• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3685-3693
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• 2021

This paper presents a disturbance observer-based robust backstepping load-following control (DO-RBLFC) scheme for modular high-temperature gas-cooled reactors (MHTGRs) in the presence of actuator saturation and disturbances. Based on reactor kinetics and temperature reactivity feedback, the mathematical model of the MHTGR is first established. After that, a DO is constructed to estimate the unknown compound disturbances including model uncertainties, external disturbances, and unmeasured states. Besides, the actuator saturation is compensated by employing an auxiliary function in this paper. With the help of the DO, a robust load-following controller is developed via the backstepping technique to improve the load-following performance of the MHTGR subject to disturbances. At last, simulation and comparison results verify that the proposed DO-RBLFC scheme offers higher load-following accuracy, better disturbances rejection capability, and lower control rod speed than a PID controller, a conventional backstepping controller, and a disturbance observer-based adaptive sliding mode controller.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.150-157
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• 1997

Uniform weld pool shape is important in determining the weld quality. And weld pool width is one of the most dominant factors of the seld pool shape. In order to control the weld pool width, the fuzzy logic controller, which is well adapted to the complicated nonlinear systems such as welding, was used in this study. The weld pool image was obtained through CCD camera, and the weld pool width was calculated by processing the image. Uaing the calculated width, welding speed, as a control input, was inferred by the fuzzy logic controller. An uniform weld pool width can be successfully obtained regardless of the disturbances in the system.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.147-156
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• 2022

Industrial control systems that manage and maintain various industries were mainly operated in closed environment without external connection, but with the recent development of the Internet and the introduction of ICT technology, the access to the industrial control system of external or attackers has become easier. Such incorrect approaches or attacks can undermine the availability, a major attribute of the industrial control system, and violation of availability can cause great damage. In this paper, when issuing commands in an industrial control system, a verification group is formed using a random list to verify and execute commands, and a trust score technique is introduced that applies feedback to the verification group that conducted verification using the command execution result. This technique can reduce overhead generated by random generation in the process of requesting command verification, give flexibility to the verification process, and ensure system availability. For the performance analysis of the system, we measured the time and gas usage when deploying a smart contract, gas usage when verifying a command. As a result, we confirmed that although the proposed system generates a random list compared to the legacy system, there was little difference in the time when it took to deploy smart contract and that the gas used to deploy smart contract increased by about 1.4 times in the process of generating a random list. However, the proposed system does not perform random operations even though the operation of command verification and confidence score technique is performed together during the command verification process, thus it uses about 9% less gas per verification, which ensures availability in the verification process.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.294-301
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• 2020

Man-In-Simulant Test(MIST) provides a test method to evaluate chemical protective equipments such as protective garments, gloves, footwear and gas mask. The MIST chamber is built to control concentration of chemical vapor that has a activity space for two persons. Non-toxic methyl-salicylate(MeS) is used to simulate chemical agent vapor. We carried out to measure inward leakage MeS vapors by using passive adsorbent dosimeter(PAD) which are placed on the skin at specific locations of the body while man is activity according to the standard procedure in MIST chamber. But more time is required for PADs and there is concern of contamination in PADs by recovering after experiment. Therefore detector for measuring in real time is necessary. In order to analyze in real time the contamination of the personal protective equipment inside the chemical environment, we have developed a wireless real-time gas detector. The detector consists of 8 gas-sensors and 1 control-board. The control-board includes a CPU for processing a signal, a power supply unit for biasing the sensor and Bluetooth-chipset for transmission of signals to external PC. All signals from gas-sensors are converted into digital signals simultaneously in the control-board. These digital signals are stored in external PC via Bluetooth wireless communication. The experiment is performed by using protective equipment worn on manikin. The detector is mounted inside protective equipment which is capable of providing a real-time monitoring inward leakage MeS vapor. Developed detector is demonstrated the feasibility as real-time detector for MIST.

• Journal of the Korean Society of Tobacco Science
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• v.7 no.2
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• pp.189-197
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• 1985

The purpose of this study is to improve the method and the apparatus for recovering freon-11 used in a process for increasing the filling capacity of tobacco. The first, the theoretical recovery rate of freon-11 was calculated from vapour pressure and thermodynamic properties of freon-11. The second, the usability of theoretical data was evaluated by the comparison between the theoretical data and the experimental data from the pilot plant. The result obtained under the present experiment condition was quite similar to the rate at 15kg/cm2 and 5$^{\circ}C$, but the theoretical data evaluated were 0.1 to 0.3% lower than the experimental data at 20 to 25kg/cm2 and 5$^{\circ}C$. The recovery rate of the cooling and the compression system was about 95% with freon-11 used in this system and that with freon-11 concentrate of waste gas was about 3 to 3.6% at 20 to 25kg/cm2 and 5$^{\circ}C$.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.359-362
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• 2003

Damping systems have been widely used to various industrial structures and are mainly hydraulic and pneumatic devices nowadays. In this work, a novel damping system based on the colloidal suspension in the field of nanotechnology is investigated. The colloidal suspension consists of Iyophobic working fluid and hydrophobic-coated porous particle. The mechanism of mechanical energy dissipation in damping system based on the colloidal suspension with nano-porous particles is different from that of the existing hydraulic damping system. The absorbed energy of the damping system using colloidal suspension can be calculated through the mechanical equilibrium condition by the superficial tensions of liquid-gas Interface in the hydrophobic surface in nano-porous particles. The results from an analytic approach have a reasonable agreement with experimental results.

• Transactions of Materials Processing
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• v.31 no.3
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• pp.124-128
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• 2022

NdFeB magnets have been positioned as the core materials in advanced technologies such as MRI (magnetic resonance imaging), FA (factory automation system), robot, motors, and so on based on the highest magnetic properties. To effectively improve the refined microstructure, the plastic deformation has been known as the good alternatives by the recrystallization. However, it has been regarded as being impossible because of the few slip systems in the RE-Fe-B magnets at room temperature. The purpose of this study was to investigate the possibility of control of grain refinement and magnetic anisotropy of NdFeB alloy powder by the severe plastic deformation. The NdFeB magnet powder was fabricated by gas atomization process, and the powder was pre-compacted at high temperature. The pre-compacted billets were deformed by HPT (high pressure torsion), and then the deformed billets were observed microstructure and magnetic properties. After the HPT process at room temperature, the grain size decreased with increasing because of the melted Nd-rich phase, and the anisotropy of Nd₂Fe₁₄B phase was formed after the HPT process.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.134-141
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• 2003

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korean Society of Visualization
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• v.15 no.1
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• pp.11-18
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• 2017

A two-phase flow observed in a heat exchanger or nuclear power generation often has a profound effect on undesirable noise or flow characteristics. Void fraction, which refers to the ratio of gas (or liquid) to the total fluid, affects heat transfer coefficient, vibration and so forth. In other words, void fraction is one of most important parameters in two-phase flow since it contributes to comprehend the characteristics of two-phase flow. We developed a two-phase flow visualization system to measure cross-sectional and volumetric void fractions by using quick closing valves and image processing software. With this system, we could observe the plug, slug, and stratified flow patterns of two-phase flow and measure a myriad of void fractions. As a consequence of the experiment, we found that the estimated void fractions were largely coincident with the predictive values by Chisholm model.