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Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 35-35页

作者： Abdel-Basset, Mohamed Mohamed, Reda Hezam, Ibrahim M. Sallam, karam M. Foul, Abdelaziz Hameed, Ibrahim A. Zagazig Univ Zagazig 44519 Sharqiyah Egypt Univ Sharjah Coll Comp & Informat Comp Sci Dept Sharjah U Arab Emirates Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra Australia King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci Alesund Norway

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs us

关键词： Multi-UAVs Mobile edge computing Trajectory planning Non-dominated solutions Pareto optimality k-means clustering algorithm

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Steady-State Interval Detection and Nonlinear Modeling for Automatic Generation Control Systems

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IEEE ACCESS 2019年 7卷 139592-139600页

作者： Cao, Pengfei Wang, Jiandong Zhang, Chao Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressure have significant impacts on the resulting active power generation of a conventional thermal generator, and the impacts may be associated with nonlinear characteristics. As a result, the control instruction requires an accurate modeling of the relationship between these three variables for a satisfactory control performance. This paper proposes a method to build a piecewise linear model for the nonlinear relationship from steady-state data hidden in historical data samples. The proposed method is composed by two main steps of steady-state interval detection and steady-state data segmentation. Historical data samples are grouped using the k-means clustering algorithm, and the time domains of each cluster are merged in a specific way to obtain the steady-state intervals. The steady-state data are taken as the samples means of data in the steady-state intervals. A bottom-up algorithm is utilized to partition the steady-state data into numbers of sets iteratively, and the parameters of the piecewise linear model for each data set are estimated by the least squares algorithm. The effectiveness of the proposed method is illustrated via industrial applications to two thermal power generation units.

关键词： Automatic generation control system piecewise linear model steady-state interval detection k-means clustering algorithm bottom-up algorithm

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A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images

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REMOTE SENSING 2022年第15期14卷 3600-3600页

作者： Wei, Yanbo Liu, Yalin Lei, Yifei Lian, Ruiyao Lu, Zhizhong Sun, Lei Luoyang Normal Univ Coll Phys & Elect Informat 6 Jiqing Rd Luoyang 471934 Peoples R China Zhengzhou Railway Vocat & Tech Coll Sch Elect Engn 56 Pengcheng Ave Zhengzhou 451460 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 Nantong St Harbin 150001 Peoples R China

To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised k-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the k-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by 10.0%, 6.3%, 2.0% and 0.6%, respectively, for the proportion of the 25% training dataset.

关键词： correlation coefficient feature vector (CCFV) k-means clustering algorithm marine radar images rainfall detection

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Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s

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SENSORS 2023年第5期23卷 2562页

作者： Liu, Deyuan Deng, Changgen Zhang, Haodong Li, Jinrong Shi, Baojun Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Hebei Key Lab Robot Sensing & Human Robot Integrat Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the

关键词： inspection robot YOLOv5s k-means clustering algorithm reflection detection pose transformation

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Spilled load detection based on lightweight YOLOv4 trained with easily accessible synthetic dataset

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Li, Feng Jiang, Zhongwang Zhou, Siqi Deng, Yutong Bi, Yufeng Beihang Univ Beijing Key Lab Cooperat Vehicle Infrastruct Syst Sch Transportat Sci & Engn Beijing 100191 Peoples R China China Univ Geosci Fac Engn Beijing 100083 Peoples R China Shandong Prov Commun Planning & Design Inst CO LT Jinan 250031 Shandong Peoples R China

The automated detection of spilled loads on the highway can help quick finding of offending vehicles and prevent traffic congestion. A real-time spilled loads detection algorithm based on the improved You Only Look Once v4 (YOLOv4) is proposed in this paper. Image synthesis method is used to manually generate dataset to solve the problem of insufficient spilled loads images. Six categories of spilled loads are divided into 35,468 samples as a dataset (80% for training and 20% for testing). k-means clustering and Convolutional Block Attention Module (CBAM) were introduced to YOLOv4 Channel pruning was carried out to reduce the computational complexity and resource consumption of the model. The proposed model, of which average precision=98.3%, recall= 0.962, precision=0.98, performs better than other 5 types classifiers and is applied to estimate spilled loads, where the model size and GFLOPs is 134.535 MB and 70 respectively. Compared with the original model, the reduction is 47.6% and 34%, saving computing resources and speeding up the detection speed on the premise of ensuring the detection accuracy. This paper paves a new way for automated spilled load detection with deep learning method, improving the efficiency and accuracy of spilled load detection.

