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IEEE 3rd Global Power, Energy and Communication Conference (IEEE GPECOM)

作者： Hu, Yi-Liang Kun Shan Univ Green Energy Technol Res Ctr Tainan Taiwan

ISBN: (纸本)9781665435123

This study focuses on the day-ahead forecasting of the power generation of the grid-connected photovoltaic (PV) system. In the literature, most PV power forecasting models depends on the information from the numerical weather prediction (NWP). The errors of the weather prediction might cause the performance of the PV power forecasting model to decrease. Therefore, in this paper, the proposed day-ahead PV power forecasting model only utilizes the historical PV power generations as the input data. The proposed forecasting model is composed of two parts, pattern analysis and the state transition. The pattern analysis evaluates the similarity of the time series of the daily PV power generations based on Euclidean distance and group the time series with similar patterns by k- means time series clustering algorithm. Each cluster is regarded as one state. Then, the probabilities of the state transitions between two consecutive days are calculated. Finally, the expected PV power pattern of tomorrow is obtained as the forecasted PV power outputs. In this study, the proposed model is compared with the traditional feed-forward neural network ( FFNN). The results of the cast study demonstrate the proposed model leads to smaller errors in 10 sets of the testing data.

关键词： PV power day-ahead forecasting Euclidean distance k-means clustering algorithm state transition

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Evaluation index system and methodology for actively responding to ageing population

Evaluation index system and methodology for actively respond...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Yang, Sijing Li, Aihua Li, Guijun Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781450391870

Ageing population is a challenge faced by the global community. According to international standards for measuring ageing society, China has entered the ageing society since 2000. Currently, actively responding to ageing population has become one of China's national strategies. Increasingly, actively responding to ageing population is gaining greater attention, but there lacks a comprehensive evaluation index system and model. Based on China's real situations, we scientifically and comprehensively construct an evaluation index system for actively responding to ageing population, using source statistical surveys, social tracking surveys and other multi-source heterogeneous data. Best-Worst Method (BWM) and k-means clustering algorithm are used here to evaluate ageing population measuring nationwide with a scientific and comprehensive index system. This paper also analyses and evaluates the measurement taken by 31 districts in China, thus carrying both theoretical and practical implications. With the emergence of big data for government administration, the rigor of the evaluation outcome will enhance.

关键词： Actively responding to ageing population BWM k-means clustering algorithm

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Oxygen saturation measurements using novel diffused reflectance with hyperspectral imaging: Towards facile COVID-19 diagnosis

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OPTICAL AND QUANTUM ELECTRONICS 2022年第5期54卷 322-322页

作者： El-Sharkawy, Yasser H. Aref, Mohamed Hisham Elbasuney, Sherif Radwan, Sara M. El-Sayyad, Gharieb S. Egyptian Armed Forces Biomed Engn Dept Mil Tech Coll Cairo Egypt Egyptian Armed Forces Nanotechnol Res Ctr Mil Tech Coll Cairo Egypt Ain Shams Univ Fac Pharm Biochem Dept Cairo Egypt Galala Univ Fac Pharm Microbiol & Immunol Dept New Galala City Suez Egypt Egyptian Armed Forces Chem Engn Dept Mil Tech Coll Cairo Egypt

Oxygen saturation level plays a vital role in screening, diagnosis, and therapeutic assessment of disease's assortment. There is an urgent need to design and implement early detection devices and applications for the COVID-19 pandemic;this study reports on the development of customized, highly sensitive, non-invasive, non-contact diffused reflectance system coupled with hyperspectral imaging for mapping subcutaneous blood circulation depending on its oxygen saturation level. The forearm of 15 healthy adult male volunteers with age range of (20-38 years) were illuminated via a polychromatic light source of a spectrum range 400-980 nm. Each patient had been scanned five times to calculate the mean spectroscopic reflectance images using hyperspectral camera. The customized signal processing algorithm includes normalization and moving average filter for noise removal. Afterward, employing k-means clustering for image segmentation to assess the accuracy of blood oxygen saturation (SpO(2)) levels. The reliability of the developed diffused reflectance system was verified with the ground truth technique, a standard pulse oximeter. Non-invasive, non-contact diffused reflectance spectrum demonstrated maximum signal variation at 610 nm according to SpO(2) level. Statistical analysis (mean, standard deviation) of diffused reflectance hyperspectral images at 610 nm offered precise calibrated measurements to the standard pulse oximeter. Diffused reflectance associated with hyperspectral imaging is a prospective technique to assist with phlebotomy and vascular approach. Additionally, it could permit future surgical or pharmacological intercessions that titrate or limit ischemic injury continuously. Furthermore, this technique could offer a fast reliable indication of SpO(2) levels for COVID-19 diagnosis.

