• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.288-290
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• 2022

As part of the SOC digitization for smart water management and flood prevention, the government reported that automatic and remote control system for drainage facilities (180 billion won) to 57% of national rivers and established a real-time monitoring system (30 billion won). In addition, they were also planning to establish a smart dam safety management system (15 billion won) based on big data at 11 regions. Therefore, research is needed for smart water management and flood prevention system that can accurately calculate the flow rate through real-time flow rate measurement of rivers. In particular, the most important thing to improve the system implementation and accuracy is to ensure the accuracy of real-time flow rate measurements. To this end, radar sensors for measuring the flow rate of electromagnetic waves in the United States and Europe have been introduced and applied to the system in Korea, but demand for improvement of the system continues due to high price range and performance. Consequently, we would like to propose an improved flow rate measurement and flood forecast system by developing a radar sensor for measuring the electromagnetic surface current meter for real-time flow rate measurement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Clean Technology
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• v.16 no.2
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• pp.103-116
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• 2010

It has been reported that the LCD panel market in the FPD industry is become growing and its panel size and production capacity are increasing, and its manufacturing technique is improved every year. FPD manufacturing process requires high cleanliness in its overall process. Especially, FPD cleaning process which accounts for 30~40% of total manufacturing process is very important in its technological and productivity aspects. It is difficult to remove residual liquid-crystal in the fine gap after liquid-crystal injection process in the cell. In this study, aqueous cleaning agents with excellent cleaning, rinsing, and penetrating abilities, but minimum ion content for LCD panel were formulated through mixing glycol ether-type and glycol dimethyl ether-type solvents and nonionic surfactants which are widely used as raw materials for alternative cleaning agents because of environmental regulation at home and abroad. And the formulated cleaning agents were applied to clean FPD liquid crystal after its injection in the cell. Physical properties, cleaning efficiencies, and rinsabilities of the formulated cleaning agents with different combination ratios of solvents, surfactants and additives were measured. As experimental results, the formulated cleaning agents showed higher wetting indices and cloud point than the traditional commercial cleaning agent. And it was found that cleaning efficiencies of the formulated cleaning agents were influenced by the structure of main solvents in them and the types of liquid crystal as soil for cleaning. The best cleaning agents among the formulated cleaning agents showed similar cleaning efficiencies and better rinsabilities compared to the conventional cleaning agent.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.1
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• pp.79-89
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• 2023

Development of efficient and stable electrocatalysts for oxygen evolution reaction (OER) under acidic conditions is desirable goal for commercializing proton exchange membrane (PEM) water electroyzer. Herein, we report iridium-doped hydrogenated titanate (Ir-HTO) nanobelts as a promising catalyst with a low-Ir content for the acidic OER. Addition of low-Ir (~ 3.36 at%) into the single-crystalline HTO nanobelts with large exposed {100} facets significantly boost catalytic activity and stability for OER under acidic conditions. The Ir-HTO outperforms the commenrcial benchmark IrO₂ catalyst; an overpotential for delivering 10 mA cm^-2 current density was reduced to about 25% for the Ir-HTO. Moreover, the catalytic performance of Ir-HTO is positioned as the most efficient electrocatalyst for the acidic OER. An improved intrinsic catalytic activity and stability are also confirmed for the Ir-HTO through in-depth electrochemical characterizations. Therefore, our results suggest that low-Ir doped single-crystalline HTO nanobelts can be a promising catalyst for efficient and durable OER under acidic conditions.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.1
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• pp.25-35
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• 2023

Considering the non-linear behavior of structure and soil when evaluating a nuclear power plant's seismic safety under a beyond-design basis earthquake is essential. In order to obtain the nonlinear response of a nuclear power plant structure, a time-domain SSI analysis method that considers the nonlinearity of soil and structure and the nonlinear Soil-Structure Interaction (SSI) effect is necessary. The Boundary Reaction Method (BRM) is a time-domain SSI analysis method. The BRM can be applied effectively with a Perfectly Matched Layer (PML), which is an effective energy absorbing boundary condition. The BRM has a characteristic that the magnitude of the response in far-field soil increases as the boundary interface of the effective seismic load moves outward. In addition, the PML has poor absorption performance of low-frequency waves. For this reason, the accuracy of the low-frequency response may be degraded when analyzing the combination of the BRM and the PML. In this study, the accuracy of the analysis response was improved by adjusting the PML input parameters to improve this problem. The accuracy of the response was evaluated by using the analysis response using KIESSI-3D, a frequency domain SSI analysis program, as a reference solution. As a result of the analysis applying the optimal PML parameter, the average error rate of the acceleration response spectrum for 9 degrees of freedom of the structure was 3.40%, which was highly similar to the reference result. In addition, time-domain nonlinear SSI analysis was performed with the soil's nonlinearity to show this study's applicability. As a result of nonlinear SSI analysis, plastic deformation was concentrated in the soil around the foundation. The analysis results found that the analysis method combining BRM and PML can be effectively applied to the seismic response analysis of nuclear power plant structures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.1-7
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• 2023

