• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• 한국정밀공학회지
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• 제22권4호
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• pp.173-180
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• 2005

In this study, changes in verbal cognitive performance, blood oxygen saturation and heart rate due to 30％ concentration oxygen supply were observed. Five male (24.6±0.9) and five female (22.2±1.9) college students were asked to perform 28 verbal cognitive tasks of the same difficulty during two types of oxygen (concentration 21％, 30％) administration. The experimental sequence consisted of Rest1 (1 min.), Control (1 min.), Task (4 min.), and Rest2 (4 min.) and the physiological signals such as blood oxygen saturation and heart rate were measured throughout the stages. The experimental result showed that the performance increased significantly at 30％'s concentration of oxygen rather than 21％'s, which shows oxygen supply has positive influence on verbal cognitive performance. When 30％ concentration oxygen is supplied, the oxygen saturation in the blood significantly increased comparing to 21％. The heart rate showed no significant difference. Significant correlations were found between changes in oxygen saturation and cognitive performance. It is suggested that 30％ oxygen can stimulate brain activation by increasing actual blood oxygen concentration in the process of cognitive performance.

• 한국통신학회논문지
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• 제29권11C호
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• pp.1498-1509
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• 2004

본 논문에서는 향후 유비쿼터스 센서 네트워크 응용 등에 적합하도록 간단한 송수신기 구조를 유지하면서, 실제 데이터를 전송하기 전에 Noise Power Calibration 및 Noise Power Windowing 방법을 통해 잡음의 영향을 고려하여 적응적인 임계값을 결정하고 이를 이용하여 전력검출 (Power Detection)을 수행하여 성능을 향상하는 임펄스 라디오 (Impulse Radio) 형태의 Noncoherent OOK (On-Off Keying) UWB (Ultra Wide Band) 시스템을 제시하고, 전형적인 UWB 실내 무선 채널 모델에서의 성능을 분석하였다. 모의실험 결과 AWGN (Additive White Gaussian Noise) 채널에서는 Noise Power Calibration 모드를 위한 슬롯수의 증가에 따라 이상적인 Ideal Adaptive Threshold를 사용하는 경우께 근접하는 우수한 성능을 보임을 확인할 수 있었고, 반면 데이터 전송률의 큰 감소를 감수해야 하는 Noise Power Windowing 방법에 의한 성능 개선은 두드러지게 나타나지 않음을 알 수 있었다. 더욱이 IEEE 802.15 Task Group 3a UWB 실내 채널 모델을 이용한 모의실험 결과, Noise Power Calibration 모드가 적용된 Noncoherent OOK UWB 시스템의 성능이 Ideal Adaptive Threshold의 경우와 비트오율 성능이 매우 근접하며, 펄스 반복 전송의 회수의 증가에 따라 비트오율 성능이 향상됨을 확인 할 수 있었다.

• 대한인간공학회지
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• 제31권2호
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• pp.371-377
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• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
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• pp.32-32
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• 1994

In this example, a message passing based multicomputer system with general interdonnedtion network is considered. After multicomputer systems are developed with morm-hole routing network, topologies of interconecting network are not major considertion for process management and resource sharing. Tehre is an independeent operating system kernel oneach node. It communicates with other kernels using message passingmechanism. Based on this architecture, the problem is how mech does performance degradation will occur in the case of processor sharing on multicomputer systems. Processor sharing between application programs is veryimprotant decision on system performance. In almost cases, application programs running on massively parallel computer systems are not so much user-interactive. Thus, the main performance index is system throughput. Each application program has various communication patterns. and the sharing of processors causes serious performance degradation in hte worst case such that one processor is shared by two processes and another processes are waiting the messages from those processes. As a result, considering this problem is improtant since it gives the reason whether the system allows processor sharingor not. Input data has many parameters in this simulation . It contains the number of threads per task , communication patterns between threads, data generation and also defects in random inupt data. Many parallel aplication programs has its specific communication patterns, and there are computation and communication phases. Therefore, this phase informatin cannot be obtained random input data. If we get trace data from some real applications. we can simulate the problem more realistic . On the other hand, simualtion results will be waseteful unless sufficient trace data with varisous communication patterns is gathered. In this project , random input data are used for simulation . Only controllable data are the number of threads of each task and mapping strategy. First, each task runs independently. After that , each task shres one and more processors with other tasks. As more processors are shared , there will be performance degradation . Form this degradation rate , we can know the overhead of processor sharing . Process scheduling policy can affects the results of simulation . For process scheduling, priority queue and FIFO queue are implemented to support round-robin scheduling and priority scheduling.

• IEIE Transactions on Smart Processing and Computing
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• 제2권6호
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• pp.367-372
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• 2013

Cloud computing technology has been developing at an increasing expansion rate. Today most of firms are using this technology, making improving the quality of service one of the most important issues. To achieve this, the system must operate efficiently with less idle time and without deteriorating the customer satisfaction. This paper focuses on enhancing the efficiency of a conventional Genetic Algorithm (GA) for task scheduling in cloud computing using Fuzzy Logic (FL). This study collected a group of task schedules and assessed the quality of each task schedule with the user expectation. The work iterates the best scheduling order genetic operations to make the optimal task schedule. General GA takes considerable time to find the correct scheduling order when all the fitness function parameters are the same. GA is an intuitive approach for solving problems because it covers all possible aspects of the problem. When this approach is combined with fuzzy logic (FL), it behaves like a human brain as a problem solver from an existing database (Memory). The present scheme compares GA with and without FL. Using FL, the proposed system at a 100, 400 and 1000 sample size*5 gave 70%, 57% and 47% better improvement in the task time compared to GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1238-1259
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• 2024

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependency-aware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

• 한국산학기술학회논문지
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• 제14권2호
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• pp.681-687
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• 2013

본 연구는 학습 환경의 실내 온도와 학습재료 색채와의 조합이 학습수행에 어떠한 영향을 미치는 지를 살펴보고자 하였다. 이를 위해 학습활동 적정온도($22.5{\sim}24^{\circ}C$)를 중심으로(중립 실내 온도 조건), 그 이상인 조건(고온 실내온도 조건), 그리고 그 이하인 조건(저온 실내 온도 조건)으로 각각 실내 온도 조건을 구분하였으며, 난색계열인 빨간색과 한색계열인 파란색, 그리고 중성인 검은색과 연두색으로 각각 색채 조건을 구분하였다. 학습과 관련된 과제로는 음운 작업기억 과제를 사용하여 집단 간 실내 온도 조건에 따른 색채 조건에서의 과제 수행을 살펴보았다. 그 결과, 학습과제의 반응시간에서는 각 독립변수들에 의한 차이가 유의하지 않은 반면, 정확률에서는 색채 조건 중 빨간색과 검은색 조건에서 보다 정확한 수행이 나타났다. 이는 빨간색이 가진 현저성과 색채 온도감 및 검정색이 가진 친숙성과 다른 색에 비해 유일하게 현저성을 가지지 않는 특이성이 존재하기 때문에 나타난 결과로 해석할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• 로봇학회논문지
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• 제17권3호
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.