• Korean Journal of Cognitive Science
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• v.28 no.1
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• pp.65-90
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• 2017

Rewards or penalties become informative only when contingent on an immediately preceding response. Our goal was to determine if the brain responds differently to motivational events depending on whether they provide feedback with the contingencies effective for learning. Event-related fMRI data were obtained from 22 volunteers performing a visuomotor categorical task. In learning-condition trials, participants learned by trial and error to make left or right responses to letter cues (16 consonants). Monetary rewards (+500) or penalties (-500) were given as feedback (learning feedback). In random-condition trials, cues (4 vowels) appeared right or left of the display center, and participants were instructed to respond with the appropriate hand. However, rewards or penalties (random feedback) were given randomly (50/50%) regardless of the correctness of response. Feedback-associated BOLD responses were analyzed with ANOVA [trial type (learning vs. random) x feedback type (reward vs. penalty)] using SPM8 (voxel-wise FWE p < .001). The right caudate nucleus and right cerebellum showed activation, whereas the left parahippocampus and other regions as the default mode network showed deactivation, both greater for learning trials than random trials. Activations associated with reward feedback did not differ between the two trial types for any brain region. For penalty, both learning-penalty and random-penalty enhanced activity in the left insular cortex, but not the right. The left insula, however, as well as the left dorsolateral prefrontal cortex and dorsomedial prefrontal cortex/dorsal anterior cingulate cortex, showed much greater responses for learning-penalty than for random-penalty. These findings suggest that learning-penalty plays a critical role in learning, unlike rewards or random-penalty, probably not only due to its evoking of aversive emotional responses, but also because of error-detection processing, either of which might lead to changes in planning or strategy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11B
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• pp.1038-1049
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• 2003

Exactly measuring traffic load is the basis for efficient traffic engineering. However, precise traffic measurement involves inspecting every packet traversing a lint resulting in significant overhead on routers with high-speed links. Sampling techniques are proposed as an alternative way to reduce the measurement overhead. But, since sampling inevitably accompany with error, there should be a way to control, or at least limit, the error for traffic engineering applications to work correctly. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level. We derive a relationship between the number of samples, the accuracy of estimation and the squared coefficient of variation of packet size distribution. Based on this relationship, we propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.12
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• pp.103-110
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• 2005

The performance of maximum likelihood(ML) detection for the given channel is analyzed in spatially multiplexed MIMO system. In order to obtain the vector symbol error rate, we define error vectors which represent the geometrical relation between lattice points. The properties of error vectors are analyzed to show that all lattice points in infinite lattice almost surely have four nearest neighbors after random channel transformation. Using this information and minimum distance obtained by the modified sphere decoding algorithm, we formulate the analytical performance of vector symbol error over the given channel. To verify the result, we simulate ML performance over various random channel which are classified into three categories: unitary channel, dense channel, and sparse channel. From the simulation results, it is verified that the derived analytical result gives a good approximation about the performance of ML detector over the all random MIMO channels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.788-791
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• 2005

In this paper, We propose performance of improvement method for eHDR-WPAN system using Interleaver. Burst error pattern caused by fading in indoor wireless channel. for the reason, using of Interleave method (make burst error to random error) can be enhance to error-rate in system. This paper is used Convolutional, Block, Random Interleaver. We make use of 9 and 27 for symbol spacing. Block-Interleaver is show that performance about 0.6dB of E$_b$/N$_o$ at $10^{-4}$. In result, the suitable Interleaver for eHDR-WPAN system is Block Interleaver of 9 symbol spacing.

• Design & Manufacturing
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• v.14 no.1
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• pp.63-68
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• 2020

Ultra-high brightness and thin displays need to optical micro-patterns which can uniformly diffuse the lights and low loss. The micro random patterns have characteristics to rise the optical efficiency such as light extraction, uniform diffusion. For this reason, various fabrication processes are studied for random patterns. In this study, the micro random patterns were machined by diamond turning which used a controlled cutting tool path with random cutting depth. The machined patterns had random shape and directionality along the circumferential direction. The average width and length of machined random pattern according to rotation radius were 40.13㎛~55.51㎛ and 37.25㎛~59.49㎛, and these results were compared with the designed result. Also, the machining error according to rotation radius in diamond turning using randomly controlled cutting depth was discussed.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.513-518
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• 1987

In this paper the error equations of the SDINS aligned by the gyrocompass are derived considering that the alignment errors are correlated to the sensor errors. Also the navigation errors due to the correlated errors are simulated by this error equations. The simulations are performed by the covariance analysis method, assumed all the sensor errors are random constants. The simulation results show that while the INS maintains the alignment attitude the cancellation takes place between the correlated errors, but once the INS changes attitude this cancellation effect is perturbed.

• The Magazine of the IEIE
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• v.11 no.2
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• pp.1-11
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• 1984

본 지 2월호에서 구술한 선형부호에 이어 이번호에서는 순회부호에 대해 기술하고자 한다. 선형블럭부호중 중요한 부류에 속하는 순회부호(cyclic code)는 그 내용이 대수적 구조를 갖고 있어 부호화 회로는 물론 부호에 필요한 오증(syndrome)계산회로 등 귀환연결이 있는 치환레지스터(shift register)를 사용한 장치화(implementation)가 매우 용이하다는 이점이 있다. 이런 순회부호는 산발오진(random error)뿐 아니라 연집오진(burst error)도 정정할 수 있는 매우 효과적인 부호로서 다중오진정정능력(multiple error correcting capability)을 갖는 BCH부호도 순회부호의 일종이다.