Earticle

Background: The purpose of this paper is to confirm the event timings and the magnitude of fission product aerosol release from the Fukushima accident. Over a few hundreds of technical papers have been published on the environmental impact of Fukushima Daiichi accident since the accident occurred on March 11, 2011. However, most of the research used reverse or inverse method based on the monitoring of activities in the remote places and only few papers attempted to estimate the release of fission products from individual reactor core or from individual spent fuel pool. Severe accident analysis code can be used to estimate the radioactive release from which reactor core and from which radionuclide the peaks in monitoring points can be generated. Materials and Methods: The basic material used for this study are the initial core inventory obtained from the report JAEA-Data/Code 2012-018 and the given accident scenarios provided by Japanese Government or Tokyo Electric Power Company (TEPCO) in official reports. In this research a forward method using severe accident progression code is used as it might be useful for justifying the results of reverse or inverse method or vice versa. Results and Discussion: The release timing and amounts to the environment are estimated for volatile radioactive fission products such as noble gases, cesium, iodine, and tellurium up to 184 hours (about 7.7 days) after earthquake occurs. The in-plant fission product behaviors and release characteristics to environment are estimated using the severe accident progression analysis code, MELCOR, for Fukushima Daiichi accident. These results are compared with other research results which are summarized in UNSCEAR 2013 Report and other technical papers. Also it may provide the physically based arguments for justifying or suspecting the rationale for the scenarios provided in open literature. Conclusion: The estimated results by MELCOR code simulation of this study indicate that the release amount of volatile fission products to environment from Units 1, 2, and 3 cores is well within the range estimated by the reverse or inverse method, which are summarized in UNSCEAR 2013 report. But this does not necessarily mean that these two approaches are consistent.

본 논문은 천리안 위성을 이용하여 운용중인 공공 테스트베드의 가용율 및 시스템 throughput 향상을 위해 포워드 링크 ACM(Adaptive Coding & Modulation) 방안에 대해 소개하고, 포워드링크에 ACM 기능을 구현하기 위한 채널 상태를 예측하기위한 알고리즘으로 기울기를 이용한 예측 기법과 LMS(Least Mean Square)를 이용한 예측 기법의 성능을 비교하였다. 시뮬레이션 결과 LMS 기법을 이용한 예측기법은 99%가 3dB 이내의 예측 오차를 보였고, 기울기를 이용한 예측 기법은 99%가 4.5dB 이내의 예측 오차를 갖음을 알 수 있다.

In this paper, we present the forward link ACM method to improve the link availability and system throughput. Also, we compare the prediction algorithm between slope based prediction and LMS algorithm. The simulation results show that the 99% of predicted values in LMS algorithm is within 3dB and that of predicted values in the slope based prediction method is within 4.5dB.

Purpose: This study aimed to provide basic clinical data by analyzing the impact of passive stretching exercise and Mckenzie exercises for improving postures on the neck alignment and pain. Method: The all subjects were participated voluntary and we divided into two groups (10 people to Mckenzie exercises group, 10 people for passive stretching exercise group). The effects on neck alignment were measured with craniovertevral angle(CVA), forward head distance(FHD) and neck pain were measured with neck disability index(NDI). 3 times a week for 4 weeks, and when all the interventions were completed after 4 weeks, neck alignment and pain were re-measured in the same way as the pre-test. Result: The comparison of changes in neck alignment and pain within the Mckenzie exercises and passive stretching exercise groups showed no statistically significant differences in the CVA, FHD, NDI. However both types of stretching exercises were effective in improving neck alignment and reducing pain within each group before and after the intervention. Conclusion: Therefore, it is believed that observation training should be used in clinical practice to improve forward head posture and relief pain in neck disability patients.

여러 분야에서 뛰어난 성능을 보이는 딥러닝은 뉴럴 네트워크의 은닉 계층을 늘려 깊은 네트 워크 구조를 형성할 수 있다. 이에 따라 복잡한 데이터를 쉽게 분류할 수 있으나, 가중치 수 증가로 인해 학습 연산량 및 메모리가 증가한다. 이처럼 많은 연산과 메모리가 필요한 딥러닝은 일반적으로 클라우드 상으로 학습하나, 클라우드는 사용자와의 통신상태가 원활해야 하며 서비스 비용에 대한 부 담감 및 개인정보 유출에 대한 위험성을 가진다. 이러한 문제를 해결하기 위해서 딥러닝을 임베디드 디바이스에 탑재해야 하며 온디바이스 탑재를 위한 네트워크 경량화가 필요하다. 경량화 기법으로는 프루닝 기법과 양자화 기법이 널리 사용되고 있으며 기존 양자화 기법은 비지도 학습 중 하나인 Kmeans를 사용하여 각 계층의 가중치들을 군집화하여 대푯값을 결정한다. K-means 기법에서 군집의 개수를 의미하는 K는 시행착오 방법을 통해 설정해야 하며 이에 따른 오버헤드가 발생한다. 또한, 기 존 양자화 기법은 네트워크 각 계층의 연결 관계를 무시한 채 독립적으로 양자화한다. 우리는 연산 오 버헤드 문제를 해결하기 위해 가중치들의 통계적 정보를 이용한 즉각적 가중치 양자화 기법을 제안하 고 네트워크 각 계층의 의존관계를 고려하여 순차 적으로 양자화함으로써 향상된 학습 성능을 보여준다.

