Published: Dec. 27, 2024
Language: Английский
Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110211 - 110211
Published: Feb. 19, 2025
Language: Английский
Citations
3
Processes, Journal Year: 2024, Volume and Issue: 12(1), P. 221 - 221
Published: Jan. 19, 2024
The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting (LightGBM), has emerged as a valuable tool achieving effective fault warning in settings. Despite its success, practical application LightGBM encounters challenges diverse scenarios, primarily stemming from multitude parameters that are intricate challenging to ascertain, thus constraining computational efficiency accuracy. In response these challenges, we propose novel innovative hybrid algorithm integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), new search strategies. This amalgamation designed optimize hyperparameters more effectively. Subsequently, seamlessly integrate this with formulate sophisticated system. Validation through case studies demonstrates our proposed consistently outperforms advanced methods prediction accuracy generalization ability. real-world water pump application, achieved rate 90%. Compared three algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural (LSTM-CNN), Grey Wolf Optimizer-Light (GWO-LightGBM), Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, 13.16%, respectively. At same time, R-Squared value increased 2.15%, 7.02%, 3.73%, Lastly, method also holds leading position success warning. accomplishment provides robust support timely detection issues, thereby mitigating risk interruptions.
Language: Английский
Citations
12
Decision Making Applications in Management and Engineering, Journal Year: 2024, Volume and Issue: 8(1), P. 42 - 81
Published: July 16, 2024
In this study, a novel risk assessment framework designed for evaluating the challenges of plastic packaging waste management in context reverse logistics is introduced. The leverages Failure Mode Effect Analysis (FMEA) to address decision-making fuzzy environment. To augment traditional FMEA criteria, encompassing severity (S), occurrence (O), and detection (D), three additional essential criteria are introduced: cost failure (C), complexity resolution (H), impact on business (I). These newly incorporated significantly enhance capacity convey multifaceted risks inherent recycling sector. Furthermore, comprehensive literature review expert validation conducted identify ten distinct modes. subjectively objectively determine weightings, combination Analytic Hierarchy Process (AHP) LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) employed. Finally, Additive Ratio Assessment (ARAS) approach applied prioritize such Recognizing imprecision uncertainty associated with human decision-making, trapezoidal set (TrFS) adopted throughout all processes. showcase proposed effectiveness, as case study manufacturer Thailand.
Language: Английский
Citations
4
Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103082 - 103082
Published: Jan. 5, 2025
Language: Английский
Citations
0
Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 103 - 128
Published: Jan. 1, 2025
Language: Английский
Citations
0
International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 27, 2025
Language: Английский
Citations
0
Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(4), P. 103342 - 103342
Published: March 1, 2025
Language: Английский
Citations
0
WSEAS TRANSACTIONS ON SYSTEMS, Journal Year: 2025, Volume and Issue: 24, P. 182 - 191
Published: April 9, 2025
This paper explores the development of an architecture and a model for building service-oriented information mobility system, which enhances user service quality by utilizing open data services in travel planning. We have developed multimodal route planning method that distinguishes it from existing systems using fuzzy set theory, ontology management, context system analysis, privacy protection, search, recommendation generation. can combine applications local, regional, national, international trips, public private transportation.
Language: Английский
Citations
0
Processes, Journal Year: 2024, Volume and Issue: 12(7), P. 1412 - 1412
Published: July 6, 2024
In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of is disassembly sequence planning problem. However, complexity arises from vast number components in leading significant proliferation potential combinations, which poses considerable challenges when devising optimal solutions for process. Consequently, improve efficiency decrease time, discrete whale optimization algorithm (DWOA) proposed this paper achieve excellent parallel (PDSP). To begin, composite nodes are added into constraint relationship graph based on characteristics equipment, time chosen as objective. Subsequently, DWOA solve PDSP problem by integrating precedence preservative crossover mechanism, heuristic mutation repetitive pairwise exchange operator. Meanwhile, hierarchical combination method used swiftly generate initial population. verify viability algorithm, classic genetic (GA), simplified teaching–learning-based (STLBO), self-adaptive swarm (SSO) were employed comparison three projects. The experimental results comparative analysis revealed that with achieved reduced only 19.96 min Experiment 3. Additionally, values standard deviation, average rate minimum 0.3282, 20.31, 71%, respectively, demonstrating its superior performance compared other algorithms. Furthermore, addresses inefficiencies dismantling processes stations enhances visual representation training Unity3D intelligent
Language: Английский
Citations
3
Batteries, Journal Year: 2024, Volume and Issue: 10(11), P. 382 - 382
Published: Oct. 30, 2024
Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due uncertainties their condition and potential risks of fire, fumes, explosions, electrical shock. To address these challenges, this paper presents robotic teleoperation system that leverages augmented reality (AR) digital twin (DT) technologies enable human operator work away from the danger zone. By integrating AR DTs, not only provides real-time visual representation robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within synchronises virtual robot with its physical counterpart an environment, enhances operator’s understanding both task statuses. In event anomalies, can interact through intuitive gestures based on information displayed interface, thereby improving decision-making efficiency operational safety. The application demonstrated case study involving disassembly busbar EoL EV battery. Furthermore, performance terms completion time workload was evaluated compared AR-based methods without informational cues ‘smartpad’ controls. findings indicate proposed reduces operation user experience, delivering broad complex industrial settings.
Language: Английский
Citations
2