• Journal of the Ergonomics Society of Korea
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• v.18 no.3
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• pp.127-140
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• 1999

In order to apply three-dimensional images to industries, the possibility of realizing three-dimensional images should be ensured and when operating a task using three-dimensional images, the intention of the observer and the result of operation should be precisely related. The aim of this paper is to investigate the task performance of a human operator during operating a robot manipulator using three-dimensional and two-dimensional image displays. From the result of this research, it was found that the accuracy of robot operation in the case of using three-dimensional displays is much higher than in the case of using two-dimensional displays and the adapting time to the operating task using three-dimensional displays is shorter than that using two-dimensional displays. From such results, we concluded that the application of three-dimensional displays, which can closely reflect real environment, to industries is desirable.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.153-158
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• 2000

This paper addresses an intelligent robot control system using an off-line programming to teach a precise assembly task of electronic components in a flexible way. The investigated task consists of three job: heat caulking test, soldering on a circuit board, and checking of soldering defects on the back cover of a camera. This study investigates the remodelling of the most complicated cell in terms of the accuracy and fault rate among the twelve cells in a camera back-cover assembly line. We have attempted to enhance back-cover assembly line. We have attempted to enhance soldering quality, to add task flexibility, to reduce failure rate, and to increase product reliability. This study modifies the cell structure, and improves the soldering condition. The developed all system implements the real-time control of assembly with vision data, and realized an easier task teaching on off-line programming.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.30-43
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• 2013

Multicore and multiprocessor systems with dynamic voltage scaling architectures are being used as one of the solutions to satisfy the growing needs of high performance applications with low power constraints. An important aspect that has propelled this solution is effective task/application scheduling and mapping algorithms for multiprocessor systems. This work proposes an energy aware, offline, probability-based unified scheduling and mapping algorithm for multiprocessor systems, to minimize the number of processors used, maximize the utilization of the processors, and optimize the energy consumption of the multiprocessor system. The proposed algorithm is implemented, simulated and evaluated with synthetic task graphs, and compared with classical scheduling algorithms for the number of processors required, utilization of processors, and energy consumed by the processors for execution of the application task graphs.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.128-130
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• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.194-204
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• 2012

Measuring task complexity of movement skill is an important factor to evaluate a difficulty of learning and/or imitating a task for autonomous robots. Although many complexity-measures are proposed in research areas such as neuroscience, physics, computer science, and biology, there have been little attention on the robotic tasks. To cope with measuring complexity of robotic task, we propose an information-theoretic measure for task complexity of movement skills. By modeling proprioceptive as well as exteroceptive sensor data as multivariate Gaussian distribution, movements of a task can be modeled as probabilistic model. Additionally, complexity of temporal variations is modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on the real robotic movement tasks.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.371-378
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• 2016

The most important things in real-time systems are that a system guarantees to meet its deadline and to operate in its predictable range. When we design a real-time system, we need to verify whether the system can meet its deadline through schedulability analysis. There are several kinds of schedulability analysis technique for fixed priority scheduling systems. But as we all know, we can't perform schedulability analysis in design time because we can't estimate upper bounds on execution time of each task. So we used a similar real-time system to estimate upper bounds on execution time for our system, and then we performed schedulability analysis and verified that our system designed can meet its deadline.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4A
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• pp.393-401
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• 2006

Energy consumption is an important design parameter for battery-operated embedded systems. Dynamic power management is one of the most well-known low-power design techniques. This paper proposes an online realtime scheduling algorithm, which we call energy-aware realtime scheduling using slack stealing (EARSS). The proposed algorithm gives the highest priority to the task with the largest degree of device overlap when the slack time exists. Scheduling result enables an efficient power management by reducing the number of state transitions. Experimental results show that the proposed algorithm can save the energy by 23% on average compared to the DPM-enabled system scheduled by the EDF algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1544-1572
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• 2011

Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.77-90
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• 2000

Real-Time verification is a procedure that verifies the correctness of specification related to requirement in time as well as in logic. One serious problem encountered in the verification task is that the state space grows exponentially owing to the unboundedness of time, which is termed the state space explosion problem. In this paper, we propose a real-time verification technique checking the correctness of specification by showing that a system model described in timed automata is equivalent to the characteristic of system property specified in timed modal-mu calculus. For this, we propose a local model checking method based on the value of the formula in initial state with constructing product graph concerned to only the nodes needed for verification process. Since this method does not search for every state of system model, the state space is reduced drastically so that the proposed method can be applied effectively to real-time system verification.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.