Ahmed Ali Ajmi^1,2, Noor Shakir Mahmood^1,2, Khairur Rijal Jamaludin^1,*, Hayati Habibah Abdul Talib¹, Shamsul Sarip¹, Hazilah Mad Kaidi¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5783-5801, 2022, DOI:10.32604/cmc.2022.021885

Abstract Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power plants. Poor performance problem in maintaining power plants is the result of both human errors, human factors and the poor implementation of automation in energy management. This problem can potentially be solved using artificial intelligence (AI) and an integrated management system (IMS). This article investigates the current challenges to improving personnel and energy management performance in power plants, identifies the critical success factors (CSFs) for an integrated intelligent framework, and develops an intelligent framework that enables power plants to improve performance. The… More >

S. Gopalakrishnan^1,*, M. Senthil Kumaran²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1435-1449, 2022, DOI:10.32604/iasc.2022.020660

Abstract In the automotive industry, multiple predictive maintenance units run behind the scenes in every production process to support significant product development, particularly among Accessories Manufacturers (AMs). As a result, they wish to maintain a positive relationship with vehicle manufacturers by providing 100 percent quality assurances for accessories. This is only achievable if they implement an effective anticipatory strategy that prioritizes quality control before and after product development. To do this, many sensors devices are interconnected in the production area to collect operational data (humanity, viscosity, and force) continuously received from machines and sent to backend computers for control operations and… More >

Jui-Chung Kao¹, Chia-Nan Wang^2,*, Viet Tinh Nguyen³ and Syed Tam Husain³

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1451-1466, 2022, DOI:10.32604/iasc.2022.021778

Abstract According to a new study by the International Labor Organization (ILO), the COVID-19 pandemic has had a strong impact on the garment industry in the Asia-Pacific region. A sharp drop in retail sales in key export markets has affected workers and businesses across supply chains. To ensure the effectiveness and efficiency of garment supply chain, choosing a sustainable supplier should be a main concern of all businesses. The supplier selection problem in garment industry involves multiple quantitative and qualitative criteria. There have been many research and literatures about the development and application of Multicriteria Decision Making (MCDM) models in solving… More >

Chia-Nan Wang¹, Chao-Fen Pan^1,*, Viet Tinh Nguyen², Syed Tam Husain²

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3005-3019, 2022, DOI:10.32604/cmc.2022.020206

Abstract As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as… More >

P. Thamizhazhagan^1,*, M. Sujatha², S. Umadevi³, K. Priyadarshini⁴, Velmurugan Subbiah Parvathy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3333-3347, 2022, DOI:10.32604/cmc.2022.020197

Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More >

Vasyl Teslyuk^1,*, Ivan Tsmots¹, Michal Gregus ml.², Taras Teslyuk³, Iryna Kazymyra¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3169-3187, 2022, DOI:10.32604/cmc.2022.019869

Abstract To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption. One of the possible ways is proposed in this research. A multi-level hierarchical system for energy efficiency management of the enterprise is designed, it is based on the modular principle providing rapid modernization. The novelty of the work is the development of new and improvement of the existing methods and models, in particular: 1) models for dynamic analysis of IT tools for data acquisition and processing (DAAP) in multilevel energy management systems, which are based on Petri nets; 2) method of synthesis of… More >

M. Iyapparaja¹, M. Sathish Kumar¹, S. Siva Rama Krishnan¹, Chiranji Lal Chowdhary¹, Byungun Yoon², Saurabh Singh², Gi Hwan Cho^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3163-3178, 2021, DOI:10.32604/cmc.2021.017302

Abstract Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps… More >

Jagannath Paramguru¹, Subrat Kumar Barik¹, Ajit Kumar Barisal², Gaurav Dhiman³, Rutvij H. Jhaveri⁴, Mohammed Alkahtani^5,6, Mustufa Haider Abidi^5,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2863-2882, 2021, DOI:10.32604/cmc.2021.016002

Abstract Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue. Various non-linearity are added to make the fossil fuel-based power systems more practical. In order to achieve an accurate economical schedule, valve point loading effect, ramp rate constraints, and prohibited operating zones are being considered for realistic scenarios. In this paper, an improved, and modified version of conventional particle swarm optimization (PSO), called Oscillatory PSO (OPSO), is devised to provide a cheaper schedule with optimum cost. The conventional PSO is improved by deriving a mechanism… More >

Liping Yan^1,*, Wei-Chiang Hong²

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1069-1102, 2021, DOI:10.32604/cmes.2021.016499

Abstract The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road. In order to obtain the scientific and real-time forecasting result, this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM. Firstly, the factors influencing investment risk of wind energy along the Belt and Road are identified from three dimensions: endogenous risk, exogenous risk and process risk. Through the fuzzy threshold method, the final input index system is selected. Secondly, the risk evaluation method… More >

Prakhar Sahu^1,*, S. K. Singh¹, Arun Kumar Singh²

Journal of Cyber Security, Vol.3, No.2, pp. 107-115, 2021, DOI:10.32604/jcs.2021.020831

Abstract Industry 4.0 is one of the hot topic of today’s world where everything in the industry will be data driven and technological advancements will take place accordingly. In the fourth phase of industrial revolution, manufacturers are dependent upon data produced by the consumers to invent, innovate or change anything for the product. Internet of things devices like OBD, RFID, IIoT, Smart devices are the major source of data generation and represents trends in the industry. Since the IoT device are vulnerable to hackers due to its limitation, consumer data security should be tighten up and enhanced. This paper gives an… More >