• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

• The Korea Journal of Herbology
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• v.34 no.3
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• pp.37-44
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• 2019

Objectives : Acne is a common disease that affects more than 70% of adolescents. Acne patients have a poor quality of life compared to patients with other skin diseases. We tried to demonstrate the effectiveness of acne treatment using cleansing soap which is easily used in daily life. Methods : We selected 20 students with acne symptoms on their facial skin. We made herbal Cp (Cold process) soaps using Seosiokyongsan, Kyungohkgo, Hwangryunhaedoktang and Baeksoooh and distributed them to experiment participants. We let them wash their face in the morning and evening for 6 weeks using herbal Cp soap. Prior to the experiment, their skin condition was checked and assessed using A-ONE Smart One-Click Automatic Facial Diagnosis System three times at 3-week intervals. Acne status was classified into 6 stages according to KAGS and acne status was also measured 3 times in total. After the experiment, the changes of skin were analyzed through facial analysis test. Results : Based on the KAGS classification, the condition of acne has improved as a whole. The state of moisture was gradually increased and the state of skin oil was significantly decreased after 6 weeks of using soap compared to before using soap. Conclusions : Cp soaps made from four kinds of herbal medicine are believed to improve the condition of acne by increasing the moisture of the facial skin and decreasing the skin oil content.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.126-142
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• 2023

The purpose of this study is to develop unmanned traffic enforcement equipment and standard specifications for the prevention of traffic accidents and violations of the two-wheeled vehicle laws. To this end, we conducted a review of the problems and new technologies of the currently operating unmanned traffic enforcement equipment on two-wheeled vehicles. And through a survey, the feasibility of introducing unmanned traffic enforcement equipment for two-wheeled vehicles and the current status of technology were investigated. In addition, the two-wheeled vehicle enforcement function was implemented through field tests of the development equipment, and the addition of enforcement targets and the number recognition rate were improved through performance improvement. Based on the results of field experiments and performance evaluation, performance standards for unmanned two-wheeled vehicle traffic enforcement equipment were prepared, and in the communication protocol, two-wheeled vehicle-related matters were newly composed in the vehicle classification code and violation items to develop standards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.264-266
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• 2022

In order to apply the RNN model to the radio fingerprint-based indoor path generation technology, the data set must be continuous and sequential. However, Wi-Fi radio fingerprint data is not suitable as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, continuity information of sequential positions should be given. For this purpose, clustering is possible through classification of each region based on signal data. At this time, the continuity information between the clusters does not contain information on whether actual movement is possible due to the limitation of radio signals. Therefore, correlation information on whether movement between adjacent clusters is possible is required. In this paper, a deep learning network, a recurrent neural network (RNN) model, is used to predict the path of a moving object, and it reduces errors that may occur when predicting the path of an object by generating continuous location information for path generation in an indoor environment. We propose a method of giving correlation between clustering for generating an improved moving path that can avoid erroneous path prediction that cannot move on the predicted path.

• Hip & pelvis
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• v.34 no.1
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• pp.45-55
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• 2022

Purpose: The aim of this study was to evaluate the short-term outcome of physiotherapy in patients with acetabular labral tears and to assess the effectiveness of physiotherapy according to the severity of the labral tear. Materials and Methods: Thirty-five patients who underwent physiotherapy for treatment of symptomatic acetabular labral tears were enrolled. We evaluated the severity of the acetabular labral tears, which were classified based on the Czerny classification system using 3-T MRI. Clinical findings of microinstability and extra-articular pathologies of the hip joint were also examined. The International Hip Outcome Tool 12 (iHOT12) was use for evaluation of outcome scores pre- and post-intervention. Results: The mean iHOT12 score showed significant improvement from 44.0 to 73.6 in 4.7 months. Compared with pre-intervention scores, significantly higher post-intervention iHOT12 scores were observed for Czerny stages I and II tears (all P<0.01). However, no significant difference was observed between pre-intervention and post-intervention iHOT12 scores for stage III tears (P=0.061). In addition, seven patients (20.0%) had positive microinstability findings and 22 patients (62.9%) had findings of extra-articular pathologies. Of the 35 patients, eight patients (22.9%) underwent surgical treatment after failure of conservative management; four of these patients had Czerny stage III tears. Conclusion: The iHOT12 score of patients with acetabular labral tears was significantly improved by physiotherapy in the short-term period. Improvement of the clinical score by physiotherapy may be poor in patients with severe acetabular labral tears. Determining the severity of acetabular labral tears can be useful in determining treatment strategies.

• Archives of design research
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• v.16 no.3
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• pp.161-174
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• 2003

Choice and use of color are very important field for designer. Present color sprang by central field of design business unlike past. Color is used mainly by three fields of sensitivity, administration, mind. But, do substantial design including all of three fields at use. Practical research field that is based on basic research when see as actuality of domestic color design is been behind real condition. Specially, color sensitivity field and color management field are very important field, it can speak that color name arid related area are most important among two. Because collar name includes sensitivity and color management. This research constructs correct data because investigate and analyze and search all compatible color names that is announced in existing or is recorded in public cosmopolitanly. As a result, it is to promise accuracy when produce creation of idea and result of design using color name. Examined laying stress on color that domestic data that is used in research is basis with Korean industrial Standard, connection literature, on-the-spot probe. International data investigated American ISCC-NBS to base. Other abroad color name data examined official data of each country all systematically with Japan, Europe. Findings about 11,000 basis color names and 33,000 application color names sorted collection. Collection method and classification system follow in international standard and arranged for user's tile convenience. Also, use frequency did laying stress on Munsell that is high color system so that can aid in industrial design business. Improved to write all international standard color values sue as RGB, CMYK, XYZ and can be applied all in each field of design. Is applying and get along with continuation improvement and development in homepage of present KIDP, it may become more worth research.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.67-82
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• 2006

We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.248-254
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• 2022

Educational innovations are first created, improved or applied educational, didactic, educative, and managerial systems and their components that significantly improve the results of educational activities. The development of pedagogical technology in the global educational space is conventionally divided into three stages. The role of innovative technologies in Higher School practice is substantiated. Factors of effectiveness of the educational process are highlighted. Technology is defined as a phenomenon and its importance is emphasized, it is indicated that it is a component of human history, a form of expression of intelligence focused on solving important problems of being, a synthesis of the mind and human abilities. The most frequently used technologies in practice are classified. Among the priority educational innovations in higher education institutions, the following are highlighted. Introduction of modular training and a rating system for knowledge control (credit-modular system) into the educational process; distance learning system; computerization of libraries using electronic catalog programs and the creation of a fund of electronic educational and methodological materials; electronic system for managing the activities of an educational institution and the educational process. In the educational process, various innovative pedagogical methods are successfully used, the basis of which is interactivity and maximum proximity to the real professional activity of the future specialist. There are simulation technologies (game and discussion forms of organization); technology "case method" (maximum proximity to reality); video training methodology (maximum proximity to reality); computer modeling; interactive technologies; technologies of collective and group training; situational modeling technologies; technologies for working out discussion issues; project technology; Information Technologies; technologies of differentiated training; text-centric training technology and others.