关键词： Spilled load Deep learning k-means clustering algorithm Attention module Channel pruning

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Changing Characteristics of Urban Heat Island Effect in Weihai City

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Meteorological and Environmental Research 2023年第6期14卷 8-15页

作者： Di WANG Qianqian SUN Wenpeng XIN Key Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong Jinan 25000China Weihai Meteorological Bureau Weihai 264200China

Based on the local climate zoning theory and the observation data of hourly temperature of 22 automatic weather stations from 2012 to 2021, k-means clustering algorithm was used to analyze the daily, monthly, seasonal, annual and spatial variation characteristics of urban heat island effect in Weihai City in the past 10 years. The results showed that in recent 10 years, the average urban heat island intensity was 1.24 ℃, showing a gradual weakening trend of -0.169 3 ℃/10 a;the summer average heat island intensity was 0.86 ℃, showing a gradual weakening trend of -0.047 5 ℃/10 a. The heat island intensity had obvious diurnal variation characteristics, that is, "it was weak in the day and strong at night". In terms of seasonal variation, heat island effect was the weakest in summer, stronger in spring and autumn, and the strongest in winter. The diurnal, seasonal and annual changes of heat island intensity showed a reverse trend to those of temperature. The high-value area of urban heat island was highly consistent with human residential activity areas and industrial and commercial intensive areas, and the extension trend of heat island intensity was the same as the direction of urban development and construction. The "cold island phenomenon" in some offshore areas was related to the geographical location, terrain and the southeast monsoon trend in summer. The adverse effects of urban heat island effect can be reduced by optimizing the types and distribution of vegetation communities, rationally planning and constructing urban water body, promoting green building materials and adjusting shape design, etc.

关键词： Urban heat island effect Local climate zoning k-means clustering algorithm Automatic weather station

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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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k-means adapted algorithm based on spatial contiguity relations

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Jisuanji Gongcheng/Computer Engineering 2006年第21期32卷 50-51+75页

作者： Wang, Haiqi Wang, Jinfeng National Key Lab. of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research Chinese Acad. of Sciences. Beijing 100101 China Dongying 257061 China Graduate School Chinese Acad. of Sciences Beijing 100039 China

Spatial object has not only non-spatial attribute properties but also spatial properties related with space coordinates and topological structures. When using the traditional clustering methods to classify spatial objects, the objects of the same class may appear in non-adjacent spatial positions because spatial relationships are not been considered. The k-means adapts algorithm based on spatial contiguity relations regards spatial contiguities of the neighboring objects as a restrained condition. So the clustering result not only reflects the similarities of attributes but also reflects spatial adjacent relations, and furthermore reviews spatial distribution patterns of different classes. Therefore, this adapted algorithm is more suitable for the clustering analysis of spatial objects than the traditional k-means method.

关键词： Contiguity matrix Contiguity relation k-means clustering algorithm Spatial object

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Face Detection using Image Morphology - A Review

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第4期10卷 89-93页

作者： Mandhala, Venkata Naresh Bhattacharyya, Debnath kim, Tai-hoon KL Univ Dept Comp Sci & Engn Vaddeswaram 522502 AP India Bharati Vidyapeeth Univ Coll Engn Dept Informat Technol Pune 411043 Maharashtra India Sungshin Womens Univ Dept Convergence Secur 249-1Dongseon Dong 3 Ga Seoul 136742 South Korea

This paper presents an approach by various algorithms stage by stage for Face Detection. It first detects the face portion, removing all other portion from an image. First it will remove the background and then the body or cloths portion of the image. To achieve this we propose an algorithm based on k-Mean clustering, Bresenham's algorithm, Graham Scan algorithm. With the help of image morphology, algorithm will detect the skin texture of the face. Image opening and closing will help to get the skin texture. Using a nose template algorithm will find the nose tip by template matching method. Feature vectors are calculated with respect to the nose tip as the origin of 8 octants. Image moment provides a measure for variation in the skin. This total process is done by the feature extraction algorithm.

关键词： k-means clustering algorithm Bresenham's algorithm Graham scan algorithm Image Morphology Template Matching

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Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-12页

作者： Pan, Lijun Zhengzhou Normal Univ Coll Elementary Educ Zhengzhou 450044 Henan Peoples R China

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial ***: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation ***: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements;then, the intelligent learning model is constructed through a k-means clustering algorithm and deep compression sparse self-encoder;finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment ***: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient ***: The results show that the proposed method improves the model's accuracy.

关键词： Intelligent learning model Model element extraction k-means clustering algorithm Deep compression sparse self-encoder

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