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Inspection of textile fabrics based on the optimal Gabor filter

SIGNAL IMAGE AND VIDEO PROCESSING 2021年第7期15卷 1617-1625页

作者： Boluki, Mina Mohanna, Farahnaz Univ Sistan & Baluchestan Dept Commun Engn Zahedan Iran

Defect detection and quality control play an important role in the textile industry. In this paper, an automatic algorithm based on the optimal Gabor filter is proposed for real-time inspection of textile fabrics. The cuckoo optimization algorithm is adopted to optimize the parameters of the Gabor filter. Also, an adaptive local binarization method is proposed, which enhances the performance of our algorithm. In order to locate the defects, the filtered image is divided into non-overlapping blocks. Then, the candidate defective blocks are binarized using adaptive thresholds, which are determined by blocks statistics. The performance of the proposed algorithm is evaluated through different types of fabrics in the TILDA database and an online Fabric Stain Dataset. The experimental results demonstrate the efficiency of the proposed method in detecting defects on the plain, regular and irregular patterned fabrics. Furthermore, the comparative results are provided to show the robustness of the proposed method.

关键词： Textile quality control Fabric defect detection Optimal Gabor filter cuckoo optimization algorithm Segmentation

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Improved regularized extreme learning machine short-term wind speed prediction based on gray correlation analysis

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WIND ENGINEERING 2021年第3期45卷 667-679页

作者： Wang, Yue Li, Yonggang Wu, Binyuan North China Elect Power Univ State Key Lab New Energy Power Syst Baoding Peoples R China

Accurate wind speed prediction is of great significance to the stable operation of the power grid when large-scale wind power is connected to the grid. This article proposes a new multi-step wind speed combination prediction model based on gray correlation analysis. First, the gray correlation analysis is performed on wind speed-related attributes, the more relevant attribute factors are selected as the input set of the prediction model, and the regularized extreme learning machine improved by the cuckoo optimization algorithm is used to perform multi-step prediction of wind speed. Then, an error self-tuning model is established to further improve the prediction accuracy. Finally, the measured results of different wind farms and seasons are selected to simulate the prediction effect of the proposed model, and the prediction accuracy and generalization ability of the proposed model are verified through comparative analysis.

关键词： Wind speed prediction gray correlation analysis regularized extreme learning machine cuckoo optimization algorithm error self-tuning mode

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A fuzzy mathematical model for hybrid inventory and purchase optimization in a reverse logistics system considering shortage and warehouse capacity

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SCIENCE PROGRESS 2023年第4期106卷 368504231201797页

作者： Tang, Hongyu Thelkar, Amruth Ramesh Guangxi City Vocat Univ Coll Management Liuzhou Guangxi Peoples R China Jimma Univ Coll Engn & Technol Dept Elect & Comp Engn Jimma Ethiopia

Making decisions about the design and implementation of a logistics network is crucial as it has long-term impacts. However, it is important to consider that demand factors and the number of returned items by customers may change over time. Therefore, it is necessary to design a logistics network that can adapt to various demand fluctuations. The main goal of this study is to calculate the quantity of products that should be sent at different times in a supply chain network to minimize the overall cost of reverse logistics and tardiness time. Accordingly, a multi-objective mathematical model is proposed that aims to optimize the total cost and the amount of delay in sending customer orders in a three-level logistics network, assuming that some parameters are uncertain. Additionally, the minimization of waiting time, considering the level of delay in sending, is applied as the second objective function. To handle the uncertainty in the reverse logistics network, a fuzzy approach is implemented, and the proposed model is solved using GAMS software. Furthermore, to solve the mathematical model in large dimensions, the cuckoo optimization algorithm (COA) is applied in MATLAB software, and the results are compared to the global optimal solution. The outcomes show that the proposed algorithm has a desirable performance, as the total values sent to the manufacturer are equal to those obtained from the exact solution, and the objective function value decreases as the number of repetitions increases.

关键词： Reverse logistics supply chain tardiness multi-objective optimization fuzzy theory cuckoo optimization algorithm

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Optimal operation of multiple energy system considering load management approach and uncertainty

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Pallathadka, Harikumar Abbood, Manal A. Tabook, Musallam Ahmed Salim Sharma, M.K. Alzubaidi, Laith H. Zearah, Sajad Ali Omran, Alaa A. Manipur International University Manipur Imphal India University of Technology Baghdad Iraq Mathematics and Sciences Department College of Arts and Applied Science Dhofar University Salalah Oman Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut India College of Technical Engineering The Islamic University Najaf Iraq College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq College of Technical Engineering The Islamic University of Babylon Babylon Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq Department of Engineering AL-Nisour University College Baghdad Iraq

