• Science of Emotion and Sensibility
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• v.8 no.3
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• pp.277-290
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• 2005

The purpose of this study is to develope a design prototype of 'Smart Battle Jacket' for the future soldier system. Future battle field is supposed to be the place where the information is the most important. Future soldiers are also supposed to get digital devices to have more possibility of survival in the battle field. To design the smart battle jacket that has digital devices inside, it's needed to forecast the body sizes and shape of the future soldiers, to research the human bodies and movements, and to study the functions of the digital devices and the relationship between the bodies and the devices. The usability of the 1st model for the Smart battle jacket had been tested, and the model had been corrected by the results from the test. After all, a smart battle jacket design prototype for the future soldier system has been developed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.35-40
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• 2010

NAND flash memory has been widely used than traditional HDD in PDA and other mobile devices, embedded systems, PC because of faster access speed, low power consumption, vibration resistance and other benefits. DiskSim and other HDD simulators has been developed that for find improvements for the software or hardware. But there is a few Linux-based simulators for NAND flash memory and SSD. There is necessary for Windows-based NAND flash simulator because storage devices and PC using Windows. This paper describe for development of simulator-NFSim for FTL performance analysis in NAND flash. NFSim is used to measure performance of various FTL algorithms and FTL wear-level. NAND flash memory model and FTL algorithm developed using Windows Driver Model and class for scalability. There is no need for another tools because NFSim using graph tool for data measure of FTL performance.

• Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.27-36
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• 2014

Similar to the essential components of many mechanical systems, the geometrical properties of the teeth of spiral bevel gears greatly influence the kinematic and dynamic behaviors of mechanical systems. Logarithmic spiral bevel gears show a unique advantage in transmission due to their constant spiral angle property. However, a mathematical model suitable for accurate digital modeling, differential geometrical characteristics, and related contact analysis methods for tooth surfaces have not been deeply investigated, since such gears are not convenient in traditional cutting manufacturing in the gear industry. Accurate mathematical modeling of the tooth surface geometry for logarithmic spiral bevel gears is developed in this study, based on the basic gearing kinematics and spherical involute geometry along with the tangent planes geometry; actually, the tooth surface is a parametric surface defined on a parallelogrammic domain. Equivalence proof of the tooth surface geometry is then given in order to greatly simplify the mathematical model. As major factors affecting the lubrication, surface fatigue, contact stress, wear, and manufacturability of gear teeth, the differential geometrical characteristics of the tooth surface are summarized using classical fundamental forms. By using the geometrical properties mentioned, manufacturability (and its limitation in logarithmic spiral bevel gears) is analyzed using precision forging and multiaxis freeform milling, rather than classical cradle-type machine tool based milling or hobbing. Geometry and manufacturability analysis results show that logarithmic spiral gears have many application advantages, but many urgent issues such as contact tooth analysis for precision plastic forming and multiaxis freeform milling also need to be solved in a further study.

• Tribology and Lubricants
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• v.32 no.3
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• pp.101-105
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• 2016

Trap effect of groove is evaluated in a lubricating system using computational fluid dynamics (CFD) analysis. The simulation is based on the standard k-ε turbulence model and the discrete phase model (DPM) using a commercial CFD code FLUENT. The simulation results are also capable of showing the particle trajectories in flow field. Computational domain is meshed using the GAMBIT pre-processor. The various grooves are applied in order to improve lubrication characteristics such as reduction of friction loss, increase in load carrying capacity, and trapping of the wear particles. Trap effect of groove is investigated with variations in cross-sectional shape and Reynolds number in this research. Various cross-sectional shapes of groove (rectangular, triangle, U shaped, trapezoid, elliptical shapes) are considered to evaluate the trap effect in simplified two-dimensional sliding bearing. The particles are assumed to steel, and defined a single particle injection condition in various positions. The “reflect” boundary condition for discrete phase is applied to the wall boundary, and the “escape” boundary condition to “pressure inlet” and “pressure outlet” conditions. The streamlines are compared with particles trajectories in the groove. From the results of numerical analysis in the study, it is found that the cross-sectional shapes favorable to the creation of vortex and small eddy current are effective in terms of particle trapping effect. Moreover, it is found that the Reynolds number has a strong influence on the pattern of vortex or small eddy current in the groove, and that the pattern of the vortex or small eddy current affects the trap effect of the groove.