An artificial intelligence (AI) method based on image deep learning is proposed to predict the entire displacement shape of a structure using the feature of partial displacements. The performance of the method was investigated through a structural test of a steel frame. An image-to-image regression (I2IR) training method was developed based on the U-Net layer for image recognition. In the I2IR method, the U-Net is modified to generate images of entire displacement shapes when images of partial displacement shapes of structures are input to the AI network. Furthermore, the training of displacements combined with the location feature was developed so that nodal displacement values with corresponding nodal coordinates could be used in AI training. The proposed training methods can consider correlations between nodal displacements in 3D space, and the accuracy of displacement predictions is improved compared with artificial neural network training methods. Displacements of the steel frame were predicted during the structural tests using the proposed methods and compared with 3D scanning data of displacement shapes. The results show that the proposed AI prediction properly follows the measured displacements using 3D scanning.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.200-208
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• 2022

In this study, an automatic information collection system and geographic information construction algorithm for the transportation disadvantaged using electric mobility aids are implemented using an object recognition model. Recognizes objects that the disabled person encounters while moving, and acquires coordinate information. It provides an improved route selection map compared to the existing geographic information for the disabled. Data collection consists of a total of four layers including the HW layer. It collects image information and location information, transmits them to the server, recognizes, and extracts data necessary for geographic information generation through the process of classification. A driving experiment is conducted in an actual barrier-free zone, and during this process, it is confirmed how efficiently the algorithm for collecting actual data and generating geographic information is generated.The geographic information processing performance was confirmed to be 70.92 EA/s in the first round, 70.69 EA/s in the second round, and 70.98 EA/s in the third round, with an average of 70.86 EA/s in three experiments, and it took about 4 seconds to be reflected in the actual geographic information. From the experimental results, it was confirmed that the walking weak using electric mobility aids can drive safely using new geographic information provided faster than now.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.196-201
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• 2023

In this study, a CNT fiber flexible electrode grafted with CuO nanoparticles (CuO NPs) and polyaniline (PANI) was developed and applied to a nonenzymatic electrochemical sensor for H₂O₂ detection. CuO NPs/PANI/CNT fiber electrode was fabricated through the synthesis and deposition of PANI and CuO NPs on the CNT fiber surface using an electrochemical method. Surface morphology and elemental composition of the CuO NPs/PANI/CNT fiber electrode were characterized by scanning electron microscope with energy dispersive X-ray spectrometry. And its electrochemical characteristics were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Compared with a bare CNT fiber as a control group, the CuO NPs/PANI/CNT fiber electrode showed a 4.78-fold increase in effective surface area and a 8.33-fold decrease in electron transfer resistance, which leads to excellent electrochemical properties such as a good electrical conductivity and an efficient electron transfer. These improved characteristics were due to the synergistic effect through the grafting of CNT fiber, PANI and CuO NPs. As a result, this electrode enhanced the H₂O₂ sensing performance.

• Tribology and Lubricants
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• v.39 no.2
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• pp.49-55
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• 2023

Currently, the demand for green technologies toward a sustainable future is rapidly increasing due to growing concern over environmental issues. Methanol is biodegradable and can provide clean combustion to reduce sulfur oxide and nitrogen oxide emissions, and therefore it is a candidate fuel for marine engines. However, the effect of methanol on tribological characteristic degradation should be addressed for methanol-fueled engines. In this study, the methanol addition effects on tribological characteristic degradation is experimentally assessed using a pin-on-disk tribo-tester. Ni-based alloy is used as a target material due to its broad applicability as an engine component material. For a lubricant, engine oil with and without methanol are used. The tests are conducted for up to 10,000 cycles under boundary lubrication while the change in friction force is monitored. Additionally, the wear rate is determined based on laser scanning confocal microscope data. An additional test in which methanol is added at regular intervals is performed with an aim to directly observe its effect on friction. Overall, the friction coefficient increases slightly with increasing methanol concentration. Furthermore, the wear rate of the pin and disk increase significantly with methanol addition. The results also indicate that the friction increases instantaneously with methanol addition at the contacting interface. These findings may be useful for better understanding the methanol effect on the tribological characteristics of Ni-based alloys for methanol-fueled engines with improved performance.