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Intelligent techniques and optimization algorithms in textile colour management: a systematic review of applications and prediction accuracy

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FASHION AND TEXTILES 2024年第1期11卷 13页

作者： Liu, Senbiao Liu, Yaohui Keane Lo, Kwan-yu Chris Kan, Chi-wai Hong Kong Polytech Univ Sch Fash & Text Hung Hom Kowloon Hong Kong Peoples R China Technol & Higher Educ Inst Hong Kong Dept Construct Technol & Engn 20A Tsing Yi Rd Hong Kong Peoples R China Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hung HomKowloon Hong Kong Peoples R China

Based on a selection of 101 articles published from 2013 to 2022, this study systematically reviews the application of intelligent techniques and optimization algorithms in textile colour management. Specifically, the study explores how these techniques have been applied to four subfields within textile colour management: colour matching and prediction, colour difference detection and assessment, colour recognition and segmentation, and dye solution concentration and decolourization. Following an introduction to intelligent techniques and optimization algorithms in textile colour management, the study describes the specific applications of these techniques in the field over the past decade. Descriptive statistics are used to analyse trends in the use of these techniques and optimization algorithms, and comparative performances indicate the effectiveness of the techniques and algorithms. The study finds that the primary intelligent techniques used in the field of textile colour management include artificial neural networks (ANN), support vector machines (SVM) such as SVM, LSSVM, LSSVR, SLSSVR, FWSVR, fuzzy logic (FL) and adaptive neuro-fuzzy inference systems (ANFIS), clustering algorithms (e.g., K-means, FCM, X-means algorithms), and extreme learning machines (ELM) such as ELM, OSLEM, KELM, RELM. The main optimization algorithms used include response surface methodology (RSM), genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). Finally, the study proposes a comparison of the performance of intelligent techniques and optimization algorithms, summarizes the relevant research trends, and suggests future research opportunities and directions, besides stating the limitations of this paper.

关键词： Intelligent techniques optimisation algorithms Textile colour management Performance comparison reference Review

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A novel optimisation model in the collaborative supply chain with production time capacity consideration

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年第3期19卷 647-658页

作者： Purnomo, Muhammad Ridwan Andi Anugerah, Adhe Rizky Aulia, Salvia Fatma 'Azzam, Abdullah Univ Islam Indonesia Dept Ind Engn Yogyakarta Indonesia Univ Putra Malaysia Inst Trop Forestry & Forest Prod INTROP Serdang Malaysia

Purpose This study aims to propose an optimal procurement model of the collaborative supply chain in the furniture industry. The final output is the total cost minimisation to produce a furniture product that covers material cost, processing cost, transportation cost and holding cost. Therefore, if companies can give the best value to customers at a low cost, then competitive advantages can be achieved. Design/methodology/approach A genetic algorithm (GA) as a metaheuristic approach was used to solve problems in this research. The optimisation was initiated by developing a mathematical model to formulate the objective function. Findings Based on the case study, the proposed GA model was able to reduce the total cost of production. The cost was reduced by 73.09% compared to the existing system. Besides, the production time of the proposed model is within the capacity of both companies;hence, no penalty cost is imposed. Practical implications The proposed GA model has been implemented and tested to minimise production costs in the Indonesian furniture industry. Originality/value To the best of author knowledge, there is no research has proposed an optimisation model that incorporates production cost, transportation cost and production time capacity together in the collaborative supply chain. This research is the first to collaborate these factors using GA in the furniture industry.

关键词： Genetic algorithms optimisation algorithms Procurement management Quality supply chain management Collaborative supply chain

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Wireless sensor nodes localiser based on sine-cosine algorithm

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IET WIRELESS SENSOR SYSTEMS 2020年第4期10卷 145-153页

作者： Hamouda, Eslam Abohamama, Abdelaziz Said Jouf Univ Dept Comp Sci Sakaka 2014 Saudi Arabia Mansoura Univ Dept Comp Sci Mansoura 35516 Egypt

Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine-cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.

