Environmental changes and increased uncertainty due to technical damage, explosions and large fires have caused the risk of an inevitable element in the gas industry. This study purposes developing a new hybrid fuzzy expert system as a decision support system to mitigate the risk associated with gas transmission stations. The designed knowledge-based system combines the procedural and descriptive rules based on experts’ judgments to analyze the complex relationships between the different components of a gas pressure reduction station. The developed fuzzy expert system is coded in C language integrated production system (CLIPS) and is linked with MATLAB software for calling fuzzy functions. A real case study of gas pressure reduction stations in Iranian gas industry is conducted to validate the proposed expert system model. The expert system provides more than one thousand rules based on expert knowledge to prevent the pressure drop and the quality loss of gas or shutting off gas flow which accordingly increases gas flow stability. The proposed expert system could minimize the risk of hazardous scenarios, such as leakage and corrosion, in the gas industry and provide an acceptable precision in the provision of periodic control strategies and appropriate response under an emergency condition.

A fuzzy expert system for mitigation of risks and effective control of gas pressure reduction stations with a real application

Building information networks using semantic based techniques to avoid tedious work and to achieve high efficiency has been a long-term goal in the information management world A great volume of research has focused on developing large scale information networks for general domains to pursue the comprehensiveness and integrity of the information However, constructing customised information networks containing subject-specific knowledge has been neglected Such research can potentially return high value in terms of both theoretical and practical contribution In this paper, a new type of network, solution-oriented information network, is coined that includes research problems and proposed techniques as nodes, and the relationship between them A lightweight Semantic-based Knowledge Fusion Model (SKFM) is proposed leveraging the power of Natural Language Processing (NLP) and Crowdsourcing to construct the proposed information networks using academic papers (knowledge) from Scopus SKFM relies on NLP in terms of automatic components while Crowdsourcing is initiated when uncertain cases arise Applying the NLP technique assists to develop a semi-automatic knowledge fusion method for saving effort and time in extracting information from academic papers Leveraging human power in uncertain cases is to make sure the essential concepts for developing the information networks are extracted reliably and connected correctly SKFM shows a theoretical contribution in terms of lightweight knowledge extraction and reconstruction framework, as well as practical value by providing solutions proposed in academic papers to address corresponding research issues in subject-specific areas Experiments have been implemented which have shown promising results In the research field of intrusion detection, the information of attack types and proposed solutions has been extracted and integrated in a graphic manner with high accuracy and efficiency

A semantic-based knowledge fusion model for solution-oriented information network development: a case study in intrusion detection field

Fault detection and severity classification are critical to gearbox structural health monitoring. A common approach to fault severity classification is to identify the patterns associated with features extracted from raw sensor data that vary with fault deterioration. Since however features only represent partial information contained in the raw data, they may indicate different interactions as faults deteriorate. Consequently, data fusion using inappropriate features may lead to decreased classification accuracy. To address this problem, a signal-level classifier based on raw sensor data through sparse representation is presented in this paper. A library containing covariance matrices calculated from the raw data corresponding to different faults or levels of fault severity is first constructed. Classification of new observations is then performed through a sparse representation of the library. A sparse mapping vector, as the result of sparse representation, reveals the classification result. Experimental study performed on a gearbox test rig with multiple vibration sensors installed is conducted to evaluate the effectiveness of the developed sparse classifier. Results confirmed good performance of the developed data fusion method for gearbox fault severity classification.

A sparse approach to fault severity classification for gearbox monitoring

Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.

/pdf/improved-dempster-shafer-evidence-theory-for-tunnel-water-2vpsi617.pdf

Improved Dempster-Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.

https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012097/pdf

Integrated Knowledge Based Expert System for Disease Diagnosis System

Expert systems are widely used for software fault diagnosis. Knowledge in these Expert Systems could be learned from different ways, such as human expert, machine learning and etc‥ The scale of the knowledge is increasing quickly, due to the development of testing tools and techniques. However, it is a big challenge to integrate the knowledge from different sources. In this paper, a novel knowledge fusion method based on D-S theory is provided to meet the challenge and its application on practical software fault diagnosis shows satisfied results.

Knowledge fusion based on D-S theory and its application on Expert System for software fault diagnosis

Many accidents occurred frequently in aerospace applications, traditional software reliability analysis methods are not enough for modern flight control software. Developing a comprehensive, effective and intelligent method for software fault diagnosis is urgent for airborne software engineering. Under this background, we constructed a fault diagnosis expert system for flight control software which combines software failure mode and effect analysis with software fault tree analysis. To simplify the analysis, the software fault knowledge of four modules is acquired by reliability analysis methods. Then by taking full advantage of the CLIPS shell, knowledge representation and inference engine can be realized smoothly. Finally, we integrated CLIPS into VC++ to achieve visualization, fault diagnosis and inference for flight control software can be performed in the human-computer interaction interface. The results illustrate that the system is able to diagnose software fault, analysis the reasons and present some reasonable solutions like a human expert.

A Fault Diagnosis Expert System for Flight Control Software Based on SFMEA and SFTA

In the research of task reliability test case generation technology, first, referring to the entity, behavior, and time sequence representation in the task scene, define the XML tag of the task reliability test case element; then, according to the task environment scene model, the task environment scene is generated, including the environment Scenario behavior and environment scenario data generation; use XML tags to describe and generate task environment scenario use cases; secondly, according to the task execution scenario model, perform task execution scenario test generation, including task-driven behavior, task incentive data and task expected output generation, using XML tags Describe and generate task execution scenario use cases; finally, design a task reliability test case framework, integrate task environment scenario use cases and task execution scenario use cases according to task scenario sequence numbers, and generate task reliability test cases.

Research on task reliability test case generation technology based on XML tag description

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Knowledge fusion based on D-S theory and its application on Expert System for software fault diagnosis

A Fault Diagnosis Expert System for Flight Control Software Based on SFMEA and SFTA

Research on task reliability test case generation technology based on XML tag description