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

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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Combined FATEMD-based band selection method for hyperspectral images

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IET IMAGE PROCESSING 2019年第2期13卷 287-298页

作者： Yu, Wenbo Zhang, Miao Shen, Yi Harbin Inst Technol Dept Control Sci & Engn Harbin Heilongjiang Peoples R China

Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high-frequency noise and signal. A stop condition of the decomposition is proposed based on the k-means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state-of-the-art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods' results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.

关键词： hyperspectral imaging image classification feature selection remote sensing geophysical image processing pattern clustering combined FATEMD-based band selection method hyperspectral images feature selection hyperspectral data hyperspectral band selection method combined fast-and-adaptive tridimensional empirical mode decomposition cFATEMD tridimensional intrinsic mode functions TIMF k-means clustering algorithm Dunn validity index RES spectral similarity residual publicly available hyperspectral datasets

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System state model based multi-period robust generation, transmission, and demand side resource co-optimisation planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第3期13卷 345-355页

作者： Dai, Chenxi Wu, Lei Zeng, Bo Liu, Cong Clarkson Univ Elect & Comp Engn Dept Potsdam NY 13699 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA ERCOT Taylor TX 76574 USA

This study discusses a multi-period co-optimised generation and transmission expansion planning (GTEP) problem while considering a proliferation of demand side resources (DSR). Uncertain renewable energy variations and load fluctuations in the long-term planning horizon are addressed, and a system state model derived via k-means clustering algorithm is adopted to capture temporal operation features. The problem is formulated as a two-stage robust optimisation model with mixed-integer recourse, in which annual investment decisions of generation, transmission, and DSR assets are determined in the first stage and short-term operation decisions of individual system states are made in the second stage. In recognising that considering DSR deployment and the system state model brings significant computational complexity, an extended column-and-constraint-generation algorithm is adopted to effectively solve the proposed planning problem. Numerical studies show that integrating DSRs into multi-period GTEP could effectively postpone or even avoid expensive generation/transmission investment in the planning stage, and improve economic efficiency in the operation stage.

关键词： power generation economics investment power generation planning power transmission economics integer programming power transmission planning pattern clustering computational complexity numerical analysis planning stage system state model multiperiod robust generation uncertain renewable energy variations long-term planning horizon temporal operation features two-stage robust optimisation model short-term operation decisions DSR deployment demand side resource cooptimisation planning generation-transmission investment multiperiod robust transmission multiperiod GTEP problem k-means clustering algorithm multiperiod cooptimised generation and transmission expansion planning problem mixed-integer recourse investment decision computational complexity extended column-and-constraint-generation algorithm

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Shipwreck Target Recognition in Side-Scan Sonar Images by Improved YOLOv3 Model Based on Transfer Learning

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IEEE ACCESS 2020年 8卷 173450-173460页

作者： Tang Yulin Jin, Shaohua Bian, Gang Zhang, Yonghou Dalian Naval Acad Dept Hydrog & Cartog Dalian 116018 Peoples R China

When used to recognize side-scan sonar images of shipwreck targets, the Faster R-CNN model is time-consuming and has low efficiency and a high missed detection rate for small targets. Considering that existing datasets of side-scan sonar images of shipwreck targets are small, we propose a YOLOv3 model that can automatically recognize side-scan sonar images of shipwreck targets based on transfer learning. Based on the Darknet-53 network, we froze part of the convolutional layer of the YOLOv3 model trained on COCO dataset images, and conducted transfer learning. Multi-scale training of shallow feature fusion was done based on multi-scale feature fusion with Feature Pyramid Networks (FPN) support, and the proportion of recognized shallow features of shipwreck targets increased. Meanwhile, the parameters and sizes of target anchor boxes were reset using k-means clustering, which allowed us to improve the speed of target recognition and the precision of smaller target recognition and positioning. Lastly, the binary classification cross entropy function was used to improve the loss function of the YOLOv3 algorithm. Experimental results show that under the same recognition target, the average precision (AP) value of the YOLOv3 model based on transfer learning reached 89.49%, which is an improvement of 0.31% and 1.77%, respectively, compared with the Faster R-CNN model and the traditional YOLOv3 model. Moreover, the YOLOv3 model based on transfer learning had the highest harmonic mean (F1), reaching 90.71%, which is 3.96% and 1.63% higher, respectively, than the harmonic means of the Faster R-CNN and the traditional YOLOv3 model. Lastly, the traditional YOLOv3 model takes an average 0.17s to identify a target. In contrast, the Faster R-CNN model takes an average of 2.8s to identify a target. Hence, our transfer learning YOLOv3 model greatly improves detection efficiency, meets the needs of real-time target recognition, and ultimately has better overall performance than exis

