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Chinese Automation Congress (CAC)

作者： Xing, Jiaming Chu, Liang Guo, Chong Hou, Zhuoran Qiao, Yiran Jiang, Siru Jilin Univ Coll Automot Engn Jilin Jilin Peoples R China Wuhan Univ Sci & Technol Evergrande Sch Management Wuhan Peoples R China

ISBN: (纸本)9781728176871

With the continuous progress of technology and development of sharing economy, sharing vehicles have rapidly gained wide attention due to their huge market potential. The management on the shared vehicles, however, is not ripe enough to support the widely arrangement. Based on big data, this paper proposes a novel method to manage parking space of the shared vehicles based on the open available data Firstly, visualizing the GPS data of the orders and analyzing the data distribution on the road. Secondly, a density-based DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to cluster the GPS data, determining number of parking spaces of the shared vehicles and obtaining the initial location of parking spaces. Finally, whale optimization algorithm is used to optimize the parking location and improve the utilization rate of parking spaces.

关键词： parking spaces management data visualization DBSCAN clustering whale optimization algorithm

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Blind Signal Extraction algorithm Based on WOA and PCA

Blind Signal Extraction Algorithm Based on WOA and PCA

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IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)

作者： Chu Dingli Chen Hong Luo Luwei Natl Univ Def Technol Coll Elect Countermeasures Hefei Peoples R China

ISBN: (数字)9781728165905

ISBN: (纸本)9781728165905

For blind extraction of independent sources in linear aliasing signals, this paper proposes an orderly blind extraction algorithm based on whale optimization and PCA. The algorithm uses the absolute value of the kurtosis as the target function, uses the whale optimization algorithm to calculate the extraction vector, and uses the PCA to reduce the dimension of the mixed signal, so as to remove the components of the extracted signal in the mixed signal, and make the blind extraction according to the order of the kurtosis in the source signal. Simulation results show that the proposed algorithm is effective for blind signal extraction, and the extraction accuracy is high, and has certain practical application value.

关键词： blind signal extraction whale optimization algorithm PCA

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Numerical simulation and optimization study of the complex fracture network in naturally fractured reservoirs

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 195卷 107726-107726页

作者： Zhang, Hao Sheng, James J. Texas Tech Univ Lubbock TX 79409 USA

Multi-fractured horizontal wells are widely used for unconventional reservoir development since the hydraulic fracture (HF) could stimulate natural fractures (NF) to form a complex fracture network (CFN), which will significantly increase the productivity. So accurately characterizing and optimally designing the fracture network configuration are essential to unconventional reservoir development. Facing this urgent request, a numerical study for the complex fracture network is proposed. The displacement discontinuity method (DDM), together with the fluid flow function, is adopted to simulate the HF dynamic propagation which couples the rock and fluid mechanics. A power-law function is adopted to describe the distribution of natural fractures. Then a fracture crossing criterion is used to determine the interaction of HF and NF to simulate the CFN configuration. Based on the CFN numerical model, the effects of different operational and geological parameters, such as the HF spacing and various NF distribution fields, are investigated. Furthermore, a CFN optimization framework is established to achieve the optimal design of CFN under complex geological conditions, in which a clustering-loop method together with a whale optimization algorithm (WOA) is employed to maintain the accuracy while reducing the calculation time. Multiple CFN placement methods are simulated and optimized and the optimal CFN under a complex naturally fractured reservoir is finally obtained. This numerical simulation and optimization approach makes an effort to the study of CFN configuration and unconventional reservoir development.

