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An Intelligent Optimization Clustering algorithm for Driver Risk Tendency Recognition in Intelligent Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Ma, Ning Wu, Kaijun Yuan, Yubin Liu, Shuoxun Li, Jiawei Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Tianjin Yingbeite Aerosp Technol Co Ltd Tianjin 300308 Peoples R China

Driving behavior assessment is of great significance in intelligent transportation systems to improve road safety and traffic efficiency. This paper proposes a novel weighted fuzzy C-means (fcm) clustering algorithm, called GSA-Wfcm, which combines a genetic algorithm and a simulated annealing algorithm to optimize the evaluation process of driving behavior. GSA-Wfcm first uses a genetic algorithm to optimize the clustering center to ensure the diversity and effectiveness of the initial selection;then, it uses a simulated annealing algorithm to avoid local optimal solutions, thereby improving the global nature of the clustering results. In order to enhance the robustness and accuracy of clustering, the algorithm introduces a point density weighting mechanism to improve the algorithm's adaptability to data features of different dimensions. By integrating the advantages of genetic algorithms and simulated annealing algorithms, the algorithm achieves intelligent initialization, optimizes the initial clustering center, and significantly improves the convergence speed and stability of the algorithm. Experimental results on multiple data sets show that GSA-Wfcm effectively overcomes the shortcomings of traditional fcm in initial cluster center selection, showing faster convergence and stronger robustness. Further verification on traffic data sets shows that the algorithm performs well in identifying different types of driving behaviors and can effectively classify cautious driving, normal driving and aggressive driving. The accuracy and stability of clustering are significantly improved, and the clustering performance reaches 0.81, providing a reliable tool for driving behavior analysis in intelligent transportation systems.

关键词： Clustering algorithms Heuristic algorithms Classification algorithms Accuracy Simulated annealing Genetic algorithms Machine learning algorithms Vehicles Sensitivity Robustness genetic algorithm fcm algorithm weighted clustering driver behavior assessment

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Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第3期22卷 1807-1816页

作者： Lv, Zhihan Qiao, Liang Cai, Ken Wang, Qingjun Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510408 Peoples R China Shenyang Aerosp Univ Sch Econ & Management Shenyang 110136 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211100 Peoples R China

To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (fcm). Then, the fcm algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the lambda value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.

关键词： Electric vehicle big data analysis fcm algorithm path guidance smart cities

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Research on the practice of innovation and entrepreneurship education in universities under the environment of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Wang, Haijun Hunan High speed Railway Vocat Technol Coll Hengyang 421002 Hunan Peoples R China

Entrepreneurship education is an educational concept and teaching mode which is formed in socio-economic development. Universal entrepreneurship education has both the long-term value of realizing the transformation and upgrading of industrial structure and the practical value of alleviating the employment problem of college students. Therefore, how establishing the innovation and entrepreneurship education model in higher education under the environment of big data analysis is a major issue at present and also a key element to achieving the construction goal of modern higher education. In this paper, the entrepreneurship data of colleges and universities are deeply mined by association rules of the Apriori algorithm and clustering analysis of the fcm algorithm. As a result, an entrepreneurship education model is established and practiced in university classrooms. According to the results of the survey after the practice of the entrepreneurship education model, it can be seen that the number of students' participation in innovation and entrepreneurship lectures has increased, and the proportion reached 50%. Innovation and entrepreneurship clubs formed on campus and discipline competitions accounted for 16.00% and 18.00%, respectively. College students' motivations for learning innovation and entrepreneurship education courses are mainly ability enhancement, interest-driven, credit demand, future planning, and influence of others, and they account for 34.00%, 26.00%, 21.00%, 14.00%, and 5.00%, in that order. These indicate that the current group of college students considers the pursuit of knowledge and the improvement of their abilities as the main reasons for undertaking entrepreneurial learning activities. After practicing the entrepreneurship education model, people have increased their knowledge of entrepreneurship theory and are more willing to participate in various entrepreneurial activities.

关键词： Data mining Apriori algorithm fcm algorithm Innovation and entrepreneurship Educational practice model

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Research on the innovation theory of college students' ideological and political education under the background of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Lai, Wenling Wuchang Univ Technol Tongshi Coll Qual Educ Wuhan 430223 Hubei Peoples R China

In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information;secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index;and finally, the data is combined with fcm (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

关键词： Big data collection and analysis technology Cloud technology education platform fcm algorithm Collaborative filtering algorithm Pearson's correlation calculation formula

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Photovoltaic power prediction model based on parallel dendritic neural model 33

Photovoltaic power prediction model based on parallel dendri...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Li, Hao Zhang, Tengfei Yu, Yang Peng, Chen Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9781665440899

Dendritic neural model (DNM) has characteristics of a simple structure and a fast convergence speed. However, when a single DNM is applied to a scene with a large data set, the number of branch layers often needs to be increased, which makes the structure of DNM larger and leads to a poor prediction accuracy. From this perspective, this paper proposes a parallel-structure based DNM with multiple sub-networks, which uses a fuzzy C-means clustering (fcm) algorithm to divide the data set. The fcm algorithm can effectively reduce the amount of data required for the training of each sub-network. Consequently, actual photovoltaic data simulation results verify that the accuracy of the photovoltaic power prediction model can be further improved, and the proposed model is effective and efficiency.

