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An Efficient Mammogram Image Retrieval System Using an Optimized Classifier

NEURAL PROCESSING LETTERS 2021年第4期53卷 2467-2484页

作者： Rayen, Sonia Jenifer Subhashini, R. Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India

The computerized examination of mammograms in the breast cancer prevention is gaining much importance. The paper which is introduced proposes an adequate mammogram image retrieval methodology utilizing the optimized classifier. At first, the info mammogram image is brought as of the database and it is then pre-handled by utilizing the Modified Weiner. This filtered image undergoes the pectoral removal. This is followed with the method of Feature extraction. The extorted features are then categorized to 3 classes namely benign, malignant and normal by utilizing an optimized classifier. The 'Modified Adaptive Neuro-Fuzzy Inference System' is optimized using the ABC algorithm ('artificial bee colony'). Score values of such classified images are determined utilizing the 'Principal Component Analysis'. Then repeat a similar process for query images and finally, the minimal distance is evaluated betwixt these 2 images This is finished using the Euclidian distance and in this way the image having less distance on diverged from the question is recovered. The proposed mammogram image retrieval methodology is implemented on the stage called MATLAB and it is evaluated by utilizing disparate database images.

关键词： Modified Weiner Filter Mammogram Euclidean distance Principal component analysis MANFIS artificial bee colony algorithm

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Optimization of Hybrid Energy System Configuration for Marine Diesel Engine

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2021年第5期E104A卷 786-796页

作者： Zeng, Guangmiao Wang, Rongjie Han, Ran Jimei Univ Sch Marine Engn Xiamen 361021 Peoples R China Fujian Prov Key Lab Naval Architecture & Ocean En Xiamen 361021 Peoples R China

Because solar energy is intermittent and a ship's power-system load fluctuates and changes abruptly, in this work, the solar radiation parameters were adjusted according to the latitude and longitude of the ship and the change of the sea environment. An objective function was constructed that accounted for the cost and service life simultaneously to optimize the configuration of the marine diesel engine hybrid energy system. Finally, the improved artificial bee colony algorithm was used to optimize and obtain the optimal system configuration. The feasibility of the method was verified by ship navigation tests. This method exhibited better configuration performance optimization than the traditional methods.

关键词： artificial bee colony algorithm hybrid energy system diesel engine optimal configuration photovoltaic power generation

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Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

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IEEE ACCESS 2021年 9卷 51818-51831页

作者： Shi, Jinmei Leau, Yu-Beng Li, Kun Chen, Huandong Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu 88400 Sabah Malaysia Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou 571126 Hainan Peoples R China Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou 571158 Hainan Peoples R China

Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable artificial bee colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.

关键词： Predictive models Prediction algorithms Telecommunication traffic Licenses Computational modeling Optimization Training Network traffic time series analysis variational mode decomposition extreme learning machine artificial bee colony algorithm

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Load Forecasting Based on Weighted Grey Relational Degree and Improved ABC-SVM

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第4期16卷 2191-2200页

作者： Ruxue, Luo Shumin, Liu Miaona, You Jican, Lin Guangdong Ocean Univ Cunjin Coll Zhanjiang 524000 Peoples R China Guilin Univ Technol Dept Mech & Control Engn Guilin 541000 Peoples R China

The present study proposes a short-term load forecasting method based on weighted grey relational degree and improved support vector machines with the artificial bee colony algorithm (ABC-SVM). The entropy weight method was employed to obtain the weight of load-related physical information, and the historical and forecast load data selected based on the weighted grey relational degree were input into the support vector machine (SVM) to build a forecasting model. Meanwhile, the SVM parameters were optimized by the improved artificial bee colony algorithm before the model was used to perform load forecasting. The experimental results show that the proposed method could effectively improve the accuracy of the forecasting model and simplify the calculation, thus having research and practical value.

