【正文】 et al., 2021) with data obtained from diagrams for determination of permitted exposure time on specific vibration level in dependence of frequency and acceleration. PROTECTOR contains the ‘‘standard’’ elements of an expert system: the knowledge base, an inference engine, the user interface and a working memory, but also a module for the interface with routines for relevant parameters determination, the Visual Basic routines themselves, and a database used by these routines (Fig. 3). Fig. 3. Architecture of PROTECTOR expert system ponent. The main purpose of the user interface is to provide means for a successful dialogue, . an exchange of information between the user and the system. It is the user interface that enables PROTECTOR to obtain all the necessary information from the user, on one hand, and that transforms system’s results and conclusions into information the user can understand, on the other. The PROTECTOR knowledge base is a formalization of the mine safety expert’s knowledge. Knowledge inexpert systems basically consists of facts and heuristics which can be represented by means of rules, frames, semantic works and other formalisms. Since knowledge is the key factor in problem solution and decision making, the quality and usability of an expert system is basically determined by the accuracy and pleteness of its knowledge base. The selection of the representation formalism is very important and plays a significant role in knowledgebased design. The problemsolving strategy is realized by the expert system’s inference engine. This reasoning mechanism infers conclusions based on knowledge from the knowledge base and the available information pertaining to the safety problem at hand. The inference engine stores intermediate results in the working memory. 4. Implementation issues The objectoriented approach in system structuring and modeling (Fowler, 2021。 Marovelli, 1981). Several studies have been conducted on hazard identification and risk assessment (South African Mining Industry, 2021。 外文資料原文 An intelligent hybrid system for surface coal mine safety analysis a b s t r a c t ： Analysis of safety in surface coal mines represents a very plex process. Published studies on mine safety analysis are usually based on research related to accidents statistics and hazard identification with risk assessment within the mining industry. Discussion in this paper is focused on the application of AI methods in the analysis of safety in mining environment. Complexity of the subject matter requires a high level of expert knowledge and great experience. The solution was found in the creation of a hybrid system PROTECTOR, whose knowledge base represents a formalization of the expert knowledge in the mine safety field. The main goal of the system is the estimation of mining environment as one of the significant ponents of general safety state in a mine. This global goal is subdivided into a hierarchical structure of subgoals where each subgoal can be viewed as the estimation of a set of parameters (gas, dust, climate, noise, vibration, illumination, geotechnical hazard) which determine the general mine safety state and category of hazard in mining environment. Both the hybrid nature of the system and the possibilities it offers are illustrated through a case study using field data related to an existing Serbian surface coal mine. 1. Introduction One of the most important and also most plex problems encountered in surface coal mines is safety analysis. An appro priate and reliable solution for this problem is vital for the working process in mines with surface exploitation. Published studies on mine safety analysis are usually based on research related to accidents statistics (Kecojevic and Radomsky, 2021。 Joy and Griffiths, 2021) which aims to provide advice on hazard identification and risk assessment within the mining industry. Systems safety analysis methods provide a proactive approach to analyze systems for potential hazards that may threaten the health and safety of miners. The systems approach to the safety problem focuses on the system taken as a whole. It involves the interaction of people, machines, and environment within proce dural constraints (Hammer, 1972). It uses a number of techni ques: the technique of operations review, the failure mode and effects analysis technique, the fault tree analysis technique. Contemporary mining theory operates with a number of methods and techniques which can be used to solve mine safety problems. These methods are used in current engineering practice with the help of appropriate software products. Software products in mine safety need expert knowledge and experience to be fully exploited. This knowledge consists of rules and heuristics experts use when they apply numerical methods, and Artificial Intelligence offers formalisms and mechanisms for its incorporation in software systems (Russell and Norvig, 2021). Formalization of knowledge representation and development of mechanisms for using this knowledge are among methods and tools developed by AI for solving plex problems (Giarratano and Riley, 2021). However, the plexity of some problems outgrows the potentials of single methods. A possible solution is to bine two or more AI methods into a hybrid intelligent system (Goonatilake and Khebbal, 1995). This approach has been adopted in the case of PROTECTOR, a hybrid system for the analysis and estimation of safety in mining environment, devel oped at the Faculty of Mining and Geology of the University of Belgrade. PROTECTOR was developed by bining neural works and expert system technology. While the mining environment estimation methodology is implemented through an expert system, some of the related estimation parameters are determined by neural works. Discussion in this paper is focused on