• 대한예방한의학회지
• /
• 제18권2호
• /
• pp.147-154
• /
• 2014

Objective : The purpose of this study is to analyze for the patent application tendency in Korean Oriental Pediatric Medicine. Recently, many studies are published in Korean Oriental Pediatric Medicine. However, in the patent area, the research results are few. Method : For patent analysis, we searched the database kipris (http://www.kipris.or.kr) which managed in the Korean Intellectual Property Office by keywords about Korean Oriental Pediatrics. They are technically analysed by year and IPC, patent's contents, main applicant and organization. Results : 1. Patents by year have been on the rise 2 in 1997 to 32 in 2011. 2. Our findings are Section A61;182, A23;46 in IPC analysis 3. In classification of content, 'application-food' is 67 cases, 'application-functional cosmetic' is 18 cases, 'application-clothes' is 5 cases. 4. Korea Institute of Oriental Medicine have 14 patent cases, that is the most patent agency in possession. Main applicants are person > company > university > laboratory > state or local government. Conclusion : This study shows that it has high frequency of daily necessity in patent cases about oriental pediatrics. The results are more distributed 'applied' field than 'treatment' field. The patent information should be connected research achievement results in Korean Oriental Medicine.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2009년도 학술대회
• /
• pp.21-26
• /
• 2009

유비쿼터스 컴퓨팅 환경의 급속한 발전은 Multi-Sensor를 이용하여 자동으로 사용자의 행동인식을 가능한 환경을 만들어주었다. 따라서 이 논문에서는 사용자가 일상생활을 하는데 있어서 기본적으로 필요한 행동인 ADL(activities of daily living)의 수행능력을 분석하고 진단할 수 있는 Multi-Sensor기반의 ADL 자동 진단 시스템을 구축하였다. 두 개의 가속도 센서를 허벅지와 손목에 부착하여 사용자의 행동 정보를 수집하고 이를 Decision-Tree를 통하여 분석하여 사용자의 행동 정보를 수집하였다. 또한 Zigbee 센서를 이용하여 개별 물체의 Object ID를 이용하여 사용자의 위치정보와 주변의 물체의 정보를 수집하여 사용자의 상태 정보를 수집하였다. 이렇게 수집된 행동 정보와 상태 정보들을 통하여 일상생활에 필요한 약 20여 가지의 행동을 인식하였고 평균적으로 96%이상의 정확도를 나타내었으며 이를 통하여 ADL 지수를 자동으로 측정하였다.

• 로봇학회논문지
• /
• 제15권4호
• /
• pp.404-412
• /
• 2020

Typically everyday human life tasks involve at least two people moving objects such as tables and beds, and the balancing of such object changes based on one person's action. However, many studies in previous work performed their tasks solely on robots without factoring human cooperation. Therefore, in this paper, we propose cooperative robot for table balancing using Q-learning that enables cooperative work between human and robot. The human's action is recognized in order to balance the table by the proposed robot whose camera takes the image of the table's state, and it performs the table-balancing action according to the recognized human action without high performance equipment. The classification of human action uses a deep learning technology, specifically AlexNet, and has an accuracy of 96.9% over 10-fold cross-validation. The experiment of Q-learning was carried out over 2,000 episodes with 200 trials. The overall results of the proposed Q-learning show that the Q function stably converged at this number of episodes. This stable convergence determined Q-learning policies for the robot actions. Video of the robotic cooperation with human over the table balancing task using the proposed Q-Learning can be found at http://ibot.knu.ac.kr/videocooperation.html.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권7호
• /
• pp.2667-2682
• /
• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• 대한의용생체공학회:의공학회지
• /
• 제30권4호
• /
• pp.294-305
• /
• 2009

The bio signals essentially have different characteristics in each person. And the main purpose of automatic diagnosis algorithm based on bio signals focuses on discriminating differences of abnormal state from personal differences. In this paper, we propose automatic ECG diagnosis algorithm which discriminates normal heart beats from premature ventricular contraction using optimization of wavelet parameterization to solve that problem. The proposed algorithm optimizes wavelet parameter to let energy of signal be concentrated on specific scale band. We can reduce the personal differences and consequently highlight the differences coming from arrhythmia via this process. The proposed algorithm using ELM as a classifier show high discrimination performance between normal beat and PVC. From the experimental results on MIT-BIH arrhythmia database the performances of the proposed algorithm are 98.1% in accuracy, 93.0% in sensitivity, 96.4% in positive predictivity, and 0.8% in false positive rate. This results are similar or higher then results of existing researches in spite of small human intervention.

• 한국환경생태학회지
• /
• 제10권2호
• /
• pp.283-296
• /
• 1997

계곡부 산림의 군집구조를 분석하여 국립공원 관리의 기초자료를 마련하고자 설악산 국립공원의 주전골계곡지역에 50개의 조사구를 설정하고 식생조사를 실시하여 얻은 자료를 대상으로 TWINSPAN에 의한 classification과 DCA ordination 기법을 적용하여 분석하였다. TWINSPAN과 DCA분석결과 군집은 신갈나무-굴참나무-소나무군집(군집 I), 소나무군집(군집 II), 서어나무군집(군집 III), 졸참나무군집(군집 IV)으로 분리되었다. 군집의 생태적 천이단계는 신갈나무-굴참나무-소나무군집과 소나무군집의 경우 소나무$\$\longrightarrow$ $신갈나무군집, 졸참나무군집의 경우 졸참나무$\$\longrightarrow$ $서어나무군집으로 추정되었으며, 서어나무군집은 계속 안정된 상태를 유지할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권6호
• /
• pp.2377-2397
• /
• 2020

Human activity recognition is widely used in smart homes, health care and indoor monitor. Traditional approaches all need hardware installation or wearable sensors, which incurs additional costs and imposes many restrictions on usage. Therefore, this paper presents a novel device-free activities recognition system based on the advanced wireless technologies. The fine-grained information channel state information (CSI) in the wireless channel is employed as the indicator of human activities. To improve accuracy, both amplitude and phase information of CSI are extracted and shaped into feature vectors for activities recognition. In addition, we discuss the classification accuracy of different features and select the most stable features for feature matrix. Our experimental evaluation in two laboratories of different size demonstrates that the proposed scheme can achieve an average accuracy over 95% and 90% in different scenarios.

• Smart Structures and Systems
• /
• 제22권2호
• /
• pp.175-183
• /
• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권3호
• /
• pp.1264-1286
• /
• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• 한국컴퓨터산업학회논문지
• /
• 제7권1호
• /
• pp.47-56
• /
• 2006

혈액에서 추출된 프로테옴 패턴(단백질 DNA 정보)는 인간 신체 기관의 병리학적 상태를 잠재적으로 반영하고 있다. 신체기관의 질병이나 이상은 이러한 프로테옴 패턴의 분석에 의해 식별될 수 있다고 알려져 있으며 프로테옴 패턴 정보를 분석하는 여러 가지 방법들이 현재 존재하고 있다. 본 논문에서는 SVM(Support Vector Machine)과 GA(Genetic Algoritm)의 융합에 근거하여 암 진단을 위한 디시전 모델의 효과적 학습(learning) 방법을 제안한다. <중략> 그 결과로서 개별적 kernel function 들보다 더 우수한 분류성능을 갖는 최적의 디시전 모델이 얻어졌다. 위암 데이터 셋 과 두 개의 일반 데이터 셋(대장암, 백혈병)을 사용한 컴퓨터 실험에서 제안된 방법이 다른 Kernel function 들에 비해 더 우수한 분류 성능을 보여주었다.