关键词： sensor placement optimisation wireless sensor networks wireless sensor networks node localisation problem multidimensional optimisation problem node localisation scheme flip ambiguity problem localisation accuracy optimisation algorithms sine-cosine optimisation algorithm wireless sensor node localiser

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Global maximum power point tracking of PV arrays under partial shading conditions using a modified particle velocity-based PSO technique

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IET RENEWABLE POWER GENERATION 2018年第5期12卷 555-564页

作者： Sen, Tanuj Pragallapati, Nataraj Agarwal, Vivek Kumar, Rajneesh Birla Inst Technol & Sci Pilani Elect & Elect Engn Dept Pilani Rajasthan India Indian Inst Technol Dept Elect Engn Bombay Maharashtra India

Modern PV arrays are generally designed with bypass diodes to avoid damage. However, such arrays exhibit multiple peaks in their P-V characteristics under partial shading conditions. Owing to the limitation in the abilities of conventional maximum power point tracking algorithms in such cases, the application of other optimisation algorithms has been explored. This study proposes a modified particle velocity-based particle swarm optimisation (MPV-PSO) algorithm for tracking the global power peak of the multiple peak P-V characteristics. The MPV-PSO algorithm is both adaptive and deterministic in nature. It eliminates the inherent randomness in the conventional PSO algorithm by excluding the use of random numbers in the velocity equation. The proposed algorithm also eliminates the need for tuning the weight factor, the cognitive and social acceleration coefficients by introducing adaptive values for them which adjust themselves based on the particle position. These adaptive values also solve problems like oscillations about the global best position during steady-state operation and particles getting trapped in local minima. The effectiveness of the proposed MPV-PSO algorithm is validated through MATLAB/Simulink simulations and hardware experiments.

关键词： maximum power point trackers particle swarm optimisation photovoltaic power systems global maximum power point tracking algorithm partial shading condition modified particle velocity-based particle swarm optimisation technique bypass diode optimisation algorithms multiple peak P-V characteristics MPV-PSO algorithm MATLAB-Simulink simulation PV array cognitive acceleration coefficient social acceleration coefficient

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Recent advances on key technologies for innovative manufacturing

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JOURNAL OF INTELLIGENT MANUFACTURING 2011年第5期22卷 675-691页

作者： Brousseau, Emmanuel Eldukhri, Eldaw Cardiff Univ Mfg Engn Ctr Cardiff S Glam Wales

Through the I*PROMS Network of Excellence which originated during the sixth Framework Programme of the European Commission, this paper introduces a European vision of the essential research areas to deliver future innovations in manufacturing. In particular, these areas are identified as Advanced Production Machines, Production Automation and Control, Innovative Design Technologies and Production Organisation and Management. Then, special attention is given to the main findings from the authors' research programme since the start of I*PROMS in October 2004 for a number of technologies belonging to these four generic research streams.

关键词： Advanced production machines Micro and nano manufacturing Rapid prototyping Production automation and control optimisation algorithms Innovative design technologies Conceptual design Automatic feature recognition Production organisation and management Fit manufacturing

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Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks 4th

Comparative Study of Different Metaheuristics on CEC 2020 Be...

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4th International Conference on Vehicle and Automotive Engineering (VAE)

作者： Alsamia, Shaymaa Albedran, Hazim Jarmai, Karoly Univ Kufa Najaf Iraq Univ Miskolc H-3515 Egyet Varos Miskolc Hungary

ISBN: (纸本)9783031152115;9783031152108

Metaheuristic algorithms have increased in usage in all the scientific fields during the last decades. Since no optimisation algorithm is valid for all optimisation problems, many metaheuristics have been developed for various applications. Accordingly, this paper presents a comparative study on CEC 2020 optimisation problems among different algorithms. The goal is to give an overall sight of selecting a specific metaheuristic algorithm for a particular application. The algorithms in this study are;dynamic differential annealed optimisation, particle swarm optimisation, fertilisation optimisation algorithm, grey wolf optimisation, whale optimisation algorithm, firefly algorithm, artificial bee colony, ant lion optimisation, harris hawks optimisation, and sine cosine optimisation algorithm. The results are discussed in the respective sections with a focus on the convergence behaviour of the algorithms.

关键词： optimisation algorithms Metaheuristics Fertilisation optimisation algorithm Artificial bee colony Dynamic differential annealed optimisation

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Entropy Regularised Deterministic Optimal Control: From Path Integral Solution to Sample-Based Trajectory optimisation

Entropy Regularised Deterministic Optimal Control: From Path...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Lefebvre, Tom Crevecoeur, Guillaume Univ Ghent B-9000 Ghent Belgium Flanders Make EEDT DC Lommel Belgium

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

Sample-based trajectory optimisers are a promising tool for the control of robotics with non-differentiable dynamics and cost functions. Contemporary approaches derive from a restricted subclass of stochastic optimal control where the optimal policy can be expressed in terms of an expectation over stochastic paths. By estimating the expectation with Monte Carlo sampling and reinterpreting the process as exploration noise, a stochastic search algorithm is obtained tailored to (deterministic) trajectory optimisation. For the purpose of future algorithmic development, it is essential to properly understand the underlying theoretical foundations that allow for a principled derivation of such methods. In this paper we make a connection between entropy regularisation in optimisation and deterministic optimal control. We then show that the optimal policy is given by a belief function rather than a deterministic function. The policy belief is governed by a Bayesian-type update where the likelihood can be expressed in terms of a conditional expectation over paths induced by a prior policy. Our theoretical investigation firmly roots sample-based trajectory optimisation in the larger family of control as inference. It allows us to justify a number of heuristics that are common in the literature and motivate a number of new improvements that benefit convergence.

