• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.35-42
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• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.

• KOMUNHWA
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• no.60
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• pp.65-99
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• 2002

The main purpose of this article is mainly by analysing the military function and role of sword in army and combat field during Chosen dynasty. Firstly, this study is a pre-survey for the overall research on the troops with military sword. The Chosun army

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1795-1805
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• 1999

In an OFDM (Orthogonal Frequency-Division Multiplexing) system, the sampling frequency offset between the transmitter and receiver is known to cause the interchannel interference (ICI), resulting in performance degradation. In this paper, we propose two time-domain techniques to estimate the sampling frequency offset, especially for a high data-rate OFDM system. The first technique estimates the sampling frequency offset by using the phase difference between two received samples with a fixed amount of time interval, corresponding to the transmitted training symbol, under the assumption of perfect symbol and carrier offset synchronization. The second technique estimates the sampling frequency offset and carrier frequency offset jointly, when the two offsets exist together, by using two training symbols with different frequency components and using a sample algebraic calculation. The proposed estimation techniques for sampling frequency offset cause no time delay due to all time-domain processing, and have a good performance due to no ICI effect. The performances of the proposed techniques are demonstrated by various simulations.

• Journal of Korean Society of Forest Science
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• v.94 no.3 s.160
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• pp.153-160
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• 2005

This study was analyze the performance of Forest Work Team from 1999 to 2003. Following recommendations were come up by reviewing the performance of Forest Work Team. The volume of forest works should be increased in order to supply the constant daily work. This would provide the increase of their salary and maintain their salary and maintain their livelihood. The Forest Work Teams should be the members of registered business with highly trained manpower. Qualified Work Team should have extra financial incentive by comparing with general forest workers who did not have adequate skills. The percentage of the certified forest workers should be increased by providing the technical training. To increase the efficiency of forest work, the forest mechanization should be accomplished and the training of forest mechanization had to be offered regularly for the forest workers, and provided them with an education allowance.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.41-53
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• 2000

In this paper, the Maekjin and the Yangdorak Diagnosis algorithm by using a neuro-fuzzy method is proposed and it is implemented on the DSP-based system. Maekjin is measured by 3-channels of the Maekjin board through Maekjin probe which is attached on Chon, Kwan and Chuk of patient's wrist. First, we experiment Chon, Kwan and Chuk, 3-parts simultaneously and second perform one part of Chon, Kwan and Chuk respectively, The experimental results show that the Maekjin signal is measured precisely with any Maekjin probe. In Yangdorak diagnosis, the pulse generated by electric stimulator stimulates a portion of body and the response signal is measured through electrodes which is attached on representative points of 12 kyungmaks. The experimental methods are (1) 1 channel-measure, (2) 2 channels-measure, (3) 6 channels-measure and (4) 24 channels-measure. A fuzzy diagnosis is performed and neural networks is learned using fuzzy values as inputs, and we show that neuro-fuzzy diagnosis method is performed well.

• Korean Journal of Applied Biomechanics
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• v.19 no.4
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• pp.719-728
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• 2009

In this study, a training program was conducted to improve the performance of the basic movement of the basket-to-handstand mount. After completion of the training program, the kinematic comparison of the before and after effects were investigated to provide scientific data about this technique. It is recommended that during P1, the center of body mass at the back should push the hip joint to flex quickly, and the shoulder joint should be maintained at a maximum angle. During P2, the body's center of mass must be accelerated so as to create enough momentum to rise efficiently for this, quick extension of both the hip and the shoulder is required. For safety during P3, it is advised that the speed upwards must be increased and that the hands, shoulders, and hip joint must be extended, as in the posture of a handstand. These results stress to coaches the importance of the bodies speed during the ascent in the motion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.503-511
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• 2013

This paper presents a speech enhancement method using non-negative matrix factorization. In training phase, we can obtain each basis matrix from speech and specific noise database. After training phase, the noisy signal is separated from the speech and noise estimate using basis matrix in enhancement phase. In order to improve the performance, we model the change of encoding matrix from training phase to enhancement phase using independent Gaussian distribution models, and then use the constraint of the objective function almost same as that of the above Gaussian models. Also, we perform a smoothing operation to the encoding matrix by taking into account previous value. Last, we apply the Log-Spectral Amplitude type algorithm as gain function.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.541-543
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• 2004

