accuracy of 0.97 + 0.06 for simulated data and 0.68 + 0.16 considering the classification of specialists as the gold standard for real data. The method requires a simple computational processing and can be useful for basic health units to triage diabetic patients helping the health-care team to reduce the number of cases of diabetic foot.

关键词： Euclidian distance k-means clustering algorithm automatic noninvasive method computational processing data classification diabetes mellitus diabetic foot diabetic patients diseases foot complications foot ulcer health care information vector medical diagnostic computing operational stage patient diagnosis pattern classification pattern clustering risk analysis self-care simulated data social scope

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Public Management Model Based on Mathematical Equation of Statistics of Probability Theory

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Dong, Jiansu China Univ Geosci Wuhan Sch Publ Adm Wuhan 430070 Peoples R China

Firstly, this paper establishes a cloud computing public opinion management business model based on distributed collection analysis, consultative decision and concurrent collaborative behavior. At the same time, this paper studies the public opinion management system from the logical framework, network structure and security index. Then this paper uses the weak correlation mining technology of probability theory statistics to establish a public health and safety public opinion prediction system composed of three dimensions. The content includes three levels of public opinion bonding, thermal and trend. Then, this paper forecasts the development of public opinion events by combining k-means clustering and grey correlation degree. The results show that this method is suitable for public opinion prediction in public health and safety.

关键词： Network public opinion Public health safety incidents k-means clustering algorithm Grey correlation analysis The warning level

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Construction of a painting image classification model based on AI stroke feature extraction

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Hu, Bowen Yang, Yafei East China Jiaotong Univ Sch Art Nanchang 330013 Peoples R China Nanchang Univ Architecture & Design Coll Nanchang 330031 Peoples R China

A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features;and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color fea

关键词： AI stroke features painting image information convolutional neural networks k-means clustering algorithm LeNet-5 support vector machine

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The application of landscape character classification for spatial zoning management in mountainous protected areas-A case study of Laoshan national park, China

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HELIYON 2023年第3期9卷 e13996页

作者： Zhao, Ye Huang, Xinyi Zhao, Yijun Liu, Xinyu Zhou, Ranjiamian Qingdao Univ Technol Coll Architecture & Urban Planning Qingdao Peoples R China

National parks provide a considerable number of co-benefits to society, including the balance of ecosystems, conservation of heritage values, and tourism. However, studies on zoning approaches for the management of national parks are lacking. The landscape characterization approach is a holistic method for identifying regional landscapes and helps improve zoning management, thus promoting sustainable planning. Here, we propose a landscape character classification (LCC) approach for national parks by integrating a k-means clustering algorithm and geographic information system (GIS). We used Laoshan National Park (LNP) as a case study and aimed to (1) quantify the major landscape factors (altitude, topography relief, soil type, and heritage impact intensity) that influence the landscape classification of mountainous protected areas;(2) create a map of landscape character types and areas to guide a zoning boundary;and (3) further examine how decision makers assign different conservation strategies to each landscape character area. Our results indicate that different landscape character areas reflect distinct ecological environments and heritage values and that differentiated zoning management can effectively mitigate the impact of natural disasters and human activities. Our study suggests that national parks require scientific landscape character zoning, rational descriptions of landscape character types, and targeted management measures to achieve the dual objectives of zoning and landscape conservation.

关键词： Landscape character classification Zoning management k-means clustering algorithm Laoshan national park Landscape conservation strategies Mountainous protected areas

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Design of New Media Event Warning Method Based on k-means and Seasonal Optimization algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第3期11卷 1-12页

作者： Gao, Zhenghan Zheng, Anzhu Qingdao Univ Technol Qingdao 266000 Shandong Peoples R China Shandong Univ Jinan 250000 Shandong Peoples R China

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, k-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction;then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing k-means clustering algorithm is proposed;finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION:Addresses the lack of comprehensiveness of current approaches to early warning of new media events.

关键词： new media event warning methods k-means clustering algorithm seasonal optimization algorithm random forests

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Two-layer coordination architecture HIF detection with μPMU data

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JOURNAL OF ENGINEERING-JOE 2018年第15期2018卷 1033-1037页

作者： Wang, Xiaojun Zhang, Yongjie Luo, Yiping He, Jinghan Ling, Ping Fang, Chen Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China State Grid Shanghai Municipal Elect Power Co Elect Power Res Inst Shanghai 200437 Peoples R China

The detection of high-impedance fault (HIF) on distribution network has been one of the most difficult problems. This study presents a data-driven method for HIF detection by using single-ended micro-phasor measurement unit (mu PMU). This approach is based on the two-layer coordination architecture, local side with PMUs and master station for further analysis. At the local side, the PMUs gather the synchronous data and achieve feature extraction with k-means clustering algorithm and principal component analysis. For determining the amounts of data categories, the authors adopt a method based on silhouette coefficient. Meanwhile, send the characteristics to the master station, then detect the HIF fault through the support vector machine. Finally, the method was tested on a 34 nodes distribution network in PSCAD/EMTDC. The results justify the effectiveness and the proposed detection scheme has >85% accuracy.

关键词： pattern clustering support vector machines feature extraction principal component analysis phasor measurement fault diagnosis power distribution faults power engineering computing detection scheme two-layer coordination architecture HIF detection PMU data data-driven method master station synchronous data principal component analysis data categories HIF fault 34 nodes distribution network high-impedance fault detection single-ended microphasor measurement unit feature extraction k-means clustering algorithm silhouette coefficient support vector machine PSCAD-EMTDC

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