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Intelligent algorithms in privacy-preserving authentication schemes and traceability with accuracy in VANETs for smart transportation

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SOFT COMPUTING 2023年第23期28卷 1-10页

作者： Shikka, Rishi Kamalraj, R. Shah, Pradeep Kumar Sutariya, Kamal Anwar, Syed Rashid Kumar, Arvind Sanskriti Univ Dept Elect Mathura Uttar Pradesh India Jain Deemed Univ Sch Comp Sci & Informat Technol Bangalore India Teerthanker Mahaveer Univ Coll Comp Sci & IT Moradabad Uttar Pradesh India Parul Univ Parul Inst Engn & Technol Dept Comp Sci & Engn Vadodara Gujarat India Arka Jain Univ Dept Comp Sci & IT Jamshedpur Jharkhand India Galgotias Univ Sch Comp Sci & Engn Greater Noida Uttar Pradesh India

Vehicle ad hoc network is used on the road where vehicles serve as mobile nodes. VANET's primary uses include intelligent transportation and active security. The framework for intelligent transportation systems relies heavily on VANETs. Intelligent transportation networks are sometimes used to refer to VANETs. In VANETs, however, authentication and user privacy remain crucial issues. This study proposes a novel method for privacy-based authentication algorithms that improve the accuracy of data transmission for smart VANET transportation. Here, a lightweight hybrid authentication-based privacy-preserving algorithm is used to improve privacy. Then, an effective congestion-based clustering algorithm is used to send the data. Throughput, QoS, latency, computational cost, and data transmission rate are the focus of the experimental analysis. Throughput, QoS, latency, computational cost, and data transmission rate were all achieved using the proposed method. The proposed method makes use of lightweight, efficient pseudonyms that can be used for both message authentication and as a trapdoor to provide conditional anonymity. The proposed method's ability to withstand a variety of security and privacy threats is demonstrated by the various attack scenarios and the proposed technique attained throughput 93%, QoS 55%, latency 62%, computational cost 63%, and data transmission rate 69%.

关键词： Privacy-based authentication Data transmission accuracy Smart transportation VANET Efficient congestion clustering algorithm

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Scenario-Transformation-Based Optimal Sizing of Hybrid Hydrogen-Battery Storage for Multi-Timescale Islanded Microgrids

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第3期14卷 1784-1795页

作者： Jiang, Sheng Wen, Shuli Zhu, Miao Huang, Yuqing Ye, Huili Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Marine Equipment Res Inst Dept Technol Manage Shanghai 200031 Peoples R China

The increasing penetration of volatile renewable energy poses a significant challenge for islanded microgrids in maintaining the seasonal power balance on a long-term timescale. To support renewable integration, seasonal energy storage techniques are expected to coordinate with short-term storage systems to compensate for power mismatches on multiple timescales. However, hybrid storage sizing is often hindered by the coupling of different timescales, which will lead to a large number of variables and greater computational complexity. Thus, in this article, a novel optimal sizing framework is proposed for a hybrid hydrogen-battery storage system, considering a year-round time horizon. To ensure reliable planning of hydrogen storage, a "seasonal-trend decomposition based on LOESS (STL)" technique is applied to preserve long-term power fluctuation characteristics during scenario clustering. Moreover, a least-squares-based scenario approximation method is developed to improve the accuracy of the clustering results. On this basis, a scenario-transformation solution method is proposed to avoid a large number of variables due to year-round hourly operation. The case studies verify the advantages and efficiency of the proposed method.

关键词： clustering algorithm hydrogen-battery energy storage multiple-timescale operation optimal sizing scenario transformation seasonal imbalance STL decomposition

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Camera Invariant Feature Learning for Unsupervised Person Re-Identification

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 6171-6182页

作者： Pang, Zhiqi Zhao, Lingling Liu, Qiuyang Wang, Chunyu Harbin Inst Technol Fac Comp Harbin 150001 Heilongjiang Peoples R China

