• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.666-671
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• 2023

As international environmental regulations on ship emissions are gradually strengthened, interest in electric propulsion and hybrid propulsion ships is increasing, and various solutions are being developed and applied to these ships, especially stabilization of the power system and system efficiency. The direct current distribution system is being applied as a way to increase the power. In addition, verification and testing of safety and performance of marine DC distribution systems is required. As a result of establishing a DC distribution test bed, verifying the performance of the DC distribution (variable speed power generation) system, and analyzing fuel consumption, this study applied a variable speed power generation system that is applied to DC power distribution for ships, and converted the power output from the generator into a rectifier. A system was developed to convert direct current power to connect to the system and monitor and control these devices. Through tests using this DC distribution system, the maximum voltage was 751.5V and the minimum voltage was 731.4V, and the voltage fluctuation rate was 2.7%, confirming that the voltage is stably supplied within 3%, and a variable speed power generation system was installed according to load fluctuations. When applied, it was confirmed through testing that fuel consumption could be reduced by more than 20% depending on the section compared to the existing constant speed power generation system.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.939-953
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• 2023

Shallow water equations (SWE) serve as fundamental equations governing the movement of the water. Traditional numerical approaches for solving these equations generally face various challenges, such as sensitivity to mesh generation, and numerical oscillation, or become more computationally unstable around shock and discontinuities regions. In this study, we present a novel approach that leverages the power of physics-informed neural networks (PINNs) to approximate the solution of the SWE. PINNs integrate physical law directly into the neural network architecture, enabling the accurate approximation of solutions to the SWE. We provide a comprehensive methodology for formulating the SWE within the PINNs framework, encompassing network architecture, training strategy, and data generation techniques. Through the results obtained from experiments, we found that PINNs could be an accurate output solution of SWE when its results were compared with the analytical method. In addition, PINNs also present better performance over the Artificial Neural Network. This study highlights the transformative potential of PINNs in revolutionizing water resources research, offering a new paradigm for accurate and efficient solutions to the SVE.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.153-166
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• 2024

The digitalization of tasks for smart construction requires the smooth exchange of digital data among stakeholders to be effective, but there is a lack of digital data standardization and utilization methods. This paper proposes a digital construction information structure to transform information from road construction sites into digital formats. The study targets include significant tasks, such as work planning, scheduling, safety management, and quality control. The key to the construction information structure is separating construction information into objects and activities, defining unit works by combining these two types of information to ensure flexibility in representing and modifying construction information. The objects and activities have their respective hierarchical structures, which are defined flexibly to match the actual content. This structure achieves both efficiency and detail. The pilot structure was applied to highway construction projects and implemented digitally using general formats. This study enables the digitalization of road construction processes that closely resemble reality, accelerating the digital transformation of the civil engineering industry by developing a digital twin of the entire road construction lifecycle.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.1-14
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• 2024

An image-reconstruction technology, involving the deployment of an unmanned mobility equipped with high-speed LiDAR (Light Detection And Ranging) has been proposed to reconstruct the shape of abandoned mine. Unmanned mobility operation is remarkably useful in abandoned mines fraught with operational difficulties including, but not limited to, obstacles, sludge, underwater and narrow tunnel with the diameter of 1.5 m or more. For cases of real abandoned mines, quadruped robots, quadcopter drones and underwater drones are respectively deployed on land, air, and water-filled sites. In addition to the advantage of scanning the abandoned mines with 2D solid-state lidar sensors, rotation of radiation at an inclination angle offers an increased efficiency for simultaneous reconstruction of mineshaft shapes and detecting obstacles. Sensor and robot posture were used for computing rotation matrices that helped compute geographical coordinates of the solid-state lidar data. Next, the quadruped robot scanned the actual site to reconstruct tunnel shape. Lastly, the optimal elements necessary to increase utility in actual fields were found and proposed.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.78-84
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• 2024

The electrochemical reduction of carbon dioxide (CO₂) is gaining attention as an effective method for converting CO₂ into high-value carbon compounds. This paper reports a facile meth od for synth esizing and characterizing g-C₃N₄-modified porous Au (pAu) electrodes for electrochemical CO₂ reduction using e-beam deposition and anodization techniques. The fabricated pAu@g-C₃N₄ electrode (@ -0.9 VRHE) demonstrated superior electrochemical performance compared to the pAu electrode. Both electrodes exhibited a Faradaic efficiency (FE) of 100% for CO production. The pAu@g-C₃N₄ electrode achieved a maximum CO production rate of 9.94 mg/s, which is up to 2.2 times higher than that of the pAu electrode. This study provides an economical and sustainable approach to addressing climate change caused by CO₂ emissions and significantly contributes to the development of electrodes for electrochemical CO₂ reduction.

