• 한국전문물리치료학회지
• /
• 제20권3호
• /
• pp.9-18
• /
• 2013

The study aimed to compare the effect of the treadmill walking training combined with obstacle-crossing (TWT-OC) and treadmill walking training (TWT) on the walking function of patients with chronic stroke. 29 subjects volunteered to participate in this study; they were randomly assigned to either TWT-OC group (15 subjects) or TWT group (14 subjects). Subjects from the TWT-OC group underwent a treadmill walking combined with obstacles-crossing for 30 minutes daily, three days a week for four weeks, whereas subjects from the TWT group received only a treadmill walking. The 10 m walk test (10MWT), 6-min walk test (6MWT), berg balance scale (BBS), timed up and go test (TUG), activities-specific balance confidence-Korean version (ABC-K), and walking ability questionnaire (WAQ) were measured before and after the 4-week training. The TWT-OC group showed significantly better functional mobility of walking and balance measured by 6MWT (p<.01), BBS (p<.01), and TUG scores (p<.05) when compared with those of the TWT group. Further, within-group comparison showed significant improvement in all variables (p<.01) except for 10MWT. These findings suggest that the TWT-OC and TWT may be helpful for improving the walking function of patients with chronic stroke, and the TWT-OC has probably more favorable outcomes for chronic stroke, however, further trials with wider range of subjects are warranted for generalization and clinical relevance.

• 방송공학회논문지
• /
• 제18권4호
• /
• pp.643-646
• /
• 2013

본 논문에서는 아다부스트의 과적합 문제를 해결하기 위해 샘플 군집화를 이용한 개선된 아다부스트 알고리즘을 제안한다. 아다부스트는 다양한 객체 검출 방법에서 좋은 성능을 보이는 방법으로 알려져 있지만 훈련 샘플에 노이즈가 존재하는 경우 과적합 현상이 발생하는 문제가 있다. 이를 해결하기 위해 제안하는 방법은 우선 훈련 샘플의 긍정 샘플을 k-평균 군집화 알고리즘을 이용하여 K개의 군집으로 나눈다. 이후 아다부스트의 약분류기 훈련 시 K개의 군집 중 훈련 오차를 최소화하는 하나의 군집만을 선택하여 사용한다. 이로써, 제안하는 방법은 매 회 반복되는 약분류기의 훈련 시 훈련 샘플들이 과분할 되는 것과 노이즈 샘플이 훈련에 사용되는 것을 방지함으로써 기존 아다부스트의 과적합 현상을 효과적으로 줄여준다. 실험 결과, 제안하는 방법은 다양한 실제 데이터셋에서 기존의 부스팅 기반 방법들에 비해 더 나은 분류 성능 및 일반화 성능을 보여주었다.

• 성인간호학회지
• /
• 제15권4호
• /
• pp.650-659
• /
• 2003

Purpose: The purpose of this study was to identify predictors of depression and quality of life among older adults with osteoarthritis. The predictors included in the model were the client's characteristics(age, pain, disease duration, ADLs), personal resources(hardiness, self-care agency and family support), and depression. Method: 150 subjects who were older than 65 years and had diagnosis of osteoarthritis participated in the study. To answer the research questions, descriptive analysis, Pearson correlation, and hierarchical multiple regression were utilized using SPSS WIN program. Result: Older adults who were younger and had lower levels of pain and dependency on ADLs, and higher levels of self care agency and hardiness reported lower levels of depression($R^2=0.517$). Older adults who had lower levels of depression, pain, and dependency on ADLs, higher levels of family support and hardiness, and who are younger reported higher levels of quality of life($R^2=0.084$). Conclusion: Based on the findings of this study, development of nursing intervention program including pain reduction, enhancing ADL abilities and personal resources (hardiness, family support) can be suggested. Further study is needed to increase the ability of generalization of the study findings to the broader population.

• 전기학회논문지
• /
• 제67권8호
• /
• pp.1071-1079
• /
• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

• 한국정보통신학회논문지
• /
• 제18권3호
• /
• pp.510-518
• /
• 2014

본 연구에서는 유체 속에서의 로봇의 방향전환 메커니즘의 성능을 개선하고 최적화하기 위하여 물 속 자연환경에 최적화되어 있는 물고기의 CST(CST:C-shape sharp turn) 패턴을 모방하여 물고기 로봇의 꼬리 관절 궤적을 신경회로망(neural network)을 사용하여 제안하였다. 물고기의 CST 패턴을 모방하기 위해 CST 패턴을 순차적으로 기록한 정보를 수치적으로 변환하여 좌표 데이터를 생성하고 함수화하였다. 함수화된 모션 함수를 물고기 로봇의 상대 관절각으로 변환하였으나, 구해진 상대 관절 궤적은 잉어의 순차적 기록에 의해 구해진 각도이므로 분해능이 떨어져 실제 물고기 로봇의 제어에 적용하기 어렵다. 그러므로 상대 관절 궤적을 일반화 기능이 뛰어난 신경회로망을 사용하여 보간하고 물고기 로봇에 적용하였다. 모의실험을 통하여 신경회로망을 이용한 상대 관절 궤적 함수가 고차의 다항식 궤적 함수에 비하여 물고기 로봇의 CST 모션에 더 좋은 성능을 나타냄을 확인하였다.

• Journal of Information Processing Systems
• /
• 제11권3호
• /
• pp.370-388
• /
• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• 한국인터넷방송통신학회논문지
• /
• 제21권1호
• /
• pp.87-92
• /
• 2021

빅데이터 기반의 추천시스템 모델링에서 바이어스, 분산, 오류 및 학습은 성능에 중요한 요소이다. 이러한 시스템에서는 추천 모델이 설명도를 유지하면서 복잡도를 줄여야 한다. 또한 데이터의 희소성과 시스템의 예측은 서로 반비례의 속성을 가지기 마련이다. 따라서 희소성의 데이터를 인수분해 방법을 활용하여 상품간의 유사성을 학습을 통한 상품추천모델이 제안되어 왔다. 본 논문에서는 이 모델의 손실함수에 대한 최적화 방안으로 max-norm 규제를 적용하여 모델의 일반화 능력을 향상시키고자 한다. 해결방안은 기울기를 투영하는 확률적 투영 기울기 강하법을 적용하는 것이다. 많은 실험을 통하여 데이터가 희박해질수록 기존의 방법에 비해 제안된 규제 방법이 상대적으로 효과가 있다는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권2호
• /
• pp.365-380
• /
• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.117-127
• /
• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Nuclear Engineering and Technology
• /
• 제55권9호
• /
• pp.3409-3416
• /
• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.