• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.39-48
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• 2014

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of Environmental Health Sciences
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• v.33 no.1 s.94
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• pp.49-56
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• 2007

The purpose of this study was to survey the distribution patterns of volatile organic compounds(VOCs) and formaldehyde in the various indoor environments using cluster analysis. We investigated VOCs and formaldehyde in subway stations, underground shopping areas, medical centers, maternity recuperation centers, public childcare centers, large stores, funeral houses, and indoor parking lots from June,2005 to May,2006. Concentration of TVOCs in maternity recuperations was 2,605.7 ${\mu}g/m^3$ that was higher than the guideline and other facilities. TVOCs in public childcare centers was 1,951.6 ${\mu}g/m^3$ also it exceeded the guideline. Moreover, concentration of TVOCs in every facility exceeded the guideline of Department of Environment, Korea. In case of formaldehyde, mean concentration, 336.5 ${\mu}g/m^3$, in only public childcare centers exceeded the 120 ${\mu}g/m^3$ of the guideline. Finally, by applying cluster analysis, three pattterns of the indoor air pollutions were distinguished. In the results of analysis, concentrations of TVOCs and formaldehyde of cluster 3 were higher than cluster 1 and 2 that were 2,561.4 ${\mu}g/m^3$ and 184.9 ${\mu}g/m^3$, respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.706-712
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• 2015

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Korean Journal of Environmental Biology
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• v.22
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• pp.30-37
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• 2004

Peysistent oyganochlorine compounds (OCs) weve determined in sediments and bivalves from Gwangyang Bay. The concentrations of ∑PCB, ∑DDT, ∑HCH and ∑CHL in sediments were in the range of f 2.25∼11.4 ng g$\^$-1/, 0.16∼1.16 ng g$\^$-1/, nd∼0.51 ng g$\^$-1/, and 0.05∼0.79 ng g$\^$-1/, respectively. The overall OCs concentrations in sediments were below the effect range -median (ER-M) values toy benthic organisms suggested by NOAA (1991). Levels of PCB compounds a re relatively lower than other industrialized bays (Pusan Bay, Ulsan Bay, and Youngil Bay). OCs accumulated in bivalves were higher than those in sediments. In bivalves, the concentration ranges of ∑PCB, ∑DDT, ∑HCH and ∑CHL were 9.97∼31.7 ng g$\^$-1/, 7.54∼22.6 ng g$\^$-1/, 0.49∼2.0 ng g$\^$-1/, and 0.82∼7.32 ng g$\^$-1/, respectively. Relatively high PCB concentrations in both environmental matrices are found at the inner bay than the outer part, indicating that the sources of PCBs were located inside the bay. DDT compound showed relatively high concentrations in the vicinity of the mouth of river and urban area, whereas other organochlorine pesticides show homogeneous distributions over the bay. Homologue profile of PCB compounds shows that low-chlorinated congeners (especially, di-, tyi- and tetra-) are abundant in Gwangynng Bay, which is diferent from other areas in Korea.

• Cartoon and Animation Studies
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• s.50
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• pp.239-273
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• 2018

There have been many topics in gamer research on gamers' game addiction, education, and psychological interest. This paper investigates how to perceive the virtual environment of gamers based on James Gibson 's theories of cognitive science. Gibson's theory is not a stimulus input through individual sensory receptors, but rather a learning process such as establishing a cognitive relationship between perceptual systems, external invariant property separation, behavioral learning, invariant property separation of events, selectiveism development. Based on this analysis tool, I collected and verified gamers' perception of game environment of by FGI survey method. The results of the analysis showed that Gibson 's perceptual learning process was perceived as a virtual environment as in reality, and there was also perceptual difference found only in games. Patterned perception develops in the direction of classifying invariant properties appearing in the game based on the purpose of the game. In this study, it can be seen as a result of the research that FGI interview can be summarized as patterning (typification) perception process based on the goal consciousness of gamers. But,The results of the study suggest that the psychological analysis of the gamer can not be presented by the FGI results alone. In the future, we need a model study to confirm the causality and the verification through statistical analysis.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1000-1015
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• 2010

In this paper, we propose a novel algorithm for hand gesture recognition. The hand detection method is based on human skin color, and we use the boundary energy information to locate the hand region accurately, then the moment method will be employed to locate the hand palm center. Hand gesture recognition can be separated into 2 step: firstly, the hand posture recognition: we employ the parallel NNs to deal with problem of hand posture recognition, pattern of a hand posture can be extracted by utilize the fitting ellipses method, which separates the detected hand region by 12 ellipses and calculates the white pixels rate in ellipse line. the pattern will be input to the NNs with 12 input nodes, the NNs contains 4 output nodes, each output node out a value within 0~1, the posture is then represented by composed of the 4 output codes. Secondly, the hand gesture tracking and recognition: we employed the Kalman filter to predict the position information of gesture to create the position sequence, distance relationship between positions will be used to confirm the gesture. The simulation have been performed on Windows XP to evaluate the efficiency of the algorithm, for recognizing the hand posture, we used 300 training images to train the recognizing machine and used 200 images to test the machine, the correct number is up to 194. And for testing the hand tracking recognition part, we make 1200 times gesture (each gesture 400 times), the total correct number is 1002 times. These results shows that the proposed gesture recognition algorithm can achieve an endurable job for detecting the hand and its' gesture.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.