• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5357-5365
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• 2013

In this paper, we propose that a model based design for a electric tractor system by using ASM(Automotive Simulation Models). Before developing a realistic electric tractor, it is essential that defining the capacities of power sources and optimizing the parameters of electric tractor. In additionally, because the electric tractor must have not only driving function but also working function, two PMSM are used at electric tractor. ASM which is based on simulink and Carsim were used to design a electric system and powertrain of electric tractor. For verifying the electric tractor system, we compared the design parameters such as max power, state of charge, drive distance, velocity which were carried out by the simulation and experimental method. The predicted results by the development model were in good agreement with the simulation results. According to simulation of tractor, it is possible to arrange the advanced research of dynamical characteristic of tractor and present the guidelines for the electrical driving system.

• The Journal of Korean Physical Therapy
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• v.22 no.6
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• pp.59-63
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• 2010

Purpose: Driving a car is an essential component of daily life. For safe driving, each driver must perceive sensory information and respond rapidly and accurately. Brake response time (BRT) is a particularly important factor in the total stopping distance of a vehicle, and therefore is an important factor in traffic accident prevention research. The purpose of the current study was (1) to compare accelerometer. BRTs analyzed by three different methods and (2) to investigate possible correlations between accelerometer-BRTs and foot switch-BRTs, which are measured method using a foot switch. Methods: Eighteen healthy subjects participated in this study. BRT was measured with either a tri-axial accelerometer or a footswitch. BRT with a tri-axial accelerometer was analyzed using three methods: maximum acceleration time, geometrical center, and center of maximum and minimum acceleration values. Results: Both foot switch-BRTs and accelerometer-BRTs were delayed. ANOVA for accelerometer BRTs yielded significant main effects for axis and analysis, while the interaction effect between axis and analysis was not significant. Calculating the Pearson correlation between accelerometer-BRT and foot switch-BRT, we found that maximum acceleration time and center of maximum and minimum acceleration values were significantly correlated with foot switch-BRT (p<0.05). The X axis of the geometrical center was significantly correlated with foot switch-BRTs (p<0.05), but Y and Z axes were not (p>0.05). Conclusion: These findings suggest that the maximum acceleration time and the center of maximum and minimum acceleration value are significantly correlated with foot switch-BRTs.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.143-162
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• 2021

Research on consumers' perception and willingness to purchase Battery Electric Vehicles (BEVs) is necessary to simulate BEVs' deployment in South Korea because South Korea's BEVs market is still in the early stage. This paper derives a theoretical framework for consumer segmentation based on consumers' willingness to purchase before and after BEV usage experience. In particular, this study empirically evaluates consumers' willingness to purchase and concerns using the survey data from BEVs users in either Seoul or the Jeju region. The empirical results from logit models show that experienced consumers' concerns about the heater and air conditioning (HAC) in BEVs decreased the consumers' willingness to buy, while greater daily driving distances increased the consumers' willingness to buy. In addition, the empirical findings from ordered probit models show that experienced consumers' concerns about the short driving distance, the availability of maintenance service (i.e., A/S service) during unexpected events, and the difficulties of driving BEVs up-hill increased the degree of concern about HAC. This paper will provide insights related to consumer segmentation, R&D, marketing strategies, and policy design for policymakers and firms.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1311-1316
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• 2021

In this paper, we present a bifocal stereo camera system combining two cameras with different focal length cameras to generate stereoscopic image and their corresponding depth map. In order to obtain the depth data using the bifocal stereo camera system, we perform camera calibration to extract internal and external camera parameters for each camera. We calculate a common image plane and perform a image rectification for generating the depth map using camera parameters of bifocal stereo camera. Finally we use a SGM(Semi-global matching) algorithm to generate the depth map in this paper. The proposed bifocal stereo camera system can performs not only their own functions but also generates distance information about vehicles, pedestrians, and obstacles in the current driving environment. This made it possible to design safer autonomous vehicles.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.142-151
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• 2022

With the advent of the 4th industrial revolution, autonomous driving technology is being commercialized in various industries. However, research on autonomous driving so far has focused on platforms with wheel-type platform. Research on a tracked platform is at a relatively inadequate step. Since the tracked platform has a different driving and steering method from the wheel-type platform, the existing research cannot be applied as it is. Therefore, a path-tracking algorithm suitable for a tracked platform is required. In this paper, we studied a path-tracking algorithm for a tracked platform based on a GPS sensor. The existing Pure Pursuit algorithm was applied in consideration of the characteristics of the tracked platform. And to compensate for "Cutting Corner", which is a disadvantage of the existing Pure Pursuit algorithm, an algorithm that changes the LAD according to the curvature of the path was developed. In the existing pure pursuit algorithm that used a tracked platform to drive a path including a right-angle turn, the RMS path error in the straight section was 0.1034 m and the RMS error in the turning section was measured to be 0.2787 m. On the other hand, in the variable LAD algorithm, the RMS path error in the straight section was 0.0987 m, and the RMS path error in the turning section was measured to be 0.1396 m. In the turning section, the RMS path error was reduced by 48.8971%. The validity of the algorithm was verified by measuring the path error by tracking the path using a tracked robot platform.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.137-147
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• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.341-353
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• 2016

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.689-700
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• 2022

Layout work for building structures requires high precision to construct structural elements in the correct location. However, the accuracy and precision of the layout position are affected by the worker's skill, and productivity can be reduced when there is information loss and error. To solve this problem, it is necessary to automate the overall layout operation and introduce information technology, and layout process automation using construction robots can be an effective means of doing this. This study develops a prototype of an automated layout robot for building structures and evaluates its basic performance. The developed robot is largely composed of driving, marking, sensing, and control units, and is designed to enable various driving methods, and movement and rotation of the marking unit in consideration of the environment on structural work. The driving and marking performance experiments showed satisfactory performance in terms of driving distance error and marking quality, while the need for improvement in terms of some driving methods and marking precision was confirmed. Based on the results of this study, we intend to continuously improve the robot's performance and establish an automation system for overall layout work process.

• Journal of the Korean Geotechnical Society
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• v.39 no.5
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• pp.27-40
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• 2023

A pile is a structural element that is used to transfer external loads from superstructures and has been widely utilized in construction fields all over the world. The method of installing a pile into the ground should be selected based on geotechnical conditions, location, site status, environmental factors, and construction costs, among others. It can be divided into two types: direct hammering and preboring. The direct hammering method installs a pile into the bearing layer, such as rock, using a few types of hammer, generating a considerable amount of pile driving-induced vibration. The vibration from pile driving influences adjacent structures and the ground; therefore, quantitatively investigating the effects of vibration is inevitably required. In this study, two-dimensional dynamic numerical modeling and analysis are performed using the finite difference method to investigate the influence on the adjacent slope, including temporary supporting system. Time-dependent loading induced by pile driving is estimated and used in the numerical analysis. Consequently, large surface displacement is estimated due to surface waves and less wave deflection, and refraction at the surface. The total displacement decreases with the increase of the distance from the source. However, lateral displacement at the top of the slope shows a larger value than vertical displacement, and the overall displacement tends to be concentrated near the face of the slope.