능동적 학습(active learning)은 제한된 시간과 인력으로 가능한 정확도가 높은 분류기(classifier)를 생성하기 위하여, 훈련집합에 추가할 예제 즉 문의예제(query example)의 선정과 확장된 훈련집합으로 다시 학습하는 과정을 반복하여 수행한다. 능동적 학습의 핵심은 사용자에게 카테고리(category) 부여를 요청할 문의예제를 선정하는 과정에 있다. 효과적인 문의예제를 선정하기 위하여 다양한 방안들이 제안되었으나, 이들은 매 문의단계마다 하나의 문의예제를 선정하는 경우에 가장 적합하도록 고안되었다. 능동적 학습이 복수의 예제를 사용자에게 문의할 수 있다면, 사용자는 문의예제들을 서로 비교해 가면서 작업할 수 있으므로 카테고리 부여작업을 보다 빠르고 정확하게 수행할 수 있을 것이다. 또한 충분한 인력을 보유한 상황에서는, 카테고리 부여작업을 병렬로 처리할 수 있어 전반적인 학습시간의 단축에 큰 도움이 될 것이다. 하지만, 각 예제의 문의예제로써의 적합 정도를 추정하면 유사한 예제들은 서로 비슷한 수준으로 평가되므로, 기존의 방안들을 복수의 문의예제 선정작업에 그대로 적용할 경우, 유사한 예제들이 문의예제로 동시에 선정되어 능동적 학습의 효율이 저하되는 현상이 나타날 수 있다. 본 논문에서는 특정 예제를 문의예제로 선정하면 이와 일정 수준이상 유사한 예제들은 해당 예제와 함께 문의예제로 선정하지 않음으로써, 이러한 문제점을 극복할 수 있는 방안을 제안한다. 제안한 방안을 문서분류 문제에 적용해 본 결과 기존 문의예제 선정방안으로 복수 문의예제를 선정할 때 발생할 수 있는 문제점을 상당히 완화시킬 있을 뿐 아니라, 복수의 문의예제를 선정하더라도 각 문의 단계마다 하나의 예제를 선정하는 경우에 비해 큰 성능의 저하가 없음을 실험적으로 확인하였다./$m\ell$로 나타났다.TEX>${HCO_3}^-$ 이온의 탈착은 서서히 진행되었다. R＆D investment increases are directly not liked to R＆D productivities because of delays and side effects during transition periods between different stages of technology development. Thus, It is necessary to develope strategies in order to enhance efficiency of technological development process by perceiving the switching pattern. 기여할 수 있을 것으로 기대된다. 것이다.'ity, and warm water discharges from a power plant, etc.h to the way to dispose heavy water adsorbent. Through this we could reduce solid waste products and the expense of permanent disposal of radioactive waste products and also we could contribute nuclear power plant run safely. According to the result we could keep the best condition of radiation safety super vision and we could help people believe in safety with Radioactivity wastes control for harmony with Environ

• 한국체육학회지인문사회과학편
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• v.54 no.5
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• pp.251-260
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• 2015

The purpose of this study was to change of Psychological skills strategy and Self-management behavior according to the level of Resourcefulness of collegiate fencing-athletes. Methods: This study was a single case for 12-week intervention. and it took about 60-90 minutes, once a week. Participants were 154 Student athletes and 20 fencing-athletes. The effect of this study was utilized a questionnaire. To analyze the responses of research, the paired t-test were used for finding the significances of mean differences(p<.05). After 12 week education session the results are as follows: Firstly, high group of Resourcefulness showed greater increase in Attention and Image. low group of Resourcefulness showed greater increase in Will power, Goal Setting, Confidence, Attention, Image. Secondly, high group of Resourcefulness showed greater increase in Physical management. low group of Resourcefulness showed greater increase in Training management. fencing-athletes were significantly increased of capacity for Performance and self-management according to the level of Resourcefulness, after 12-weeks. This study suggested useful information about sport psychological skills training and intervention of fencing-athletes.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.