关键词： Target recognition Feature extraction Training Sonar detection Object detection Predictive models Side-scan sonar shipwreck target YOLOv3~model transfer learning shallow feature fusion k-means clustering algorithm binary classification cross entropy

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Heart Disease Prediction using Exploratory Data Analysis

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Procedia Computer Science 2020年 173卷 130-139页

作者： R. Indrakumari T. Poongodi Soumya Ranjan Jena Associate Professor School of Computing Science & Engineering Galgotias University Greater Noida U.P. India

Healthcare industries generate enormous amount of data, so called big data that accommodates hidden knowledge or pattern for decision making. The huge volume of data is used to make decision which is more accurate than intuition. Exploratory Data Analysis (EDA) detects mistakes, finds appropriate data, checks assumptions and determines the correlation among the explanatory variables. In the context, EDA is considered as analysing data that excludes inferences and statistical modelling. Analytics is an essential technique for any profession as it forecast the future and hidden pattern. Data analytics is considered as a cost effective technology in the recent past and it plays an essential role in healthcare which includes new research findings, emergency situations and outbreaks of disease. The use of analytics in healthcare improves care by facilitating preventive care and EDA is a vital step while analysing data. In this paper, the risk factors that causes heart disease is considered and predicted using k-means algorithm and the analysis is carried out using a publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate and four types of chest pain. To predict the heart disease, k-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances and evaluation metrics. In the result section, the visualized data shows that the prediction is accurate.

关键词： Tableau Exploratory Data Analysis k-means clustering algorithm

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An adaptive segmentation method combining MSRCR and mean shift algorithm with k-means correction of green apples in natural environment

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Information Processing in Agriculture 2019年第2期6卷 200-215页

作者： Sashuang Sun Huaibo Song Dongjian He Yan Long College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the k-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** with the manifold ranking algorithm,the k-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

关键词： Green fruit Adaptive segmentation MSRCR algorithm Mean shift algorithm k-means clustering algorithm Manifold ranking algorithm

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Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2020年 131卷 109967-109967页

作者： Yang, Ying Campana, Pietro Elia Yan, Jinyue Malardalen Univ Sch Business Soc & Engn S-72123 Vasteras Sweden KTH Royal Inst Technol Dept Chem Engn S-10044 Stockholm Sweden

This paper analyzes if solar photovoltaic technology is economically feasible enough to compete with coal-fired power in Chinese cities in the subsidy-free context. Considering this, this paper further investigates how profitable investing in solar PV projects is. This paper firstly analyzes to what degree local coal-fired power plants can be replaced by distributed solar power in 344 prefectural-level cities in China. Levelized Cost of Electricity of solar PV power and the local desulfurized coal benchmark price are used for simplified cost crossover math to identify the replacement risk of local coal-fired power plants. Four risk-levels and their corresponding cities are identified, i.e. deemed no cost-risk, potentially at cost-risk, at cost-risk, and substantially at cost-risk. As a whole, 85.17% of current coal-fired power plants from the investigated cities are under cost-risk. Levelized Profit of Electricity, Net Present Value, Internal Rate of Return, and Discounted Payback Period are calculated for each city, and grouped using k-means algorithm. The cities are clustered into four groups, i.e. high return, medium return, moderate return, and low return. The results show that 65.99% of all the cities could achieve a moderate or higher financial return. The cost-risk and investment profit results are mapped for a better understanding of the regional variation in China.

