• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.26-41
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• 2024

To ensure safer road conditions, understanding the human factors influencing driving behavior is crucial. However, there are many difficulties in deriving the characteristics of individual human factors that affect actual driving behaviors. Therefore, this study analyzes self-reported dangerous-driving propensities in order to explore potential correlations with drivers' behaviors. The goal is to propose a method for assessing driving tendencies based on varying traffic scenarios. The study employed a questionnaire to gauge participants' propensity to drive dangerously, utilizing a simulator to analyze their driving behaviors. The aim is to determine any notable connections between dangerous-driving propensity and specific driving behaviors. Results indicate that individuals exhibiting a high propensity for reckless driving, as identified by the Korean DBQ, tend to drive at higher speeds and display more aggressive acceleration patterns. These findings contribute to a potential method for assessing reckless driving drivers.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.1156-1159
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• 2008

Local dimming driving has advantages in reducing power consumption and improving contrast ratio(CR). In an LED backlight unit(BLU), many small LED blocks are implemented in 2-dimmensional space, and luminance of the blocks is controlled by a local dimming algorithm. However, such a BLU can induce various recognizable artifacts. A new novel algorithm is proposed for exact block luminance calculation to correct local dimming artifacts. Also we discuss modified low-gray-level dimming to achieve much aggressive power saving in a local dimming BLU system.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.104-104
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• 2011

The fuel economy estimates essentially serve two purposes : to provide consumers with a basis on which to compare the fuel economy of different vehicles, and to provide consumers with a reasonable estimate of the range of fuel economy they can expect to achieve. The current fuel economy label values utilize measured fuel economy over city driving cycles. However, this test driving mode can not be evaluated the variety factor of the real-world. These factors include differences between the way vehicles are driven on the road and over the test cycles, air conditioning use, widely varying ambient temperature and humidity, widely varying trip lengths, wind, precipitation, rough road conditions, hills, etc. The purpose of this paper is to account for three of these factors on the fuel economy : 1) on-road driving patterns (i.e. higher speeds and more aggressive driving (higher acceleration rates)), 2) air conditioning, and 3) colder temperatures. The new test methods will bring into the fuel economy estimates the test results from the five emissions tests in place today : CVS-75, HWFET, US06, SC03 and Cold CVS-75. Based on these new test methods, this paper discusses the characteristics of driving condition on Hybrid electric vehicle (HEV). And this paper assesses the fuel economy label of HEV.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• 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 Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.51-56
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• 2020

This study examines the strategies employed by GM, who experienced bankruptcy in 2008. Specifically, we explore the autonomous driving-related technologies and execution, which GM began developing later than other car manufacturing companies. This study found that GM implemented aggressive M&A in search of vertical industrial integration for the development and production of autonomous vehicles. GM selected candidate firms to complement its technological gaps for the development and implementation of the autonomous vehicle. Secondly, GM achieved executive capacity by attempting to build a vertical integration in the wider scope of components, solution, service, and sales. Thirdly, the consistent governmental support and policies, such as the connected car project, M-City, and NCHRP Program expedited the development process. This study provides practical and policy implications for Korean companies and policymakers related to the automotive industry.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.94-100
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• 2017

Due to the environmental problems caused by the greenhouse effect, regulation of $CO_2$ emissions is getting stronger day by day. In paricular, regulations of automobiles $CO_2$ emissions are being strengthen. However, existing $CO_2$ measurement methods do not reflect the environment and operating conditions on actual roads. Emissions of $CO_2$ can be increased by various conditions such as environmental condition(temperature and humidity) and driver's tendency(aggressive and passive). Therefore it is necessary to reflect the conditions of various actual roads such as 5-cycle test method on behalf of the existing $CO_2$ emission measurement method. The 5-cycle measurement method has five test modes; FTP-75, HWFET, US06, SC03, Cold FTP-75. The method reflects the following three environments and operating conditions as compared to conventional method; Using heater at low temperature, Aggressive driving such as rapid acceleration or deceleration, Using air conditioner at high temperature. Because of these various conditions of each test cycle, the 5-cycle method can reflect actual environments and operating conditions. This paper attempt to analyze $CO_2$ emission characteristics based on the results measured through the 5-cycle mode and develop the correction formula that can derive the results of the 5-cycle test method using existing test methods. As a result, the developed correction formula is expected to reduce $CO_2$ emissions and cut down expense for testing 5-cycle mode.

