• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.10
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• pp.89-97
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• 1997

To extract time-varying evoked potential (EP), segement latency corrected average method is presented. This method is composed of three steps. First, adaptive filtering for reducing the effect of artifacts and removing background noise is performed. Next, validated intervals of individual segments are aligned, and latency components are detected by cross-correlation between the previously obtained and measured EPs within the intervals. Finally, after the detected latency component, responses of segments are groupe and averaged, the shole corrected EP signal is obtained. In the experiments, the resutls of the conventional methods including simple averaging, Woody's method, and peak component latency corrected averaging are obtained, the results compared with the present method for evaluating performance. Therefore, the presented method confirms that it reflects the latency variations of fundamental peaks and gets the improved EP.

• Sleep Medicine and Psychophysiology
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• v.3 no.1
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• pp.79-84
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• 1996

Kleine-Levin syndrome is a disorder characterized by recurrent episodes of hypersomnia, hyperphagia and hypersexuality that typically occur weeks or months apart. A 17-years-old male showed these episodes and took nocturnal polysomnography(NPSG) and multiple sleep latency test(MSLT). As results of NPSG, sleep latency was 82.5min, sleep efficiency was 82.5min, sleep efficiency was 82.5%, latency and percentage of REM sleep were 106.5min and 14.6% and percentage of slow wave sleep was 12.7%. In 4 times MLST, average of sleep latency and REM latency were 8min 7sec and 5min 20sec with 3 times sleep onset REM period(SOREMP). These findings are consistent with these of Keine-Levin syndrome. And the possible causes and classification of this syndrome were discussed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.60-69
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• 1997

We present an average method based on the multirate signal processing and dynamic allocation average for the purpose of monitoring event-related potentials(ERP) and continuously and dynamically. In the proposed method, first, latency shifts are detected through the cross correlation between a current response and the reference response. Then, the multirate signal processing which is composed of up-sampler, lowpass filter, and down sampler is performed to compensate the latency shifts of the reference response, therefore we obtain the reference response with a peak latencies compenated by those of a current response. Finally, the single response is obtained by averaging the compensated reference response and a current response. In the simulation, the results of quantitative evaluation by simulation and the results using linical data are presented. From the result, the proposed method reflects dynamic time-varying ERP more exactly than previous methods and is also effective in consecutive monitoring of ERP.

• Journal of Communications and Networks
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• v.6 no.2
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• pp.147-155
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• 2004

In this paper, we propose an efficient sequencing technique, namely minimum Row time scheduling (MFTS), to manage variable-Iength message transmissions for single-hop passive starcoupled WDM optical networks. By considering not only the message length but also the state of the receivers and the tuning latency, the proposed protocol can reduce the average delay of the network greatly. This paper also introduces a new channel assignment technique latency minimizing scheduling (LMS), which aims to reduce the scheduling latency. We evaluate the proposed algorithm, using extensive discrete-event simulations, by comparing its performance with shortest job first (SJF) algorithm. We find that significant improvement in average delay could be achieved by MFTS algorithm. By combining the proposed message sequencing technique with the channel selection technique, the performance of the optical network could be further improved.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.1-8
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• 2019

In this paper, we propose a performance isolation of shared space for virtualized SSD based storage systems, to solve the weakness in a VSSD framework. The proposed scheme adopts a CFQ scheduler and a shared space-based FTL for the fairness and the performance isolation for multiple users on virtualized SSD based storage systems. Using the CFQ scheduler, we ensure SLOs for the storage systems such as a service time, a allocated space, and a IO latency for users on the virtualized storage systems. In addition, to improve a throughput and reduce a computational latency for garbage collection, a shared space-based FTL is adopted to maintain the information of SLOs for users and it manages shared spaces among the users. In our experiments, the proposal improved the throughput of garbage collection by 7.11%, on average, and reduced the computational latency for garbage collection by 9.63% on average, compared to the previous work.