In recent years, the advancement of technology has led to the development of energy networks' infrastructure, resulting in the transformation of conventional networks into smart networks. By incorporating communication equipment, each component of the network can now exchange information with other components, creating an environment where innovative ideas can be implemented. One such idea is the utilization of multi-carrier energy systems, also known as Energy Hubs. Integrating energy hubs into traditional networks used to be a challenging task. However, with the expansion and enhancement of smart networks, the potential for incorporating and locating energy hubs within the power grid has significantly increased. In these systems, the focus shifts from optimizing a single energy carrier to ensuring the optimal operation of a system that encompasses multiple energy carriers, such as natural gas, electric grid, and thermal energy. Energy hubs act as intermediaries between energy supply companies and various consumers, including residential, commercial, and industrial sectors. These energy suppliers can encompass different energy networks, such as electricity, gas, and local heating. The integration of residential and commercial energy hubs has become a crucial aspect of modern energy systems, particularly in the context of load response applications. This paper proposes a novel approach for effectively utilizing residential and commercial energy hubs, taking into account the time of use (ToU) load response program. In the proposed system, the inherent uncertainties of the system are addressed using the two-point estimation method of the model. To achieve the desired objective, the cuckoo optimization algorithm, a well-established meta-heuristic algorithm for solving optimization problems, is employed. This algorithm aids in determining the optimal scheduling of energy resources while considering ToU tariffs and load management plan signals. The results demonstrate th

关键词： cuckoo optimization algorithm Energy system operation Load management program Residential and commercial energy hub integration

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An Autonomous Evolutionary Approach to Planning the IoT Services Placement in the Cloud-Fog-IoT Ecosystem

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JOURNAL OF GRID COMPUTING 2022年第3期20卷 32-32页

作者： Hong, Xiaobin Zhang, Jiali Shao, Yerong Alizadeh, Yeganeh Guangzhou Coll Technol & Business Guangzhou 510800 Guangdong Peoples R China Islamic Azad Univ Dept Comp Engn Robat Karim Branch Robat Karim Iran

The growth of network-based computing devices and the development of massive services are both results of the Internet of Things (IoT) by deploying Internet-connected devices at the edge. These devices are limited in terms of storage and computing, where the resources they need are provided by the cloud computing paradigm. Unfortunately, the two-tier cloud-IoT architecture is not efficient enough to provide the resources needed for latency-sensitive applications. Consequently, Fog Computing Paradigm (FCP) has been proposed to complement cloud computing and support such IoT-generated applications at the network edge. Heterogeneity, geographical distribution, and large-scale of fog nodes need the development of new methodologies for deploying and running IoT applications on fog nodes. A collection of IoT services, which have varying Quality of Service (QoS) needs, make up applications for the IoT, so finding an autonomous IoT Fog Service Placement (IoT-FSP) scheme in such an infrastructure can be challenging. The current study presents a distributed conceptual computing framework to address this problem. It is based on an autonomous approach, which improves resource management in three-tier cloud-fog-IoT architecture. Besides, we use the cuckoo optimization algorithm (COA) as a meta-heuristic approach to efficiently solve the FSP. We refer to the proposed strategy as the FSP-COA. The FSP-COA formulates the problem as a multi-objective problem to reconcile various objectives, including SLA violations, service latency, response time, fog utilization, service cost, and energy consumption. Finally, the performance of FSP-COA is evaluated compared to state-of-the-art algorithms using the iFogSim simulator. According to experiments, FSP-COA is more efficient than other algorithms regarding various metrics, including latency, energy and cost, and on average, outperforms the better of existing algorithms ranging from 4% to 16%.

关键词： Fog computing IoT services Fog service placement cuckoo optimization algorithm Autonomous approach

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Analysis of optimized response time in a new disaster management model by applying metaheuristic and exact methods

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Smart and Resilient Transportation 2022年第1期4卷 22-42页

作者： Armin Mahmoodi Milad Jasemi Zergani Leila Hashemi Richard Millar Department of Industrial Engineering Islamic Azad University Masjed Soleyman Iran Department of Computer Informatics Stephens College of Business University of Montevallo Montevallo Alabama USA Department of Industrial Management Islamic Azad University Tehran Iran Department of Engineering Management and Systems Engineering George Washington University Washington DC District of Columbia USA

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

关键词： Humanitarian logistics Drones delivery Operation research in disaster relief Additive manufacturing cuckoo optimization algorithm Genetic algorithm

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Physical model test validation of dynamic similarity truncation method for an internal turret

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OCEAN ENGINEERING 2022年 245卷 110328-110328页

作者： Wang, Hongwei Zhang, Zizhao Ma, Gang Kang, Zhuang Hu, Kaiye Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Yantai Res Inst Yantai 265501 Shandong Peoples R China Harbin Engn Univ Grad Sch Yantai 265501 Shandong Peoples R China

The mooring system needs to be truncated in physical model test to adapt to the limited dimensions of offshore basins. Especially, the drag force and inertial force are required to be included in the dynamic mooring truncation methods, and the method should be verified by model test comparison. Here an effective dynamic truncation method is proposed based on slender rod theory and multi-objective cuckoo search optimization algorithm. The line tensions in time domain are transferred into frequency domain to implement the dynamic similarity. The proposed process can promote optimization efficiency in a great extent. In order to demonstrate the accuracy of the proposed method, a horizontal truncation is carried out to an internal turret moored FPSO working in 420 m water depth. The model test in deep-water basin is performed with static tests and irregular wave tests. It can be found that model test and numerical results show consistence on the static stiffness characteristics and dynamic response. The comparison verifies the reliability and accuracy of the proposed truncation method well.