• Tribology and Lubricants
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• v.39 no.1
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• pp.8-12
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• 2023

This study presents a molecular simulation of adhesion control between carbon nanotube (CNT) and Ag thin film deposited on silicon substrate. Rough and flat Ag thin film models were prepared to investigate the effect of surface roughness on adhesion force. Heat treatment was applied to the models to modify the adhesion characteristics of the Ag/CNT interface based on thermal wetting. Simulation results showed that the heat treatment altered the Ag thin film morphology by thermal wetting, causing an increase in contact area of Ag/CNT interface and the adhesion force for both the flat and rough models changed. Despite the increase in contact area, the adhesion force of flat Ag/CNT interface decreased after the heat treatment because of plastic deformation of the Ag thin film. The result suggests that internal stress of the CNT induced by the substrate deformation contributes in reduction of adhesion. Contrarily, heat treatment to the rough model increases adhesion force because of the expanded contact area. The contact area is speculated to be more influential to the adhesion force rather than the internal stress of the CNT on the rough Ag thin film, because the CNT on the rough model contains internal stress regardless of the heat treatment. Therefore, as demonstrated by simulation results, the heat treatment can prevent delamination or wear of CNT coating on a rough metallic substrate by thermal wetting phenomena.

• Tunnel and Underground Space
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• v.24 no.6
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• pp.449-463
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• 2014

There are many case studies and application cases in abandoned mines for hydraulic filling method filled by slurry or paste form, but research on the pneumatic filling is not applied in Korea. The damage of steel pipe is occurred by wear due to the flow of filling material in the bent area of steel pipe in traditional pneumatic filling method. In this study, the new pneumatic filling method was developed using a newly devised improved nozzle to improve the above problem. The model test for mine filling was performed in the laboratory for the simulated accessible or inaccessible mine cavities, and the filling efficiency by the results obtained from the test was calculated. The filling efficiency was analyzed from the variation of outlet angle, feed rate and grain size of sand in model test of simulated accessible mine cavity. The superiority of improved pneumatic filling method was proved through the analysis of filling efficiency by the results obtained from each model tests of gravitational, traditional, and improved filling method in simulated inaccessible mine cavity.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.9
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• pp.251-260
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• 2021

Gait analysis is one of the research fields for obtaining various information related to gait by analyzing human ambulation. It has been studied for a long time not only in the medical field but also in various academic areas such as mechanical engineering, electronic engineering, and computer engineering. Efforts have been made to determine whether there is a problem with gait through gait analysis. In this paper, as a pre-step to find out gait abnormalities, it is investigated whether it is possible to differentiate whether experiment participants wear elderly simulation suit or not by applying gait data to machine learning models for the same person. For a total of 45 participants, each gait data was collected before and after wearing the simulation suit, and a total of six machine learning models were used to learn the collected data. As a result of using an artificial neural network model to distinguish whether or not the participants wear the suit, it showed 99% accuracy. What this study suggests is that we explored the possibility of judging the presence or absence of abnormality in gait by using machine learning.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.433-439
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• 2017

Urban railway tunnels are polluted by resuspension of deposited bottom dust or newly generated wear dust. A hybrid type dust collector consisting of a baffle and an electrostatic precipitator was developed to remove these types of airborne dust when trains are running in the tunnel. Since dust collection efficiency of the hybrid dust collector is inversely proportional to the airflow rate, the relationship between airflow rate and dust collection efficiency was experimentally investigated for two baffle models. Collection efficiencies for dust larger than $0.5{\mu}m$ for the hybrid dust collector model A1, operated at 3.4 m/s, were greater than 30%; those for the hybrid dust collector model A2, operated at 4.7 m/s, were higher than 20%. When the applied voltage was 13 kV, the amounts of $PM_{10}$ collected with model A1 and model A2 dust collectors were estimated at $253{\mu}g$ and $242{\mu}g$ per hour, respectively.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.