• International Journal of Highway Engineering
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• v.14 no.5
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• pp.101-111
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• 2012

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1324-1331
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes and gateway are deployed with Microstrain G-Link system. Five wireless sensor nodes and gateway are installed at the man-made slope to detect landslide. It is found that the acceleration data of each sensor node can be obtained via wireless sensor networks. Additionally, thresholds to determine whether the slope will be stable or not are proposed using finite element analysis. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.79-84
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• 2011

The purpose of this study was to investigate the relevance of the prediction equations derived from the relationship between metabolic energy expenditure and kinetic energy, for different speeds of walking and running over the treadmill. Seven male subjects participated in this study. A tri-axial accelerometer was attached on between the left and right posterior superior iliac spines. Kinetic energy was calculated by the integration of acceleration data and compared with the metabolic energy measured by a gas analyzer. Correlation coefficients were determined to find a relationship between the kinetic energy and the metabolic energy expenditure. Also, the difference between measured and predicted values was used to find the relevance for individual and group equations. Results showed a relatively good correlation between the measured metabolic energy and the calculated kinetic energy. In addition, a dramatic increase in kinetic energy was observed at the transition speed of walking and running (6 km/h). There was no difference in how to predict the kinetic energy expenditure for individual and group even though people have different physical characteristics. This study would be useful to predict metabolic energy expenditures by the regression analysis with acceleration data.

• Geomechanics and Engineering
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• v.36 no.6
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• pp.575-587
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• 2024

The contact pressure between the surrounding rock and the support is an important indicator of the surrounding rock pressure. There has been a bottleneck in the prediction of contact loads between surrounding rock and primary support in deep-buried mountain tunnels. The main reason is that a reliable method wasn't existed to quantify the contact loads. This study had been taken into account the flexible support role of the primary support, and the fitting curve of surrounding rock deformation for dynamic tunnel construction was proposed. New formulas for the calculation of contact loads between surrounding rock and primary support were obtained by inversion. Comparative analysis of the calculation results with numerical simulation verified the reliability of the calculation method in this study. It can be seen from the analyses that the contact load between surrounding rock and primary support increases, remains unchanged and decreases during acceleration, uniform velocity and deceleration, respectively, and the deformation of the surrounding rock in the acceleration and deceleration stages cannot completely converted into contact loads. The contact loads between surrounding rock and primary support of medium-strength and weak surrounding rock tunnels are generally within 150 kPa and 1 MPa, respectively. For tunnels with weak surrounding rock, advanced support can be installed to reduce the unique release coefficient λ₀ and the value of the constant D, with the purpose of reducing the contact loads between surrounding rock and primary support. Changes in support parameters have a small effect on the contact loads between surrounding rock and primary support, but increase or decrease the safety factor, resulting in a waste of resources or a situation that threatens the safety of the support. The results of this research provide guidance for the prediction of contact loads between surrounding rock and primary support for dynamic tunnel construction.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.139-146
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• 2007

Many researches had been gone about method to track moving object using wireless sensor network. We examined tradeoffs that exist between quantity of energy and correctness of tracking, and we confirmed that can get more energy sayings through improved motion prediction method. The consumed energy in the tracking is used by sensor node for sensing the object, and tracking correctness is a differ once of actual object position from calculated value by sensing. Some tracking methods and controlling parameters causes a variation of tracking correctness and energy consuming, we can get best energy effectiveness by motion prediction algorithm. Furthermore, we get better tracking quality and energy effectiveness through using a motion prediction algorithm that consider acceleration. By the simulation, we know that if we use an accurate motion prediction algorithm, node activation range that is used for target's predicted position should be restricted to sensing range of sensor is better.

• Earthquakes and Structures
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• v.4 no.1
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• pp.109-132
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• 2013

Current codes incorporate simplified methods for the prediction of acceleration demands on secondary structural and non-structural elements at different levels of a building. While the use of simple analysis methods should be advocated, damage to both secondary structural and non-structural elements in recent earthquakes have highlighted the need for improved design procedures for such elements. In order to take a step towards the formation of accurate but simplified methods of predicting floor spectra, this work examines the floor spectra on elastic and inelastic single-degree of freedom systems subject to accelerograms of varying seismic intensity. After identifying the factors that appear to affect the shape and intensity of acceleration demands on secondary structural and non-structural elements, a new series of calibrated equations are proposed to predict floor spectra on single degree of freedom supporting structures. The approach uses concepts of dynamics and inelasticity to define the shape and intensity of the floor spectra at different levels of damping. The results of non-linear time-history analyses of a series of single-degree of freedom supporting structures indicate that the new methodology is very promising. Future research will aim to extend the methodology to multi-degree of freedom supporting structures and run additional verification studies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.6
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• pp.483-492
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• 2014

Uncomfortable feelings of occupants by indoor floor impact noise in a residential building are not accurately represented by the floor impact noise from a standard impact source. It is due to the characteristics of standard impact sources, which are different from the impact forces produced by occupants. It varies significantly by impact source, and it is not easy to be replicated for testing. As a result, the indoor floor impact noise under different acoustic conditions cannot be directly compared. Using frequency response function(FRF), which represents the input-output relationships of a dynamic system, it is possible to examine the characteristics of the system. Especially, FRF can predict the response of a linear dynamic system subjected to various excitation. To determine the relationship between impact force and the corresponding response of dynamic system in residential building, the acceleration response of a concrete slab and the floor impact noise in the living room, produced by bang-machine and rubber-ball excitation, were measured. The test results are compared to the estimates based on FRF and impact force spectrum.