• Journal of the Korea Society of Computer and Information
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• v.24 no.4
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• pp.1-9
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• 2019

LRR(Loose Round Robin) warp scheduling policy for GPU architecture results in high warp-level parallelism and balanced loads across multiple warps. However, traditional LRR policy makes multiple warps execute long latency operations at the same time. In cases that no more warps to be issued under long latency, the throughput of GPUs may be degraded significantly. In this paper, we propose a new warp scheduling policy which utilizes latency hiding, leading to more utilized memory resources in high performance GPUs. The proposed warp scheduler prioritizes memory instruction based on GTO(Greedy Then Oldest) policy in order to provide reduced memory stalls. When no warps can execute memory instruction any more, the warp scheduler selects a warp for computation instruction by round robin manner. Furthermore, our proposed technique achieves high performance by using additional information about recently committed warps. According to our experimental results, our proposed technique improves GPU performance by 12.7% and 5.6% over LRR and GTO on average, respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.3
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• pp.45-58
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• 2020

In the current trend of increasing network traffic due to the popularization of cloud service and mobile devices, WAN bandwidth is very low compared to LAN bandwidth. In a WAN environment, a WAN optimizer is needed to overcome performance problems caused by transmission protocol, packet loss, and network bandwidth limitations. In this paper, we analyze the data deduplication algorithm of WANProxy, an open source WAN optimizer, and evaluate its performance in terms of network latency and WAN bandwidth. Also, we evaluate the performance of the two-stage compression method of WANProxy and Zstandard. We propose a new method to improve the performance of WANProxy by revising its data deduplication algorithm and evaluate its performance improvement. We perform experiments using 12 data files of Silesia with a data segment size of 2048 bytes. Experimental results show that the average compression rate by WANProxy is 150.6, and the average network latency reduction rates by WANProxy are 95.2% for a 10 Mbps WAN environment and 60.7% for a 100 Mbps WAN environment, respectively. Compared with WANProxy, the two-stage compression of WANProxy and Zstandard increases the average compression rate by 33%. However, it increases the average network latency by 2.1% for a 10 Mbps WAN environment and 5.27% for a 100 Mbps WAN environment, respectively. Compared with WANProxy, our proposed method increases the average compression rate by 34.8% and reduces the average network latency by 13.8% for a 10 Mbps WAN and 12.9% for a 100 Mbps WAN, respectively. Performance analysis results of WANProxy show that its performance improvement in terms of network latency and WAN bandwidth is excellent in a 10Mbps or less WAN environment while superior in a 100 Mbps WAN environment.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.179-184
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• 2003

One of the remarkable problems in PIM-DM (Protocol Independent Multicast - Dense Mode) is the join latency time, increasing for specific periods. The reason of this problem is proved to the confusion of flooding prune message and leave prune message. We propose a new solution to this problem, reducing the average join latency by 37.4%, and prove the proposed solution by network simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1296-1309
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• 2023

In this paper, based on the soft cancellation (SCAN) bit-flipping (SCAN-BF) algorithm, a generalized SCAN bit-flipping (GSCAN-BF-Ω) decoding algorithm is carried out, where Ω represents the number of bits flipped or corrected at the same time. GSCAN-BF-Ω algorithm corrects the prior information of the code bits and flips the prior information of the unreliable information bits simultaneously to improve the block error rate (BLER) performance. Then, a joint threshold scheme for the GSCAN-BF-2 decoding algorithm is proposed to reduce the average decoding complexity by considering both the bit channel quality and the reliability of the coded bits. Simulation results show that the GSCAN-BF-Ω decoding algorithm reduces the average decoding latency while getting performance gains compared to the common multiple SCAN bit-flipping decoding algorithm. And the GSCAN-BF-2 decoding algorithm with the joint threshold reduces the average decoding latency further by approximately 50% with only a slight performance loss compared to the GSCAN-BF-2 decoding algorithm.