• Journal of Information Processing Systems
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• 제14권1호
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• pp.129-139
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• 2018

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

• 융합보안논문지
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• 제21권3호
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• pp.93-104
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• 2021

오늘날 인공지능과 같은 4차 산업혁명의 발달로 인하여 군 조직의 보안위협이 증대되고 있다. 이렇게 변화하거나 새롭게 등장하는 보안환경 속에서 발생하는 영향요인들이 보안 스트레스와 보안준수행동에 미치는 영향을 연구함으로써 군사보안을 준수하는데 기여될 수 있는 연구가 필요하다. 기존 선행연구에서 보안 스트레스를 유발하는 환경적인 영향요인 중 업무 과중, 업무 복잡성, 업무 불확실성, 업무 갈등을 추출하였다. 이러한 영향요인이 보안 스트레스에 어떠한 영향을 미치는지와 보안 스트레스의 매개역할 여부를 실증적으로 분석하였다. 분석결과, 업무 과중이 보안 스트레스에 통계적으로 유의미하게 정(+)의 영향을 미치는 것으로 나타났다. 보안 스트레스는 업무 과중과 보안준수행동간의 관계를 매개하는 것으로 나타났다. 이는 군 조직에서 과중한 보안업무로 인하여 보안 스트레스가 발생하고, 결국은 보안준수행동이 낮아지는 결과를 초래한다. 따라서 이런 상황을 관리하는 보안정책이 우선 추진되어야 할 것이다.

• Nuclear Engineering and Technology
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• 제35권1호
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• pp.80-90
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• 2003

According to related studies, it was revealed that the procedural deviation plays a significant role in initiating accidents or incidents. This means that, to maximize safety, it is indispensable to be able to answer the question of “why the operators deviate from procedures\ulcorner” In this study, the SC (step complexity) measure is introduced to investigate its applicability for studying the procedural deviation, since it was shown that the change of the operators' performance is strongly correlated with the change of SC scores. This means that the SC measure could play an important role for researches related to the procedural deviation, since it is strongly believed that complicated procedures would affect both the operator' performance and the possibility of the procedural deviation. Thus, to ensure this expectation, the meaning of the SC measure is investigated through brief explanations including the necessity, theoretical basis and verification activities of the SC measure. As a result, it is confirmed that the SC measure can be used to explain the change of the operators' performance due to the task complexity implied by procedures. In addition, it seems that the SC measure nay be useful for various purposes, particularly for scrutinizing the relationship between the procedural deviation and complicated procedures.

• 한국정보과학회논문지:시스템및이론
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• 제37권2호
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• pp.76-89
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• 2010

분산 이기종 시스템의 성능은 DAG로 주어지는 입력 그래프를 스케줄링 하는 알고리즘의 성능에 좌우된다. 많은 스케줄링 알고리즘 중에 리스트 스케줄링 알고리즘은 낮은 복잡도를 가지면서 우수한 성능을 보이고 있다. 리스트 스케줄링은 태스크 우선순위 결정 단계와 프로세서 할당 단계로 이루어져 있으나 대부분의 연구들은 태스크 우선순위 결정 단계만을 연구하고 있다. 본 논문에서는 기존의 할당 정책과 동일한 복잡도를 가지면서 성능이 향상된 새로운 프로세서 할당 정책인 LIP 정책을 제안한다. 기존의 리스트 스케줄링 알고리즘인 HEFT, HCPT, GCA, PETS의 태스크 우선순위 결정 정책에 본 논문에서 제안한 LIP 정책을 적용하여 실험한 결과 기존의 프로세서 할당 정책인 삽입 정책과 비 삽입 정책보다 성능 향상이 있는 것을 확인할 수 있었다.

