• 대한전자공학회논문지SD
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• 제46권7호
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• pp.22-28
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• 2009

멀티프로세서가 임베디드 시스템에서 널리 쓰임에 따라 지원되는 전력 최소화 기법을 이용하여 태스크를 수행하기 위해 필요한 에너지의 소모량을 줄여야 할 필요성이 대두된다. 본 논문은 동적 전압 스케일링 및 전력 셧다운을 이용하여 에너지 소모를 최소화 하는 태스크 스케줄링 알고리즘을 멀티프로세서 환경을 위해 제안하였다. 제안된 알고리즘에서는 전력 셧다운시의 에너지 및 타이밍 오버헤드를 고려하여 반복적으로 태스크 할당 및 태스크 순서화를 수행한다. 제안된 반복적인 태스크 스케줄링을 통해 전체 에너지 소모를 줄이는 가장 좋은 해를 얻을 수 있었다. 전체 에너지 소모는 리니어 프로그래밍 모델 및 전력 셧다운의 임계 시간을 고려하여 계산되었다. 실제 어플리케이션으로부터 추출된 표준 태스크 그래프에 기반을 둔 실험 결과를 통해 하드웨어 자원 및 시간제한에 따른 에너지 소모 관계를 분석하였다. 실험 결과를 볼 때 제안된 알고리즘은 기존의 우선권 기반의 태스크 스케줄링에 대해서 의미 있는 성능 향상을 얻을 수 있었다.

• International Journal of Aeronautical and Space Sciences
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• 제12권2호
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• pp.163-174
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• 2011

Task assignments of multiple unmanned aerial vehicles (UAVs) are examined. The phrase "task assignment" comprises the decision making procedures of a UAV group. In this study, an on-line decentralized task assignment algorithm is proposed for an autonomous UAV group. The proposed method is divided into two stages: an order optimization stage and a communications and negotiation stage. A genetic algorithm and negotiation strategy based on one-to-one communication is adopted for each stage. Through the proposed algorithm, decentralized task assignments can be applied to dynamic environments in which sensing range and communication are limited. The performance of the proposed algorithm is verified by performing numerical simulations.

• Journal of information and communication convergence engineering
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• 제18권4호
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• pp.222-229
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• 2020

Recent multi-core processors for smart devices use per-core dynamic voltage and frequency scaling (DVFS) that enables independent voltage and frequency control of cores. However, because the conventional task scheduler was originally designed for per-core DVFS disabled processors, it cannot effectively utilize the per-core DVFS and simply allocates tasks evenly across all cores to core utilization with the same CPU frequency. Hence, we propose a novel task scheduler to effectively utilize percore DVFS, which enables each core to have the appropriate frequency, thereby improving performance and decreasing energy consumption. The proposed scheduler classifies applications into two types, based on performance-sensitivity and allows a performance-sensitive application to have a dedicated core, which maximizes core utilization. The experimental evaluations with a real off-the-shelf smart device showed that the proposed task scheduler reduced 13.6% of CPU energy (up to 28.3%) and 3.4% of execution time (up to 24.5%) on average, as compared to the conventional task scheduler.

• 한국정밀공학회지
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• 제28권6호
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• pp.657-682
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• 2011

This paper presents the dynamic analysis on the joint torque of a finger for the tip pinch task. The dynamic model on finger movement was developed in order to predict the joint torques of an index finger, and the finger was assumed as a three-link planar manipulator. Analysis of the model revealed that the joint stiffness was one of the most important parameters affecting the joint torque. The stiffness of the finger joint was experimentally measured, and it was used in analyzing the finger joint torque required for performing the tip pinch task. The obtained joint torque for the tip pinch task will be used as the design requirements of the finger exoskeletal orthosis actuated by the polymer actuator whose allowable torque limit is relatively low compared to that of a mechanical actuator.

