Pengendalian Cacat Produk Kain Tenun Menggunakan Statistical Process Control (Studi Kasus: PT. Kusuma Mulia Plasindo Infitex)

Quality is a primary consideration for consumers when choosing products and is essential for effective product competition and increasing customer satisfaction for companies. PT. Kusuma Mulia Plasindo Infitex is a woven fabric manufacturer facing numerous defective products. To address this issue, the company needs to implement quality control. This study aims to apply Statistical Process Control (SPC) to control defective products in the production of woven fabric at PT. KMPI. The research is limited to defect data from the inspection stage at the end of production and product control at the end of the production process. SPC tools used include p-charts, Pareto diagrams, and fishbone diagrams. Scoring techniques using the Urgency, Seriousness, Growth (USG) method are employed to determine priority causal factors. The research findings indicate that textile production at the company is not fully controlled, except for September and December. Four types of defects contribute to 80% of the priority defect products, including dirty + stitched rings, torn fabric, and loose weft. From the scoring method ranking, machine collisions emerged as the crucial cause of product defects. The proposed improvement from this research involves using the kaizen 5W+1H method by providing notes on machines as reminders for operators about the buttons to use when starting the machine

Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review

An efficient system that is capable to detect driver fatigue is urgently needed to help avoid road crashes. Recently, there has been an increase of interest in the application of electroencephalogram (EEG) to detect driver fatigue. Feature extraction and signal classification are the most critical steps in the EEG signal analysis. A reliable method for feature extraction is important to obtain robust signal classification. Meanwhile, a robust algorithm for signal classification will accurately classify the feature to a particular class. This paper concisely reviews the pros and cons of the existing techniques for feature extraction and signal classification and its fatigue detection accuracy performance. The integration of combined entropy (feature extraction) with support vector machine (SVM) and random forest (classifier) gives the best fatigue detection accuracy of 98.7% and 97.5% respectively. The outcomes from this study will guide future researchers in choosing a suitable technique for feature extraction and signal classification for EEG data processing and shed light on directions for future research and development of driver fatigue countermeasures

Vehicle driver monitoring through the statistical process control.

This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver?s behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ?60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations