• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• Journal of the Korean Chemical Society
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• v.36 no.1
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• pp.113-124
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• 1992

The pattern recognition methods of chemometrics have been applied to multivariate data, for which ninety four Korean ancient glass pieces have been determined for 12 elements by neutron activation analysis. For the purpose, principal component analysis and non-linear mapping have been used as the unsupervised learning methods. As the result, the glass samples have been classified into 6 classes. The SIMCA (statistical isolinear multiple component analysis), adopted as a supervised learning method, has been applied to the 6 training set and the test set. The results of the 6 training set were in accord with the results by principal component analysis and non-linear mapping. For test set, 17 of 33 samples were each allocated to one of the 6 training set.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.346-351
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• 2023

With the release of numerous open driving datasets, the demand for domain adaptation in perception tasks has increased, particularly when transferring knowledge from rich datasets to novel domains. However, it is difficult to solve the change 1) in the sensor domain caused by heterogeneous LiDAR sensors and 2) in the environmental domain caused by different environmental factors. We overcome domain differences in the semi-supervised setting with 3-stage model parameter training. First, we pre-train the model with the source dataset with object scaling based on statistics of the object size. Then we fine-tine the partially frozen model weights with copy-and-paste augmentation. The 3D points in the box labels are copied from one scene and pasted to the other scenes. Finally, we use the knowledge distillation method to update the student network with a moving average from the teacher network along with a self-training method with pseudo labels. Test-Time Augmentation with varying z values is employed to predict the final results. Our method achieved 3rd place in ECCV 2022 workshop on the 3D Perception for Autonomous Driving challenge.

• Journal of the Korean Society of Physical Medicine
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• v.19 no.1
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• pp.1-10
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• 2024

PURPOSE: This pre-post intervention study aimed to examine the effects of a 12-week supervised modified Taekwondo exercise program on the functional fitness of community-dwelling older adults. METHODS: 10 participants (mean age: 72.3 ± 4.6 years) completed the program over a 12-week period. The intervention consisted of Taekwondo-based exercises modified for older persons. Changes to functional fitness were measured using the finger-to-nose test, functional reach test, timed up and go test, 30-second sit-to-stand test, 30-second arm curl test, Chester step test, chair sit-and-reach test, and back scratch test. Participants were assessed prior to the training and after the final training session, changes were measured using descriptive statistics and paired sample 't' tests. RESULTS: Effect sizes ranged from small to large (Cohen's d = .22 to 1.23). The exercise program was well- tolerated by participants, with a high level of engagement and no attrition for the duration of the program. Results showed significant improvements in most measures of functional fitness (p < .05) except for the back scratch test (p = .051). CONCLUSION: These findings suggest a well-designed, supervised, modified Taekwondo exercise program can significantly improve functional fitness in older adults.

• Physical Therapy Korea
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• v.10 no.1
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• pp.29-35
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• 2003

Body weight support treadmill training is a new and promising therapy in gait rehabilitation of patients with hemiplegia. The purpose of this study was to identify the effects of body weight support treadmill training on gait and standing balance in patients with hemiplegia. Eighteen patients with hemiplegia participated in the study. A 10 m-timed walk test, measurements of step length and standing balance score were administered. Intervention consisted of body weight support treadmill training five times a week for 2 weeks. The data were analyzed by paired t-test. Body weight support treadmill training scoring of standing balance, step length and 10 m-timed walk test showed a definite improvement. Body weight support treadmill training offers the advantages of task-oriented training with numerous repetitions of a supervised gait pattern. The outcomes suggest that patients with hemiplegia can improve their gait ability and standing balance through body weight support treadmill training.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2751-2756
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• 2012

Selection of training set influences the classification accuracy in supervised classification using satellite imagery. Generally, if pure pixels which character of training set is clear were selected, whole accuracy is high while if mixed pixels were selected, accuracy is decreased because of low-resolution imagery or unclear distinguishment. However, it is too difficult to choose the pure pixels as training set actually. Accordingly, this study should be suggested the suitable classification method in case of mixed pixels choice. To achieve this, a few pure pixels were chosen as training set and classification accuracy was calculated which was compared with classification result using an equal number of mixed pixels. As a result, accuracy of SVM was the highest among the classification method using mixed pixels and it was a relatively small difference with the result of classification using pure pixels. Therefore, imagery classification using SVM is most suitable in the mixed area of construction and green because it is high possibility to choose mixed pixels as training set.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.