Fully unsupervised person re-identification (ReID) methods aim to learn discriminative features without using labeled ReID data. Because these methods are easily affected by camera discrepancies, similar studies have typically designed optimization methods to enable the model to learn camera-invariant features. However, they often ignore the impact of camera discrepancies on clustering results. Specifically, camera discrepancies will reduce the intra-class camera diversity and promote the generation of noise labels. To solve the above problems, we propose a unified unsupervised learning framework: camera invariant feature learning (CIFL) framework. First, we designed a novel DBSCAN-NN algorithm in the CIFL framework that improves the intra-class camera diversity by forcibly merging samples from different cameras. Then, we designed feature ensemble clustering that improves the accuracy of the pseudo-labels by clustering feature ensembles. In addition, we designed an optimization method for camera discrepancies: stochastic pulled loss. With the stochastic pulled loss, the ReID model is forced to learn camera-invariant features. We verified the effectiveness and generalization of CIFL on four ReID datasets (Market-1501, DukeMTMC-reID, MSMT17 and CUHK03-NP). The experimental results show that CIFL not only outperforms the existing fully unsupervised methods but also is superior to the unsupervised domain adaptation methods.

关键词： Cameras Data models clustering algorithms Representation learning Feature extraction Stochastic processes Optimization methods clustering algorithm person re-identification representation learning unsupervised learning

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Minimum cost consensus model with loss aversion based large-scale group decision making

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2023年第7期74卷 1712-1729页

作者： Liang, Yingying Ju, Yanbing Qin, Jindong Pedrycz, Witold Dong, Peiwu Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Wuhan Univ Technol Sch Management Wuhan Hubei Peoples R China Wuhan Univ Technol Res Ctr Data Sci & Intelligent Decis Making Wuhan Hubei Peoples R China Wuhan Univ Technol Res Inst Digital Governance & Management Decis In Wuhan Hubei Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

In minimum cost consensus problems, to accomplish the group consensus, decision makers accommodate their initial discrepant opinions based on the unit adjustment costs. During this process, decision makers may exhibit loss aversion, which results in extra expenses when participants feel loss in contrast to their reference opinion adjustments. Existing minimum cost consensus models pay little attention to the loss-averse preference. Hence, to fill up this gap, a minimum cost consensus model with loss aversion (MCCM-LA) is established and the desired properties are analyzed. To manage large-scale group decision making problems, we first propose an integrated opinion similarity, connectivity similarity and behavior similarity clustering algorithm to divide decision makers into multiple subgroups. Balancing the individual adjustment willingness and consensus reaching efficiency, a two-stage consensus reaching mechanism is further designed based on MCCM-LA to realize the accordant opinion. Finally, the effectiveness and feasibility of the proposed method are demonstrated by sensitivity and comparative analyses with an illustrative example.

关键词： Minimum cost consensus model loss aversion large-scale group decision making consensus reaching mechanism clustering algorithm

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Collaborative Contrastive Refining for Weakly Supervised Person Search

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 4951-4963页

作者： Jia, Chengyou Luo, Minnan Yan, Caixia Zhu, Linchao Chang, Xiaojun Zheng, Qinghua Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Shaanxi Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Univ Technol Sydney Fac Informat Technol Ultimo NSW 2007 Australia Mohamed Bin Zayed Univ Artificial Intelligence MBZ Dept Comp Vis Abu Dhabi U Arab Emirates

Weakly supervised person search involves training a model with only bounding box annotations, without human-annotated identities. clustering algorithms are commonly used to assign pseudo-labels to facilitate this task. However, inaccurate pseudo-labels and imbalanced identity distributions can result in severe label and sample noise. In this work, we propose a novel Collaborative Contrastive Refining (CCR) weakly-supervised framework for person search that jointly refines pseudo-labels and the sample-learning process with different contrastive strategies. Specifically, we adopt a hybrid contrastive strategy that leverages both visual and context clues to refine pseudo-labels, and leverage the sample-mining and noise-contrastive strategy to reduce the negative impact of imbalanced distributions by distinguishing positive samples and noise samples. Our method brings two main advantages: 1) it facilitates better clustering results for refining pseudo-labels by exploring the hybrid similarity;2) it is better at distinguishing query samples and noise samples for refining the sample-learning process. Extensive experiments demonstrate the superiority of our approach over the state-of-the-art weakly supervised methods by a large margin (more than 3% mAP on CUHK-SYSU). Moreover, by leveraging more diverse unlabeled data, our method achieves comparable or even better performance than the state-of-the-art supervised methods.

