• 혜화의학회지
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• 제22권2호
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• pp.47-56
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• 2014

Objectives : The purpose of this study is to raise the proper recognition of Qigong and expand the area of Medical Qigong in the korean Medicine by investigating and analyzing the current state of Javaldonggong training which has a high medical value but there has not been little research on. Method : The survey of this study was conducted by doing a search on the internet - Naver(www.naver.com) and Daum(www.daum.net), Nate(www.nate.com), trying question-and-answer on the websites and over the phone, visiting the organizations and reading their publications. Results : None of the teachers of these selected organizations are doctors. One thing all these organizations have in common is that they are, ultimately, aiming to gain the individual enlightenment and to contribute to public welfare although the terms they use are different. As for training contents, most of these organizations use breathing, meditation, gymnastics, circuit training in addition to Javaldonggong training and they work on Javaldonggong training programs to prevent problems that Qigong training can result in. 7 organizations have published the books of the theories, which are based on their own Javaldonggong training experience. Conclusions : Applying Javaldonggong training to the therapy for the diseases is the role of a doctor of Korean medicine. A further study of and a great interest in Javaldonggong training are required for Korean medical doctors to gain a firm foothold in using it as the medical Qigong therapy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2784-2799
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• 2012

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권10호
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• pp.2650-2662
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• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4270-4284
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• 2019

This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.5087-5102
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• 2015

Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone models of the training sets in multi-class classifier, a one-class classifier is orderly used in the framework. Feature vectors including color filter array (CFA) interpolation coefficients estimation and multi-feature fusion is employed to verify the effectiveness of the classifier combination strategy. Experimental results demonstrate that for different feature sets, our method presents high accuracy of source identification both for the cell phone in the training sets and the outliers.

• 정보처리학회논문지A
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• 제9A권1호
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• pp.121-128
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• 2002

인터넷을 통한 웹 기반 교수-학습 시스템은 상호작용 증진을 통한 학습자 위주의 교육환경으로 지속적인 변화를 지향해 왔다. 그 결과 학습자는 시간과 공간의 제약에서 벗어나 다양한 교육정보를 접할 수 있는 기회를 가질 수 있게 되었다. 결과적으로 인터넷의 발달은 교육환경의 변화를 가져왔다. 인터넷을 통한 웹 기반 원격교육은 이제 교육의 여러 분야에 폭넓게 적용되고 있다. 사실, 인터넷을 통한 원격교육은 이제 새로운 교육패러다임으로 자리를 잡았다. 본 논문에서는 교사를 대상으로 하는 원격 교육 시스템에 한정하여 시스템 개발에서 운영에 이르기까지의 모든 과정에 대한 경제적, 교육적 효율성 확보를 목적으로 하였다. 따라서 시스템 개발에서는 주요 모듈의 컴포넌트화를 시도하여 재사용성 증대를 통한 설계 기간 및 비용의 감소를 이끌어 내어 개발의 효율성을 높이고, 구성주의 원리를 교육학적 기반으로 함으로써 교육의 효율성을 최대로 끌어올릴 수 있도록 설계하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.