关键词： Photovoltaic power prediction Dendritic neural model Parallel sub-networks fcm algorithm

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Modeling of thermal error electric spindle based on KELM ameliorated by snake optimization

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CASE STUDIES IN THERMAL ENGINEERING 2022年 40卷

作者： Dai, Ye Pang, Jian Li, Zhaolong Li, Weiwei Wang, Qinghai Li, Shikun Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automatic Machinery Prod Dev Harbin 150080 Peoples R China

In this article, to heighten the accuracy of thermal error forecast model of motorized spindle, a thermal error modeling method of kernel extreme learning machine based on snake optimization is advanced. ANSYS simulation software is used to simulate the thermal characteristics of the electric spindle, and the temperature field distribution is acquired. Then, according to the experimental requirements, a platform is built to obtain the axial temperature and thermal displacement data of the spindle. The fcm algorithm and grey relation analysis are utilized to optimize the survey points, and ten measuring points are reduced to four. The SO-KELM model is established based on the snake-optimized kernel extreme learning machine. Comparing the established model with the KELM model and PSO-KELM model, it is proved that SO-KELM model has good prediction accuracy and stability.

关键词： Snake optimization algorithm Kernel extreme learning machine fcm algorithm Grey relation analysis Thermal error modeling

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Research on design of customer portrait system for E-commerce

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 275-288页

作者： Cai, Lingjie Li, Jun Hubei Univ Technol Engn & Technol Coll Wuhan 430068 Hubei Peoples R China

Customer portrait is the customer information that is labelled and digitised. Collecting and studying consumer data is highly helpful to enterprises because it empowers them to use these data to locate target groups and meet various customer needs. With the continuous rise of the e-commerce market, major e-commerce websites list providing customer value and being data-driven as company values. Traditional customer portraits rely excessively on objective and past experience;so, a simple but powerful customer portrait system is required to make the customer analysis platform simple and intelligent, and to make the data accumulated at ordinary times bring practical value to e-commerce companies. Based mainly on addressing this requirement, this paper proposes an effective customer portrait system for e-commerce. First, the research background and significance is expounded, which lays a theoretical foundation for this research. Then, the customer portrait system is designed, including the overall design scheme and functional framework of the system. The improved fcm algorithm is used as the basis for building the customer portrait model, and the performance of this algorithm is analysed and compared with that of the improved algorithm used in this paper.

关键词： customer portrait system fcm algorithm cluster analysis

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Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

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COMPUTER COMMUNICATIONS 2020年 155卷 40-47页

作者： Liu, Zhendong Jia, Hongfei Wang, Yanxia Jilin Univ Sch Transportat Changchun 130000 Peoples R China Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (fcm) algorithm Because the fcm algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional fcm algorithm. Make improvements. Through the experi

关键词： Intelligent transportation system Pattern recognition Image processing fcm algorithm

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Interference-graph and fuzzy C-means based resource allocation scheme in LTE-V2V communication networks

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2021年 142卷 153982-153982页

作者： Liu, Zhixin Wang, Yongkang Xie, Yuan-ai Yuan, Yazhou Chan, Kit Yan Yang, Yi Ma, Kai Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Curtin Univ Dept Elect & Comp Engn Perth WA 6102 Australia

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (fcm) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the fcm algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity.

关键词： Vehicular network Resource allocation fcm algorithm KM algorithm Graph theory

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Anti-noise fcm image segmentation method based on quadratic polynomial

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SIGNAL PROCESSING 2021年 178卷 107767-107767页

作者： Zhang, Xijing Ning, Yang Li, Xuemei Zhang, Caiming Shandong Univ Software Coll Jinan 250101 Peoples R China Shandong Coinnovat Ctr Future Intelligent Comp Yantai 264025 Peoples R China

Image segmentation is an important and challenging task in computer vision and image understanding, in which the segmentation model is the key to improve the quality of image segmentation. In this paper, a new fuzzy c-means clustering(fcm) algorithm based on quadratic polynomials is proposed, which can better distinguish the weak edge region in the image and has certain noise resistance. Firstly, the algorithm proposes to define the segmentation center using a quadratic polynomial surface,and divide the set of data points according to the algebraic distance between data points and the segmentation surfaces. The existing model with constant as the segmentation center is a special case with quadratic polynomial surface as the segmentation center, so the new model has higher segmentation accuracy. Secondly, based on the quadratic polynomial surface as the segmentation center, a new fuzzy factor is designed, and the deviation value is used to represent the difference between the mean algebraic distance of neighborhood points and the algebraic distance of center pixel. By calculating the deviation value, the influence of neighborhood points on the center point can be better measured and the segmentation accuracy can be improved. Experimental results show that the new algorithm has better anti-noise ability as well. Thirdly, select a local window on the edge of the global segmentation result for local window segmentation, which is equivalent to using a clustering center that is more in line with local information to do segmentation in a local small window, optimize misclassified pixels, and obtain the final segmentation result. The experimental results show that in the final segmentation results of medical images with 5% noise, segmentation accuracy can reach above 96%, partition coefficient V has been increased by 0.14. So the algorithm can get the membership matrix with less ambiguity, which means more reliable segmentation results, and can effectively eliminate noise e

关键词： fcm algorithm Classification Quadratic polynomial Local window

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