• Applied Chemistry for Engineering
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• v.25 no.5
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• pp.455-462
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• 2014

This work investigated the hydrophobic coating of silicate yellow phosphor powder in the form of divalent europium-activated strontium orthosilicate ($Sr_2SiO_4:Eu^{2+}$) by using an atmospheric pressure dielectric barrier discharge (DBD) plasma with argon as a carrier and hexamethyldisiloxane (HMDSO), toluene and n-hexane as precursors. After the plasma treatment of the phosphor powder, the lattice structure of orthosilicate was not altered, as confirmed by an X-ray diffractometer. The coated phosphor powder was characterized by scanning electron microscopy, fluorescence spectrophotometry and contact angle analysis (CAA). The CAA of the phosphor powder coated with the HMDSO precursor revealed that the water contact angle increased from $21.3^{\circ}$ to $139.5^{\circ}$ (max. $148.7^{\circ}$) and the glycerol contact angle from $55^{\circ}$ to $143.5^{\circ}$ (max. $145.3^{\circ}$) as a result of the hydrophobic coating, which indicated that hydrophobic layers were successfully formed on the phosphor powder surfaces. Further surface characterizations were performed by Fourier transform infrared spectroscopy and X-ray photoelectron spectrometry, which also evidenced the formation of hydrophobic coating layers. The phosphor coated with HMDSO exhibited a photoluminescence (PL) enhancement, but the use of toluene or n-hexane somewhat decreased the PL intensity. The results of this work suggest that the DBD plasma may be a viable method for the preparation of hydrophobic coating layer on phosphor powder.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.5
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• pp.25-33
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• 2011

This work proposes a skinny-type 10b 50MS/s 0.13um CMOS three-step pipeline ADC for CIS applications. Analog circuits for CIS applications commonly employ a high supply voltage to acquire a sufficiently acceptable dynamic range, while digital circuits use a low supply voltage to minimize power consumption. The proposed ADC converts analog signals in a wide-swing range to low voltage-based digital data using both of the two supply voltages. An op-amp sharing technique employed in residue amplifiers properly controls currents depending on the amplification mode of each pipeline stage, optimizes the performance of op-amps, and improves the power efficiency. In three FLASH ADCs, the number of input stages are reduced in half by the interpolation technique while each comparator consists of only a latch with low kick-back noise based on pull-down switches to separate the input nodes and output nodes. Reference circuits achieve a required settling time only with on-chip low-power drivers and digital correction logic has two kinds of level shifter depending on signal-voltage levels to be processed. The prototype ADC in a 0.13um CMOS to support 0.35um thick-gate-oxide transistors demonstrates the measured DNL and INL within 0.42LSB and 1.19LSB, respectively. The ADC shows a maximum SNDR of 55.4dB and a maximum SFDR of 68.7dB at 50MS/s, respectively. The ADC with an active die area of 0.53$mm^2$ consumes 15.6mW at 50MS/s with an analog voltage of 2.0V and two digital voltages of 2.8V ($=D_H$) and 1.2V ($=D_L$).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1297-1308
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• 2015

In Korea, the local traffic safety master plan has been established and implemented according to the Traffic Safety Act. Each local government is required to establish a customized traffic safety policy and share roles for improvement of traffic safety and this means that local governments lead and promote effective local traffic safety policies fit for local circumstances in substance. For implementing efficient traffic safety policies, which accord with many-sided characteristics of local governments, the prediction of community-based traffic accidents, which considers local characteristics and the analysis of accident influence factors must be preceded, but there is a shortage of research on this. Most of existing studies on the community-based traffic accident prediction used social and economic variables related to accident exposure environments in countries or cities due to the limit of collected data. For this reason, there was a limit in applying the developed models to the actual reduction of traffic accidents. Thus, this study developed a local traffic accident prediction model, based on smaller regional units, administrative districts, which were not omitted in existing studies and suggested a method to reflect traffic safety facility and policy variables that traffic safety policy makers can control, in addition to social and economic variables related to accident exposure environments, in the model and apply them to the development of local traffic safety policies. The model development result showed that in terms of accident exposure environments, road extension, gross floor area of buildings, the ratio of bus lane installation and the number of crossroads and crosswalks had a positive relation with accidents and the ratio of crosswalk sign installation, the number of speed bumps and the results of clampdown by police force had a negative relation with accidents.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.