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    CFD—Assisted Expert System for N2-Controlled Atmosphere Process of Rice Storage Silos
    (2024-03-01)
    Angsrisuraporn, Phakkawat
    ;
    Samakkarn, Chawit
    ;
    Lekawat, Lertsak
    ;
    Singkhornart, Sasathorn
    ;
    Thongsri, Jatuporn
    Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N<inf>2</inf>, reducing O<inf>2</inf> concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O<inf>2</inf> concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD’s credibility. Significantly, CFD results revealed that the ES controlled N<inf>2</inf> distribution throughout the silos and effectively reduced O<inf>2</inf> concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP’s efficacy in controlling O<inf>2</inf> concentration and insect extermination in the actual silos. The experimental results and owner’s feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES.
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    Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes
    (2024-01-01)
    Kirdponpattara, Suppakrit
    ;
    Sooraksa, Pitikhate
    ;
    Boonjing, Veera
    The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.
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    Classification of in Vitro Blood Stages of Plasmodium Falciparum Based on Fuzzy Inference System
    (2018-08-06)
    Tantisatirapong, Suchada
    ;
    Phothisonothai, Montri
    This paper proposes the automated texture based classification of Malaria parasites in Giemsa-stained thin blood film images based on fuzzy inference system (FIS). The proposed expert and knowledge based framework includes the segmentation, feature extraction and classification of erythrocytes. First-order statistical analysis includes mean, standard deviation, skewness and kurtosis have been proposed as input parameters of FIS. The effectiveness of classifier is compared to find appropriate parame- ters for classification of normal cells and infected cells, both ring and trophozoite stages. The proposed method can provide 96.28% accuracy rate for binary classification of normal and infected cells. The results also yield 97.55% accuracy for ring stage classification, and 98.54% accuracy for trophozoite stage classification.