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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2023年 4卷 520-527页

作者： Zhang, Qian He Zhang, Qing He Shen, Zhao Yang China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Hubei Peoples R China

For the problem of maximizing beam collection efficiency in microwave wireless power transmission, a clustering algorithm based on a combination of the particle swarm algorithm and the k-means algorithm is proposed for the subarray partitioning of planar arrays. By minimizing the difference between the subarray beam collection efficiency and the reference beam collection efficiency, the subarray partitioning problem is transformed into an excitation matching problem. Then, the optimal subarray configuration is obtained by using a clustering algorithm that combines the particle swarm algorithm and the k-means algorithm. The effectiveness of the proposed method is evaluated by integrating the beam collection efficiency with parameters such as the receiving area and the size of the planar array. The superiority of the proposed method is demonstrated by a comparison with other advanced methods.

关键词： clustering algorithms Microwave antenna arrays Transmitting antennas Partitioning algorithms Receiving antennas Planar arrays Wireless power transmission Microwave wireless power transmission (MWPT) planar array antenna INDEX TERMS particle swarm optimization algorithm (PSO) k-means clustering algorithm beam collection efficiency (BCE)

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Single source bins detection-based localisation scheme for multiple speech sources

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ELECTRONICS LETTERS 2017年第6期53卷 430-432页

作者： Jia, Maoshen Sun, Jundai Deng, Feng Sun, Junyue Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Dolby Labs Beijing Peoples R China Dialog Semicond Tianjin Peoples R China

A single source bins (SSBs) detection based multiple source localisation scheme is proposed. This scheme is based on detecting the SSBs in mixture signals that are only derived from one source. Specifically, after proposing a DOA convergence' assumption, k-means clustering algorithm is used for SSBs detecting. Thus, the multiple source localisation is converted to a single source one among these SSBs. Moreover, the proposed SSBs detection is applicable to other localisation methods and not limited to specific microphone topology. Experimental results demonstrate the localisation accuracy of the proposed method outperforms the localisation approaches which are based on single source zone detection.

关键词： direction-of-arrival estimation speech processing pattern clustering single source bins detection-based multiple localisation scheme multiple speech sources SSB DOA convergence k-means clustering algorithm

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Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation algorithm

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SENSORS 2023年第10期23卷 4714-4714页

作者： Cai, Ziming Sun, Liang An, Baosheng Zhong, Xin Yang, Wei Wang, Zhongyan Zhou, Yan Zhan, Feng Wang, Xinwei Guangxi Univ Sch Resources Environm & Mat Nanning 530004 Peoples R China Chinese Acad Sci Inst Semicond Optoelect Syst Lab Beijing 100083 Peoples R China Chinese Acad Sci Inst Tibetan Plateau Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

关键词： dammed lake automatic monitoring alarm k-means clustering algorithm region growing algorithm water level recognition

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SMEER: Secure Multi-tier Energy Efficient Routing Protocol for Hierarchical Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2019年第1期105卷 17-35页

作者： Dhand, Geetika Tyagi, S. S. Manav Rachna Int Inst Res & Studies CSE Faridabad Haryana India

Energy consumption and the secure transmission are the increasing fields of research challenge in wireless sensor network (WSN) applications. Heterogeneous WSN is an efficient network strategy that has sensor nodes with different processing, memory power, and transmission capacity. In order to provide effective transmission for this type of network, clustering associated with secure routing is enabled to transfer the data packets safely to the endpoint. Data gathering and clustering help to group the network and control transmission overhead during data transmission. Hybridization of k-means clustering algorithm with Ant Lion Optimizer for grouping of nodes and optimal CH selection is utilized for better energy efficiency. Thus, a Miscegenation of Ant Lion optimizer within k-means for clustering and Spherical grid based multi-curve Elliptic curve cryptographic routing (MALOkSER) is determined for effective clustering and secure routing of data packets within time to the base station. The main aim of our work is to enhance network security and energy savage in the wireless network communication system. Elliptic curve cryptography with spherical grid multi-tier routing ensure secure transmission by encrypting the messages with two different keys and forward the packets in a spherical format. The performance is evaluated under metrics such as packet deliver ratio, minimum energy consumptions, communication overheads, throughput and end to end delay with the existing standards shows better results. This proves the proposed technique can actively reduce the energy utilization with an efficient and safe routing of data over the network.

