• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.15-24
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• 2022

The article clarifies the conditions for information, digital and educational accessibility for higher education seekers with disabilities in terms of distance learning caused by quarantine restrictions. It is established that such conditions are regulated by international and Ukrainian legal documents (The Standard Rules on the Equalization of Opportunities for Persons with Disabilities, Convention on the Rights of Persons with Disabilities, Sustainable Development Goals, Law of Ukraine "On Education", Law of Ukraine "On Higher Education", Strategy for the Development of Higher Education in Ukraine 2021-2031, Development Strategy areas of innovation for the period up to 2030, Development strategy of the sphere of innovation activity for the period up to 2030). As a part of information barrierlessness, Higher Education Institutions (HEI) should provide access to information in various formats and using technologies, in particular Braille script, large-type printing, audio description (audio descriptive commenting), sign language interpretation, subtitling, a format suitable for reading by screen access programs, formats of simple speech, easy-to-read formats, means of alternative communication. The experience of Pavlo Tychyna Uman State Pedagogical University is described. In particular, special attention is paid to the study of sign language: in view of this, the initiative group implemented the project "Learning to hear and overcome social isolation together" with the financial support of the British Council in Ukraine. Within the framework of digital accessibility, the official website of the Faculty of Social and Psychological Education has been adapted for the visually impaired in accordance with WCAG 2.0 World Standards. In 2021, Pavlo Tychyna Uman State Pedagogical University implemented the project "Cultural, Recreational and Tourist Cherkasy Region: Inclusive Social 3D Map" funded by the Ukrainian Cultural Foundation; a site with available content for online travel in the region to provide barrier-free access to the historical and cultural heritage of Cherkasy region was created. Educational accessibility is achieved by increasing the number of people with special educational needs, receiving education in inclusive groups; activities of the Center for Social and Educational Integration and Inclusive Rehabilitation Social Tourism "Bez barieriv" ("Without barriers"); implementation of a research topic for financing the Ministry of Education and Science of Ukraine: "Social and psychological rehabilitation of children and youth with special educational needs by means of inclusive tourism"; implementation of the project "Social inclusion of distance educational process"; development of information campaigns to popularize the ideas of accessibility, the need for its implementation, ongoing training programs and competitions, etc.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.133-144
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• 2023

The goal of this study was to improve environmental awareness by systematically developing a hands-on online lesson for adults on making drink bags by upcycling discarded umbrella cloth. The lesson was developed using an ADDIE model. During the analysis stage, the instructional design direction was established based on the findings of previous studies. In the design stage, the operation of practical classes in the online environment was specifically planned. The contents of education and the training time were also determined. The materials developed during the development stage included a kit and theoretical information containing images to raise awareness of environmental pollution and the significance of upcycling, as well as videos and photos. During the implementation stage, two sessions were held three months apart. A total of 36 adults participated, with 18 participants in each session. In the evaluation stage, the first session participants provided feedback on class satisfaction, which led to improvements. Positive feedbacks were received from the second session participants, who expressed satisfaction with the smooth communication and easy approaches to the learning materials. In both instances, the surveys on environmental consciousness and attitudes yielded an overall average score of 4.27, indicating a generally positive evaluation.

• The Korean Journal of Franchise Management
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• v.8 no.2
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• pp.17-24
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• 2017

Purpose - In recent years, research has been conducted on the conflict resolution strategies of the franchise headquarters and the franchisees, but there is a lack of research on how the power structure and cultural factors play a role in resolving conflicts. From this perspective, this study is to examine the structural relationship between franchisors' cultural orientation and relationship power, and conflict resolution strategies, relationship trust, and relationship performance using. The findings of this study suggest how franchise headquarters should establish long-term relationship with franchisees and share information. Research design, data, methodology - The data were collected from April 1 to April 15, 2013. Because this study examined franchise industries from the franchisee perspective, we contacted franchisee store owner and managers located in Seoul and Gyeonggi Province. Interviewers trained contacted a total of 200 franchisees, and 196 franchisees responded. Out of 196 respondents, 13 respondents were deleted due to missing information. Thus, a total of 183 franchisee were used for this study. he data were analyzed using frequency analysis, confirmatory factor analysis, correlation analysis, and structural equational modeling with SPSS 24.0 and Amos 23.0 statistical program. Results - The results showed that cooperation orientation and relational power of franchisor had significant effects on conflict resolution strategies. Cooperating, obliging, and compromising strategies of conflict resolution strategy had significant effects on relationship trust. Also, relationship trust had significant effect on relationship performance. Conclusions - This study shows that the franchise headquarters and the franchisees share necessary information for common purposes and that continuous two-way communications play an important role in resolving conflicts. In other words, the result of this study suggests that if the franchise headquarters and the franchisee actively consider the position of the other party and strive to achieve the goal, conflict resolution may be more successful. In order to do this, the franchise headquarters will have to consider how to build and maintain continuous communication with the franchisees, and continuous education is also needed so that employees can have a cooperative attitude. However, since the culture of these organizations is not made up of simple staff training and is not formed within a short time, the CEO of the franchisee headquarters should take the lead in establishing a cooperative culture with the merchants over the long term.

• Industry Promotion Research
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• v.8 no.1
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• pp.83-94
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• 2023

This study explored the effect of corporate field teacher and corporate education satisfaction on apprenticeship education satisfaction and student job competency development focusing on NCS job standards and industry-academia-integrated apprenticeship school project group. As a result of the study, satisfaction with corporate field teachers in the electrical, electronic, and food service sectors was found to have a positive influence on improving students' job competency, while satisfaction with corporate education was important in the management, accounting, office, and information and communication sectors. In the analysis by type of project group, the satisfaction of corporate field teachers in the joint practice type and industry-led type had a strong influence on improving job competency, but in the base school type and single school type, corporate education satisfaction had a greater influence on capacity improvement. Therefore, it is necessary to redefine the competencies of corporate field teachers and to establish and implement an industry-academic integrated apprenticeship school operation plan with the relationship between the type of project group and NCS job standard classification.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.