关键词： Optimal control optimisation algorithms

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A Method for the Prediction of Process Parameters for minimal Distortion in Welded Frame Structures using a FE-Simulation

A Method for the Prediction of Process Parameters for minima...

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1st International Conference on Manufacturing of Lightweight Components

作者： Belitzki, A. Schweier, M. Zaeh, M. F. Tech Univ Munich Inst Machine Tools & Ind Management D-85748 Garching Germany

Welded frame structures are often subject to unintended distortions due to the thermal joining process. In order to precisely quantify and reduce the distortion of welded frame structures using Finite Element (FE) simulation, a fast and reliable method is required, especially for industrial applications. This paper presents a methodical, simulation based and time optimised framework for the prediction of appropriate process parameters for minimal component distortion of complex welded frame structures by means of e.g. a variation of the process parameters or the weld seam sequences. To achieve a minimal distortion of the final structure, different optimisation algorithms will be used in combination with a database. (C) 2014 Elsevier B.V.

关键词： welded frame structures FE-simulation component distortion optimisation algorithms

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A hybrid machine learning model for detecting cybersecurity threats in iot applications

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3359-3370页

作者： Usoh, Midighe Asuquo, Philip Ozuomba, Simeon Stephen, Bliss Inyang, Udoinyang University of Uyo Akwa Ibom State Uyo Nigeria

The introduction of the Internet of Things has led to the connectivity of millions of devices with less human interaction. This demand in connectivity has resulted in a surge in network attacks as IoT is susceptible to several cyberattacks. Due to their resource-constrained nature, traditional security mechanisms are inappropriate for securing IoT systems. Hence, the need for pervasive security mechanisms that are robust to mitigate attacks and secure IoT networks. One of the emerging potential solutions to network security is Machine Learning (ML). Recently, ML has been applied to mitigate cybersecurity threats in Cyber-Physical Systems (CPS). This paper presents a hybrid ML model for the efficient and effective detection of anomalies in IoT systems. The proposed model combines Random Forest algorithm, XGB, KNN and two decision tress with equal weights assigned to enhance the detection of anomalies in IoT systems. Experimental results show that the proposed hybrid model achieves a higher misbehaviour detection rate when compared to the other ML models in terms of accuracy, precision, recall and f1-score. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Anomaly detection Feature selection Network attacks Network intrusion optimisation algorithms

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Research on Site Planning and Design of Mobile Communication Network Based on Multi-Objective Planning

Research on Site Planning and Design of Mobile Communication...

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International Conference on AI and Metaverse in Supply Chain Management (AIMSCM)

作者： Wen, Zilu Zhu, Qigang Liu, Jinyu Shandong Univ Sci & Technol Qingdao Shandong Peoples R China

ISBN: (纸本)9798400708251

With the development of 5G, the bandwidth of communication is getting bigger and bigger, but the coverage area of the base station is getting smaller and smaller, making the number of base stations needed to cover the same area become more. In addition, the types of base stations and antennas have become more complex. This makes the problem of base station site selection more and more complex. In the actual network planning, considering the construction cost of the base station and some other factors, it is then necessary to try to prioritise the weak coverage areas with high service volume. In order to facilitate the calculation, the given area is divided by a very small grid, and only the centre point of each grid is considered, i.e. any given area can be divided into a finite number of points. And the new station can not be smaller than the given threshold value between new stations and there is a certain limit to the area covered by the new station. In reality, the coverage area of each station is not considered circular coverage, but there are three sectors on each base station that can be adjusted in the direction, which changes the geometric relationship to judge whether a weak coverage point is covered or not. Therefore, this paper first derives the polar coordinate equation of the coverage range about the rotation angle, then takes the maximum coverage rate of the total service volume, the minimum total cost of construction and the minimum duplicate coverage as the optimisation objective, determines whether the weak coverage points are covered or not according to the polar coordinate equation of the coverage range and other constraints as the constraints, and establishes a multi-objective planning model under the premise of satisfying the priority of the weak coverage points with high service volume to be covered to solve the problem of The coverage rate of business volume is 91.76% and the optimal rotation angle is 59.8 degrees.

关键词： Multi-objective planning Site planning Coverage optimisation algorithms Mobile communication networks

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