• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.273-281
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• 2019

The energy consumed by buildings among the total national energy consumption is more than 10% of the total. For this reason, Korea has adopted the zero energy building policy since 2025, and research on the energy saving technology of buildings has been demanded. Analysis of buildings' energy consumption patterns shows that lighting, heating and cooling energy account for more than 60% of total energy consumption, which is directly related to solar power acquisition and window opening and closing operation. In this paper, we have developed a low - power IoT sensor module for window system to transfer acquired information to building energy management system. This module transmits the external environment and window opening / closing status information to the building energy management system in real time, and constructs the network to actively take energy saving measures. The power used in the module is designed as an independent power source using solar power among the harvest energy. The topology of the power supply is a Buck converter, which is charged at 4V to the lithium ion battery through MPPT control, and the efficiency is about 85.87%. Communication is configured to be able to transmit in real time by applying WiFi. In order to reduce the power consumption of the module, we analyzed the hardware and software aspects and implemented a low power IoT sensor module.

• Journal of Navigation and Port Research
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• v.43 no.4
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• pp.231-236
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• 2019

As ships become bigger, faster, and diverse, transportation has increased the usage of marine vehicles. However, ship accidents are increasing. Ship accidents cause loss of life and property as well as environmental disasters. The occurrence of ship accidents causes enormous economic and environmental impacts. Notably, in the case of passenger ships, methods for preventing ship accidents are being discussed to avoid losing numerous human lives. The purpose of this study is to provide essential data for evacuation before reaching the dangerous time by predicting the time to reach the risk of capsizing based on the heeling angle of the passenger ship. Based on sinking accidents between 2012 and 2016, we set up specific scenarios and simulated the PRR1 data using commercial software MOSES V20. In the case of the linear equation, the simulation results showed a low error rate because the simulation data showed the linear graph. In the case of the quadratic equation, the error rate was low at the beginning but showed a high error rate at the subsequent angle.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.4
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• pp.457-467
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• 2022

Electron bombardment to silicate glass during electron probe microanalysis (EPMA) causes outward migration of Na from the excitation volume and subsequent decrease in the measured X-ray count rates of Na. To acquire precise Na₂O content of silicate glass, one should use proper analytical technique to avoid or minimize Na migration effect or should correct for decreases in the measured Na X-ray counts. In this study, we analyzed 8 silicate glass standard samples using automated Time Dependent Intensity (TDI) correction method of Probe for EPMA software that can calculate zero-time intercept by extrapolating X-ray count changes over analysis time. We evaluated an accuracy of TDI correction for Na measurements of silicate glasses with EPMA at 15 kV acceleration voltage and 20 nA probe current electron beam, which is commonly utilized analytical condition for geological samples. Results show that Na loss can be avoided with 20 ㎛-sized large beam (<0.1 nA/㎛²), thus silicate glasses can be analyzed without TDI correction. When the beam size is smaller than 10 ㎛, Na loss results in large relative errors up to -55% of Na₂O values without correction. By applying TDI corrections, we can acquire Na₂O values close to the reference values with relative errors of ~ ±10%. Use of weighted linear-fit can reduce relative errors down to ±6%. Thus, quantitative analysis of silicate glasses with EPMA is required for TDI correction for alkali elements such as Na and K.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.104-108
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• 2023

Because of recent expansion of the electric car market. it is highly growing that should be supplemented its performance and safely issue. The EMI problem due to the interlocking of electrical components that causes various safety problems such as fire in electric vehicles is emerging every time. We strive to achieve optimal charging efficiency by combining various technologies and reduce radioactive noise among the EMI noise of a weirless charging control module, one of the important parts of an electric vehicle was designed and tested. In order to analyze the EMI problems occurring in the wireless charging control module, the optimized wireless charging control module by applying the optimization design technology by learning the accumulated test data for critical factors by utilizing the Python-based script function in the Ansys simulation tool. It showed an EMI noise improvement effect of 25 dBu V/m compared to the charge control module. These results not only contribute to the development of a more stable and reliable weirless charging function in electric vehicles, but also increase the usability and efficiency of electric vehicles. This allows electric vehicles to be more usable and efficient, making them an environmentally friendly alternative.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.716-727
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• 2004

Component-based software development (CBD) technology has been widely accepted as a new effective paradigm for building software systems with reusable components, consequently reducing efforts and shortening time-to-market. Hence, components should provide standard or common functionalities in a domain, yielding a higher level of reusability. Especially, micro-level variability within the commonality should also be modeled so that a product member-specific business logic or requirement can be supported through component tailoring or customization The importance of commonality and variability (C&V) analysis has been emphasized in several CBD methods, but they lack of well-defined systematic process, detailed instructions, and standard artifact templates. As the result, the development of components has been carried out in ad-hoc fashion, depending on developer's experience. In this paper, we propose a systematic process and work instructions to design components. The process consists of phases and their activities and each activity is specified with detailed instructions and artifact templates in order to facilitate effective development of components. To verify a feasibility of the propose method, a case study in a banking domain and comparison and assessment between the proposed method and other methods are additionally provided. With proposed processes and instructions, reusability and efficiency of developing components can be better supported.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.53-58
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• 2013

Purpose: To optimize correction method for SPECT/CT, image quality consisting of resolution and contrast was evaluated using three radioisotopes ($^{99m}Tc$, $^{201}Tl$ and $^{131}I$) and three different correction methods; attenuation correction (AC), scatter correction (SC) and both attenuation and scatter correction (ACSC). Materials and Methods: Images were acquired with a SPECT/CT scanner and a conventional CT protocol with an OESM reconstruction algorithm (2 iterations and 10 subsets). For resolution measurement, fixed radioactivity (2.22 kBq) was infused into a spatial resolution phantom and full width at half maximum (FWHM) was measured using a vendor-provided software. For contrast evaluation, radioactive source with a ratio of 1:8 to background was filled in a Flanged Jaszczak phantom and percent contrast (%) were calculated. All the parameters for image quality were compared with non-correction (NC) method. Results: As compared with NC, image resolution of all three isotopes were significantly improved by AC and ACSC, not by SC. In particular, ACSC showed better resolution than AC alone for $^{99m}Tc$ and $^{201}Tl$. Image contrast of all three radioisotopes in a sphere with the largest diameter were enhanced by all correction methods. ACSC showed the highest contrast in all three radioisotopes, which was the most accurate in $^{99m}Tc$ (85.9%). Conclusion: Image quality of SPECT/CT was improved in all the radioisotopes by CT-based attenuation correction methods, except SC alone. SC failed to improve resolution in any radioisotopes, but it was effective in contrast enhancement. ACSC would be the best correction method as it improved resolution in radioisotopes with low energy levels and contrast in radioisotope with low energy levels. However, in radioisotope with high energy level, AC would be better than ACSC for resolution improvement.