Search Results (1,196)

Addressing the problems of complex forms, low structurization and insufficient reliability of automatic acquisition of professional knowledge sources in the manufacturing industry, this paper proposes an improved multi-person collaborative knowledge acquisition method for professional fields. Drawing on the quality control concept of “three reviews and three proofs” in the publishing industry and combining the characteristics of professional knowledge acquisition tasks, this method constructs a knowledge acquisition process with the collaborative participation of editors and professionals. This paper designs a quality assurance mechanism from three dimensions, namely personnel quality, process quality and result quality; introduces triangular fuzzy numbers to evaluate personnel quality; and establishes a process quality control model under multi-level inspection. Taking the knowledge acquisition project of CNC Machining Manual as an example, 39 professionals completed large-scale professional knowledge processing tasks within 60 working days. Compared with the traditional manual knowledge acquisition method, under similar workload conditions, the proposed method reduces the task completion time by approximately 40% and improves knowledge quality by approximately 10%. The research results show that this method can enhance the organization, inspectability and result stability of the complex professional knowledge acquisition process, and is suitable for constructing vertical domain knowledge bases with high quality requirements. Full article

In casting processes, mechanical properties such as hardness are highly sensitive to both chemical composition and process parameters, making parameter design a complex and uncertain task during the embodiment stage of engineering design. Conventional trial-and-error-based approaches are often costly, time-consuming, and impractical in industrial environments. To address these challenges, this study proposes an optimized fuzzy artificial neural network (FANN)-based decision-support approach for hardness-oriented parameter design in a casting process. The developed model uses chemical composition variables, including carbon, silicon, manganese, phosphorus, sulfur, chromium, copper, and tin, together with process parameters such as casting temperature and casting time as inputs, while Brinell hardness is considered as the output. A dataset consisting of 170 experimental casting samples was employed; 128 samples were used for model development and hyperparameter selection, and 42 samples were reserved as an independent final test set. The proposed model was implemented as a scaled direct FANN weighted ensemble, in which fuzzified input variables were used to predict standardized continuous hardness values. A total of 300 FANN configurations were evaluated using five-fold cross-validation, and the five best-performing configurations were combined through RMSE-based weighted ensemble averaging. The final model was compared with Random Forest, Linear Regression, Ridge Regression, and SVR-RBF models using MSE, RMSE, MAE, R², MAPE, normalized RMSE, and ±5% prediction success rate. The results showed that the optimized FANN ensemble achieved the lowest mean RMSE in the full-data five-fold cross-validation analysis, slightly outperforming the Random Forest benchmark. In the independent final test set, Random Forest produced the lowest prediction error, whereas the proposed FANN ensemble remained competitive and achieved the same ±5% prediction success rate as Random Forest, Linear Regression, and Ridge Regression. Furthermore, a target-hardness case study demonstrated that the proposed approach could identify candidate casting conditions very close to a desired hardness level, with the nearest prediction reaching 202.985 HB for a target value of 203 HB. These findings indicate that the proposed FANN-based framework can serve not only as a hardness prediction model but also as a practical fuzzy decision-support tool for target-hardness-oriented parameter design in casting processes. Full article

To achieve optimal performance of load frequency control (LFC), a data-driven scheme is proposed for renewable power systems in this paper. A multi-agent Actor-Double-Critic deep reinforcement learning approach is developed to ensure real-time scheduling that complies with system safety operation constraints within the multi-area LFC power system. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. A Self-Critic and Cons-Critic network is employed to improve the convergence speed during the multi-agent training process. Simulations on two-area and three-area LFC power systems are performed to verify and validate the analytical results. Comparisons with conventional PI and fuzzy PI controllers demonstrate that the presented approach effectively reduces training difficulties, guarantees the satisfaction of system safety constraints, and significantly improves the dynamic frequency regulation performance of the power system. Full article

The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article

In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then used to predict the mass of the target object. During operation, real-time processing and calculation of the robotic arm’s joint motor current data are performed. Based on the mathematical relationship between the identified basic parameter set from the dynamic parameters and the end-effector payload, the second fuzzy compensation system was used to calculate the root mean square error (RMSE) of the predicted versus collected current data of the 6-th joint motor, thereby predicting and compensating for the payload mass. The final prediction is generated upon completion of the operation. The overall experiment is conducted on the HSR-CR607 robot. The experimental results indicated that the proposed prediction algorithm consistently operates within the acceptable error range (15%) in most test cases. Full article

