• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.120-128
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• 2022

Printed circuit boards (PCBs) are a basic component in the electronics industry and are widely used in nearly all electronic products, such as mobile phones, tablet computers, and digital cameras, as well as in electric equipment. PCB manufacturing involves the use of many chemicals and chemical processes and therefore has more risks than other manufacturing sectors. This study aims to identify the causes of possible accidents during PCB manufacturing through risk assessment, develop and implement safety measures, and evaluate the effectiveness of these measures. Note that the safety measures developed to mitigate the risks of a certain process were also implemented for other similar processes. The risk assessments conducted over seven years, from 2015 to 2021, at a PCB manufacturing company identified 361 hazardous processes. Between 2016 and 2019, 41-56 hazardous processes were identified per year; such processes decreased to fewer than 20 per year after 2020. Application of the risk assessment results to the improvement of the hazardous processes with the similar characteristics seems to be effective in decreasing the risks. Equipment-related factors such as lack of appropriate maintenance, low work standards, and defective protection devices were responsible for 59.8% of all possible accidents. Because PCB manufacturing involves many chemicals, skin contact with hazardous substances, electric shock, fire, and explosion were the most common types of possible accidents (81.7%). In total, 505 safety measures were implemented, including 157 related to purchase and improvement of equipment and devices for safety (31.1%), 147 related to the installation/modification of fire prevention facilities (29.1%), and 69 related to the use of standard electrical appliances (13.7%). Risk assessment conducted after implementing the safety measures showed that these measures significantly decreased risk; 247 processes (68.4%) had a risk level of 3, corresponding to "very low," and 114 processes (31.6%) showed a risk level of 4, corresponding to "low." In particular, risk assessment of 104 processes with risk scores of 12 and 10 other processes with risk score of 16 showed that the risk decreased to 4 after implementing the safety measures. Thus, implementing these measures in similar manufacturing sectors that involve chemical processes can mitigate risk.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.243-248
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• 2022

The purpose of this paper is to etching SUS MASK by adding an additive (F300) to FeCl3. The equipment used in the experiment is a self-made automatic liquid management system. The automatic liquid management system is a device capable of controlling the Oxidation Reduction Potential (ORP) and specific gravity in real time and supplying FeCl3 and additives in a quantitative manner. SUS MASK was etched in units from 10 sheets up to 200 sheets for 1 minute. It was confirmed that when the initial SUS MASK was 10 sheets, the ORP value started with 628 mV and measured at 611 mV from the time of 40 sheets being injected, and maintained close to 610 mV up to 200 sheets. The specific gravity was maintained near 1.640. And the SUS MASK was measured close to 0.4 mm from 50 sheets to 200 sheets. The experimental conditions of ORP had a specific gravity of 610 mV, 1.463, an etching pressure of 3.0 kg/cm2, an additive (F300) ratio of 1.2%, and the hole size was measured by up to 200 sheets of 10 sheets at once etching. As a result, the diameter approached 0.4 mm from 20 sheets. Even if the number of SUS MASK was increased, the ORP and specific gravity were well controlled, and it was confirmed that the experimental target value was close to 0.4 mm.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.49-59
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• 2022

In this paper, we designed a new enzyme function prediction model PSCREM based on a study that compared and evaluated CNN and LSTM/GRU models, which are the most widely used deep learning models in the field of predicting functions and structures using protein sequences in 2020, under the same conditions. Sequence evolution information was used to preserve detailed patterns which would miss in CNN convolution, and the relationship information between amino acids with functional significance was extracted through overlapping RNNs. It was referenced to feature map production. The RNN family of algorithms used in small CNN-RNN models are LSTM algorithms and GRU algorithms, which are usually stacked two to three times over 100 units, but in this paper, small RNNs consisting of 10 and 20 units are overlapped. The model used the PSSM profile, which is transformed from protein sequence data. The experiment proved 86.4% the performance for the problem of predicting the main classes of enzyme number, and it was confirmed that the performance was 84.4% accurate up to the sub-sub classes of enzyme number. Thus, PSCREM better identifies unique patterns related to protein function through overlapped RNN, and Overlapped RNN is proposed as a novel methodology for protein function and structure prediction extraction.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.271-282
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• 2022

In this paper, we propose a digital human content creation system. The digital human content creation system works with 3D AI modeling through whole-body scanning, and is produced with 3D modeling post-processing, texturing, rigging. By combining this with virtual reality(VR) content information, natural motion of the virtual model can be achieved in virtual reality, and digital human content can be efficiently created in one system. Therefore, there is an effect of enabling the creation of virtual reality-based digital human content that minimizes resources. In addition, it is intended to provide an automated pre-processing process that does not require a pre-processing process for 3D modeling and texturing by humans, and to provide a technology for efficiently managing various digital human contents. In particular, since the pre-processing process such as 3D modeling and texturing to construct a virtual model are automatically performed by artificial intelligence, so it has the advantage that rapid and efficient virtual model configuration can be achieved. In addition, it has the advantage of being able to easily organize and manage digital human contents through signature motion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.283-290
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• 2022