关键词： Load forecasting Entropy weight method Grey relational degree Support vector machine artificial bee colony algorithm

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A data-driven corrosion prediction model to support digitization of subsea operations

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 153卷 413-421页

作者： Li, Xinhong Zhang, Luyao Khan, Faisal Han, Ziyue Xian Univ Architecture & Technol Sch Resources Engn 13 Yanta Rd Xian 710055 Peoples R China Mem Univ Ctr Risk Integr & Safety Engn C RISE St John NF A1B 3X5 Canada

Corrosion is an important factor leading to the failure of subsea process operations especially subsea crude oil pipelines. Developing a data-driven corrosion prediction model is urgently required by the digitization of subsea process system in the industry 4.0 environment, which is critical to improve the intelligent level of risk management of subsea process system. This paper proposed a new data-driven model based on hybrid techniques to model corrosion degradation of subsea operations. The model is built integrating three data-driven methods: principal component analysis (PCA), artificial bee colony algorithm (ABC) and support vector regression (SVR). The developed model is tested on the corrosion rate prediction of subsea crude oil pipelines. This model can realize effective prediction of corrosion rate. In the proposed hybrid model, PCA is used to reduce the dimension of corrosion influencing factors. The obtained principal components are selected as the input variables of the model. The ABC algorithm is adopted to optimize the hyper-parameters of the SVR. The model is trained using fraction of the historical data;subsequently, the model performance is tested on the remaining set of the data. A case study demonstrates the feasibility and effectiveness of the proposed model. The model is compared with the four different models SVR, PCA-SVR, PCA-GA-SVR, PCA-PSO-SVR. The PCA-ABC-SVR model performed superior in terms of prediction accuracy and robustness of results (MAE = 7.10 %;RMSE = 9.19 %;R-2 = 0.976). The proposed model will serve as a useful online tool to support safety and digitization of process system. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Corrosion rate prediction Subsea crude oil pipelines Principal component analysis artificial bee colony algorithm Support vector regression

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Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第4期43卷 343-350页

作者： Xiao, Yonghao Cao, Yunfei Yu, Weiyu Tian, Jing South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Foshan Univ Foshan 528000 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Jiangsu Peoples R China

Image threshold segmentation based on artificial bee colony algorithm (ABCA) and maximum entropy is presented in this paper. The entropy function is simplified with several parameters. The ABC is applied to search the maximum value of entropy function. According to the maximum function value, the optimal image thresholds are obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

关键词： image threshold maximum entropy artificial bee colony algorithm ABCA image segmentations

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REBOR: A new sketch-based 3d object retrieval framework using retina inspired features

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第15期80卷 23297-23311页

作者： Shi, Xin Chen, Huijuan Zhao, Xueqing Xian Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Xian Polytech Univ Sch Comp Sci Shaanxi Key Lab Clothing Intelligence Xian 710048 Peoples R China Xian Polytech Univ Natl & Local Joint Engn Res Ctr Adv Networking & Xian 710048 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Natl Engn Lab Video Technol Beijing 100871 Peoples R China

With the rapid development of data science and modeling engineering, the capacity of cyberspace has significantly expanded which enables the online storage of increasing number of 3D models. Hence, the development of effective and efficient approaches to search 3D models is becoming increasingly important and urgent. In this paper, we propose a new sketch-based 3D retrieval framework named REBOR under the inspiration of retina which is not only consistent with human perception sensitivity but also simplifies the requirement of retrieval query by enabling hand-drawn sketch. The feature extraction process incorporates human visual system by simulating the ganglion perceptive mechanism in retina. Support Vector Machine is used to classify the query sketches and is further optimized by means of an global optimization algorithm so as to acquire optimal results automatically. Experiments are done on the database generated by ourselves with 15 categories of 3D objects, and the results indicate the effectiveness of REBOR in terms of retrieval accuracy.