The extensive development of high-speed railway (HSR) networks often necessitates construction activities adjacent to operational lines. However, existing studies have mostly focused on the substructure construction phase, lacking systematic consideration of cumulative effects throughout the construction process. This study proposes an integrated framework for risk-informed monitoring throughout the full construction process. The framework integrates the Analytic Hierarchy Process (AHP), triangular fuzzy numbers, and fuzzy comprehensive evaluation to construct a quantitative risk assessment model, decomposing the construction process into hierarchical risk factors and quantifying the weights of each factor. Furthermore, a non-contact real-time monitoring system based on Digital Image Correlation (DIC) is designed and deployed, enabling high-frequency, high-precision three-dimensional pier deformation measurement. Applied to a new bridge crossing the Beijing–Shanghai HSR, the risk model identified pile cap and pier construction as the highest-risk stage (weight: 0.311). The DIC system, validated against total station measurements (relative error < 5%), recorded cumulative pier deformations across 31 construction stages, all remaining within the ±1.2 mm early warning threshold, thereby validating the proposed risk assessment model. The integrated AHP-Fuzzy and DIC framework provides a robust paradigm for proactive risk management, confirming that risk-informed monitoring ensures construction impacts on existing HSR infrastructure remain within safe limits. Full article

Small and medium enterprises (SMEs) in the entertainment sector face significant challenges managing seat assignments through manual processes that are error-prone and time-consuming. This paper presents an intelligent agent-based system that automates seat assignment, while providing natural language support for operational staff. The system integrates a large language model (Gemini 2.5 Flash) for conversational interaction with a constraint-based optimization algorithm that considers capacity, accessibility, revenue, and business priorities. A fuzzy matching engine combining spaCywith the fuzzy string matching library FuzzyWuzzy consolidates duplicate reservations from multiple channels. The cloud-based architecture leverages AWS managed serverless services (ECS Fargate for container orchestration and Lambda for event-driven pipelines) with PostgreSQL for data management. Technology Readiness Level 4 (TRL4) validation demonstrated 94% precision in duplicate detection, successful assignment of 87% of reservations with 82% average capacity utilization, and effective natural language query handling. The system reduces manual processing time by 65%, while improving assignment quality through systematic enforcement of constraints. This work demonstrates the feasibility of AI-powered operations management for resource-constrained SMEs, offering a practical reference architecture combining conversational AI with algorithmic optimization. Full article

To address the problems of false negatives, false positives, and delayed response in traditional fire detection systems, this paper proposes a warehouse fire detection scheme based on multi-sensor information fusion. By constructing a ZigBee wireless sensor network and integrating temperature, CO concentration and smoke sensors, fire simulation data are collected in the warehouse. At the data processing level, an improved Grubbs criterion is innovatively adopted to eliminate outliers, and the median is used instead of the average to effectively suppress the same-side shielding effect. At the feature layer fusion stage, a BP neural network model optimized by the cosine decreasing inertia weight particle swarm optimization algorithm (CIW-PSO) is designed. By dynamically adjusting the learning factors (c₁, c₂) and inertia weight (w), the convergence speed and global optimization ability are significantly improved. At the decision-making level, a fuzzy logic reasoning mechanism is introduced to integrate multi-parameter membership functions, thereby reducing the probability of misjudgment. Field tests have verified that the system can achieve early fire warning in a 50 m × 100 m warehouse environment, with a false alarm rate reduced by 42% compared to a single sensor and a response time shortened by 35%, providing an efficient and reliable intelligent solution for warehouse fire safety. Full article

High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to interpret in weekly control settings. This study presents a single-enterprise methodological demonstration of a weekly fuzzy monitoring model with dual benchmarks for explainable operational control. The empirical panel covers 156 weeks from 2023 to 2025 across three plants, five product families, and 27 KPIs grouped into Operational Agility, Sustainable Responsiveness, and Socio-Market Adaptability. KPI and benchmark weights were elicited through a two-round Delphi process with 24 experts. The model combines fixed target-centered B1 compliance thresholds with percentile-calibrated B2 thresholds for direction-adjusted week-to-week adaptation. In the calibrated specification, the overall index mean is 0.618 with a range of 0.489 to 0.741, while the mean B1 and B2 values are 0.619 and 0.617. Matched-level validation at the plant–product–week level (N = 2340) shows a positive association with EBIT (Pearson r = 0.222, 95% CI [0.200, 0.245]), and time-safe calibration checks preserve the substantive interpretation of the index. The results support the model as an explainable, human-in-the-loop instrument for within-case weekly monitoring and diagnosis rather than as a broadly validated predictive model. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