关键词： Displacement discontinuity method Complex fracture network propagation Reservoir uncertainty whale optimization algorithm Clustering-loop optimization

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From traditional to deep learning methods for skin lesion segmentation: A Literature Review

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Scientific African 2025年

作者： Abdelkrim Hammimou Hassan Ezzahori Abdelghani Boudaoud Mounaim Aqil Engineering and Applied Physics Team Superior School of Technology Sultan Moulay Slimane University Beni Mellal Morocco

Due to their similarity, skin lesions are challenging for early and precise diagnosis, and segmenting them manually is a laborious and time-consuming task. Therefore, several methods, including traditional and artificial intelligence methods, have been proposed for segmenting the skin lesions automatically. The majority of recent reviews, however, have overlooked the traditional methods and instead only briefly discussed their advantages and disadvantages. Consequently, this review aims to provide a thorough overview of this field and offer insights about the role and performance of the traditional methods in the era of artificial intelligence. We collected 128 peer-reviewed and high-quality publications using inclusion and exclusion criteria. These publications have been selected from four well-known databases, IEEE Xplore, ScienceDirect, Springer, and MDPI, and classified systematically into three categories: traditional, artificial intelligence, and hybrid methods. The main contributions of this paper are: (1) Exploring the most used traditional methods and performance enhancement techniques. (2) Providing the State of the art of the convolutional neural network and deep learning methods. (3) Highlighting the role of traditional methods in hybrid frameworks. (4) Providing an extensive performance analysis and discussing the computational cost and real-world applicability challenges associated with segmentation methods. The results highlight the superiority of deep learning methods over traditional methods, while also recognizing the relevance of traditional methods like Active contours and clustering as trusted tools for skin lesion segmentation. These methods benefit from improvements such as ensembling techniques and optimization algorithms. Additionally, 9% of reviewed papers were motivated by the combination of benefits from traditional methods and deep learning methods to develop more robust models. In conclusion, significant challenges remain in developing

关键词： Skin lesion segmentation Automatic Segmentation Traditional methods Convolutional neural network Deep Learning Ensembling methods optimization algorithms Hybrid frameworks ABCDE Asymmetry, Border, Color, Diameter, and Evolution method AC Active contour ACC Accuracy ACO Ant colony optimization algorithm AI Artificial intelligence AK Actinic keratosis BCC Basal cell carcinoma BUZOA Buzzards optimization algorithm CAD Computer aided diagnosis CNN Convolutional neural network COVIDOA Coronavirus optimization algorithm CPU Central Processing Unit CST Color space transformation DC Dice coefficient DF Dermatofibroma FCM Fuzzy c-means FLOP Floating-point operation FN False negative FP False positive FPS Frames per second FRCN Full-resolution convolution network GA Genetic algorithm GAN Generative adversarial networks GPU Graphical processing unit IoU Intersection over union IPO interior-point optimization JI Jaccard index KM K-means MAC Multiply-accumulate operation Mel Melanoma Nev Nevus PM Performance metric PSO Particle swarm optimization ROI Region of interest SCC Squamous cell carcinoma SENS Sensitivity SFS Stochastic fractal search SGO Social group optimization SIFT Scale-invariant feature transform SK Seborrheic keratosis SLC Skin lesion classification SLS Skin lesion segmentation SLIC Simple linear iterative clustering SPEC Specificity SSL Self supervised learning Swin-transformer Shifted window-based vision transformers TN True negative TP True positive WOA whale optimization algorithm

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Short-Term Traffic Flow Prediction Based On IWOA-WNN

Short-Term Traffic Flow Prediction Based On IWOA-WNN

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第33届中国控制与决策会议

作者： Qin Yu Yuepeng Chen Qingyong Zhang Li Li Wenqing Ma Wuhan University of Technology School of Automation

Aiming at the nonlinear and random characteristics of short-term traffic flow,an improved whale optimization algorithm(IWOA) is proposed to replace the gradient descent method to optimize the wavelet neural network(WNN) for short-term traffic flow ***,in view of the slow convergence speed and low convergence accuracy of the traditional whale optimization algorithm(WOA),a nonlinear convergence factor a is introduced to balance the global search and local search ability of the *** the same time,Kent chaotic mapping is used to increase population diversity and enhance the ability of jumping out to fall into local ***,aiming at the problem that the gradient descent method in the wavelet neural network is sensitive to the initial values of the weights and wavelet factors,and is easy to fall into local minimum values,the network weights of the wavelet neural network are optimized by improving the whale optimization ***,the wavelet threshold denoising algorithm is used to process the noise in the raw traffic flow sequence data,and the IWOA-WNN is used to test the short-term traffic flow data set after *** results show that the mean square error(MSE),mean absolute error(MAE) and mean absolute percentage error(MAPE) of the model are 18.03,2.82 and 13.13 %,*** results show that the improved algorithm has higher accuracy than the raw algorithm,and the model can effectively improve the prediction accuracy of short-term traffic flow.