Deep learning, which has excellent performance in various fields, can form deep network structure by increasing hidden layer of neural network. Thus, complex data can be easily classified, but the amount of learning computation and memory increases due to increase of the number of weights. Deep learning, which requires a lot of computation and memory, usually is trained on cloud environments. However, the cloud environment must be maintained in good communication state with user and have burden on service cost and risk of leakage of personal information. To solve this problem, deep learning needs to be learnt on the embedded device, and be light-weighting. Pruning and quantization methods are widely used for the light-weighting. Existing quantization methods usually use K-means, one of the unsupervised learning methods, in order to determine representative values of clustering weights. In the K-means method, the number of groups is set through trial-and-error method, which results in the computation overhead. To alleviate the overhead, we propose an instant weight quantization method using statistical information of weights Also, it can improve learning performance by quantizing sequentially with considering the dependency between layers in the network.

This paper presents a recognition algorithm combining the vehicle detection and the color difference of RGB color space to recognize the brake-lights state of moving vehicles in order to achieve the intelligent-car’s rear-end collision warning about the vehicle in front of it. Firstly, we train and build AdaBoost cascade classifier by haar features samples and scaling sub-windows are used to detect the target vehicle from the region of interest of the resized image. Then we compare the adjacent frames to recognize brake light status, which including using color, shape, structural features to identify the third brake light; comparing the center of gravity coordinates and the color difference threshold to rear brake lights when vehicles are not red or yellow; according to subtraction of each RGB corresponding channel, binarization, and the color difference threshold of RGB color space to identify the red or yellow vehicles’ brake lights. Finally, experiments show that the algorithm can detect the front vehicle’s braking quickly and accurately.

Together with the GPS-based approach, geo-location through mobile communication networks is a key technology for location-based service. To save the cost, most geo-location system is implemented on the existed network service, which has a cellular structure. Still, multilateration is limited in cellular structure because it is difficult for the mobile terminal to acquire distance measurements from multiple base stations. This low hearability in the receiver is caused by co-channel interference and multipath environment. Therefore, hearability enhancement is necessary for multilateration under multipath and interference environment. Former time domain based hearability methods were designed for real signals. However, orthogonal frequency division multiplexing (OFDM) signal, which its usage has been increased in digital wireless communication, is a complex signal. Thus, different hearability enhancement method is needed for OFDM signals. This paper proposes a hearability enhancement method for forward-link multilateration using OFDM signals, which employ interference cancellation and multipath mitigation. A novel interference cancellation and multipath mitigation strategy for complex-valued OFDM signals is presented that has an iterative structure. Simulation results show that the proposed multilateration method provides the user's position with an accuracy of less than 80m through the mobile WiMAX cellular network in multipath environment.

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital signal processing approach. From the 20 second query audio file, 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS (Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we verify the superior performance of the SFS method that provides near $90{\%}$ success rate for the genre classification which means $10{\%}$-$20{\%}$ improvements over the previous methods

PURPOSE: This paper aims to present the available angle to evaluate the severity of forward head posture (FHP) with the observation method and photographic method. METHODS: A cross-sectional observation research design study consisted of 29 subjects who was divided two groups (slight FHP group, moderate FHP group) in Eulji university was used. We evaluated the FHP and the angles including CranioVertebral Angle (CVA), Head Tilting Angle (HTA), Head Position Angle (HPA) and Forward Shoulder Angle (FSA) with the Body style S-8.0 (South Korea, LU Commerce). RESULTS: The mean of CVA, FSA from the slight FHP group was shown higher than moderate severe FHP group. According to independent t-test result, but there was no difference among all angles in two groups. The linear discriminate analysis showed the size of distinction of FSA was the biggest, and then CVA, HTA and HPA were in the order. 55.6% of FSA is properly classified in the slight FHP group. CONCLUSION: The FSA is the best to distinguish the severity of FHP and then CVA as the second best. Therefore, FSA is recommended to check the FHP.