• 한국정보통신학회논문지
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• 제20권12호
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• pp.2401-2409
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• 2016

안드로이드 CPU 거버너는 CPU 주파수를 낮추어 CPU 공급 전압을 감소시키는 DVFS (Dynamic Voltage Frequency Scaling) 기반 전력 관리 기법을 사용한다. 그러나 CPU 주파수의 감소는 태스크의 실행 속도 지연을 유발한다. 이로 인해 태스크의 응답 시간 및 마감 시한 초과율이 증가하여 태스크가 제공하는 서비스의 품질 하락이 발생한다. 이에 본 논문에서는 다양한 안드로이드 CPU 거버너들을 전력 소비와 태스크의 응답성 및 마감 시한 측면에서 분석하였다.

• 한국인터넷방송통신학회논문지
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• 제20권3호
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• pp.83-88
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• 2020

최근 자동차는 관련 기술과 통신 환경의 발달로 차량과 도로 주변 인프라 구조와의 통신, 차량 간 통신이 가능해지고 있다. 자율주행차가 개발되면서 많은 센서와 고성능 연산장치가 탑재되고 있으며 이러한 자동차의 가용자원을 활용하여 클라우드 서비스를 제공하는 연구도 진행되고 있다. 본 논문에서는 연결형 자동차로 클라우드 서버를 구성하여 각 차량에 적합한 작업을 분배하는 과정에서 차량의 가용자원뿐만 아니라 기지국과의 통신 환경을 고려하여 동적으로 작업의 크기를 결정하는 기법을 제안하였다. 제안 기법의 모델을 기반으로 시뮬레이션한 결과 가용자원만을 고려하는 것보다는 통신 환경을 함께 고려해서 작업을 할당해야 마감 시간 내에 할당된 작업을 완료할 수 있다는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• 한국전문물리치료학회지
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• 제23권4호
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• pp.16-26
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• 2016

Background: Deficiencies in the ability to maintain balance are common in elderly. Augmented feedback such as knowledge of results (KR) can accelerate learning and mastering a motor skill in older people. Objects: We designed this study to examine whether one session of Wii-Fit game with self-regulated KR is effective for elderly people, and to compare the effect of two different timings of self-regulated KR conditions. Methods: Thirty-nine community-dwelling elders, not living in hospice care or a nursing home, participated in this study. During acquisition, two groups of volunteers were trained in 10 blocks of a dynamic balancing task under the following 2 conditions, respectively: (a) a pre-trial self-regulated KR ($n_1=18$), or (b) a post-trial self-regulated KR ($n_2=21$). Immediate retention tests and delayed retention tests of balancing performance were administered in 15 minutes and 24 hours following acquisition period, respectively. Results: In both groups, significant improvements of balancing performances scores were observed during the acquisition period. Regardless of the group, mean of balancing performance scores on retention tests were well-maintained from the final session. There were no significant differences between groups in balancing performance scores during the acquisition period (p>.05); however, the post-trial self-regulated KR group exhibited significantly higher balancing performance scores in both the immediate retention test and delayed retention test than that of the pre-trial self-regulated KR group (p<.05). Conclusion: Therefore, subjects who regulated their feedback after a dynamic balancing task, during the acquisition period, experienced more efficient motor learning during the retention period than did subjects who regulated their feedback before a dynamic balancing task. Accordingly, in case of presenting the KR of motor learning in clinical settings to elders who reduced dynamic balance abilities, the requesting time of KR is imperative according to self-estimation processes as well as types of KR and practice.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• International journal of advanced smart convergence
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• 제11권3호
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• pp.23-27
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• 2022

In this paper, we explore a new type of Android evasive malware based on the Sequential Probability Ratio Test (SPRT) that does not perform malicious task when it discerns that dynamic analyzer is input generator. More specifically, a new type of Android evasive malware leverages the intuition that dynamic analyzer provides as many inputs within a certain amount of time as possible to Android apps to be tested, while human users generally provide necessary inputs to Android apps to be used. Under this intuition, it harnesses the SPRT to discern whether dynamic analyzer runs in Android system or not in such a way that the number of inputs per time slot exceeding a preset threshold is regarded as evidence that inputs are provided by dynamic analyzer, expediting the SPRT to decide that dynamic analyzer operates in Android system and evasive malware does not carry out malicious task.