关键词： sketch-based object retrieval Human visual system Retina based feature extraction SVM artificial bee colony algorithm

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The dynamic bike repositioning problem with battery electric vehicles and multiple charging technologies

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 131卷 103327-103327页

作者： Wang, Yue Szeto, W. Y. Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat Shenzhen Peoples R China Guangdong Hong Kong Macau Joint Lab Smart Cities Hong Kong Peoples R China

The bike-repositioning problem (BRP) primarily involves determining the routes and loading instructions for a fleet of vehicles that transport bikes between stations in a bike sharing system (BSS), to mitigate the mismatch between the demand for and supply of public bikes. However, the use of fossil-fueled vehicles for this repositioning task generates pollutants and greenhouse gases, which harm the environment. The use of battery electric vehicles (BEVs) instead of fossil-fueled vehicles for repositioning bikes can mitigate this negative environmental impact. On the other hand, user demand for bikes is dynamic during the daytime. Therefore, this study addresses the dynamic BRP with battery electric vehicles. Multiple charging technologies are available at charging stations to allow repositioning vehicles to recharge en-route at different speeds and costs. The objective is to solve the problem by minimizing the weighted sum of the penalty costs of unmet user demand and the charging costs of repositioning vehicles. A rolling horizon framework is adopted to incorporate the revealed inventory levels at bike stations, the BEV load of bikes, and the battery energy levels of BEVs at regular intervals. An artificial bee colony algorithm with an embedded dynamic programming method for computing the loading instructions is proposed to generate solutions. Computational experiments are conducted on a real-world BSS, and three aspects of the problem properties are analyzed: the horizon settings, the various penalties for failed rentals and returns, and the charging-related settings. The results provide practical insights into the use of BEVs for daily bike-repositioning tasks in BSSs.

关键词： Dynamic bike-repositioning problem Rolling horizon method Demand forecasting Battery electric vehicles artificial bee colony algorithm

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Current sharing optimization of multilayer high temperature superconducting cable

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PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS 2023年第1期611卷

作者： Wang, Lina Liu, Jun Sun, Dong Li, Wei Xu, Hongguang Working Stn Postdoctoral Sci Res Shengli Oil Adm Dongying 257000 Peoples R China SINOPEC Tech Inspection Ctr Shengli Oilfield Co Dongying 257000 Peoples R China Shengli Oil Adm Dongying 257000 Peoples R China Petr Engn Technol Res Inst Shengli Oilfield Co SINOPEC Dongying 257000 Peoples R China

Large-capacity high-temperature superconducting (HTS) cables generally adopt a multilayer structure to increase the current-carrying capacity. However, the multilayer structure of the HTS cables will increase the uneven current distribution, decreasing the critical current and heighten the AC loss. Therefore, the current sharing technology between each layer of the HTS cable is essential to improve transmission performance. This paper proposes two hybrid HTS current sharing optimization algorithms based on particle swarm optimization and artificial bee colonies. The current distribution of the HTS cable in each layer under pre-and post-optimization is estimated and the structure parameters are obtained. A 3D model is established to evaluate the characteristics of the optimized HTS cable. The results show that the proposed hybrid current sharing optimization schemes can well improve the inner current sharing of the HTS cable, and the feasibility and effectiveness are therefore demonstrated.

关键词： High -temperature superconductor HTS tape Particle swarm algorithm artificial bee colony algorithm Hybrid algorithm HTS current sharing

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A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期236卷

作者： Hsieh, Tsung-Jung Natl Formosa Univ Ctr Gen Educ Yunlin Taiwan

Mixed redundancy strategies (MRSs) are based on leveraging a diverse combination of active and cold-standby components to improve system reliability. This study integrates three designed MRSs and employs them to develop a hybrid model to solve redundancy allocation problems (RAPs) and reliability-RAPs (RRAPs). The MRS best suited for a specific subsystem is difficult to determine. To resolve this issue, in the process of system reliability optimization, the state of limited resource utilization (e.g., cost, weight, and volume) in each sub-system facilitates the adoption of MRSs and is used as a learnable factor. To realize this learning process, Q-learning is used in this study to build a knowledge library (i.e., a Q-table) of MRS usage, where the Q-table guides the main optimization technique, the artificial bee colony algorithm (ABC), to expedite the convergence in searching for near-optimal solutions. For convenience, the Q-learning-guided ABC search method is abbreviated as QABCS, and the new MRSs obtained by QABCS are called Q-mixed. The experimental results show that Q-mixed not only improves system reliability but also reveals the preferences of each subsystem for the MRSs.

关键词： Q-learning Mixed redundancy strategy Reliability optimization Markov model artificial bee colony algorithm

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