The appropriate radiation conditions of ship motion problem with advancing speed in frequency domain are investigated from a theoretical and practical point of view. From extensive numerical experiments that have been conducted for evaluation of the relevant radiation conditions, a hybrid radiation technique is proposed in which the Sommerfeld radiation condition and the free surface damping are mixed. Based on the comparison with the results of the translating and pulsating Green function method, the optimal damping factor of the hybrid radiation technique is selected, and the observed limitations of the proposed hybrid radiation technique are discussed, along with its accuracy obtained from the numerical solutions. Comparative studies of the forward-speed seakeeping prediction methods available confirm that the results of applying the hybrid radiation technique are relatively similar to those obtained from the translating and pulsating Green function method. This confirmation is made in comparisons with the results of solely applying either the free surface damping, or the Sommerfeld radiation condition. By applying the proposed hybrid radiation technique, the wave patterns, hydrodynamic coefficients, and motion responses of the Wigley III hull are finally calculated, and compared with those of model tests. It is found that, in comparison with the model test results, the three-dimensional Rankine source method adopting the proposed hybrid radiation technique is more robust in terms of accuracy and numerical stability, as well as in obtaining the forward speed seakeeping solution.

이 연구의 목적은 전방머리자세를 가진 젊은 성인을 대상으로 슬링뉴렉안정화운동이 중재방법이 통증 및 목뼈 앞굽음각, 중력중심선에 어떠한 영향을 미치는지 알아보고자 실시되었다. 연구의 대상자는 슬링뉴렉안정화운동그룹(SNSEG) 10명, 통제그룹(CC) 10명으로 선정하였으며, 8주간 주3회 70분간 실시하였다. 이 연구 결과 슬링뉴렉안정화운동은 통증은 감소(p<.001), 목뼈 앞굽음각은 증가(p<.001)와 중력중심선은 감소에(p<.01) 유의한 효과가 나타났다. 이상의 결과 종합해 보면 슬링뉴렉안정화운동은 속근육(LM)군의 고유수용성감각 개선 및 근활성화에 효과적이며, 대근육(GM)군과 공동활성화하여 앞굽은각과 중력중심선에 개선에 효과적인 것으로 나타났다. 따라서 깊은목굽힘근과 뒤통수밑근의 재활성화는 통증 조절과 자세 정렬에 중요한 요소로 전방머리자세 개선시킬 수 있는 효과적인 중재방법으로 제안한다.

The purpose of this study was to the effects of the Sling Neurac stabilization exercise(SNSE) on pain, cervical lordosis angle(CLA), and cervical gravity line(CGL) in young adults with a forward head posture. The subjects of the study were selected as SNSEG(n=10) and control group(n=10), they were conducted for 70 minutes three times a week for 8 weeks. Study results showed that pain(p<.001), CGLe(p<.01) were significantly decreased and CLA(p<.001) were also significantly increased in the SNSEG. In conclusion, SNSE is effective in improving the proprioceptive sense of the LM group and activating the muscle. Co-activation with the GM group was found to be effective in improving the CLA and CGL. Therefore, reactivation of the deep neck flexors and suboccipital muscles is an important factor in pain control and postural alignment, and is suggested as an effective intervention method to improve forward head posture.

유전 발현 데이터는 생명체의 특정 조직에서 채취한 샘플을 microarray상에서 측정한 것으로 유전자들의 발현 정도가 수치로 나타난 데이터이다. 일반적으로 정상조직과 이상조직에서 관련 유전자들의 발현 정도는 차이를 보이기 때문에, 유전발현 데이터를 통하여 암을 분류할 수 있다. 하지만 분류에 모든 유전자가 관여하지는 않으므로 관련성 있는 유전자만을 선별해내는 작업인 특징 선택방법이 필요하다. 본 논문에서는 회귀분석의 변수선택방법중 하나인 전진 선택법(forward selection method)을 사용하여 유전자들을 선택하고 분류하는 방법을 제안한다. 실험데이터는 대장암 데이트를 사용하였고, 분류기는 KNN을 사용하였다. 이 방법과 상관계수를 이용한 특징 선택 방법인 피어슨 상관계수와 스피어맨 상관계수방법과 비교해본 결과 전진 선택법에 의한 특징 선택방법이 암의 분류에 있어서 더 효과적인 유전자 선택을 한다는 사실을 확인하였다. 실험결과 90.3%의 높은 인식률을 보였다.

