• Physical Therapy Korea
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• v.11 no.2
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• pp.1-8
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• 2004

Motor skill learning can be acquired implicitly without consciousness of what is being learned. The purpose of this study was to examine the characteristics of implicit motor learning in young and elderly people using a perceptual-motor task. Forty normal young and elderly subjects participated. A modified version of the Serial Reaction Time Task (SRTT) using six blocks of twelve perceptual motor sequences was administered. The paradigm consisted of the first random sequence block followed by the four patterned blocks and another random block. In each block, the go signal consisted of an asterisk displayed in the one of the four parallel arrayed boxes in the middle of the screen. Subjects were instructed to push the corresponding response buttons as quickly as possible. Young subjects demonstrated shorter reaction times during the consecutive patterned blocks reflecting appropriate learning accomplished. Elderly subjects were able to learn a perceptual-motor task with implicit knowledge, but the performance was lower than that of the young persons. These results indicated that implicit sequence learning is still preserved in elderly adults, but the rate of learning is slower.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5948-5971
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• 2019

Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.269-273
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• 1999

Combination of mental stress task with noise background is a traditional tool employed in psychophysiology. However, intensity of background noise is a factor affecting both performance on test and psychophysiological responses associated with stress evoked by mental load in noisy environment. In the current study on 7 subjects we analyzed the influence of white noise (WN) intensity (55, 70, and 85 dB[A] ) on psychophysiological responses during word recognition test performed on noise background. There were recorded following physiological variables: electrodermal activity (EDA) , namely, skin conductance level (SCL), skin conductance response (SCR) amplitude (SCR-A), rise time and total number of SCRs (N-SCR); cardiovascular activity, e.g., heart rate (HR), respiratory sinus arrhythmia (RSA) index, pulse transit time (PTT), finger pulse volume (PV), skin temperature (SKT) and respiratory activity, such as respiration rate (RESP-R) and inspiration wane amplitude (RESP-A) during baseline resting state and 40 s long performance on 3 similar Korean word recognition tests with different WN intensity (55, 70, and 85 dB). Electrodermal responses (SCR-A, SCL, N-SCR) demonstrated gradual increment with increased intensity of noise, and this increase of response magnitude with higher intensity of noise was typical also for r skin temperature (phasic SKT decrease) and pulse volume (phasic and tonic PV decrease). However, some cardiovascular and respiratory responses did not exhibit same tendency of gradual increase of reactivity , namely HR, as well as RESP-R and RESP-A showed decrement of response magnitudes. Important finding in terms of cardiovascular reactivity was that 55 and 70dB evoked similar profiles, while 85dB WN resulted in significantly different profile of reactions, suggesting that there exists a threshold level after which intensive auditory stimulation elicits psychophyslological responses pattern of different quality. There are discussed potential autonomic mechanism involved in mediation of observed physiological responses.

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.155-160
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• 2011

In distributed source localization where sensors transmit measurements to a fusion node, we address the sensor selection problem where the goal is to find the best set of sensors that maximizes localization accuracy when quantization of sensor measurements is taken into account. Since sensor selection depends heavily upon rate assigned to each sensor, joint optimization of rate allocation and sensor selection is required to achieve the best solution. We show that this task could be accomplished by solving the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. Then we solve this rate allocation problem by using the generalized BFOS algorithm. Our experiments demonstrate that the best set of sensors obtained from the proposed sensor selection algorithm leads to significant improvements in localization performance with respect to the set of sensors determined from a sensor selection process based on unquantized measurements.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.107-112
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• 2004

As the internet service evolves fast recently, guarantee request of QoS (Quality of Service) by characteristic of traffic source as well as high rate of data is going greatest. Accordingly intemet service technology also is changing rapidly, technology that guarantee QoS in existent network service technology is developed or network model that guarantee new QoS is presented. By DiffServ-aware-MPLS network that present in IETF (Internet Engineering Task Force) to guarantee QoS in this treatise does comparative analysis with existent network model, relative show that is superior, and present direction that compose next generation network wish to.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.4
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• pp.252-261
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• 2001

Room air flow and temperature distribution was numerically investigated where a PAC(personal air conditioning) system was installed. The calculated results were compared with those from experiments. The effects of the important operation parameters such as the air flow rate, velocity, and temperature at the diffuser on the thermal performance of the system were studied. The possibility of energy saving using the PAC system was verified from the results, It was shown that the warm air from the diffuser could not spread over the whole task area if the inlet temperature was too high.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.