• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.72-79
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• 2014

This paper presents a method for constructing a pipeline-aware quasi-cyclic irregular repeat accumulate low-density parity-check (PA-QC-IRA-LDPC) codes and efficient rate-1/2 (2016, 1008) PA-QC-IRA-LDPC decoder architecture. A novel pipeline scheduling method is proposed. The proposed methods efficiently reduce the critical path using pipeline without any bit error rate (BER) degradation. The proposed pipeline-aware LDPC decoder provides a significant improvement in terms of throughput, hardware efficiency, and energy efficiency. Synthesis and layout of the proposed architecture is performed using 90-nm CMOS standard cell technology. The proposed architecture shows more than 53% improvement of area efficiency and much better energy efficiency compared to the previously reported architectures.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1157-1169
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• 2021

Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

• The Journal of Engineering Geology
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• v.19 no.1
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• pp.63-70
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• 2009

The optical fiber cable acting as a sensor was embedded in the underground research tunnel and portal area in order to monitor their stability and the spatial temperature variation. This system includes two types of sensing function to monitor the distributed strain and temperature along the line, where sensor cable is installed, not a point sensing. According to the results of one year monitoring around the KURT, there is no significant displacement or movement at the tunnel wall and portal slope. However, it would be able to aware of some phenomena as an advance notice at the tunnel wall which indicates the fracturing in rockmass and shotcrete fragmentation before rock falls accidently as well as movement of earth slope. The measurement resolution for rock mass displacement is 1 mm per 1 m and it covers 30 km length with every 1m interval in minimum. In temperature, the cable measures the range of $-160{\sim}600^{\circ}C$ with $0.01^{\circ}C$ resolution according to the cable types. This means that it would be applicable to monitoring system for the safe operation of various kinds of facilities having static and/or dynamic characteristics, such as chemical plant, pipeline, rail, huge building, long and slim structures, bridge, subway and marine vessel. etc.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.