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This study considers a parallel dedicated machine scheduling problem towards minimizing the total tardiness of allocated jobs on machines. In addition, this problem comes under the category of NP-hard. Unlike classical parallel machine scheduling, a job is processed by only one of the dedicated machines according to its job type defined in advance, and a machine is able to process at most one job at a time. To obtain a high-quality schedule in terms of total tardiness for the considered scheduling problem, we suggest a machine scheduler based on double deep Q-learning. In the training phase, the considered scheduling problem is redesigned to fit into the reinforcement learning framework and suggest the concepts of state, action, and reward to understand the occurrences of setup, tardiness, and the statuses of allocated job types. The proposed scheduler, repeatedly finds better Q-values towards minimizing tardiness of allocated jobs by updating the weights in a neural network. Then, the scheduling performances of the proposed scheduler are evaluated by comparing it with the conventional ones. The results show that the proposed scheduler outperforms the conventional ones. In particular, for two datasets presenting extra-large scheduling problems, our model performs better compared to existing genetic algorithm by 12.32% and 29.69%.

Proposed kiosk touch system adopts both the ultra-high resolution (UHD) LCD display and the advanced in-cell (AIT) touch scheme which uses the display common voltage plate as touch electrodes. In this kiosk system, a several touch performance limitations are occurred by the large display size and the long display driving time. The number of touch node is optimized in order to resolve the limitations. In addition, the cost of the kiosk system should be considered concurrently. As the number of touch electrode node decreases, the size of touch electrode increases. This increases the parasitic capacitance of touch electrode. To drive this large parasitic capacitance, the load free driving scheme has been adopted and the operation amplifier with both the high current driving ability and the low power dissipation has been also chosen. To deal with the transportation of a number of touch data, proposed bus type touch data interface is used between readout and algorithm operation block.

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