关键词： FPSO Mooring truncation Dynamic similarity Slender rod cuckoo optimization algorithm Model test

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Optimizing laser triangulation displacement sensor of 3D positioning and posture using COA Based BPNN

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SENSOR REVIEW 2020年第1期40卷 112-120页

作者： Selami, Yassine Lv, Na Tao, Wei Yang, Hongwei Zhao, Hui Shanghai Jiao Tong Univ Dept Instrument Sci & Technol Minhang Campus Shanghai Peoples R China

Purpose The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. The 3D positioning and posture sensor allows access to the third dimension through depth measurement;the performance of the sensor varies according to the level of nonlinearities presented in the system, which leads to inaccuracies in measurement. Design/methodology/approach While applying optimization approach, the mathematical model and the relationship between the key parameters in the laser triangulation ranging and the indexes of the measuring system were analyzed. Findings Based on the performance of the parametric optimization method, the measurement repeatability reached 0.5 mu m with an STD value within 0.17 mu m, an expanded uncertainty of measurement was within 5 mu m, the angle error variation of the object's rotational plane was within 0.031 degrees and nonlinearity was recorded within 0.006 per cent in a full scale. The proposed approach reduced the effect of the nonlinearity presented in the sensor. Thus, the accuracy and speed of the sensor were greatly increased. The specifications of the optimized sensor meet the requirements for high-accuracy devices and allow wide range of industrial application. Originality/value In this paper, COA-based BPNN is proposed for laser triangulation displacement sensor optimization, on the basis of the mathematical model, clarifying the working space and working angle on the measurement system.

关键词： Neural network Back propagation cuckoo optimization algorithm Laser triangulation displacement sensor

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Multi-objective availability and cost optimization by PSO and COA for series-parallel systems with subsystems failure dependencies

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MICROPROCESSORS AND MICROSYSTEMS 2022年 89卷 104422-104422页

作者： Arezki Mellal, Mohamed Zio, Enrico MHamed Bougara Univ Fac Technol LMSS Boumerdes Algeria PSL Res Univ CRC Mines ParisTech Sophia Antipolis France Politecn Milan Milan Italy

System availability and cost are two of the elements of system dependability. Most systems involve subsystems with failure dependencies. The failure dependencies complicate the optimization of those elements. In this paper, the multi-objective optimization problem of availability and cost is addressed for series-parallel systems with subsystems failure dependencies in case of strong dependency. The problem is solved by applying the particle swarm optimization (PSO) algorithm and the cuckoo optimization algorithm (COA). The multi-objective opti-mization problem is converted to a single one using the weighted-sum method. The results of a system consisting of six subsystems are analyzed with a comparison of the methods. The best value of the system availability and cost, number of function evaluations, CPU time, and standard deviation reveal that the COA has outperformed the PSO.

关键词： system availability system cost series -parallel system failure dependencies weighted-sum method particle swarm optimization cuckoo optimization algorithm

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Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm

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RESULTS IN CONTROL AND optimization 2023年 12卷

作者： Basil, Noorulden Marhoon, Hamzah M. Mustansiriyah Univ Coll Engn Depart Elect Engn Baghdad Iraq Al Nahrain Univ Coll Informat Engn Depart Syst Engn Jadriya Baghdad Iraq

The study of unmanned systems has garnered significant academic attention worldwide. The advancements in tracking technologies, particularly the unmanned aerial vehicle (UAV) developed by the US Air Force, have motivated scientists to extensively explore this field due to the abundance of available aerial resources. However, the UAV's effectiveness in motion is influenced by its three fundamental motions: pitch, roll, and yaw. To address this, scientists have been developing optimization algorithms and models to enhance the UAV's performance. Nonetheless, in practical scenarios, each motion requires specific measurements, and their relative importance varies. This presents several challenges, including the existence of multiple criteria for selecting optimization values, determining the criteria's significance, and evaluating trade-offs between performance across different optimization cases. Consequently, the selection and evaluation of UAV motion control algorithms becomes complex. This study proposes a novel selection process that employs the cuckoo optimization algorithm (COA) and real measured resizable margins for goal detection instead of traditional fixed-size evaluation criteria. The framework consists of enhancing PID motion gains using the COA algorithm, focusing on altitude and attitude functional PID controllers. The results of the selection process demonstrate the significance of various criteria for different motions. Rigorous evaluation and analysis were conducted to validate the proposed research framework.

关键词： Unmanned Aerial Vehicle PID Criteria cuckoo optimization algorithm Observation and Tracking Head Altitude and Attitude Controller

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