关键词： Wireless sensor networks Ant lion optimizer k-means clustering algorithm Elliptic curve cryptographic Security enhancement key generation

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Mechanical response and damage mechanism of C/SiC composites impacted by high-velocity water jet

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2023年第8期43卷 3158-3171页

作者： Zhao, Renxi Hou, Naidan Wang, Xuan Yue, Yifan Wang, Bo Li, Yulong Zhang, Chengyu Northwestern Polytech Univ Sch Aeronaut Xian Peoples R China Joint Int Res Lab Impact Dynam & Engn Applicat Xian Peoples R China Shaanxi Key Lab Impact Dynam & Engn Applicat Xian Peoples R China Northwestern Polytech Univ Sch Civil Aviat Suzhou Peoples R China Northwestern Polytech Univ Sch Mat Sci & Engn Xian Peoples R China

Rain erosion is a potential hazard for supersonic vehicles, with severe damage to materials that may be impacted by raindrops. In this paper, a series of impact tests of 413-572 m/s are carried out on a 3 mm-thick 2D C/SiC composite specimen using a single impact waterjet apparatus. The typical morphology of C/SiC specimen is obtained by single jet impact test. Under the multi-drop impact, the stress wave interaction is enhanced, and the internal damage of the specimen is severe, showing a funnel-shaped damage. Moreover, the C/SiC specimen is penetrated after 5 drops of impact. Quasi-static tensile tests were employed to quantify the post-impact strength of the specimen, during which the digital image correlation (DIC) method was used to obtain the strain value, at the same time acoustic emission (AE) signal was detected and processed by the k-means to reveal the damage evolution.

关键词： Waterjet impact Ceramic matrix composites k-means clustering algorithm Residual tensile strength

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Research on Urban Storm Flood Simulation by Coupling k-means Machine Learning algorithm and GIS Spatial Analysis Technology into SWMM Model

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WATER RESOURCES MANAGEMENT 2024年第6期38卷 2059-2078页

作者： Liu, Chengshuai Hu, Caihong Zhao, Chenchen Sun, Yue Xie, Tianning Wang, Huiliang Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China

Accurate flood simulation has significant practical implications for urban flood management. The focus of this study is to develop a new flood model (k-SWMMG) based on the Storm Water Management Model (SWMM), which innovatively couples the k-means clustering machine learning algorithm and GIS spatial analysis techniques. The k-means clustering machine learning algorithm is used to determine the uncertain parameters of the SWMM model, while GIS spatial analysis techniques enhance the two-dimensional realism of flood simulation. We applied the k-SWMMG model to six historical observed flood events in a specific catchment area in Zhengzhou City, using rainfall and flow data. The study shows that: 1) k-SWMMG optimizes the sub-basin division method of urban stormwater models, avoiding the tedious and complex parameter calibration process, and improving modeling efficiency to some extent. 2) The two-dimensional visualization of inundation provided by GIS spatial analysis techniques better meets the production requirements of current urban flood simulation. 3) k-SWMMG outperforms SWMM in terms of simulation performance, with improvements in absolute error (AE), relative error (RE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2) by 0.019m, 5.36%, 0.068, and 0.042, respectively. The findings can provide scientific decision-making references for urban flood forecasting and early warning.

关键词： Urban flood forecast k-SWMMG Uncertainty parameters City functional area k-means clustering algorithm GIS

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High-velocity impact behaviors and post-impact tension performance of 2D-C/SiC composite beams

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CERAMICS INTERNATIONAL 2020年第9期46卷 13282-13291页

作者： Hu, Wei Guan, Tianhao Gao, Mengting Ren, Tengfei Huang, Jinzi Suo, Tao Wang, Bo Zhang, Chao Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Joint Int Res Ctr Impact Dynam & Engn Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Key Lab Impact Dynam & Engn Applicat IDEA Xian 710072 Peoples R China

2D-C/SiC composites have widely been used in aeronautical and aerospace engineering, but their mechanical behaviors under small-mass and high-speed impact have not been thoroughly studied yet. In this paper, 2D-C/SiC beam specimens were impacted by a single-stage light-gas gun and the fracture processes were captured by a high-speed camera. Post-impact internal and surface damage morphologies were scanned by a CT and a SEM, respectively. Similar damage modes were revealed by high-speed images. Subsequently, quasi-static post-impact tension tests were conducted to understand the residual mechanical properties. Acoustic emission (AE) signals of specimens were detected during the tests and then classified by the k-means algorithm. Therefore, evolutions of matrix damage, interfacial delamination and fiber fracture were recognized. At the same time, strain value was obtained by digital image correlation (DIC) method and main crack propagations were obvious in strain contours. A combination of the AE and DIC methods very well monitored the real-time damage during post-impact testing, which further revealed the damage during impact phase.