• 한국멀티미디어학회논문지
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• 제8권12호
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• pp.1589-1596
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• 2005

The imprecise real-time system provides flexibility in scheduling time-critical tasks. Most scheduling problems of satisfying both 0/1 constraint and timing constraints, while the total error is minimized, are NP complete when the optional tasks have arbitrary processing times. Liu suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on uniprocessors for minimizing the total error. Song et al suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on multiprocessors for minimizing the total error. But, these algorithms are all off-line algorithms. On the other hand, in the case of on line scheduling, Shih and Liu proposed the NORA algorithm which can find a schedule with the minimum total error for a task system consisting solely of on-line tasks that are ready upon arrival. But, for the task system with 0/1 constraint, it has not been known whether the NORA algorithm can be optimal or not in the sense that it guarantees all mandatory tasks are completed by their deadlines and the total error is minimized. So, this paper suggests an optimal algorithm to search minimum total error for the imprecise on-line real-time task system with 0/1 constraint. Furthermore, the proposed algorithm has the same complexity, O(N log N), as the NORA algorithm, where N is the number of tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3423-3438
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• 2014

In recent years, wearable sensor devices are reshaping the way people live, work, and play. A wearable sensor device is a computer that is subsumed into the personal space of the user, and is always on, and always accessible. Therefore, among the most salient aspects of a wearable sensor device should be a small form factor, long battery lifetime, and real-time characteristics. Thereby, sophisticated applications of a wearable sensor device use real-time operating systems to guarantee real-time deadlines. The deterministic multi-dimensional task scheduling algorithms are implemented on ARC (Actual Remote Control) with relatively limited hardware resources. ARC is a wearable wristwatch-type remote controller; it can also serve as a universal remote control, for various wearable sensor devices. In the proposed algorithms, there is no limit on the maximum number of task priorities, and the memory requirement can be dramatically reduced. Furthermore, regardless of the number of tasks, the complexity of the time and space of the proposed algorithms is O(1). A valuable contribution of this work is to guarantee real-time deadlines for wearable sensor devices.

• 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.

• 로봇학회논문지
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• 제14권2호
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• pp.81-86
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• 2019

Robots are used in various industrial sites, but traditional methods of operating a robot are limited at some kind of tasks. In order for a robot to accomplish a task, it is needed to find and solve accurate formula between a robot and environment and that is complicated work. Accordingly, reinforcement learning of robots is actively studied to overcome this difficulties. This study describes the process and results of learning and solving which applied reinforcement learning. The mission that the robot is going to learn is bottle flipping. Bottle flipping is an activity that involves throwing a plastic bottle in an attempt to land it upright on its bottom. Complexity of movement of liquid in the bottle when it thrown in the air, makes this task difficult to solve in traditional ways. Reinforcement learning process makes it easier. After 3-DOF robotic arm being instructed how to throwing the bottle, the robot find the better motion that make successful with the task. Two reward functions are designed and compared the result of learning. Finite difference method is used to obtain policy gradient. This paper focuses on the process of designing an efficient reward function to improve bottle flipping motion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• 한국콘텐츠학회논문지
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• 제21권4호
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• pp.67-76
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• 2021

모바일 앱으로 과제를 수행할 때에 트리-뷰 메뉴의 외적인 구조에 따라 사용성이 달라지고, 메뉴의 배열이 사용자가 기대하는 스크립트와 다를 때에 사용성이 떨어지고, 그리고 과제의 복잡성에 따라 이 둘의 효과가 달라진다는 연구가설을 검증하였다. 이를 위하여 대학생 참가자들이 기대하는 숙박 예약 스크립트를 작성하였으며, 과제완료시간과 오반응률을 종속변인으로 하는 실험을 설계하였다. 실험은 예약 시스템 프로토타입을 스마트폰에 구현하여 실시하였다. 그 결과로 과제가 복잡할 때에 수행이 전반적으로 불리한 가운데, 구획메뉴의 수월성이 과제가 복잡할 때에 두드러졌으며 복잡한 과제에서 메뉴의 배열이 기대와 일치할 때에 긍정적인 반응이 관찰되었다. 이러한 결과를 논의하여 모바일 화면에 절차적인 메뉴를 설계할 때에는 작업기억의 부담을 최소화하는 방안을 마련하고, 과제에 맞는 트리-뷰가 무엇인지를 고민하고, 그리고 복잡한 과제에서는 사용자가 기대하는 스크립트에 준하여 메뉴를 배열하여야 한다는 사실을 발견할 수 있었다.