• Journal of the Korean Society of Safety
• /
• v.37 no.4
• /
• pp.28-35
• /
• 2022

This study is aimed to develop a safety diagnosis system for fire pumps that detects normal and abnormal signals for the stable operation of the system. Hence, the following activities were carried out: first, a threshold value was identified for the normal operation and six abnormal operations (adherence of impeller, absence of water source, separation of pump and motor, run-stop operation, air inflow into the casing, and reverse-phase loss of the power line) reflecting changes in the current, flow and pressure of fire pumps; secondly, based on the identified signals, an algorithm capable of detecting three abnormal signals was developed and in terms of hardware, a current, pressure and flow sensor suitable for the analogue input values of NI-6009 was designed and installed. This combination of the hardware and software is applicable as a diagnosis system to ensure the stable operation of fire pumps.

• Nuclear Engineering and Technology
• /
• v.54 no.12
• /
• pp.4644-4651
• /
• 2022

Due to its short half-life, thoron gas has been assumed to have negligible health hazards on humans compared to radon. But, one of the decay products with a long half-life can make it to be transported to a long distance and to cause a severe internal dose through respiration. Since most commercial radon detectors can not discriminate thoron signals from radon signals, it is very common to overestimate radon doses which in turn result in biased estimation of lung cancer risk in epidemiological studies. Though some methods had been suggested to measure thoron and radon separately, they could not be used for real-time measurement because of CR-39 or LR-115. In this study, an effective method was suggested to measure radon and thoron separately from the free air. It was observed that the activity of thoron decreases exponentially due to delay time caused by a long pipe between two chambers. Therefore from two ion chambers apart in time, it was demonstrated that thoron and radon could be measured separately and simultaneously. We also developed a collimated alpha source and with this source and an SBD, we could convert the ion chamber reading to count rate in cps.

• International Journal of Computer Science & Network Security
• /
• v.23 no.6
• /
• pp.115-120
• /
• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.39-44
• /
• 2008

In this paper, heat transfer coefficients were measured in a channel with one side dimpled surface. The sphere type dimples were fabricated and the diameter and depth of dimple was 16mm and 4mm, respectively. Two channel heights of about 0.6 and 1.2 time of the dimple diameter, two dimple configuration were tested. The Reynolds numbers based on the channel hydraulic diameter was varied from 30000 to 50000. The improved hue detection based transient liquid crystal technique was used in the heat transfer measurement. Heat transfer measurement results showed that high heat transfer was induced downstream of dimples due to flow reattachment. Due to the flow recirculation on the upstream side in the dimple, the heat transfer coefficient was very low. As the Reynolds increased, the overall heat transfer coefficients also increased. With same dimple arrangement, the heat transfer coefficients and the thermal performance factor were higher for the lower channel height. As the distance between dimples became smaller, the overall heat transfer coefficient and the thermal performance factor were increased.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.107-112
• /
• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Tribology and Lubricants
• /
• v.40 no.1
• /
• pp.24-27
• /
• 2024

A monotype valve body for a dual clutch transmission has the potential to reduce costs, weight, and manufacturing time by modularizing various parts, including those of existing solenoid packs and valve bodies, into one through the application of super-precision die casting technology. However, this approach may lead to challenges such as reduced rigidity and increased interference due to modularization and compactness, impacting both product performance due to the reduced weight as well as durability and reliability. Unlike existing products, this approach requires a high-precision thin-wall block to avoid more complicated flow line formation, interference between flow lines, and leaks, as well as a strict quality requirement standard and precise inspections including detection of internal defects. To conduct precise inspections, we built an equivalent model corresponding to a driving distance of 300,000 km. Testing involved simulating actual road loads using a real vehicle and a chassis dynamometer in the FTP-75 mode (EPA Federal Test Procedure). The aim of the study was to establish a vehicle load-based part durability model for manufacturing a mono-type valve body and to develop fundamental technology for part weight reduction through preliminary design by introducing analytical weight reduction technology based on the derived results.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.48-60
• /
• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Journal of Sensor Science and Technology
• /
• v.33 no.5
• /
• pp.384-390
• /
• 2024

Conventional vibration-measurement methods used for vibration testing typically employ accelerometers, which offer the significant advantage of accurately measuring vibrations at specific positions. However, they can only measure one point at a time as simultaneously measurements of multiple points can be economically disadvantageous. This study aims to overcome these limitations by analyzing the vibration outputs of accelerometers attached to a product and those obtained through image processing. The analysis involved assessing the measurement uncertainties and verifying the low-frequency vibration testing according to KS standards. The results validated and confirmed the reliability of the proposed camera-based image-processing vibration-measurement method, which exhibited a notable vibration-detection performance and measurement errors within 5% compared to accelerometers for low-frequency vibrations. This method has the potential for application across various vibration-response and durability evaluations. Future research should focus on expanding it to high-frequency vibration testing using high-speed cameras and further enhancing image-based vibration-analysis techniques.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.11
• /
• pp.297-304
• /
• 2021

Commercial obfuscation tools (protectors) aim to create difficulties in analyzing the operation process of software by applying obfuscation techniques and Anti-reversing techniques that delay and interrupt the analysis of programs in software reverse engineering process. In particular, in case of virtualization detection and anti-debugging functions, the analysis tool exits the normal execution flow and terminates the program. In this paper, we analyze Anti-reversing techniques of executables with Debugger Detection and Viralization Tools Detection options through VMProtect 3.5.0, one of the commercial obfuscation tools (protector), and address bypass methods using Pin. In addition, we predicted the location of the applied obfuscation technique by finding out a specific program termination routine through API analysis since there is a problem that the program is terminated by the Anti-VM technology and the Anti-DBI technology and drew up the algorithm flowchart for bypassing the Anti-reversing techniques. Considering compatibility problems and changes in techniques from differences in versions of the software used in experiment, it was confirmed that the bypass was successful by writing the pin automation bypass code in the latest version of the software (VMProtect, Windows, Pin) and conducting the experiment. By improving the proposed analysis method, it is possible to analyze the Anti-reversing method of the obfuscation tool for which the method is not presented so far and find a bypass method.

• Journal of Korean Society of Environmental Engineers
• /
• v.39 no.5
• /
• pp.282-287
• /
• 2017

The purpose of this study was predicted the effects of mixing and diffusion due to the operation of the apparatus before the development of the mixed diffusion device for the decontamination absorbent to minimize the influence of contaminant inflow due to radiation accident. The tracer used for the flow characteristics was $^{68}Ga$, $^{99m}Tc$, which is a radioactive isotope, and 2 inch NaI radiation detector was used to detect it. The impeller of the decontamination mixed diffusion system applied to this study was made into three types and the mixing diffusion effect was compared. As a result of analyzing the flow characteristics of the radio-isotope with decontamination mixed diffusion device, mixing, diffusion and flow pattern were obtained. The radial mixing type impeller was able to diffuse to the water surface by the upflow flow, and the fin structure was adjusted for finding optimal conditions. The model 3 type consists of a fin guiding part and an auxiliary fin so that the diffusion speed is higher than that of other types of impellers. It also showed a short time to reach complete mixing.