关键词： Person search unsupervised person Re-ID weakly supervised learning clustering algorithm

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A higher satisfaction product customization method for different customer groups

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期83卷 36571页

作者： Wang, Zhengyu Dai, Mingzhi Sun, Xin Zhou, Meiyu East China Univ Sci & Technol Sch Art Design & Media Meilong Rd 130 Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Meilong Rd 130 Shanghai 200237 Peoples R China Qinghai Univ Sch Mech Engn Ningda Rd 251 Xining 810016 Qinghai Peoples R China

Product customization in response to the widely varying perceptual expectations of different customers is an effective means to obtain customer satisfaction. Rapidly optimizing products to meet the personalized needs of different customers while controlling costs and improving design efficiency remains a challenge. In the present paper, a higher satisfaction product customization model was proposed with aim of efficiently reducing the number of target research customers and rapidly generating customer-oriented product design. In this model, a clustering algorithm based on emotional preference and migratory behavior (EPMC) was combined with a coupled model of interactive genetic algorithm with hesitancy-based interval individual fitness (IGA-HIIF) and Kansei Engineering (KE) method. This is the first implementation of EPMC in the customer research field, and the output content is optimized, which makes it capable of quickly dividing the multi-dimensional customer data space constructed in this study and accurately identifying different types of representative customers (RCs). And then, coupled the IGA-HIIF and KE model was adopted to establish a customer-oriented product evolution design system (PEDS). Finally, the most satisfactory products were auto-generated by PEDS with the participation of the RCs. The proposed method was applied to a case of social robot design. The result verifies that the approach could reduce the number of target research customers effectively without reducing diversity, allow direct customer involvement in the design process, and accurately extracts customers' personalized emotional implicit information and preference differences.

关键词： Product customization clustering algorithm Interactive genetic algorithm Kansei engineering Customers satisfaction diversity

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Exploring the associations of socioeconomic characteristics and distance decay effects with two-Steps spatial interaction model

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APPLIED GEOGRAPHY 2025年 179卷

作者： Zhang, Bowen Zhong, Chen Gao, Qi-li Shabrina, Zahratu Kings Coll London Dept Geog London England Alan Turing Inst London England UCL Ctr Adv Spatial Anal London England Shenzhen Univ Shenzhen Audencia Financial Technol Inst Shenzhen Peoples R China

The Spatial Interaction (SI) model is a prominent tool for predicting trip flows based on the distance decay effect. Despite extensive discussions on spatial heterogeneity and spatial structure, existing SI models are still exploring ways to incorporate local distance decay variations within small urban areas. Furthermore, non-spatial factors, such as socioeconomic characteristics, are typically underestimated in SI and other travel flow prediction models. To tackle these issues, this study introduces a novel two-step SI model that enhances travel flow predictions. This study utilises a k-means clustering algorithm to group areas based on residents' socioeconomic characteristics, then calibrates the localised distance-decay parameter in the origin-specific gravity model for each group and predicts the travel flows. Demonstrated by a case study of the Greater London Area (GLA), we uncovered local distance decay patterns in commuting trips and explained their associations with spatial structure and nonspatial factors using census data. Most importantly, the results proved that our two-step SI model could significantly improve the accuracy of flow predictions without considerably increasing computational complexity.

关键词： Distance decay effects Spatial interaction model Socioeconomic characteristic Commuting behaviour clustering algorithm

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Fermatean fuzzy vehicle routing problem with profit: solution algorithms, comparisons and developments

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第3期48卷 1-17页

作者： Singh, V. P. Sharma, Kirti Singh, Bishwajeet Ebrahimnejad, Ali Chakraborty, Debjani VNIT Nagpur Dept Math Nagpur 440010 India Islamic Azad Univ Dept Math Qaemshahr Branch Qaemshahr Iran IIT Kharagpur Dept Math Kharagpur 721302 India

In this work, a fermatean fuzzy vehicle routing problem with profits (FFVRPP) is considered. Fermatean fuzzy numbers are used to represent the various coefficients of the problem. The objectives of FFVRPP is to obtain a set of routes that originates and terminates at the source node while keeping in mind the objective function and route restriction constraints. The objective function here is to maximizes the difference between the profit function and the expense function. The route restriction constraints provides an upper bound on the number of customers that can be served in single visit of any vehicle. We propose two stage solution methodologies for solving FFVRPP where the first stage corresponds to the selection of subset of customers which gives the maximum profit and the second stage corresponds to formation of routes for the selected customers such that the expenses incurred in serving them comes out to be a minimum. The inclusion of qualitative factors like relationship of customers with the seller and the risk of transportation of goods across various edges with the help of fermatean fuzzy numbers makes the model more realistic and applicable in real life situations. Three different methodologies based on various clustering and routing algorithms are proposed to solve the problem. The feasibility and richness of the proposed algorithms is demonstrated with the help of a numerical example.

