首页|Research from Kokushikan University Has Provided New Data on Machine Learning (F eature extraction for machine learning to detect floating scrap during stamping using accelerometer)
Research from Kokushikan University Has Provided New Data on Machine Learning (F eature extraction for machine learning to detect floating scrap during stamping using accelerometer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting from Kokushikan Unive rsity by NewsRx journalists, research stated, “This study entails an examination and comparison of features observed in an accelerometer signal obtained from fl oating-scrap detection during stamping with those obtained using the ‘center-of- gravity’ method, which is conventionally used for anomaly detection via machine learning. The samples are detected using the Mahalanobis-Taguchi system.” The news journalists obtained a quote from the research from Kokushikan Universi ty: “If using the center-of-gravity method, the unit space that contains normal samples without scraps cannot be separated from the error samples. Based on an e stimated threshold for detecting all the error samples, the falsepositive rate of the abovementioned method is 0.9 %. In this study, a suitable th reshold that allows the system to detect 100 % of the error sample s (and no normal samples) is estimated using six features. Detection using the s ix suggested features is more effective than that using only three features asso ciated with the downward journey of the press slide. Features selected from two different events (i.e., the downward and upward journeys of the press slide) may result in more effective detections than features selected from only one event (i.e., the downward journey). To confirm the effect of tool wear, six experiment s based on normal samples are conducted after all error samples are created.”
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