This research aims to improve real-time decision-making, process-state reconstruction, and multi-objective operational optimization in intelligent composting systems through an integrated framework based on hierarchical digital twin and Edge–Cloud architecture. Unlike previous studies that mainly focused on data monitoring or static optimization, the proposed framework enables dynamic reconstruction of process state, predictive decision-making, and intelligent assignment of tasks between Edge and Cloud layers simultaneously. The main innovation of the research lies in the combination of multilayer digital twin, dynamic decision rule for Edge–Cloud orchestration, and fuzzy multi-objective optimization in an integrated structure. In the proposed model, biological and operational uncertainties are modeled using triangular fuzzy numbers and control decisions are updated in real-time based on the actual system state. The results, compared to the baseline system without hierarchical digital twin and without Edge–Cloud orchestration, showed that the proposed framework was able to reduce the composting process time by about 28%, significantly reduce energy consumption, increase the compost quality index by 0.91, and effectively control the emission of undesirable compounds. The results also showed that the hierarchical Edge–Cloud architecture, by transferring time-sensitive decisions to the Edge layer and performing complex analyses in the Cloud, simultaneously improved the response time, process state reconstruction accuracy, and decision-making stability under dynamic and uncertain conditions. This research is an effective step in the development of intelligent, predictive, and self-adaptive systems for biological processes and sustainable waste management. Full article

This study presents a novel integrated methodology for optimizing forest fire emergency rescue vehicle scheduling through the synergistic combination of a multi-criteria demand urgency grading framework and mechanistic fire spread propagation modeling, enhancing spatiotemporal resource allocation efficiency under evolving wildfire scenarios. The research focuses on three core aspects: First, a multi-dimensional demand urgency evaluation system is established, incorporating fire threat, response efficiency, and path factors. Subjective and objective weights are determined through fuzzy analytic hierarchy process and entropy method, respectively, while grey relational analysis TOPSIS method is employed for prioritizing affected areas. The model’s validity is verified using wildfire data from the Greater Khingan Mountains. Second, a multi-objective vehicle scheduling model is developed, combining total rescue time, cost, and urgency ranking index via weighted sum method. A fire spread model is innovatively introduced to dynamically adjust urgency classification, with genetic algorithm (GA) and Genetic Simulated Annealing Algorithm (GASA) designed for solution optimization. Finally, empirical analysis of 13 fire cases in the Greater Khingan Mountains (2020) demonstrates that GASA outperforms GA, achieving 17% reduction in rescue time, 1% cost savings, 22% shorter travel distance, and 0.7% improvement in urgency ranking. Incorporating the fire spread model enhances the urgency ranking index by 10.78%, where the improvement is defined as the percentage increase in the achieved objective function value f3 compared to the solution obtained without dynamic fire propagation information. By integrating dynamic urgency assessment with intelligent algorithms, this research constructs a spatiotemporal-aware emergency scheduling framework aligned with forest fire evolution patterns, providing theoretical foundations and practical strategies to enhance rescue efficiency and resource allocation, with significant implications for disaster management. Full article

The research presented provides an overview of the latest progress in data-driven control methods used for industrial heating furnaces. Although the data-driven methodologies reviewed provide good performance metrics compared to conventional control strategies, they lack the integration of energy efficiency considerations into the controller design process. This research presents a comprehensive control design framework for a novel energy-efficient data-driven controller applied to an industrial heating furnace. It proposes a novel Hybrid Mamdani–ANFIS controller developed using real-time data from an industrial heating furnace. A novel ANFIS-based energy model is also presented in this work to evaluate the energy efficiency of the presented controller models. The results demonstrated that the proposed novel Hybrid Mamdani–ANFIS controller outperforms both the Fuzzy PID and conventional Fuzzy controller in terms of energy efficiency, achieving approximately 30% energy savings and exhibiting a faster disturbance response time. This study makes a considerable contribution to the field of control theory by synthesizing existing knowledge, addressing identified research gaps, and introducing a novel control design framework that enhances energy efficiency, robustness, and adaptability across a wide spectrum of control applications in industrial heating furnace systems. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

With the rapid growth of short-video platforms, it has become increasingly important to understand the psychological processes that sustain prolonged engagement and contribute to individual evaluative responses. This study examines the dual pattern of associations involving perceived time spent on TikTok by investigating whether it is positively associated with overall user satisfaction while also being linked to psychological cost-related responses, including privacy concerns, health consciousness, social interaction anxiety, and social media fatigue. Data were collected through an online survey administered via Prolific and analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The findings show that perceived time spent on TikTok is significantly associated with health consciousness and social interaction anxiety. Perceived time spent on TikTok is also directly and positively associated with overall user satisfaction. Moreover, privacy concerns and social media fatigue are negatively associated with overall user satisfaction. The fsQCA results further reveal six configurations associated with high user satisfaction. These configurations illustrate the principle of equifinality and indicate that no single condition reached the conventional threshold for necessity. Overall, the findings suggest that high user satisfaction can coexist with different combinations of psychological cost-related responses, thereby offering a more nuanced account of how users experience short-video platforms. Full article