In this study, an IoT inspection system that can be linked with a server was developed using a gas timer and ESP-01 Wi-Fi module installed on a gas valve in the home. The server environment of the gas leak IoT inspection system was installed with APM (Apache, PHP, MySQL) to collect gas pressure data by generation so that leakage checks could be performed. In order to control the gas leak IoT inspection system, the app inventory was used to manage the gas leak check value in real time. In addition, user convenience has been enhanced so that membership management, WiFi settings, and leakage check values can be checked through mobile apps. In order to manage subscribers by region, the user list was checked by logging in in in the administrator mode so that the information on whether or not the leak test was conducted and the results could be provided. In addition, when the user presses the gas leak check button, the pressure is automatically checked, and the measured value is stored in the server, and when a gas leak occurs, the leakage check is performed after alarm and repair so that it can be used if normal. In addition, in order to prevent overlapping membership, membership management can be performed based on MAC addresses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.452-460
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• 2021

As the global electric vehicle (EV) market expands, eco-friendly EV that complement performance and safety problems continue to be released and the market is growing. However, in the case of EVs, the inconvenience of charging, safety problems such as electric shock, and electromagnetic interference (EMI) problems caused by the interlocking of various electronic components are problems that must be solved in EVs. The use of wireless power transmission technology can solve the problem of safety by not dealing with high current and high voltage directly and solving the inconvenience of charging EVs. In this paper, in order to reduce EMI a wireless charging control module, which is a key electronic component of WPT of EV. EMI reduction was designed through simulation of problems such as resonance and impedance that may occur in the power supply and signal distortion between high-speed communication that may occur in the signal part. Therefore, through the EMI reduction design with power integrity and signal integrity, the WPT wireless charging control module for electric vehicles reduces 10 dBu V/m and 15 dBu V/m, respectively, in 800 MHz to 1 GHz bands and 1.5 GHz bnad.

• 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.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.1-10
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• 2022

Learning C programming language in electronics education is an important basic education course for understanding computer programming and acquiring the ability to use microprocessors in embedded systems. In order to focus on understanding basic grammar and algorithms, it is a common teaching method to write programs based on C standard library functions in the console window and learn theory and practice in parallel. However, if a student wants to start a project activity or go to a deeper stage after acquiring some basic knowledge of the C language, using only the C standard library function in the console window limits what a student can express or control with the C program. For the purpose of making it easier for a student to use graphics or multimedia resources and increase educational value, this paper studies a case of applying Simple DirectMedia Layer (SDL), an open source software, into the C programming language learning process. The SDL-based programming course applied after completing the basic programming curriculum performed in the console window is introduced, and the educational value is evaluated through a survey. As a result, more than 56% of the respondents expressed positive opinions in terms of improved application ability, stimulating interest, and overall usefulness, and less than 4% of them had negative opinions.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.267-277
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• 2022

Achievement at university is recognized in a comprehensive sense as the level of qualitative change and development that students have embodied as a result of their experience in university education. Therefore, the academic achievement of university students will be given meaning in cooperation with the historical and social demands for diverse human resources such as creativity, leadership, and global ability, but it is practically an indicator of the outcome of university education. Measurement of academic achievement by such credits involves many problems, but in particular, standardization of academic achievement by credits based on evaluation methods, contents, and university rankings is a very difficult problem. In this study, we present a model that uses machine learning techniques to predict whether or not academic achievement is excellent for D-University graduates. The variables used were analyzed using up to 96 personal information and bachelor's information such as graduation year, department number, department name, etc., but when establishing a future education course, only the data after enrollment works effectively. Therefore, the items to be analyzed are limited to the recommended ability to improve the academic achievement of the department/student. In this research, we implemented an academic achievement prediction model through analysis of core abilities that reflect the philosophy, goals, human resources image, and utilized machine learning to affect the impact of the introduction of the prediction model on academic achievement. We plan to apply the results of future research to the establishment of curriculum and student guidance conducted in the department to establish a basis for improving academic achievement.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.555-562
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• 2022

This study measured and analyzed the propagation characteristics at frequencies 6, 10, and 17 GHz to discover the new propagation demands in a semi-basement indoor corridor environment for meeting the 4th industrial revolution requirements. The measured indoor environment is a straight corridor consisting of three lecture rooms and glass windows on the outside. The measurement scenario development and measurement system were constructed to match this environment. The transmitting antenna was fixed, and the frequency domain and time domain propagation characteristics were measured and analyzed in the line-of-sight environment based on the distance of the receiving antenna location. In the frequency domain, reliability was determined by the parameters of the floating intercept (FI) path loss model and an R-squared value of 0.5 or more. In the time domain, the root mean square (RMS) delay spread and the cumulative probability of K-factor were used to determine that 6 GHz had high propagation power and 17 GHz had low propagation power. These research results will be effective in providing ultra-connection and ultra-delay artificial intelligence services for WIFI 6, 5G, and future systems in a semi-basement indoor corridor environment.