Long-term hybrid prediction method based on multiscale decomposition and granular computing for oxygen supply network

Rule Extraction for Screening of COVID-19 Disease Using Granular Computing Approach

In the epidemic status of an unknown virus called Coronavirus, one of the main problems is inadequate access to treatment centers. Statistics show that many people are infected with the virus through unseasonable visits to medical centers immediately after noticing the initial symptoms similar to those reported for Coronavirus. Besides, unnecessary congestion at health centers reduces the quality of service to patients in urgent need of care. Since any external factor, including the virus, appears to have some symptoms after the onset of activity in the affected person, early diagnosis is possible. This paper presents an approach to classifying patients and diagnosing disease by symptoms, based on granular computing. One of the vital features of this method is the extraction of correct rules with zero entropy. This process is done based on a predefined classification of training datasets collected by experts. Granular computing has been a helpful approach in rule extraction and variety in recent years. Experimental results show that the proposed method can successfully detect COVID-19 disease according to its observed symptoms.

A Design of CGK-Based Granular Model Using Hierarchical Structure

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models.

Energy Saving Benefit Analysis of the Compressor Short-Stop Adjustment Method Based on TGNET

Aiming at the problem of gas emissions caused by the high-pressure operation of a large industrial gas (O₂, N₂, and Ar) pipeline network, this study establishes a mathematical model of the oxygen transmission and distribution system (OTDS) based on TGNET software. In addition, the study conducts transient simulation, comprehensively considering theoretical constraints and actual operation requirements, and adopts a large air separation company for the OTDS as a case study. After comparing two traditional adjustment methods, a compressor short stop adjustment strategy is proposed to reduce the peak pressure of the pipe network system. This study determines the energy-saving benefits and the difference in the scope of application of compressor short-stop adjustment. Compared with the medium-pressure release and inlet guide vane opening adjustment (IGVOA) strategies, the compressor short-stop adjustment strategy reduced oxygen emission by 3850.9 Nm³ and increased by 927.1 Nm³. Furthermore, the compressor operating energy consumption was reduced by 3349 and 2919 kW h. Compared with the IGVOA strategy, the compressor short-stop adjustment strategy has increased the application range of compressor inlet pressure and medium-pressure pipeline pressure by more than 70%. This strategy is effective for reducing the emission of pipeline gases caused by fluctuations in user demand.