• Journal of the Korea Society for Simulation
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• v.14 no.1
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• pp.1-8
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• 2005

In the semiconductor manufacturing, yield enhancement is an urgent issue. It is ideal to prevent all the failures. However, when a failure occurs, it is important to quickly specify the cause stage and take countermeasure. Reviewing wafer level and composite lot level yield patterns has always been an effective way of identifying yield inhibitors and driving process improvement. This process is very time consuming and as such generally occurs only when the overall yield of a device has dropped significantly enough to warrant investigation. The automatic method of failure pattern extraction from fail bit map provides reduced time to analysis and facilitates yield enhancement. The automatic method of failure pattern extraction from fail bit map provides reduced time to analysis and facilitates yield enhancement. This paper describes the techniques to automatically classifies a failure pattern using a fail bit map.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.304-310
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• 2016

This paper proposes a fault diagnosis method for an open-fault in inverter driving five-phase induction motor. The five-phase induction motor has a high output torque and small torque ripple in comparison to three-phase. The best advantage of the five-phase induction motor is fault diagnosis and tolerant control using redundancy of phases. This paper uses an inverter as a power converter for driving a five-phase induction motor. If a switch of inverter occurs to the open-fault, this problem is the influence on the output current and output torque. To solve this problem, there is need of an accurate diagnosis and fault switch distinction. Therefore, this paper propose a fault detection method of the open-fault switches for the fault diagnosis. First, analyzing the pattern for the open-circuit fault of one phase. next, analyzing the pattern for the open-circuit fault of each inverter switches. Through the pattern analysis, It defines the scope of each of the failure switch. Thereafter, By using an algorithm that proposes to perform a fault diagnosis method. The proposed algorithm is verified from the experiment with the 1.5 kW five-phase induction motor.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.111-122
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• 2013

PURPOSES : The purpose of this system (Rear Lighting System) is to provide illumination for the driver to operate the commercial vehicle safely after dark in highway, to increase the conspicuity of the vehicle, and especially be suggesting the finest observable improvement method, depending on color and pattern of rear lighting system of truck for midnight highway traffic. METHODS : Rear lightning system as an improving way for forward commercial vehicles lighting the securing sight from human factors and the surrounding environment in midnight driving. For this one, basic materials were collected from the data analysis about many types of problems, and filed investigation for establishing Driving Simulator. also taking statistic test to human volunteers after finding recognizable distance of them. RESULTS : As a result, color with the highest visuality is amber followed by green-red-blue as in order for all road types. Especially almost no difference is found between red and green, also when the light is turn off, recognizable distances is wide difference compared to turn on the light. One more thing about study per pattern, upper and entire lighting have similar recognizable distances, but under lighting shows short distance with difficulty securing sight from medians. And straight section shows similar recognizable distances. By finding visuality improvement method depending on color and pattern of supplement taillight, it is expected to suggest quantitative judgement standard for introducing regulation and improvement of supplement taillight. CONCLUSIONS : Night time vehicle conspicuity to the rear is provided by rear position lamps. this study is showed that the color of light ramp is not important to be safe driving, most important is to turn on the light, recognizable distances is big different compared to turn off the rear light, so when the drivng dark in highway, have to turn on the light for reducing risk.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1149-1156
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• 2012

In this study, we proposed the most efficient driving posture based on the analysis of quantitative muscular strength and fatigue degree according to posture. Since driving include complicated actions required by a variety of ability and cause by extremes concentration or strain, drivers tend to feel tired easily. However, drivers can't recognize the fatigue degree by themselves. Moreover, the method for measuring the quantitative fatigue degree exactly is quite difficult to be secured. 9 professional bus drivers were participated. We analyzed the quantitative legs' muscular strength when operating each pedal. And then we also analyzed the muscular strength and muscular fatigue degree according to driving pattern during bus driving. Therefore, we suggested the most efficient driving posture.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.74-87
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• 2020

Overloaded trucks are the main source that threatens road safety and directly affects the reduction of pavement life. The On-board truck scale is the only equipment that could prevent overloading by measuring and adjusting the loading weight before driving. Legislation is needed to encourage its installation so that the driver can prevent overloading. In this study, an on-board truck scale system was installed on 30 dump trucks for transporting construction waste, such as soil and aggregates, which are major loads of 36.55% for overloading, and the trucks were monitored remotely. The overload prevention effect was analyzed by comparing driving data for 1 month before distribution of the weight display app that can recognize the weight to the driver and 1 month after distribution. After installation, overloading could be 6.1% reduced, and the transportation efficiency could be increased by checking the weight provided from the On-board truck scale system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• Journal of Korean Society of Transportation
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• v.30 no.4
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• pp.43-59
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• 2012

Assuming a completed Smart Highway road & communication environment that allows real-time information collection and transmission of road traffic condition ahead, the purpose of this study is to develop a plan for inducing a network-level safe driving pattern by providing road traffic condition and safety information to multiple drivers through a road information provision device. In this study, the device with a function that displays different colors according to the hazard level to the existing delineator has been named 'Smart Delineator'. Smart Delineator is a device that provides not only alignment information but also safety information for drivers to receive real-time warning information and intuitively recognize road traffic condition ahead so that drivers can respond. To examine the effects of safety driving inducement level on drivers, a simulation test was conducted using driving simulator as well as a satisfaction survey. The result showed that the Smart Delineator was able to identify the location of occurrence and affecting driving according pattern, either adhering to recommended speed or reducing speed according to the pre-defined hazard level.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.174-183
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• 2005

A train driving requires to n the fixed distance within given time, and it is desirable to consume low energy if necessary. Reducing energy consumption depends on the train operation modes by either manual or automatic operation. In this article, an operation to reduce energy consumption by changing modes of train operation by a driver without changing the train operation requirement is investigated. The powering model, braking model and consumed energy calculation model are developed, then simulated by using a Matlab software. The accuracy of the train dynamic model established by the simulations is verified by comparing with the real experimental data. Several simulations by various operations in the real track are executed, then the desirable pattern of train driving is found.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.107-118
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• 2022

As connected and automated driving(CAD) technology is entering its commercialization stage, service platforms providing CAD-based mobility services have increased these days. However, CAD-baded mobility services with these platforms need more consideration for the demand for mobility services when determining target areas for CAD-based mobility services because current CAB-based mobility design focus on driving performance and driving stability. For a more efficient design of CAD-based mobility services, we analyzed the applicability for the introduction of CAD-based mobility services in terms of driving difficulty of CAD and demand patterns of current non-CAD based-mobility services, e.g., taxi, demand-responsive transit(DRT), and special transportation systems(STS). In addition, for the spatial analysis of the applicability of the CAD-based mobility service, we propose the Index for Autonomous Driving Applicability (IADA) and analyze the characteristics of the spatial distribution of IADA from the network perspective. The analysis results show that the applicability of CAD-based mobility services depends more on the demand patterns than the driving difficulty of CAV. In particular, the results show that the concentration pattern of demand in a specific road link is more important than the size of demand. As a result, STS service shows higher applicability compared to other mobility services, even though the size of demand for this mobility service is relatively small.