도시의 방범 대책의 일환으로 거리감시 카메라의 설치가 진행되고 있다. 이때 가능하면 넓은 범위를 포함하고, 사각을 없애기 위해 다수의 카메라가 필요하기 때문에 기기 도입에 많은 비용이 발생하게 된다. 또한 카메라에 모터를 장착하여, 상하 좌우로 움직이는 기계적인 장치를 추가하여, 기기의 대수를 줄이는 경우에도 많은 유지 보수비용이 필요하다. 그러나 360도를 관찰할 수 있는 어안렌즈 카메라를 이용하면 다수의 카메라 대신 한 대의 카메라로 대응할 수 있어 유지보수 비용을 절약할 수 있다. 그러나 어안 렌즈를 이용할 경우에는 영상이 굴절되어 보이기 때문에 감시 카메라용 렌즈로써는 부적합하다. 따라서 본 논문에서는 굴곡 되어 있는 영상의 개선을 위하여 Forward Warping을 이용하여 파노라마 영상으로 변환하고 파노라마 영상으로 변환 중에 손실되는 영상 정보를 복원하기 위하여 양선형 보간법을 적용하여 개선된 파노라마 영상을 얻을 수 있는 방법을 제안한다. 본 논문에서 제안한 어안 렌즈 영상 재구성 방법의 성능을 평가하기 위하여 다양한 어안 렌즈 영상을 대상으로 실험한 결과, 기존의 방법보다 영상을 재구성하는데 효과적인 것을 확인하였다.

Active control method is applied to a flexible beam excited by a shock impulse in order to reduce the residual vibrations after the shock event. It is assumed that the shock input can be measured and is always occurred on the same point of the beam. If the system is well identified and the corresponding inverse system is designed reliably, it has shown that a very simple feed-forward active control method may be applied to suppress the residual vibrations without using error sensors and adaptive algorithm. Both numerical simulations and experimental results show a promising possibility of applying to a practical problem. Also, the performance of the method is examined by considering various practical aspects : shock duration, shock magnitude, and control point.

This introduces a improved method of the forward kinematics analysis, which finds the 6DOF motions and velocities from the given six cylinder lengths in the Steward platform. The numerical method(Newton Raphson Mehotd)of the forward kinematics analysises has the disadvantage of the long calculated time. To overcome this, we propose the competitive method that determine a proper initial value. Through the competitive method, we can select a proper initial value so that the calculate time is reduced. therefore we can give the property of the real time process to the forward kinematics analysis. We show the result comparing between general Newton-Raphson method and proposed one. From the result we verify the performance of the proposed method.

This paper presents a method to analyze a forward converter. A transformer is coupled with the forward converter electric circuit and then the finite element analysis considering a hysteresis phenomenon of magnetic core is carried out when the primary or the reset winding conducts current. The analytical method is used to reduce the computation time when the reset winding circuit of the transformer turns off. As a result, the simulation results show a good agreement with experimental ones.

원격제어는 명령을 전달하는 마스터 장치와 원격지에 위치한 로봇을 슬레이브로 간주하는 방식이다. 이러한 원격제어의 경우, 양 에이전트 간의 동특성, 전송속도 차이에 의해 가상환경을 이용한 가상 슬레이브를 통해 조작자가 원격제어의 특수성을 선행 수행하는 것이 일반적이다. 기존의 가상 슬레이브는 연산량의 한계에 의해 동역학적 효과를 제거한 그래픽 모델로 구성하는 것이 일반적이나 이는 원격지 로봇의 동특성이 무시되어 실제 원격제어시의 실재감을 살리기 어렵다. 본 논문에서는 로봇의 정동역학 모델을 이용하여 이를 원격제어에 활용하고, 가상 환경에서 상대적으로 느린 제어 주기에 의해 발생하는 정동역학의 수치오류를 보완하는 보상 기법을 제안하고 이를 실제 환경과의 원격제어와 비교하고자 한다.

Teleoperation is defined with a master device that gives control command and a slave robot in a remote site. In this field, it is common that a human operator executes and experiences teleoperation with a virtual slave, and preliminary learns dynamic characteristic and network environment from both agents. Generally, a virtual slave has neglected forward dynamics and its kinematic model has been implemented in computer graphics. This makes a operator to experience actual feelings. This paper proposes a dynamic teleoperation model in which a robotic forward model is applied. Also, a novel compensation method is proposed to reduce the numerical error problems in forward dynamics caused by low control sampling rate. Finally, its results will be compared to the teleoperation in an actual environment.

현재에 시스템의 결함을 분석하는 안전성 분석 기법들이 여러 가지가 있다. 본 논문에서는 많은 결함 분석 기법 중에서 FTA(Fault Tree Analysis)와 FMECA(Failure Modes, Effect and Criticality Analysis)을 통하여 안전성 분석을 하고자 한다. 또한 이 두 가지 기법을 따로 사용하지 않고 전방에서 FTA 분석을 하고 후방에서 FMECA 분석을 수행하는 통합 기법을 제안한다. 이를 통해 각 기법의 단점을 최소화하고 장점을 최대화 하여 안전성 분석의 효율성을 제고하고자 한다.