Browsing by Author "Chen, Yu-Hsiang"

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A 10-Gb/s, 1.24 pJ/bit, Burst-Mode Clock and Data Recovery With Jitter Suppression
(2015-03-01)
Su, Ming-Chiuan
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Chen, Wei-Zen
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Wu, Pei-Si
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Chen, Yu-Hsiang
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Lee, Chao-Cheng
A burst mode clock and data recovery (BMCDR) circuit for 10 Gbps passive optical network (10G-PON) is presented. The proposed BMCDR is reconfigurable between data gating mode and phase tracking mode to achieve instantaneously phase-locked with jitter suppression capability. Incorporating selectively gating VCO (SGVCO), the BMCDR operates at 1/5-rate and accomplishes 1:5 demultiplexing with a high energy efficiency of 1.24 pJ/bit. With a 4 MHz, 0.22UI(pp) jitter stressed input data at 10 Gbps, the recovered clock jitter at 2 GHz is 2.94 ps(rms). The prototype is fabricated using 55 nm CMOS technology. The core area is 0.03 mm(2) only. It dissipates 12.4 mW from 1 V supply.
A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts
(2017-11-01)
Lin, Chun-Cheng
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Shu, Lei
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Deng, Der-Jiunn
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Yeh, Tzu-Lei
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Chen, Yu-Hsiang
Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.
Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity
(2020-01-01)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Chen, Yu-Hsiang
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工業工程與管理學系
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Department of Industrial Engineering and Management
Diversification of products has increased the involvement of reentrant manufacturing processes, in which a job returns multiple times to a machine at the preceding workflow stage to continue the manufacturing process. Reentrant flow shop manufacturing can substantially improve manufacturing efficiency when scheduled properly. In practice, advanced manufacturing companies (e.g., semiconductor foundries) have introduced automated material handling system (AMHS), including stockers that serve as centralized inventory buffer space for temporarily storing the inventories owing to limited buffer capacity of each machine. However, no previous studies on reentrant flow shop scheduling have considered the impact of limited buffer capacity or stockers on scheduling efficiency. Consequently, this study investigated the application of stockers in solving the reentrant hybrid flow shop scheduling problem with limited buffer capacity. With the objective of optimizing the makespan and mean flowtime of a schedule, this problem is NP-hard because it generalizes the flow shop problem. Therefore, this study developed a hybrid harmony search and genetic algorithm (HHSGA) for the problem, in which limited buffer capacity and stockers cause solution decoding to be non-trivial. Experimental comparison on scheduling problems with different numbers of jobs showed that the HHSGA performed better than conventional algorithms. Moreover, among three manufacturing conditions (i.e., with buffers and stockers, with buffers only, and with stockers only), the results indicated that the condition using inventory buffers and stockers was more beneficial.