关键词： Distributed solar photovoltaic Zero subsidies Cost-risk Investment-profit Crossover math k-means clustering algorithm

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What are the critical barriers to the development of hydrogen refueling stations in China? A modified fuzzy DEMATEL approach

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ENERGY POLICY 2020年 142卷 111495-111495页

作者： Xu, Chuanbo Wu, Yunna Dai, Shuyu North China Elect Power Univ Sch Econ & Management Beijing Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing Peoples R China State Power Investment Corp Hydrogen Energy Co Ltd Beijing Peoples R China

Hydrogen fuel cell vehicle is one of the important modalities of the next generation of new energy vehicles. As the basic infrastructure of hydrogen fuel cell vehicles, hydrogen refueling stations (HRSs) have drawn attention from all social circles. However, hindered by various barriers, the development of HRSs is much slower than the Chinese government's expectation. Hence, this study aims to explore critical barriers of the HRS development in China based on a modified fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Firstly, a barrier list is identified through a literature survey and expert consultation, which consists of four categories (economic, technological, social and political-regulatory aspect) and 12 potential barriers. Later, considering the subjectivity of threshold setting in the traditional fuzzy DEMATEL method, the k-means clustering algorithm is utilized to optimize the threshold value, and thus a modified fuzzy DEMATEL approach is proposed to investigate the prominence of these barriers and the cause-effect relationships among them. The results show that six barriers are regarded as key barriers to China's HRS development, and corresponding policy measures are formulated to mitigate or eliminate these critical barriers.

关键词： Hydrogen refueling stations Critical barriers Modified fuzzy DEMATEL k-means clustering algorithm Policy measures

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Research of Flexible Load Analysis of Distribution Network Based on Big Data 4

Research of Flexible Load Analysis of Distribution Network B...

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4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Sun RongQi Xiao XueQuan Zhou Feng Zhou YuShan State Grid XuZhou Power Supply Co Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781728114101

Aiming at the problem of insufficient computing power in the analysis of flexible load under the condition of massive user data in distribution network, a flexible load analysis method based on big data is proposed. Firstly, the data fusion and feature extraction of the massive user data in distribution network are carried out to realize the preprocessing of the original data. Then, on the basis of MapReduce parallel computing model, the k-means algorithm is used to cluster the load characteristic data and calculate the industry flexibility coefficient and adjustable load. According to the results of load flexibility analysis based on the historical data of a power supply company in a certain city, this method can make statistics on load flexibility (load adjustable space) from industry dimension and time dimension, judge the current load flexibility level, and combine external data and temperature data to carry out load flexibility analysis. According to historical data, the adjustable load level of a certain area, a certain industry and a certain period in the future is predicted.

关键词： big data MapReduce model k-means clustering algorithm Load flexibility

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Data Compression Scheme for Fronthaul Based on Vector Quantization 19

Data Compression Scheme for Fronthaul Based on Vector Quanti...

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International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

作者： Feng, Chenwei Lin, Mingxia Xie, Xinlin Zhang, Mingjiang Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen Peoples R China

ISBN: (纸本)9781450372985

With the rapid development of the mobile communication technology, the number of long-term evolution (LTE) users and the volume of data is continually increasing, which greatly increases the amount of data transferred by fronthaul, resulting in a huge investment in optical fiber resource. In order to control the consumption of optical fiber resource, lower the cost of operator and avoid congestion in the premise of increasing data volume, it is necessary to compress the data for fronthaul. According to the characteristics of the LTE baseband signal, this paper proposes a data compression scheme on the basis of discrete cosine transform (DCT) and vector quantization. Firstly, the time domain signal is transformed in DCT. And then, the k-means clustering algorithm is used for vector quantization on the transformed signal. Finally, the Huffman coding is applied to further increase the compression ratio (CR) under the promise of an acceptable error. The simulation results show that the scheme proposed has a good performance in both compression ratio and error vector Magnitude (EVM) for the LTE baseband signal base on orthogonal frequency division multiplexing (OFDM).

关键词： Fronthaul Data compression Vector quantization Discrete cosine transform k-means clustering algorithm Huffman coding

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