• Applied Chemistry for Engineering
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• v.22 no.5
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• pp.566-574
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• 2011

Coal gasifier is a key component for achieving high efficiency in integrated gasification combined cycle and indirect coal liquefaction. Although there have been several successful coal gasifiers that were commercially proven, many different design configurations are still possible for a simple and reliable gasifier operation. Four different gasifier design concepts of dry-feeding were compared in terms of residence time, exit syngas temperature and syngas composition. First, cold-flow simulation was applied to pre-select the configuration concepts, and the hot-flow simulation including chemical reactions was performed to compare the concepts at more actual gasifier operating conditions. There are many limitations in applying CFD method in gasifier design, particularly in estimating slag behavior and slag-tap design. However, the CFD analysis proved to be useful in comparing the widely different gasifier design concepts as a pre-selection tool.

• Journal of Industrial and Engineering Chemistry
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• v.67
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• pp.301-311
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• 2018

In this study, the ion-gelation method was applied to fabricate novel Fe-carbon-bentonite-alginate beads ($Fe^0$/C-BABs). $Fe^0$/C-BABs could effectively control Fe release during persulfate (PS) activation in N-acetyl-p-aminophenol (APAP) oxidation. A novel two-stage approach that combined $Fe^0$/C-BABs and an oyster-shell-filled bed (OSFB) column was developed to address the low pH and high Fe concentration of the effluent of the traditional PS process. The application of the $Fe^0$/C-BABs and OSFB column regulated pH levels and Fe release during the advanced oxidation of APAP. The characteristics of $Fe^0$/C-BABs were also investigated through scanning electron microscopy, energy dispersive spectrometry, and Fourier transform infrared spectroscopy. The long-term operation performance of $Fe^0$/C-BABs in a continuous fixed-bed reactor under simultaneous PS and APAP feeding was also evaluated. The effects of initial PS concentration, pH, fixed-bed weight, in-flow rate, and dissolved oxygen (DO) were investigated. Under selected conditions, 86.3% efficiency was achieved during the first stage of APAP degradation (effluent pH of 3.05, Fe contents: $106.25mgL^{-1}$). Water quality improved after the effluent was passed through the OSFB column (effluent pH of 6.32, Fe contents: $21.43mgL^{-1}$). Moreover, this study analyzed the free radicals and intermediates produced during APAP degradation to identify the possible routes of APAP degradation.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.51-60
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• 2021

Effective nonpoint source (NPS) pollution management requires frequent water quality monitoring, which is, however, often costly to be implemented in practice. Statistical techniques and machine learning methods allow us to identify and focus on fundamental environmental variables that have close relationships with NPS pollutants of interest. This study developed surrogate models to predict the concentrations of suspended sediment (SS) and total phosphorus (T-P) from turbidity and runoff discharge rates using multiple linear regression (MLR) and random forest (RF) methods. The RF models provided acceptable performance in predicting SS and T-P, especially when runoff discharge rates were high. The RF models outperformed the MLR models in all the cases. Such finding highlights the potential of RF techniques and models as a tool to identify fundamental environmental variables that are measured in relatively inexpensive ways or freely available but still able to provide information required to quantify the concentrations of NP S pollutants. The analysis of relative importance rates showed that the temporal variations of SS and T-P concentrations could be more effectively explained by that of turbidity than runoff discharge rate. This study demonstrated that the advanced statistical techniques such as machine learning could help to improve the efficiency of NPS pollutants monitoring.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.1-8
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• 2022

RISC-V is an open-source instruction set architecture which has a simple base structure and can be extensible depending on the purpose. In this paper, we designed a small and low-power 32-bit RISC-V processor to establish the base for research on RISC-V embedded systems. We designed a 2-stage pipelined processor which supports RISC-V base integer instruction set except for FENCE and EBREAK instructions. The processor also supports privileged ISA for trap handling. It used 1895 LUTs and 1195 flip-flops, and consumed 0.001W on Xilinx Zynq-7000 FPGA when synthesized using Vivado Design Suite. GPIO, UART, and timer peripherals are additionally used to compose the system. We verified the operation of the processor on FPGA with FreeRTOS at 16MHz. We used Dhrystone and Coremark benchmarks to measure the performance of the processor. This study aims to provide a low-power, high-efficiency microprocessor for future extension.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1603-1611
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• 2021

As IMO environmental regulations are tightened, the need to establish a system that can reduce emissions is increasing, and for this purpose, various power control management systems have been studied and implemented as a new energy management system for ships. In this study, we design a control process through modeling for Bi-Directional Converter (BDC) application with bi-directional power flow to link batteries, which are energy storage devices, to conventional generator power systems, and propose mechanisms for batteries optimized for varying loads. This work models MATLAB/Simulink as a BDC and simulates current control and state of charge (SOC) optimization at the time of charging and discharging batteries according to load scenarios. Through this, the battery, power, and load were interlocked so that the generator operated on board could be operated in the optimal operation range, and power control management was performed to enable the generator to operate in the high fuel efficiency range.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.85-90
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• 2022

In this study, to reduce the time and cost disadvantages of the traditional motion graphic production method in order to realize the movement of a virtual camera identical to that of the real camera, motion graphics virtualization using AR Core-based mobile device real-time tracking data A method for creating a camera is proposed. The proposed method is a method that simplifies the tracking operation in the video file stored after shooting, and simultaneously proceeds with shooting on an AR Core-based mobile device to determine whether or not tracking is successful in the shooting stage. As a result of the experiment, there was no difference in the motion graphic result image compared to the conventional method, but the time of 6 minutes and 10 seconds was consumed based on the 300frame image, whereas the proposed method has very high time efficiency because this step can be omitted. At a time when interest in image production using virtual augmented reality and various studies are underway, this study will be utilized in virtual camera creation and match moving.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Membrane Journal
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• v.33 no.6
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• pp.344-351
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• 2023

The membrane separation process for carbon dioxide capture from hydrogen reformer exhaust gas has been developed. Using a commercial membrane module, a multi-stage process was developed to achieve 90% of carbon dioxide purity and 90% of recovery rate for ternary mixed gas. Even if a membrane module with being well-known properties such as material selectivity and permeability, the process performance of purity and recovery widely varies depending on the stage-cut, the pressure at feed and permeate side. In this study, we verify the limits of capture efficiency at single-stage membrane process under various operating conditions and optimized the two-stage recovery process to simultaneously achieve high purity and recovery rate.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1307-1312
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• 2023

In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

• Journal of Space Technology and Applications
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• v.3 no.4
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• pp.355-372
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• 2023

As the need for low-cost, high-efficiency cubesats develops in the new space age, commercial paradigms are shifting in the private sector. This paper examines the challenges of launching and operating both domestic and foreign cubesats, and proposes practical solutions to ensure the robustness and reliability of the satellites from a practical perspective. In particular, the paper deals with checkpoints that are easy to miss, focusing on key events that can occur from the satellite deployment process through normal mode to mission mode in the operation scenario. Although the contents presented in this paper may not be technically applicable to all cubesat systems due to the different nature of each satellite bus system, they will be of some help during satellite assembly, integration and testing.