关键词： Short-Term Traffic Flow Prediction whale optimization algorithm Wavelet Neural Network Kent Chaos Mapping Wavelet Threshold Denoising

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IoT Resource Allocation and optimization Based on Heuristic algorithm

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SENSORS 2020年第2期20卷 539-539页

作者： Sangaiah, Arun Kumar Hosseinabadi, Ali Asghar Rahmani Shareh, Morteza Babazadeh Bozorgi Rad, Seyed Yaser Zolfagharian, Atekeh Chilamkurti, Naveen VIT Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Islamic Azad Univ Behshahr Branch Dept Comp Engn Behshahr 51148515 Iran Islamic Azad Univ Babol Branch Dept Comp Engn Babol Sar *** Iran La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of "total communication cost", is better than other ones.

关键词： internet of things resource allocation problem whale optimization algorithm communication cost

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Exploring DC microgrid: Advanced applications and their control strategies

Energy 360

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Energy 360 2025年 3卷

作者： C. Elwely Salem A. Et-taleby Y. Chaibi N. Ayadi B. Elkari M. Benslimane Z. Chalh Sidi Mohamed Ben Abdellah University Fez Morocco ESME Research Lab Paris France IDEA School Euromed University Fez Morocco

The increasing reliance on microgrids (MG) as a power delivery system underscores the critical importance of advanced control strategies and application-specific solutions. With a focus on their technological advantages, possible uses and control mechanisms, this review evaluates the emerging role of DC microgrids as a viable substitute for conventional AC systems. Examining recent advancements, the paper explores key applications, including electric vehicle (EV) charging, smart buildings, hydrogen production, data centers and residential energy systems, highlighting the versatility of DC microgrids in optimizing renewable energy integration and enhancing energy efficiency. The review further categorizes and analyzes different control methods, including centralized, decentralized and distributed control, detailing their key features, limitations and mathematical representations. The findings emphasize that DC microgrids offer improved energy efficiency, reduced conversion losses and enhanced power reliability. Additionally, advanced control strategies play a crucial role in ensuring optimal energy management, system stability and operational resilience. By providing a critical analysis of these aspects, this review serves as a guide for future research and innovation in DC microgrid control and application optimization, contributing to the development of more sustainable and efficient energy systems.

关键词： AC, Alternative current AEMS, Automated energy management system BEMSs, Building energy management systems BESS, Battery energy storage system CAC, Centralized automated control CEMPC, Centralized economic model predictive control COS, Cooperative Online Scheduling CRAC, Computer room air conditioning CSWG, Climate Sustainability Work Group CVC, Clusters of vehicle control DC, Direct current DEMPC, Distributed economic model predictive control DERs, Distributed energy resources DGs, Distributed generators DMM, Direction method of multipliers DMS, Distributed management system DRC, Demand response control DTLR, Dynamic thermal line rating EMS, Energy management system ESS, Energy storage system EVCSs, Electric Vehicle Charging Stations EVs, Electric Vehicles FLC, Fuzzy logic control GA, Genetic algorithm GTBA, Game-theory based approach HDR, Holistic Demand Response HEMS, Hybrid energy management system HPWOA, Hybrid particle whale optimization algorithm HRSO, Hybrid Robust-Stochastic optimization HV, High voltage HVAC, Heat, ventilation and air conditioning IRENA, International Renewable Energy Agency IT, Information technology LC, Local control LCOE, Levelized Cost of Energy LV, Low voltage LVAC, Low voltage alternative current LVDC, Low voltage direct current MCO, Multiple criteria optimization MG, Microgrid MINLP, Mixed-integer non-linear programming MPPT, Maximum power point tracking MREAI, Microgrid renewable energy availability index MREEI, Microgrid renewable energy energy index MRI, Microgrid resiliency index MV, Medium voltage NPC, Net present cost NZB, Net zero building NZEB, Net-zero energy buildings OCO, Online Convex optimization P2P, Peer to peer PED, Peak energy demands PEVs, Plug-in electric vehicles PFF, Power fluctuation factor PHEVs, Plug-in hybrid electric vehicles PMS, Power management system PSO, Particle Swarm optimization PV, Photovoltaic RESs, Renewable energy sources RMOO, Robust Multi-Objective optimization SIU, Smart interlinking unit SLA, Service legal agreement SMC, Sliding mode cont

