• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.3
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• pp.201-206
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• 2019

This study presents the design and experimental result of a GaN-based DC-DC converter with an integrated gate driver. The GaN device is attractive to power electronic applications due to its superior device performance. However, the switching loss of a GaN-based power converter is susceptible to the common source inductance, and converter efficiency is severely degraded with a large loop inductance. The objective of this study is to achieve high-efficiency power conversion and the highest power density using a multiphase integrated half-bridge GaN solution with minimized loop inductance. Before designing the converter, several GaN and Si devices were compared and loss analysis was conducted. Moreover, the impact of common source inductance from layout parasitic inductance was carefully investigated. Experimental test was conducted in buck mode operation at 48 -12 V, and results showed a peak efficiency of 97.8%.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.3
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• pp.139-147
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• 2017

In recent years, non-volatile memory express (NVMe), a new host controller interface standard, has been adapted to overcome performance bottlenecks caused by the acceleration of solid state drives (SSD). Recently, performance breakthrough cases over AHCI based SATA SSDs by adapting NVMe based PCI Express (PCIe) SSD to servers and PCs have been reported. Furthermore, replacing legacy eMMC-flash storage with NVMe based storage is also considered for next generation of mobile devices such as smartphones. The Linux kernel includes drivers for NVMe support, and as the kernel version increases, the implementation of the NVMe driver code has changed. However, mobile devices are often equipped with older versions of Android operating systems (OSes), where the newest features of NVMe drivers are not available. Therefore, different features of different NVMe driver implementations are not well evaluated on Android OSes. In this paper, we analyze the response time of the NVMe driver for various Linux kernel version.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.1-8
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• 2013

Recently, the automotive technology has developed with electronics and information technology as convergence technology while vehicles had been regarded as machines. Moreover, vehicles are becoming more intelligent and safer devices, assembly of advanced technologies by customers' demand. Even though all of installations of vehicle have attracted as diverting devices, it cause drivers' mistakes like delay of response on traffic condition. Here, we proposed the Field Operational Test (FOT) environment which could be used as driving and road conditions collector(Vehicle motion, Traffic condition, Driver input, Driver state, etc.) for researches about Driver Friendly Intelligent System(SCC, LDWS, etc.), Human Vehicle Interface(Driving Workload, etc.) and Economic Drive Model. Furthermore driving patten and fuel consumption patten of drivers were analyzed by measured data and direction of future research was suggested.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.138-139
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• 2017

This paper describes the design and implementation of a trigger circuit which can be series connected with main pulse circuit for a xenon flash lamp driver. For generating high voltage, the trigger circuit is designed as an inductive energy storage pulsed power modulator with 2 state step-up circuit consisting of a boost converter and a flyback circuit. In order to guarantee pulse width, a resonant capacitor on the output side of the flyback circuit is designed. This capacitor limits the output voltage to protect the flyback switch. In addition, to protect another power supply of xenon flash lamp driver from trigger pulse, the high voltage transformer which can carry the full current of main pulse is designed. To verify the proposed design, the trigger circuit is developed with the specification of maximum 23 kV, 0.6 J/pulse output and tested with a xenon flash lamp driver consisting of a main pulse circuit and a simmer circuit.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.165-171
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• 2011

A Consider the dead man's switch installed in each and every locomotive cab, which support operational safety on railways around the world. The concept is very simple - every 150 to 180 seconds an illuminated push-button demands to be acknowledged so as to know that the Train Driver is alive and active. In the absence of a response over a period of minutes, the vigilance control will automatically apply the train brakes and bring the train to a stand. If we multiply the resetting of the vigilance control 60 times per hour by a 10-hour shift it equals 600 presses of the button during the shift that a Train Driver must pay attention to and acknowledge. This adds a fair bit of pressure on the train driver's job, particularly when he/she is driving through stations, with passengers moving about on platforms in an environment of complex signaling arrangements - all the while looking out for restricting signals. From this perspective, the Vigilance System's demand to be acknowledged every 150/180 seconds is disturbing and can unnecessarily take a driver's attention away from what is happening outside the confines of the cab. A much more dramatic situation can happen when a train driver is driving hour after hour at night when, by Mother's Nature request - people need to sleep. Experience and research shows that the the dead man's switch can be pressed by train driver in a state of deep relaxation and 'micro-sleep'. The vigilance control system which is applied to reduce the drive load considering bio-response multiple unit train is proposed.

• Journal of the Ergonomics Society of Korea
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• v.32 no.3
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• pp.217-227
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• 2013

Objective: The purpose of this study was to analyze 'response time', 'peak response time' and 'overshoot value' for each muscle by applying the EMG signal to the vehicle response in ISO 7401 and to quantify the response of the driver according to vehicle characteristics by comparing vehicle characteristics and muscle responses of the driver. Background: The Open-loop test defined in international standards ISO 7401 is the only method for evaluating the performance of the vehicle. However, this test was focused only on mechanical responses, not driver's ones. Method: One skilled male driver(22 yrs. experience) was participated in this experiment to measure muscle activities of the driver in transient state. Then the seven muscle signals were applied to calculate 'response time', 'peak response time', and 'overshoot value'. Results: In the analyses of the EMG data, the effects of vehicle type and muscle were statistically significant on the 'response time' and 'peak response time'. Also, the effects of vehicle type, muscle, and lateral acceleration level were statistically significant on the 'overshoot value' in this study. According to the analyses of the vehicle motion data, vehicle motion variable(LatAcc, Roll, YawVel) was statistically significant on the 'response time' and vehicle type, vehicle motion variable, and lateral acceleration level were statistically significant on the 'peak response time', respectively. Conclusion: In the analyses of the 'response time' and 'overshoot value', the data of muscle activities(EMGs) was better index that could evaluate the vehicle characteristic and performance than the data of vehicle motion. In case of peak response time, both EMG and vehicle motion data were good index. Application: The EMGs data from a driver might be applicable as index for evaluation of various vehicle performances based on this study.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.25-34
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• 2006

For decades, simulation technique has been well validated in areas such as computer and communication systems. Recently, the technique has been much used in the area of transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and diversities of driver characteristics have never been considered sufficiently in these methods, although they are considered important factors in traffic flow analysis. In this paper, we propose a traffic simulation tool called Multi-Agent for Traffic Simulation with Vehicle Dynamics Model (MATDYMO). Road transport consultants, traffic engineers and urban traffic control center managers are expected to use MATDYMO to efficiently simulate traffic flow. MATDYMO has four sub systems: the road management system, the vehicle motion control system, the driver management system, and the integration control system. The road management system simulates traffic flow for various traffic environments (e.g., multi-lane roads, nodes, virtual lanes, and signals); the vehicle motion control system constructs the vehicle agent by using various vehicle dynamic models; the driver management system constructs the driver agent capable of having different driving styles; and lastly, the integrated control system regulates the MATDYMO as a whole and observes the agents running in the system. The vehicle motion control system and driver management system are described in the companion paper. An interrupted and uninterrupted flow model were simulated, and the simulation results were verified by comparing them with the results from a commercial software, TRANSYT-7F. The simulation result of the uninterrupted flow model showed that the driver agent displayed human-like behavior ranging from slow and careful driving to fast and aggressive driving. The simulation of the interrupted flow model was implemented as two cases. The first case analyzed traffic flow as the traffic signals changed at different intervals and as the turning traffic volume changed. Second case analyzed the traffic flow as the traffic signals changed at different intervals and as the road length changed. The simulation results of the interrupted flow model showed that the close relationship between traffic state change and traffic signal interval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.115-122
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• 2016

This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.