• BMB Reports
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• v.51 no.10
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• pp.481-483
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• 2018

Glioblastoma (GBM) is the most common and aggressive form of human adult brain malignancy. The identification of the cell of origin harboring cancer-driver mutations is the fundamental issue for understanding the nature of GBM and developing the effective therapeutic target. It has been a long-term hypothesis that neural stem cells in the subventricular zone (SVZ) might be the origin-of-cells in human glioblastoma since they are known to have life-long proliferative activity and acquire somatic mutations. However, the cell of origin for GBM remains controversial due to lack of direct evidence thereof in human GBM. Our recent study using various sequencing techniques in triple matched samples such as tumor-free SVZ, tumor, and normal tissues from human patients identified the clonal relationship of driver mutations between GBM and tumor-free SVZ harboring neural stem cells (NSCs). Tumor-free SVZ tissue away from the tumor contained low-level GBM driver mutations (as low as 1% allelic frequency) that were found in the dominant clones in its matching tumors. Moreover, via single-cell sequencing and microdissection, it was discovered that astrocyte-like NSCs accumulating driver mutations evolved into GBM with clonal expansion. Furthermore, mutagenesis of cancer-driving genes of NSCs in mice leads to migration of mutant cells from SVZ to distant brain and development of high-grade glioma through the aberrant growth of oligodendrocyte precursor lineage. Altogether, the present study provides the first direct evidence that NSCs in human SVZ is the cell of origin that develops the driver mutations of GBM.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.5
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• pp.125-135
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• 2004

The validity of simulation has been well-established for decades in areas such as computer and communication system. Recently, the technique has become entrenched in specific areas such as transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and their driver's characteristics, even though it is known that they are important factors for any traffic flow analysis, have never been considered sufficiently. In this paper, the traffic simulation using a multi-agent approach with considering vehicle dynamics is proposed. The multi-agent system is constructed with the traffic environment and the agents of vehicle and driver. The traffic environment consists of multi-lane roads, nodes, virtual lanes, and signals. To ensure the fast calculation, the agents are performed on the based of the rules to regulate their behaviors. The communication frameworks are proposed for the agents to share the information of vehicles' velocity and position. The model of a driver agent which controls a vehicle agent is described in the companion paper. The vehicle model contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation has proceeded for an interrupted and uninterrupted flow model. The result has shown that the driver agent performs human-like behavior ranging from slow and careful to fast and aggressive driving behavior, and that the change of the traffic state is closely related with the distance and the signal delay between intersections. The system developed shows the effectiveness and the practical usefulness of the traffic simulation.

• Molecules and Cells
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• v.46 no.3
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• pp.153-164
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• 2023

Cancer stem cells (CSCs) are a small population of tumor cells characterized by self-renewal and differentiation capacity. CSCs are currently postulated as the driving force that induces intra-tumor heterogeneity leading to tumor initiation, metastasis, and eventually tumor relapse. Notably, CSCs are inherently resistant to environmental stress, chemotherapy, and radiotherapy due to high levels of antioxidant systems and drug efflux transporters. In this context, a therapeutic strategy targeting the CSC-specific pathway holds a promising cure for cancer. NRF2 (nuclear factor erythroid 2-like 2; NFE2L2) is a master transcription factor that regulates an array of genes involved in the detoxification of reactive oxygen species/electrophiles. Accumulating evidence suggests that persistent NRF2 activation, observed in multiple types of cancer, supports tumor growth, aggressive malignancy, and therapy resistance. Herein, we describe the core properties of CSCs, focusing on treatment resistance, and review the evidence that demonstrates the roles of NRF2 signaling in conferring unique properties of CSCs and the associated signaling pathways.