关键词： Fermatean fuzzy numbers Vehicle routing problem with profits clustering algorithm Network K-nearest neighbour algorithm

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A dynamic method to predict driving risk on sharp curves using multi-source data

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ACCIDENT ANALYSIS AND PREVENTION 2023年第1期191卷 107228页

作者： Ma, Yongfeng Wang, Fan Chen, Shuyan Xing, Guanyang Xie, Zhuopeng Wang, Fang Southeast Univ Sch Transportat Jiangsu Key Lab Urban ITS Nanjing 211189 Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing 211189 Peoples R China Ningxia Univ Sch Civil & Hydraul Engn 489 Helan West Rd Yinchuan 750021 Peoples R China

Traffic accidents are likely to occur on sharp curves under poor driving conditions, and the severity level of such accidents is high. Therefore, predicting the risk associated with driving on curved roadways in real time can effectively improve driving safety. This paper aims to develop a dynamic real-time method that fuses multiple data sources to predict risk when driving on sharp curves in the context of the connected vehicle environment. Six curves with three small radii (60 m, 100 m, 150 m) and two driving directions (left and right) were designed for a driving simulation experiment. Driver maneuvering data, vehicle kinematic data, and physiological data of 55 drivers were collected for this study. The data were combined and spatially and dynamically segmented. The mean value of the critical lateral acceleration of the vehicle was set as the risk assessment index. K-means clustering was used to classify the driving risk into three levels: low, medium, and high. Then, the risk level was predicted using the maneuvering data, vehicle kinematic data, and physiological data as well as road alignment characteristics as input features for the proposed model that employs the long short-term memory (LSTM) network algorithm. Models with different combinations of observation window (lookback) and interval window (delay) were compared to derive the best window combination. The algorithms selected for comparison against the LSTM algorithm are random forest, XGBoost, and LightGBM. The results show that the proposed LSTM-based method can effectively predict dangerous driving behavior on sharp curves. The optimal window combination derived using the LSTM algorithm is lookback = 20 m and delay = 20 m. The prediction performance of the proposed model is significantly better than that of the other three compared algorithms, with F1-scores of 84.8% and 86.0% for the medium and high risk categories, respectively. In addition, the proposed LSTM-based model that fuses multiple data

关键词： Driving risk Sharp curve clustering algorithm Dynamic prediction Long short-term memory (LSTM) algorithm

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Data Integration Method of Multi-source Feedback Evaluation for Remote Teaching Quality (Aug, 10.1007/s11036-023-02189-5, 2023)

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MOBILE NETWORKS & APPLICATIONS 2023年第3期28卷 935-935页

作者： Chen, Lili Baza, Mohamed Alshahrani, Hani Jilin Univ Architecture & Technol Dev & Planning Off Changhun 130114 Peoples R China Coll Charleston Dept Comp Sci Charleston SC 29414 USA Najran Univ Coll Comp Sci & Informat Syst Dept Comp Sci Najran 61441 Saudi Arabia

Distance teaching is a teaching method widely concerned and used in the current education field, and accurate evaluation of the quality of distance teaching is the key to improve the effect of distance teaching. Therefore, a multi-source feedback evaluation data integration method for the quality of distance teaching is proposed. The remote teaching of organic chemistry experiment was taked as the research object, the initial multi-source feedback evaluation index system is constructed through the research of experts and scholars. After the initial evaluation index is standardized, the K means clustering is used to reduce the initial evaluation index and determine the final evaluation index system. Then the final used evaluation index data are checked and coded to determine whether the final used evaluation index data set matches the actual data. In this way, artificial intelligence (AI) is used to realize the integration of multi-source feedback evaluation data of distance teaching quality. The experimental results show that the information contribution rate of the evaluation index system constructed in this method is 90.93%, the correlation coefficient value with the actual results is 0.982, and the evaluation accuracy is as high as 99.48%.

关键词： Organic Chemistry Experiment Distance Teaching Quality Multi-source Feedback Evaluation clustering algorithm Analytic Hierarchy Process

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