关键词： Vein viewer Hyperspectral imaging Oxygen concentration k-means clustering algorithm Tissue characterization

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Optimized FPGA-Based Implementation of Brain Tumor Detection by Combining k-means and Grey Wolf Optimization algorithms

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TRAITEMENT DU SIGNAL 2022年第6期39卷 1879-1891页

作者： Jarrah, Amin Amri, Screen Yarmouk Univ Hijjawi Fac Engn Technol Dept Comp Engn Irbid 21163 Jordan

There is a need for fast, accurate, and real-time algorithms to detect brain tumors effectively to support the physician's decision-making for treatment purposes. A brain tumor is a life-threatening uncontrolled growth of cells and tissues that may cause death due to inaccurate and late detection. k-means clustering is one of the clustering techniques that is widely used in brain tumor detection, but it has some drawbacks such as dependency on initial centroid values and a tendency to fall on local optima. This research proposes a new model that uses grey wolf optimization to find the optimal value of k (clusters number) of the k-means algorithm to avoid local optima. A parallel implementation of the k-means clustering algorithm on a field-programmable gate array (FPGA) is also proposed to enhance the performance by reducing the processing time and the power consumption. Moreover, the proposed algorithm is implemented using the Vivado HLS tool on Xilinx kintex7 XC7k160t FPGA 484-1 where different optimization techniques are adopted and applied, such as loop unrolling, loop pipelining, dataflow, and loop merging. The achieved speed-up of the parallel implementation compared with sequential implementation was 88.17, where the obtained average clustering accuracy was 97.11%.

关键词： brain tumor detection grey wolf optimization k-means clustering algorithm optimization techniques parallel implementation

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Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Zhiyong, Lv Liu, Tongfei Wang, Robert Yu Benediktsson, Jon Atli Saha, Sudipan Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Univ Iceland Fac Elect & Comp Engn IS-107 Reykjavik Iceland Tech Univ Munich TUM Munich AI Future Lab AI4EO Artificial Intelligenc D-82024 Taufkirchen Germany

Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.

关键词： Terrain factors Remote sensing Training Shape Tuning clustering algorithms Standards k-means clustering algorithm land-cover change detection (LCCD) landslide inventory mapping (LIM) very-high-spatial-resolution (VHR) images

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An efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network

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Internet of Things (The Netherlands) 2025年 32卷

作者： Mohapatra, Prajna Paramita R., Bala krishnan Priyadarsini, Madhukrishna Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur 492010 India

Internet of Things (IoT) is the interconnection of billions of devices over the Internet. It is an umbrella of various concepts, protocols, and technologies that are used to create numerous benefits in productivity and automation. Despite the benefits it provides, there are challenges such as high cost of IoT devices, time-constraints, and overuse of Internet protocols and technologies which attackers often take advantage. To address this, IoT networks must have secure routing protocols that can provide security to the network and also utilize the benefits of existing technologies. However the lack of infrastructure, dynamic topology changes, resource constraints, and unreliable links make even the best existing protocol “Routing Protocol for Low Power and Lossy Network (RPL)” to be vulnerable for various attacks. Besides the trust management, that ensures only the reliable and legitimate nodes to participate in routing decisions, is another critical aspect that many existing solutions fail to consider. Hence, in this research, we propose a novel secure routing technique “Genetic algorithm-based Trusted framEwork for RPL (GATE-RPL)” that supports multi-topology routing and provides security to various devices in the IoT network. To overcome the security issues, the proposed work:(i) provides a “dynamic trust management technique” that maximizes the trust of nodes, links, and routing performance using a combination of k-means clustering and extended Genetic algorithm;and subsequently (ii) finds a trusted routing path between every node in the network. The experimental results indicate an average of 0.012% packet loss, 10.5 Mbps throughput, and 99% accuracy in identifying trustworthy routing paths. © 2025 Elsevier B.V.

关键词： Genetic algorithm IoT k-means clustering algorithm RPL Trust calculation

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Intelligent Site Detection Based on Improved YOLO algorithm

Intelligent Site Detection Based on Improved YOLO Algorithm

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International Conference on Big Data Engineering and Education (BDEE)

作者： Sun, Shidan Zhao, Shijia Zheng, Jiachun Jimei Univ Xiamen 361021 Peoples R China

ISBN: (纸本)9781665439572

An improved YOLOv4 algorithm is proposed to improve the detection accuracy of helmets and reflective clothing for safety problems on construction sites that require simultaneous automatic detection of helmets and reflective clothing. Firstly, the clustering algorithm in YOLOv4 network is optimized, and weighted kernel k-means clustering algorithm is used to analyze the dataset, so as to select the anchor box more suitable for small targets and improve the average accuracy and speed of detection;secondly, the Darknet feature network layer inside the YOLO network is optimized, and the feature maps extracted by quadruple downsampling are doubly upsampled, then convolved and fused with the doubled downsampling, and transported to the subsequent network together with quadruple downsampling, eightfold downsampling, and sixteenfold downsampling to achieve the reduction of the probability of missing detection of small targets. Experimental results show that, the average detection accuracy of the improved algorithm is improved by 8.4% when the helmets and reflective clothing are worn at the same time.

