Display panels manufactured in the FAB process are assembled with various films and components in the module (MOD) process, and their final quality is a result of accumulated the quality of hundreds of preceding proce...
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With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffe...
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Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This r...
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Display panels manufactured in the FAB process are assembled with various films and components in the module (MOD) process, and their final quality is a result of accumulated the quality of hundreds of preceding proce...
详细信息
Display panels manufactured in the FAB process are assembled with various films and components in the module (MOD) process, and their final quality is a result of accumulated the quality of hundreds of preceding processes. Due to these limitations, it is hard to analyze the cause of MOD's outliers relevant to the FAB process and much time and money are consumed to maintain the cause process to eliminate MOD outliers. Therefore, we propose a new explainable ai (Xai) approach to detect and improve the module process's outliers by finding the correlation between FAB and MOD in this paper. The proposed approach was verified for MOD outlier cases in the real world's OLED development and manufacturing process. As a result, it confirmed 96.8% of the average accuracy for classifications. This approach presents the FAB factor that caused MOD's outliers based on the correlation between the MOD and the FAB.
With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffe...
With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans.
Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This r...
Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.
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