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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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A New clustering-Based Approach for Target Tracking to Optimize Energy Consumption in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2020年第4期114卷 3337-3349页

作者： Hosseini, Rahim Mirvaziri, Hamid Shahid Bahonar Univ Comp Engn Dept Kerman Iran

The emergence of wireless sensor networks is counted among the most significant achievements of the late 20's. Nowadays, by the ever increasing utilization of new technologies, accessibility of distributed processing technologies and the rising demand for these technologies in regions unreachable or hard-to-reach for humans, their application is greatly expanded. In this study, the objective application is target tracking in protected zones. In this respect, tracking and accurate position reporting of targets in the defined time unit is the main objective. Nevertheless, one of the challenges in these networks is their short lifetime. This is due to the energy limitation of the power supply in each unit. The topic of target tracking in wireless sensor networks is of great importance because on one hand, for their lack of recharge ability, sensor nodes get depleted quite quickly, while on the other hand, losing the target is a highly undesirable event that may severely hamper the functionality of the network. Thus, we are searching for a solution that minimizes tracking errors. In this paper, we propose a strategy for target tracking in wireless sensor networks that is able to generate the least possible tracking error while consuming the minimum energy.

关键词： Target tracking k-means clustering algorithm Wireless sensor networks Energy optimization

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Hybrid clustering algorithms with GRASP to Construct an Initial Solution for the MVPPDP

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Computers, Materials & Continua 2020年第3期62卷 1025-1051页

作者： Abeer I.Alhujaylan Manar I.Hosny Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia College of Computer Qassim UniversityBuraydahSaudi Arabia

Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any *** demand for e-commerce increases tremendously,the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer *** important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem(MVPPDP),where a selected set of pickup and delivery customers need to be served within certain allowed trip *** this paper,we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure(GRASP)to construct an initial solution for the *** approaches first cluster the search space in order to reduce its dimensionality,then use GRASP to build routes for each *** compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this *** results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time.

关键词： Multi-vehicle profitable pickup and delivery problem k-means clustering algorithm ant colony optimisation greedy randomised adaptive search procedure metaheuristic algorithms

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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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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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Segmentation of thermal infrared images of cucumber leaves using k-means clustering for estimating leaf wetness duration

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 161-167页

作者： Dongmei Wen Aixin Ren Tao Ji Isabel Maria Flores-Parra Xinting Yang Ming Li Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/National Meteorological Service Center of Urban Agriculture Beijing 100097China College of Plant Protection China Agricultural UniversityBeijing 100193China Department of Sustainable Crop Production UniversitàCattolica del Sacro Cuore29122 PiacenzaItaly Department of Informatics University of AlmeriaAlmeriaCP04120Spain

Leaf wetness duration(LWD)is a critical parameter used to predict plant disease,but its determination under actual field conditions is a major *** this study,a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar *** images of the plant leaves were captured using an infrared scanning camera,and a leaf wetness area segmentation method consisting of two procedures was ***,a color space conversion was performed automatically by an image-processing ***,the k-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal ***,to enable overall thermal image analysis,an initial leaf wetness threshold(LWT)of 5%was defined(where wetness values higher than 5%indicated that the leaf was in a wet state).The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values,because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning *** these differences were not found to be statistically significant in this study,the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses.

关键词： thermal imaging k-means clustering algorithm leaf wetness duration cucumber

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VkECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and k-means algorithm

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SENSORS 2023年第2期23卷 573-573页

作者： Gou, Pingzhang Guo, Baoyong Guo, Miao Mao, Shun Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VkECE-3D, based on 3D-Voronoi partitioning and the k-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the k-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VkECE-3D improves network coverage and greatly lengthens the network's lifetime.

