• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.1-4
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• 2015

This paper aims to present the mathematical model to find the mean web object transfer latency in the slow-start phase of TCP congestion control mechanism, which is one of the main control techniques of Internet. Mean latency is an important service quality measure of end-user in the network. The application area of the proposed latency model is the narrowband environment including multi-hop wireless network and Internet of Things(IoT), where packet loss occurs in the slow-start phase only due to small window. The model finds the latency considering initial window size and the packet loss rate. Our model shows that for a given packet loss rate, round trip time and initial window size mainly affect the mean web object transfer latency. The proposed model can be applied to estimate the mean response time that end user requires in the IoT service applications.

• Electronics and Telecommunications Trends
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• v.13 no.3 s.51
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• pp.61-70
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• 1998

The obvious way to make a computer system more powerful is to make the processor as fast as possible. Furthermore, adopting a large number of such fast processors would be the next step. This multiprocessor system could be useful only if it distributes workload uniformly and if its processors are fully utilized. To achieve a higher processor utilization, memory access latency must be reduced as much as possible and even more the remaining latency must be hidden. The actual latency can be reduced by using fast logic and the effective latency can be reduced by using cache. This article discusses what the memory latency problem is, how serious it is by presenting analytical and simulation results, and existing techniques for coping with it; such as write-buffer, relaxed consistency model, multi-threading, data locality optimization, data forwarding, and data prefetching.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.227-237
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• 2019

Ultra-low latency control is one of the characteristics of 5G cellular network services, which means that the control loop is handled in milliseconds. To achieve this, it is necessary to identify time delay factors that occur in all components related to CPS control loop, including new 5G cellular network elements such as MEC, and to optimize CPS control loop in real time. In this paper, a novel CPS architecture for ultra-low latency control of CPS is designed. We first define the ultra-low latency characteristics of CPS and the CPS concept model, and then propose the design of the control loop performance monitor (CLPM) to manage the timing information of CPS control loop. Finally, a case study of MEC-based implementation of ultra-low latency CPS reviews the feasibility of future applications.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.1
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• pp.1-9
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• 2014

This paper proposes a low-latency Sample Adaptive Offset filter (SAO) architecture and its Single Instruction Multiple Data (SIMD) optimization scheme to achieve fast High Efficiency Video Coding (HEVC) decoding in a multi-core environment. According to the HEVC standard and its Test Model (HM), SAO operation is performed only at the picture level. Most realtime decoders, however, execute their sub-modules on a Coding Tree Unit (CTU) basis to reduce the latency and memory bandwidth. The proposed low-latency SAO architecture has the following advantages over picture-based SAO: 1) significantly less memory requirements, and 2) low-latency property enabling efficient pipelined multi-core decoding. In addition, SIMD optimization of SAO filtering can reduce the SAO filtering time significantly. The simulation results showed that the proposed low-latency SAO architecture with significantly less memory usage, produces a similar decoding time as a picture-based SAO in single-core decoding. Furthermore, the SIMD optimization scheme reduces the SAO filtering time by approximately 509% and increases the total decoding speed by approximately 7% compared to the existing look-up table approach of HM.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.1-6
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• 2020

This paper aims to integrate the previous models about mean object transfer latency in one framework and analyze the result through the computational experience. The analytical object transfer latency model assumes the multiple packet losses and the Internet of Things(IoT) environment including multi-hop wireless network, where fast re-transmission is not possible due to small window. The model also considers the initial congestion window size and the multiple packet loss in one congestion window. Performance evaluation shows that the lower and upper bounds of the mean object transfer latency are almost the same when both transfer object size and packet loss rate are small. However, as packet loss rate increases, the size of the initial congestion window and the round-trip time affect the upper and lower bounds of the mean object transfer latency.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.165-170
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• 2017

Many studies have utilized GPGPU (General-Purpose Graphic Processing Unit) and its high computing power to compute complex tasks. The characteristics of GPGPU programs necessitate the operations of memory copy between the host and device. A high latency period can affect the performance of the program. Thus, it is required to significantly improve the performance of GPGPU programs by optimizations. By executing multiple GPGPU programs simultaneously, the latency hiding effect of memory copy is achieved by overlapping the memory copy and computing operations in GPGPU. This paper presents the results of analyzing the latency hiding effect for memory copy operations. Furthermore, we propose a performance anticipation model and an algorithm for the limitations of using pinned memory, and show that the use of the proposed algorithm results in a 41% performance increase.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.4
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• pp.329-333
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• 2003

In this paper we analyze the performance of the low latency pre-registration handoff method of mobile IP by computer simulation. Packet losses and delays are evaluated in terms of system utilization. Foreign agents that participate in the handoff process can be modeled as queues representing input and output ports. Therefore, we propose an analytical model of pre-registration handoff by using open queueing network model. Simulation results are shown for validating analytical estimates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.473-493
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• 2019

5G vehicular communication is one of key enablers in next generation intelligent transportation system (ITS), that require ultra-reliable and low latency communication (URLLC). To meet this requirement, a new hybrid vehicular network structure which supports both centralized network structure and distributed structure is proposed in this paper. Based on the proposed network structure, a new vehicular network utility model considering the latency and reliability in vehicular networks is developed based on Euclidean norm theory. Building on the Pareto improvement theory in economics, a vehicular network uplink optimization algorithm is proposed to optimize the uplink utility of vehicles on the roads. Simulation results show that the proposed scheme can significantly improve the uplink vehicular network utility in vehicular networks to meet the URLLC requirements.