• Journal of Veterinary Clinics
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• v.27 no.2
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• pp.125-129
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• 2010

A 2-year-old female Pomeranian dog was referred with multiple pelvic fractures. The surgical correction was performed for the fractures. However, after the surgery, purulent exudation was occurred in the surgical site. Antibiotic susceptibility test revealed that the isolated bacteria are resistant to penicillins, cephalosporins, aminoglycosides, quinolones, and trimethoprim/sulfamethoxazole. Bacterial identification and extended-spectrum $\beta$-lactamase (ESBL) confirming test indicated that the isolated bacteriae is ESBL-producing Klebsiella pneumoniae. Minimum inhibitory concentration (MIC) and maximum bactericidal concentration (MBC) tests revealed that meropenem, one of carbapenems, is the only effective antibiotic. The patient was treated with meropenem for 5 days. After 10 days, the exudation was disappeared and the infection was cured. The molecular typing of the ESBL revealed that TEM-1 ESBL is present in the bacteria isolated from the patient. The bacteria isolated from the owner's palm also revealed that TEM-1 and SHV-1 ESBLs are present.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.434-442
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• 2018

In this paper, we propose a road lane color recognition method from the image obtained from a driving vehicle. In autonomous vehicle techniques, lane information becomes more important as the level of autonomous driving such as lane departure warning and dynamic lane keeping assistance is increased. In particular the lane color recognition, especially the white and the yellow lanes, is necessary technique because it is directly related to traffic accidents. In this paper, color information of lane and road area is mapped to a 2-dimensional S-color space based on lane detection. And the center of the feature distribution is obtained by using an improved mean-shift algorithm in the S-color space. The lane color is determined by using the distance between the center coordinates of the color features of the left and right lanes and the road area. In various illumination conditions, about 97% color recognition rate is achieved.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.411-419
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• 2003

This paper aims at developing 'gyro-mouse' which provides decent and comfortable human-computer interface that supports the usage of such software as an internet-browser in PC for the people paralyzed in upper limbs. This interface operates on information collected from head movement to get the cursor control. The interface is composed of two modules. One is hardware module in which the head horizontal and vertical angular velocities are detected and transmitted into PC. The other is a PC software that translates the received data into movement and click signals of the mouse. The ANN (artificial neural network) learns the quick nodding pattern of each user as click input so that it can provide user-friendly interface. The performance of the system was evaluated by three indices that are click recognition rate. error in cursor position control. and click rate of the moving target box. The performance result of the gyro-mouse was compared with that of the optical-mouse to assess the efficiency of the gyro-mouse. The average click recognition rate was 93%, average error in cursor position control was 1.4∼5 times of optical mouse. and the click rate with 50 pixels target box was 40%(30 clicks/min) to that of optical mouse. The click rate increased monotonously with the number of trial from 35% to 44%. The suggested system is expected to provide a new possibility to communicate with the society.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.93-100
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• 2006

This paper aims at the development and clinical evaluation of the wireless gyro-mouse system. The wireless gyro-mouse system is a computer interface with gyro-sensor and wireless communication, for the patients with upper-extremity disabled from the traffic accident or stroke to use the computer software i.e. internet browser. In the development, we focused on, firstly, to make the system wireless for the patients to manipulate the mouse easily even on the bed or wheelchair, secondly, to insert the gyro-sensor into a headband for easy don-and-doff and aesthetic appearance, thirdly, to devise a click switch in case of $C5{\sim}C6$ patients and a head nodding detection in case of C4 patients for sending click message to computer operating system. We performed evaluation experiment for patients with upper-extremities disabled from spinal cord injury. The results show that the displacement error of the cursor position against the target position during linear (vertical/horizontal) movement manipulation decreased with trial number. The click rate per minute also increased with trial number. This indicates the developed wireless gyro-mouse system would be more useful to the patients with repetitive use.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.183-190
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• 1999

The purpose of this study was to develop a portable and convenient closed-loop contrel type electrical stimulator for patients with foot drop. This system restores walking movement as well as prevents from atrophy or necrosis of lower limb muscles and increases blood circulation in hemiplegic patients caused by traffic accident, industrial disaster or stoke. This system detects the changes of the ankle joint angle during walking, and then controls the stimulus intensity automatically to maintain the programmed level of the ankle joint angle. Also, this automatic system controls the stimulus intensity which is affected by increased electrode impedance resulting from long time use. The system detects the joint angle by an optical sensor and includes modified PID control which adjusts the stimulus intensity if the joint angle deviates from the preset value. Stimulus parameters are 30~80 volt, 40 Hz, and 0.2 ms. The system was applied to five hemiplegic patients for 42 days. Duration of stimulation was 15 min/day for the first week and then the duration was gradually increased to 30, 60, 90 and 120 min/day. The muscle force was increased up to 29.7%, muscle fatigue was decreased compared with the level before stimulation and the pattern of locomotion was improved. These results suggest that the electrical stimulator with closed-loop control type is more convenient and effective in restoration of locomotion of patients with foot drop than open-loop system.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.3-9
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• 2017

Recently, in 3rd Generation Partnership Project(3GPP), there is a study of the Long Term Evolution(LTE) based vehicle communication which has been actively conducted to provide a transport efficiency, telematics and infortainment. Because the vehicle communication is closely related to the safety, it requires a reliable communication. Because vehicle speed is very fast, unlike the movement of the user, radio channel is rapidly changed and generate a number of problems such as transmission quality degradation. Therefore, we have to continuously updates the channel estimates. There are five types of conventional channel estimation scheme. Least Square(LS) is obtained by pilot symbol which is known to transmitter and receiver. Decision Directed Channel Estimation(DDCE) scheme uses the data signal for channel estimation. Constructed Data Pilot(CDP) scheme uses the correlation characteristic between adjacent two data symbols. Spectral Temporal Averaging(STA) scheme uses the frequency-time domain average of the channel. Smoothing scheme reduces the peak error value of data decision. In this paper, we propose the novel channel estimation scheme in LTE based Vehicle-to-Vehicle(V2V) environment. In our Hybrid Reliable Channel Estimation(HRCE) scheme, DDCE and Smoothing schemes are combined and finally the Linear Minimum Mean Square Error(LMMSE) scheme is applied to minimize the channel estimation error. Therefore it is possible to detect the reliable data. In simulation results, overall performance can be improved in terms of Normalized Mean Square Error(NMSE) and Bit Error Rate(BER).