• International journal of advanced smart convergence
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• v.12 no.2
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• pp.159-166
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• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• Journal of Physiology & Pathology in Korean Medicine
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• v.21 no.5
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• pp.1356-1363
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• 2007

The purpose of the study was to identify the general status of Kigong organizations introduced on Internet. We have used www.naver.com, the biggest portal site in Korea and www.nice114.co.kr, which has the longest list of the telephone numbers to look up the organizations with the index of "Kigong and Danhak" and "Mediation". Among them we screened the organizations to have the list of organizations which have been established for more than 5 years, with more than 100 trainees and whether they published books or booklets regarding Kigong by the means of telephone conversation or home page access. The number of organizations identified by telephone and Internet with the indexes of Kigongdanhak and mediation was 852. The number of organizations that passed the primary criterion was 22, and that passed the secondary criterion was 8. Among the primarily screened organization, there are 5 focusing on mediation, 5 focusing on breathing, 3 focusing on Haenggong, 4 focusing on mediation and Haenggong, 4 focusing on breathing and Haenggong and 1 focusing on mediation and breathing. In secondarily screened organizations, they called their training method as Seondo, Shinseondo or Seonhak and origin of the training method as Dangun and Hwangwung. As Sambeop training of Jigam, Josik and Geumchok provide training methods which are a little different each other, the utilization rate was low although there are some organizations that have special training using Three Bibles. It was identified that there were many texts and writings that they took as training methods other than Three Bibles. Kigong training organizations based on Korean traditional ideologies are grounded on the Three Textbooks such as , , and and the concept of Hongikingan. This ideological ground is the study of Completion of Human Beings through physical and mental training and goes with Seondo, Pungryudo and Hyunmyojido.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.89-100
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• 2007

Scoring the constructed responses in large-scale assessments needs great efforts and time to reduce the various types of error in Paper-based training and scoring. For the purpose of eliminating the complexities and problems in Paper and pencil based training and scoring, many of countries including U.S.A and England already have applied online scoring system. There, however, has been few studies to develop the scoring system for the constructed responses items in Korea. The purpose of this study is to develop the basic design of the Internet-based scoring system for the constructed responses. This study suggested the algorithms for assigning scorers to constructed responses, employing methods for monitoring reliability, etc. This system can ensure reliable, quick scoring such as monitor scorer consistency through ongoing reliability checks and assess the quality of scorer decision making through frequent various checking procedures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Journal of Engineering Education Research
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• v.18 no.6
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• pp.18-23
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• 2015

Nowadays, IT that leads information era is closely connected to our lives. The importance of training human resources for global empowerment on IT is being emphasized all over the world. With the growth of international society, the cultivation of global human resources now includes the cultivation of future human resources and the educations related with the career development program. However, the programs that colleges are operating for the cultivation of global human resources have been limited only to supporting the participation in international training program, operating camp and conferences, etc. On the contrary, to give many learners chances of experiencing courses for global empowerment and employment capability, We are operating the Internet of Everything online education program with Cisco Networking Academy Korea. Based on the management cases of Internet of Everything online education program, this thesis is suggesting a career program on global empowerment that many learners can experience, different from the limitation of the existing program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).