Development of a novel Electrical Industry Safety Risk Index (EISRI) in the electricity power distribution industry based on fuzzy analytic hierarchy process (FAHP)

An effective electricity worker identification approach based on Yolov3-Arcface

To address the issues of low efficiency and high complexity of detection models for electric power workers in distribution rooms, the electric power worker identification approach is proposed. The ArcFace loss function is used as the coordinate regression loss of the target box. According to the score, the template box with the highest score is selected for prediction, which speeds up the rate of convergence. Dimensional clustering is used to set template boxes for bounding box prediction. The experimental results show that the improved YOLOv3 is a high-performance and lightweight model. The electric power worker identification approach proposed in this paper has a high-speed recognition process, accurate recognition results. The effectiveness of the approach is verified with better detection performance and robustness.

Determining the appropriate natural fibers for intelligent green wearable devices made from biomaterials via multi-attribute decision making model

Intelligent and green wearable technology becomes essential for new modern societies. This work introduces a multi criteria decision making model to properly assess and compare relative desired criteria for selecting the most suitable constituents for green body wearable bio-products made from bio-based materials. It aims to enhance the sustainability of intelligent green wearable devices by providing support in the selection process of lightweight, eco-friendly materials suitable for personal body wearable bio-products made of natural fiber composites to improve qualities that may help in better monitoring human vital signs and thereby address the health care concern. The relative intrinsic characteristic and merits of various natural fibers were utilized to compare and evaluate their relative performance in bio-composites. The model considered several evaluation factors like mechanical performance including tensile strength and modulus of elasticity, comfortability including size and weight, availability, fiber orientation, cellulose content, and cost. Results have demonstrated different priorities of the considered natural fibers relative to each evaluation factor. However, the model was capable of properly evaluating and ranking the best fibers relative to the whole conflicting evaluation criteria simultaneously. The closeness of priorities in several cases emphasizes upon using such decision making models to be able to judge the relative merits of natural fibers for such applications. It can also help designers to avoid bias during determining the best alternatives considering several conflicting evaluation criteria.

Why is the risk of industrial non-routine activities high and how can it be evaluated? An integrated approach

Non-routine activities such as startup, shutdown, maintenance, and operation commissioning require increased human interaction with the corresponding process. Owing to operator or procedural violations, the risk of accidents can be high during non-routine activities, even though they are performed less frequently. To identify and evaluate the hazards of non-routine processes, an integrated method combining job hazard analysis (JHA), hazard and operability analysis (HAZOP), and deviation degrees is proposed. JHA is applied to break down an operational process into steps, which are further defined as nodes in HAZOP for hazard scenario analysis. The concept of deviation degree is defined by integrating the operational and control function deviations to quantify the deviation analysis. Finally, the heating-furnace startup process in an oil and gas gathering and transmission station was selected to illustrate the proposed integrated method. The results show that this method constitutes a systematical and intuitive approach to identify hazard scenarios and evaluate risks, as well as to establish preventive measures for non-routine processes.

Risk assessment of firefighting job using hybrid SWARA-ARAS methods in fuzzy environment

Identification and risk assessment of hazards using classic methods have various flaws, such as ambiguity and uncertainty in the data, inability to correctly reflect the human thinking style, failure to assign weight to criteria, use of predetermined data and tables, and the evaluator's role in the results of the risk assessment process. Therefore, developing these methods and creating approaches with higher accuracy and sensitivity is necessary. This study utilized a developed method that integrated SWARA's weighting methods and ARAS prioritization using Fine-Kinney criteria in a fuzzy environment to evaluate the risks of the firefighting job. The sensitivity analysis results confirmed the accuracy and applicability of the proposed method. Based on the study's results, the inhalation of harmful gases and smoke (R3) was identified as the most critical risk, while concern about the optimal operation (R21) was considered the least dangerous risk in firefighting.