• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.451-469
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• 2014

The purpose of this study is to implement and develop the integrated Economic Value Added (EVA) and Time-Driven Activity-Based Costing (TDABC) model to seek both improvement of Net Operating Profit Less Adjusted Tax (NOPLAT) and reduction of Capital Charge (CC). Net Operating Profit Less Adjusted Tax (NOPLAT) can be maximized by reducing the indirect cost of an unused resource capacity increased by Cost Capacity Ratio (CCR) of TDABC. On the other hand, Capital Charge (CC) can be minimized by improving the efficiency of Invested Capital (IC) considered by Weighted Average Cost of Capital (WACC) of EVA. In addition, the integrated system of TDABC using Balance Scorecard (BSC) and EVA is developed by linking between the lagging indicators and the three leading indicators. The three leading indicators include customer, internal process and growth and learning perspectives whereas the lagging indicator includes NOPLAT and CC in terms of financial perspective. When the Critical Success Factor (CSF) of BSC is cascading as a cause and an effect relationship, time driver of TDABC and capital driver of EVA can be used efficiently as Key Performance Indicator (KPI) of BSC. For a better understanding of the proposed EVA/TDABC model and BSC/EVA/TDABC model, numerical examples are derived from this paper. From the proposed model, the time driver of TDABC and the capital driver of EVA are known to lessen indirect cost from comprehensive income statement when increasing the efficiency of operating IC from the statement of financial position with unified KPI cascading of aligned BSC CSFs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.149-155
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• 2020

The Korea Highway Traffic Authority provides statistics that analyze the causes of traffic accidents that occurred since 2015 using the Traffic Accident Analysis System (TAAS). it was reported Through TAAS that the driver's forward carelessness was the main cause of traffic accidents in 2018. As statistics on the cause of traffic accidents, 51.2 percent used mobile phones and watched DMB while driving, 14 percent did not secure safe distance, and 3.6 percent violated their duty to protect pedestrians, representing a total of 68.8 percent. In this paper, we propose a system that has improved the advanced driver assistance system ADAS (Advanced Driver Assistance Systems) by utilizing CNN (Convolutional Neural Network) among the algorithms of Deep Learning. The proposed system learns a model that classifies the movement of the driver's face and eyes using Conv2D techniques which are mainly used for Image processing, while recognizing and detecting objects around the vehicle with cameras attached to the front of the vehicle to recognize the driving environment. Then, using the learned visual steering model and driving environment data, the hazard is classified and detected in three stages, depending on the driver's view and driving environment to assist the driver with the forward and blind spots.

• Journal of The Korean Association For Science Education
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• v.29 no.6
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• pp.712-729
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• 2009

This study aims to research high school students' misconception of botanic photosynthesis and respiration, and as the measure of rectifying the misconception, to develop the teaching program based on Driver's conceptual change model, applying it to classes and observing the effect. Selected as the research subject was sixty-six students in 1st year of a highschool located in Busan who had chosen Biology Learning as discretionary subject, with their conceptual level on botanic photosynthesis and respiration researched through tests in drawing and descriptive writing. As a consequence of applying drawing as a way of classifying the levels of students' misconception on photosynthesis and respiration, many students' drawings included their misconception caused by textbooks or scientists, but after application of Driver's conceptual change model, they drew scientific drawings including the fundamental factors of botanic photosynthesis and respiration such as light, carbon dioxide, water, glucose, oxygen, leaf, chloroplast, mitochondria, stoma, and energy. Likewise, as a result of the descriptive writing test implemented for researching the students' conception on the various aspects of botanic photosynthesis and respiration, many students in the pretest showed misconception on the point of time and location at which botanic photosynthesis and respiration occur, botanic nutrient, the role of a leaf in photosynthesis, and the relation between botanic photosynthesis and respiration, but after teaching based on Driver's conceptual change model, their misconceptions on photosynthesis and respiration were rectified to a high degree.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1055-1060
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• 2009

In this paper, we design an e-Learning system for the Driver's License Teste studying through the Internet. The proposed system make users to be arrived at the goal for the license in a shorter time by offering learning contents and items according to the item-responses made by the users based on the Item Response Theory. Moreover we design the scheme to give the optimum items and the most necessary content to the user during the learning procedure in the form of concept-based objects. All the items in the problem bank DB maintain their difficulties, discriminations, and guessing parameters as is the case of 3-parameter logistic model. In addition user profile DB stores users' status informations, item responses, and ability parameters. Using these structures and combining agents, we can offer the optimum learning process or dynamic personalized studying structure to the user. We can construct interface agent and content selection and feedback agent with the DB's described above. User can study without any awareness of system operations or personal fitting scheme.

• 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.