关键词： YOLO v4 algorithm safety helmet wearing test k-means clustering algorithm reflective clothing wear detection

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Adaptive multilabel image segmentation model with prior constraints based on geometric structure characteristics

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JOURNAL OF ELECTRONIC IMAGING 2022年第6期31卷 063031-063031页

作者： Tian, Yuzhu Pang, Liping Pang, Yanbo Zhang, Hongwei Dalian Univ Technol Sch Math Sci Dalian Peoples R China Key Lab Computat Math & Data Intelligence Liaonin Dalian Peoples R China Univ Tokyo Ctr Spatial Informat Sci Tokyo Japan

Image segmentation is an essential digital image processing problem closely related to computer vision. The limitations of the total variational regular term for the continuous Potts model make the transition of segmentation results over-smooth. In this paper, we propose an improved model with prior conditions to realize the improvement of image segmentation results quality. To extract the structural features of the images, we utilize the simple and practical k-mean clustering (kM) algorithm to set the corresponding volume structure as a prior condition, which is also crucial for the initial label selection of the proposed model. Then, we choose two convex relaxation methods to solve the original nonconvex variational problem. Using these methods, we verify that increasing the constraint of volume structure can maintain slender structures in the original image and achieve a good balance between image segmentation quality and computation. Consequently, we use the unified, convergent primal-dual (PD) algorithm to solve the minimization problem in the proposed model. Extensive experimental comparisons between our method and the pure kM method, the graph-cut method, and the corresponding Potts model without volumetric structure are provided. The segmentation results illustrate that our model performs better in terms of both visualization and evaluation metrics. (c) 2022 SPIE and IS&T

关键词： image segmentation k-means clustering algorithm total variation regularization volume constraint

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Performance enhanced probabilistically shaped PAM4 signal based on nonlinear differential coding for IM/DD system

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OPTICAL FIBER TECHNOLOGY 2022年 71卷

作者： Liu, Tianyi Ma, Jie Lu, Jia Liu, Jianfei Zeng, Xiangye Wang, Yang Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China

In this paper, the probabilistically shaped pulse amplitude modulation (PS-PAM) signal is proposed in intensity modulation and direct detection (IM/DD) system. For the traditional shaping scheme with constant composition distribution matcher (CCDM), there are several problems that high-precision algorithm for long block coding, the loss of information rate for short block coding. In addition, it can increase nonlinear interference. In order to reduce computational complexity and alleviate the influence of nonlinear interference on the system, the nonlinear differential coding (NLDC) scheme is used to realize PS, which can be better integrated with the costeffective optical access network. This paper evaluates seven shaping spectrums obtained by different NLDC coding coefficients. As the coding coefficient increases, the performance of the system gradually improves. Moreover, this paper proposed the machine learning includes neural network (NN) and improved k-means clustering algorithm at the receiver. The results show that the PS-PAM4 signal is significantly better than the ordinary PAM4 signal in improving the channel bandwidth tolerance and signal transmission rate. Under the same data rate, using NLDC can save 5.8 GHz bandwidth for the system. For the bandwidth-limited system, the PS-PAM4 signal can obtain 19Gbit/s bit rate gain compared with the ordinary PAM4 signal. Compared with the typical shaping scheme using CCDM, this scheme can achieve 2.2 GHz band tolerance gain and 5Gbit/s rate gain in nonlinear fiber channel.

关键词： Intensity modulation and direct detection Probabilistic shaping Nonlinear differential coding Neural network k-means clustering algorithm

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An integrated approach to the selection of municipal solid waste landfills through GIS, k-means and multi-criteria decision analysis

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ENVIRONMENTAL RESEARCH 2020年 185卷 109348-109348页

作者： Eghtesadifard, Mahmood Afkhami, Payam Bazyar, Arash Shiraz Univ Technol Fac Ind Engn Shiraz Iran

Selecting the proper site for disposing of solid waste is one of the serious environmental and public health concerns in metropolises. This multifaceted issue encompasses environmental, economic, social, geographical, technical, and legal criteria. Some of these criteria, however, are less influenced by experts' subjective judgments because they display scientific specifications. This study drew on a novel, integrated method for the selection of municipal solid waste (MSW) landfills in the Iranian metropolis, Shiraz;the study relied on the GIS and multi-criteria decision-making methods, which helped to reduce the number of comparisons in collecting experts' opinions, simplified the selection process, and enhanced the assessment method. The method proposed was regulated by DEMATEL and ANP. Primarily 13 criteria were identified in five groups through the Delphi method. Next, using the integrated method, the weight of each criterion was determined and was assigned to the corresponding layer in ArcGIS 10.5. By combining these layers through a fuzzy logic, the sites satisfying the disposal conditions were identified. The sites were then divided into six areas through the k-means clustering algorithm, while MOORA, WASPAS and COPRAS methods were used to discover the best sites based on their priorities. Finally, to confirm the reliability of the results, compare and verify them, and conduct sensitivity analysis on them, 13 scenarios were used.

关键词： Sustainable municipal solid waste management Landfill siting Geographic information system (GIS) Multi criteria decision analysis k-means clustering algorithm

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