关键词： heterogeneous wireless sensor networks highly destructive polynomial mutation strategy 3D-Voronoi partitioning k-means clustering algorithm polling working mechanism

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MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint kashin-Beck disease

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JOURNAL OF SUPERCOMPUTING 2021年第8期77卷 9088-9101页

作者： Dang, Chenpo Yi, Guirong Zhu, Zhaomin Zhou, Peng Shao, Hongbin Yao, Yanbin Zhao, Maosheng Li, Lintao Li, Shensong Chinese Peoples Liberat Army Hosp Joint Logist Support Force 90 Dept Orthoped Sport Med Lanzhou 730050 Peoples R China Lanzhou Univ Dept Gastroenterol Affiliated Hosp 2 Lanzhou 730050 Peoples R China Gen Hosp Tibet Mil Reg Dept Orthoped Lasa 850007 Peoples R China Gansu Univ Chinese Med Clinical Med Coll Lanzhou 730050 Peoples R China Nanjing Univ Jinling Hosp Sch Med Dept Orthoped Nanjing 210093 Peoples R China

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of kashin-Beck Disease (kBD) of the knee joint. Based on the shortcomings of the traditional k-means clustering algorithm (kCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare kCA of MapReduce (MR-kCA) and Improved MR-kCA (IMR-kCA). With the same data, the sum of squared errors of MR-kCA and IMR-kCA decreased with the increase in the number of center points. Compared with MR-kCA, the quality of IMR-kCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-kCA and IMR-kCA increased with the increase in the number of center points. Compared to MR-kCA, the total execution time of IMR-kCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-kCA could reduce the total execution time by 50%. Through experiments, IMR-kCA was proved to better present the diagnosis and treatment data of patients with knee joint kBD. The scalability rates of MR-kCA and IMR-kCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-kCA, IMR-kCA had significantly higher scalability. The IMR-kCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of kBD diagnosis and treatment.

关键词： MapReduce distributed parallel computing framework knee joint kashin-Beck disease k-means clustering algorithm Data mining Center point

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Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s

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SENSORS 2023年第5期23卷 2562页

作者： Liu, Deyuan Deng, Changgen Zhang, Haodong Li, Jinrong Shi, Baojun Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Hebei Key Lab Robot Sensing & Human Robot Integrat Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the

关键词： inspection robot YOLOv5s k-means clustering algorithm reflection detection pose transformation

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Changing Characteristics of Urban Heat Island Effect in Weihai City

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Meteorological and Environmental Research 2023年第6期14卷 8-15页

作者： Di WANG Qianqian SUN Wenpeng XIN Key Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong Jinan 25000China Weihai Meteorological Bureau Weihai 264200China

Based on the local climate zoning theory and the observation data of hourly temperature of 22 automatic weather stations from 2012 to 2021, k-means clustering algorithm was used to analyze the daily, monthly, seasonal, annual and spatial variation characteristics of urban heat island effect in Weihai City in the past 10 years. The results showed that in recent 10 years, the average urban heat island intensity was 1.24 ℃, showing a gradual weakening trend of -0.169 3 ℃/10 a;the summer average heat island intensity was 0.86 ℃, showing a gradual weakening trend of -0.047 5 ℃/10 a. The heat island intensity had obvious diurnal variation characteristics, that is, "it was weak in the day and strong at night". In terms of seasonal variation, heat island effect was the weakest in summer, stronger in spring and autumn, and the strongest in winter. The diurnal, seasonal and annual changes of heat island intensity showed a reverse trend to those of temperature. The high-value area of urban heat island was highly consistent with human residential activity areas and industrial and commercial intensive areas, and the extension trend of heat island intensity was the same as the direction of urban development and construction. The "cold island phenomenon" in some offshore areas was related to the geographical location, terrain and the southeast monsoon trend in summer. The adverse effects of urban heat island effect can be reduced by optimizing the types and distribution of vegetation communities, rationally planning and constructing urban water body, promoting green building materials and adjusting shape design, etc.

关键词： Urban heat island effect Local climate zoning k-means clustering algorithm Automatic weather station

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