• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.65-70
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• 2020

This paper describes hierarchically stratifying and analyzing haplotrees of haplogroups from haplotypes on the Y and X chromosomes of human cells for genetic and Korean traditional and genealogical trees. The specific region is Chungcheong province, and the Y-DNA of the paternal lines has high frequency of O3a∗ and O2b∗ in the O group, and the mtDNA of the maternal line has a relatively high frequency of D∗ and M∗ in the L3 group. Each combination of these constructs the family tree of the father lines and mother lines. Genetic distances using Nei's standard genetic distance, are very close relatives of less than 0.1 and close relatives of 0.1 to 0.8. Provided, the distance is more than 1.0, it is difficult to estimate relatives. STR has the identified kinship, and SNP has the personal genetic identification. A scientific stratification of the Korean genealogical tree is created by the three factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.963-969
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• 2020

Cities in the region have a problem in that they cannot accurately monitor small areas because the number of air pollution is differently observed depending on variables such as population, season, traffic volume, and industrial complexes. In order to solve this problem, in this paper, comparative analysis was performed on small areas where representative air pollutants SO2, PM10, NO2, CO, and O3, which adversely affect the human body, are observed through coefficient of determination. In addition, based on Bonferroni's multiple comparative analysis, the air pollution level by period is shown. The map for the monitoring system was linked with the coordinates of each small city to visualize air pollutants for small cities based on the analysis data. Through this, it is possible to provide the user with a monitoring system of air pollutants for the region more precisely, and to prevent them from accidents that may occur due to air pollution in everyday life.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.85-106
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• 2021

The purpose of this study is to examine the concept and definition of fake news focusing on misinformation/false information and is to examine the ways in which our society can respond to the distortion of social reality and damage to democracy caused by information distortion such as fake news. To do this, the concept of fake news was examined based on the level of facticity and intention to device, and our social environment in which fake news was created and spread was examined from the perspective of datafication. In this environment, the library community, which plays a pivotal role in human access to and use of information, argued that it should strive to establish and provide universal literacy education in order to realize the Sustainable Development Goals of the UN 2030 agenda. The core of universal literacy education is to understand the society by investigating and analyzing data communication types according to the degree of datafication and the political, economic, social, and cultural background of society. For this reason, it was concluded that universal literacy should be implemented flexibly according to the degree of datafiation and users of each society.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.221-227
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• 2021

Recently, interests in mask-type skin care devices using light-emitting diodes have been increasing and optical stimuli at certain wavelengths have been known to have various therapeutic effects, such as skin whitening, acne treatment, elasticity and wrinkle improvement by controlling the exposure to wavelengths of light and irradiation time. In particular, light sources of different wavelengths are applied in masks for the purpose of suppressing skin aging, inducing cell proliferation, and alleviating skin inflammation. In this paper, we developed a light-emitting diode control system that is actively used in skin regeneration masks using a microcontroller. Optical stimuli with different manners were applied to skin fibroblast cells in a single or complex wavelengths, and then confirmed how they are effective in the cell differentiation. In addition, we found a specific wavelength that has a positive effect on cell proliferation rates, and confirm the effectiveness of cell proliferation by image processing based quantitative analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1013-1018
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• 2021

Due to the recent accident caused by the automated vehicle, discussions on the ethical aspects of AI have been actively underway. This paper confirms that AI is inevitably connected to ethical components through the concepts and techniques related to robots-AI, and argues that ethical aspects are built-in, not post facto. Furthermore, this devises a solution to the trolley dilemma that can serve as a clue to ethical problems associated with automated vehicles. Preferentially, that process contains writing Bayesian networks. Next, only important and influential data are left after the pre-processing stage, and crowd-sourcing & extrapolation is used to calculate the exact figures of the networks. Through this process, this argues that humans' subjects are certainly included in implementing algorithms and models and discusses the necessity and direction of engineering liberal arts, especially education of ethics that distinguished from major education to prevent distortions and biases abouts AI systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.83-90
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• 2022

Diabetes is a common disease today, and there are also many cases of developing into serious complications called Diabetic Foot Ulcers(DFU). Diagnosis and prevention of DFU in advance is an important task, and this paper proposes the method. Based on existing studies introduced in the paper, it can be seen that foot pressure and temperature information are deeply correlated with DFU. Introduce the process and architecture of SmarTinsole, an IoT device that measures these indicators. Also, the paper describes the preprocessing process for AI-based diagnosis of DFU. Through the comparison of the measured pressure graph and the actual human step distribution, it presents the results that multiple information collected in real-time from SmarTinsole are more efficient and reliable than the previous study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.49-57
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• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.850-858
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• 2022

To deploy Gate Recurrent Units (GRU) on resource-constrained embedded devices, this paper presents a reconfigurable FPGA-based GRU accelerator that enables structured compression. Firstly, a dense GRU model is significantly reduced in size by hybrid quantization and structured top-k pruning. Secondly, the energy consumption on external memory access is greatly reduced by the proposed reuse computing pattern. Finally, the accelerator can handle a structured sparse model that benefits from the algorithm-hardware co-design workflows. Moreover, inference tasks can be flexibly performed using all functional dimensions, sequence length, and number of layers. Implemented on the Intel DE1-SoC FPGA, the proposed accelerator achieves 45.01 GOPs in a structured sparse GRU network without batching. Compared to the implementation of CPU and GPU, low-cost FPGA accelerator achieves 57 and 30x improvements in latency, 300 and 23.44x improvements in energy efficiency, respectively. Thus, the proposed accelerator is utilized as an early study of real-time embedded applications, demonstrating the potential for further development in the future.