关键词： Ceramic matrix composites Foreign object damage k-means clustering algorithm Digital image correlation

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Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第5期10卷 331-337页

作者： Sun, Jie Sun, Jian Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban SiPaiLou 2 Nanjing 210096 Jiangsu Peoples R China

The traffic safety on expressways is crucial for the efficient operation of the expressway system, and there is a close relationship between traffic states and crashes on expressways, and the occurrence of crashes may be influenced by the interaction of different combinations of traffic states upstream and downstream of the crash location. Based on the crash data and the corresponding traffic flow detector data collected on expressways in Shanghai, this study proposes a hybrid model combining a support vector machine (SVM) model with a k-means clustering algorithm to predict the likelihood of crashes. The random forest (RF) model is employed to select the important and significant variables for model construction from the data of the traffic flow 5-10 min before the crash occurred. Then, the cross-validation and transferability of different models (SVM model without variable selection, SVM model with variable selection, and hybrid SVM model with variable selection) are determined using 577 crashes and 5794 matched non-crash events. The results show that the crash prediction model along with the four most important variables selected using the RF model can obtain a satisfactory prediction performance for crashes. With the combination of the clustering algorithm and SVM model, the accuracy of the crash prediction model can be as high as 78.0%. Moreover, the results of the transferability of the three different models imply that the variable selection and clustering algorithm both have an advantage for crash prediction.

关键词： road accidents road safety roads support vector machines learning (artificial intelligence) pattern clustering traffic engineering computing road traffic RF model hybrid SVM model random forest model k-means clustering algorithm Shanghai traffic flow detector data traffic safety hybrid support vector machine model urban expressways real-time crash prediction

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An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第31-32期79卷 22277-22292页

作者： kumar, R. Biji Marikkannu, P. Pentagram Res Ctr Pvt Ltd Hyderabad India Anna Univ Dept Informat Technol Chennai Tamil Nadu India

Content Based Image Retrieval (CBIR) is a popular method to search and retrieve the similar images. For medical applications, it plays an important role to find the diseasessuch as breast cancer in human body. Many existing methods were presented for improving the performance of CBIR method. Nevertheless, retrieval time and accuracy of CBIR are further to be improved. To solve this issue, an optimal classifier is to be used in CBIR. In this paper, Artificial Neural Network based on Particle Swarm Optimization based (PSO-ANN) is presented as an optimized classifier. Also, the features of images such as shape, texture, mean and standard deviation are extracted. To increase the speed of the classification, these extracted features are to be clustered using k-means clustering algorithm. From the clustered features, similar images of query image are retrieved using the proposed PSO-ANN classifier. Simulation results prove that performance of this proposed CBIR outperforms than that of existing methods in terms of accuracy and CBIR time.

关键词： CBIR Particle Swarm Optimization Artificial Neural Network PSO-ANN k-means clustering algorithm Gray Level Co-occurrence Matrix

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Discriminant image resolution: a novel multivariate image analysis method utilizing a spatial classification constraint in addition to bilinear nonnegativity

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1-2期77卷 18-31页

作者： Hancewicz, TM Wang, JH Unilever Res & Dev Edgewater NJ 07020 USA JHW Consulting Cardinal ON K0E 1E0 Canada

Spectroscopic image analysis can be characterized as two different and distinct problems depending on the kind of information required from the solution. In its simplest form, the data can be decomposed into two submatrices, each of which carries different aspects of pure component information. Typically, this means information about pure spectra and pure intensities are obtained from the solution. This is the well-known and well-characterized bilinear form that by itself cannot guarantee a unique solution due to the rotational ambiguity inherent in the mathematical solution. This problem has been addressed in a number of ways by different authors using novel constraints applied to the least-squares solution. This usually takes the form of natural constraints as suggested by Tauler as the standard methodology to improve the resolution of data during alternative least squares (ALS) iterative process. A second type of multivariate image analysis problem is proposed in this paper that is quite different from the tradition methods and in some ways potentially is more useful. This involves the solution as a class problem in which the relevant information is not necessarily contained in pure component information, but rather, in unique combinations of the pure components that are allowed to be spatially collocated. This discriminant image resolution (DIR) method theoretically can be treated as a more generalized solution to the problem because the distribution of components is allowed to freely mix in simplified combinations of solutions. The result is a constrained least-squares solution where the constraints are more limited and therefore less restrictive. The constraints in this case employ the results of probabilistic class partition information by applying Bayesian discriminant clustering to the intensity submatrix. This amounts to a spatial constraint because the probability of class association is used as a way of limiting the components that are allowed to appear

关键词： discriminant image resolution multivariate curve resolution multivariate image analysis k-means clustering algorithm Fuzzy c-means clustering algorithm Bayes classifier modified alternating least squares regression

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