• YAKHAK HOEJI
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• v.54 no.6
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• pp.455-460
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• 2010

We evaluated the inappropriateness of metformin use in patients with type 2 diabetes and chronic medical conditions to identify the frequency of the prescription metformin in violation of the food and drug administration (FDA) black box warning. We reviewed medical records of 307 outpatients who received metformin at endocrinology department in a hospital setting between January 1, 2005 and August 30, 2009. Of the 307 outpatients, 25 discontinued treatment of metformin due to elevated serum creatinine level (Scr${\geq}$1.5 mg/dl in male, Scr${\geq}$1.4 mg/dl in female), cancers, and/or liver disease. 5 were lost to follow-up. 89 (29.0%) of the patients had cardiovascular disease, 54.1% for hypertension, 9.8% for liver disease, and 60 (20.8%) for chronic kidney disease. 12 patients (3.9%) with chronic kidney disease and/or elevated serum creatinine level, and 1 patient (0.3%) with lactic acidosis were contraindicated to metformin use. Metformin should be avoided in 7 outpatients (2.3%) with active hepatitis and 1 patient (2.6%) with liver cirrhosis. Of the 307 outpatients, 13 (4.2%) patients who received metformin at the first visit and 16 (8.7%) patients who received metformin at the last visit violated to black box warning. 8 (2.6%) of the patients were in precautionary conditions to metformin use. Adjusted mean difference of serum creatinine was -0.16 mg/dl [95% CI: -0.22 to -0.11 (p<0.05)] and adjusted mean difference of alanine aminotransferase was 4.46 IU/l [95% CI: 2.47 to 6.44 (p<0.05)] between the first visit and the last visit. Critical number of elderly patients who are at the high risks of drug-disease and drug-laboratory interaction is exposed to the inappropriate metformin use in violation of black box warning. The periodic evaluation of metformin use and monitoring prescription through drug utility review (DUR) system is needed to improve patients' safety and to reduce adverse drug events.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2942-2950
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• 2010

Car black box helps to investigate the cause of accident by recording time, position and videos as well as shock information. In addition, the car black box need a function to support safe driving for preventing accident. The representative driving support function is a lane departure warning. In order to implement the function, it is necessary to carry out the image processing to detect the lane first. The image processing algorithm requires computational burden to handle so much data and complicated structure of algorithm. This paper describes the efficient image processing algorithm with relatively low amount of computation for car black box embedded platform to detect lanes from the real-time lane image.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.440-448
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• 2015

A pedestrian collision warning system which makes a judgement of pedestrian's intention to help avoiding hitting accidents is proposed. This system uses the image sequences obtained from a car black box as well as vehicle's speed obtained from a GPS. It detects pedestrians, if any, based on the Histogram of Gradient method and extracts several information such as the pedestrian's relative positions, the direction of motion vectors, and distance between vehicle and pedestrian . A fuzzy logic based on these extracted information is applied to analyze the pedestrian's safety levels. When the safety level is determined to be danger, an alarm is triggered to the driver. The performance of the proposed algorithm is tested under various driving scenarios, which shows it works successfully in real-time.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.19 no.2
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• pp.83-88
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• 2008

Antidepressants, in particular selective serotonin reuptake inhibitors (SSRIs), are one of the most commonly used classes of psychotropic drugs for treating children and adolescents. The US Food and Drug Administration has issued a black box warning concerning the increased risks of suicidal ideation and behavior associated with antidepressant treatment in children and adolescents. The aim of this review is to assess the risks and benefits of antidepressants in the treatment of child and adolescent psychiatric disorders.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.837-846
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• 2006

In this paper, axial flow fans which applied at coal-thermal power plant(500[MW]) cause a unique phenomenon called 'Stall' under normal operation and this causes abnormal operation and damages the blades. In order to prevent these abnormal operation, this study estimates the reliability of new system which is applying control logic on each parameter with existing black-box-type by field test.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.103-109
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• 2012

Digital speedometer which is supposed to provide the basic data for analyzing human factors of drivers has a limitation for human behavior studies of drivers, because it records limited driving information including GPS velocities. Besides, Black Box, which is currently being actively commercialized in the market, records mostly vehicles' risky patterns rather than drivers' behaviors. As a result, it also shows a limit to analyze dangerous driving patterns. This study performed a risky driving study for human factor analysis. This study conducted before and after comparisons for real time warning study using a risky driving judgment device. The analysis was conducted based on Longitudinal acceleration, Lateral acceleration, and Yaw rate of vehicles.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.105-115
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• 2009

According to the accident statistics published by the National Police Agency in 2007, the number of commercial vehicle(city, suburb and other buses) accidents consumes 3.5 percent of the total number of traffic accidents in this year. Since the commercial vehicles are responsible for not only the drivers but also the passengers, it leads more serious social and economic problems. There have been various forms of systems such as a digital speedometer or a black box to meet the social requirement for reducing traffic accidents and safe driving. however the system based on the data after accident control the driver by analyze dangerous drive behaviors, so there is a limit to control driver in real-time. Also speedometer currently managed provide the driver warning information in real-time, but using only the speed of vehicle and RPM information regardless of actual dangerous drive behaviors, disappear the effectiveness. In this study performed a simulation for drivers in general using a simulator programed with dangerous driving types we had developed in the previous study and judging the types. It'd be more effective system to provide the drivers warning information using weight valued in this study. However in this study is limited to apply weight as a result of simulation of drivers in general in actual situation should be made up the deficit based on information of driving type of actual commercial vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.15-28
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• 2008

According to the accident statistics published by the National Police Agency in 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, although many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors, it is also true that it still lacks in obvious categories for dangerous driving types and then, the efficiency of the categories to be studied has been low. In this study, dangerous driving types are redefined. They are grouped into 7 classifications in the first level, and the seven classifications are regrouped into 16 in more detail. To verify the redefined dangerous driving types, a Data-logger is developed to receive and analyze the data that occur from the driving behaviors of the test vehicle. The developed Data-logger can be used to construct a real time warning system and safe driving management system with dangerous driving patterns based on acceleration, deceleration, Yaw rate, image data, etc.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.