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A multi-compartment capacitated arc routing problem with intermediate facilities for solid waste collection using hybrid adaptive large neighborhood search and whale algorithm

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WASTE MANAGEMENT & RESEARCH 2019年第1期37卷 38-47页

作者： Mofid-Nakhaee, Elham Barzinpour, Farnaz Iran Univ Sci & Technol Sch Ind Engn Tehran 16844 Iran

Municipal solid waste collection is an increasingly difficult task and has the highest operation cost in the solid waste management process;thus, finding the optimal routes for the waste collection is a most tactically significant decision that should be focused on due to the population growth. This research investigated the multi-compartment capacitated arc routing problem with intermediate facilities (MCCARPIF) in the context of solid waste collection. This problem has been researched rarely in the past in the real world. The MCCARPIF develops the capacitated arc routing problem (CARP) by considering both the multi-compartment vehicles and intermediate facilities together. In case of waste separation, the fleet of vehicles should have multiple parts to avoid mixing waste together. In developing countries, the process of separating the wastes is not carried out comprehensively, so this subject is almost new and research about it can improve their waste collection process. Due to the complexity of this model, two algorithms are developed to solve it: an adaptive large neighborhood search algorithm (ALNS) and the hybrid ALNS with whale optimization algorithm. Results showed that hybrid ALNS with whale optimization algorithm got higher quality solutions in comparison to ALNS. A real case study in one of the districts of Tehran municipality has been considered and the results obtained show that the use of multi-compartment vehicles is more cost-effective than the use of single-compartment vehicles, reducing the total distance traveled.

关键词： Arc routing problem multi-compartment vehicle adaptive large neighborhood search whale optimization algorithm separation of waste municipal solid waste

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UNIQUE DRAGONFLY optimization algorithm FOR HARVESTING AND CLUSTERING THE KEY FEATURES 18

UNIQUE DRAGONFLY OPTIMIZATION ALGORITHM FOR HARVESTING AND C...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Devarakonda, Nagaraju Anandarao, Sarvani Kamarajugadda, Raviteja Wang, Yingxu LBR Coll Engn Mylavaram Andhra Pradesh India Univ Calgary Calgary AB Canada

ISBN: (纸本)9781728114194

In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered "Sequential Forward Selection" for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis optimization algorithm (TOA).We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.

关键词： Dragonfly algorithm sequential forward selection convergence Ilinction benchmark functions Tornadogenesis optimization algorithm whale optimization algorithm

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A WOA-SVDD based Axial Compressor Instability Detection

A WOA-SVDD based Axial Compressor Instability Detection

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第32届中国过程控制会议(CPCC2021)

作者： Xinyuan Dong Peng Lin Machine Learning and I-health International Cooperation Base of Zhejiang Province and with the Artificial Intelligence InstituteHangzhou Dianzi University

As one of the most critical module in aircraft engine,axial compressor instability limits the whole performance mostly,so the detection and control of it is particularly *** that the compressor instability is abnormal,an established one-class classifier called support vector data description(SVDD) is chosen for anomaly detection due to few observation data of compressor *** whale optimization(WOA) algorithm is taken to optimize the parameters of SVDD,and a WOASVDD model is proposed for the detection of axial compressor *** validity of the proposed WOA-SVDD model is proved by simulation on the data from a low speed axial compressor.

关键词： anomaly detection Support vector data description whale optimization algorithm Compressor instability

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