• Proceedings of the KOR-BRONCHOESO Conference
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• 1982.05a
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• pp.8.2-8
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• 1982

Hoarseness is the change of voice quality which represents the abnormal function of phonation and is the main symtom of the laryngeal diseases. The etiology of hoarseness are known more than 50 causes, among them, viral upper respiratory infection is the main cause of hoarseness and the laryngeal nodule and polyp, laryngeal paralysis, laryngeal cancer, laryngeal papilloma and the laryngeal tuberculosis are the other causes of hoarseness in that order. Recently, the authors experienced 4 cases of uncommon etiology of hoarseness, so we present the cases with the brief review of literatures. Case 1. 29 years old male Admitted in Dept. of neurosurgery due to Traffic Accident. He had a trauma on the anterior neck. Hoarseness was developed on 1 month after the accident. Laryngoscopic finding; Paramedian paralysis of left vocal cord. Displacement of left arytenoid cartilage. Case 2. 53 years old male Admitted in Dept. of General Surgery due to Clonorchis Sinensis, under the general endotracheal anesthesia, Choledochostomy was performed. Laryngoscopic finding; Median paralysis of left vocal cord. Case 3. 56 years old male Admitted in Dept. of Internal Medicine due to Aortic Aneurysm. Hoarseness was developed on 3 months prior to admission. Laryngoscopic finding; Intermediated position paralysis of left vocal cord. Displacement of left arytenoid cartilage. Case 4. 74 years old male Admitted in Dept. of Internal Medicine due to Bronchogenic carcinoma. Hoarseness was developed on 3 years prior to admission. Laryngoscopic finding; Paramedian paralysis of right vocal cord.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.630-642
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• 2013

To transmit vital signal stream of mobile patients remotely, it requires mobility of patient and watcher, sensing function of patient's abnormal symptom and self-organizing service binding of related computing resources. In the existing relative researches, the vital signal stream is transmitted as a centralized approach which exposure the single point of failure itself and incur data traffic to central server although it is localized service. Self-organizing middleware platform based on heterogenous overlay network is a middleware platform which can transmit real-time data from sensor device(including vital signal measure devices) to Smartphone, TV, PC and external system through overlay network applied self-organizing mechanism. It can transmit and save vital signal stream from sensor device autonomically without arbitration of management server and several receiving devices can simultaneously receive and display through interaction of nodes in real-time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.732-742
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• 2016

This study suggests methods of defense against low-level high-bandwidth DDoS attacks by adding a solution with a time limit factor (TLF) to an existing high-bandwidth DDoS defense system. Low-level DDoS attacks cause faults to the service requests of normal users by acting as a normal service connection and continuously positioning the connected session. Considering this, the proposed method makes it possible for users to show a down-related session by considering it as a low-level DDoS attack if the abnormal flow is detected after checking the amount of traffic. However, the service might be blocked when misjudging a low-level DDoS attack in the case of a communication fault resulting from a network fault, even with a normal connection status. Thus, we made it possible to reaccess the related information through a certain period of blocking instead of a drop through blacklist. In a test of the system, it was unable to block the session because it recognized sessions that are simply connected with a low-level DDoS attack as a normal communication.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• 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 Wetlands Research
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• v.25 no.2
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• pp.83-90
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• 2023

Due to climate change, the occurrence of abnormal rainfall is increasing, and the intensity and scale of damage caused by heavy rain are increasing every year. In addition, as the frequency of heavy rains becomes more frequent, heavy rains often occur continuously, resulting in large flooding damage that has never been seen before in urban area. When near rivers and coastal areas are impermeable areas, the maximum flow increases rapidly as the rainfall intensity increases, so a comprehensive flood risk evaluation is needed considering the characteristics of the basin. In this study, the flood inundation risk evaluation was analyzed by giving scores on evaluation factors as a measure to prevent inundation in subway stations. Through the flood inundation risk evaluation process considering the comprehensive evaluation index, the flood risk evaluation was conducted on five urban railway stations with a large amount of traffic and floating population that had been inundated in the past. It is judged that by comprehensively analyzing this and establishing a inundation risk grade (grade 1 to 4) to establish a flood measure suitable for the risk grade.

• Journal of Korean Academy of Nursing
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• v.24 no.4
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• pp.545-556
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• 1994

Medical insurance and health care delivery system enabled Korean people to get the necessary medical service, but it caused increased needs for medical service, and resulted in the occurence of some problems such as a lack of manpower and medical facilities. In order to solve these problems, many countries, which already had medical insurance system had developed home care system and it has been regarded effective both in reducing costs and in increasing the rates of turnover of bed. Recently, Korea has included home nursing care in its health care delivery system, and some models of the hospital based home nursing care had been tried and its effects had been evaluated. So, author tried to run a home nursing care for the Cesarean section mothers and evaluate Its effects both in the mother's health and costs. This study was designed as a Quasi-experimental study. Subjects were thirty mothers who got Cesarean section operation in hospital in Seoul. Experimental group consisted of 15 volunteers, and control group were selected by means of matching technique. Data were gathered from February 1st to March 26th by two assistants who were trained by author. Experimental group were discharged on the 4th day after their operation, and got nursing care and assessment about their home three times on the 5th, 6th, and 7th day. Control group stayed in the hospital until 7th day as usual and were checked on the same day as above mentioned To evaluate the state of physiological recovery, vital signs, H.O.F, presence of edema in the legs, bathing, appetite, sleep, presence of pain or discomfort in the breasts, amount of lochia, color of lochia, defecation urination. To compare incidence of complication in experimental group with that in control group, specific assessment was done such variables as smell of lochia, presence of inflammation of operation wound, dizziness, and presence of immobilization in the extremities. The activities of daily living were checked Satisfaction of nursing were checked To calculate costs, author asked subjects to specify expenditure including hospital charge, traffic enpenses, and food expenses. The results were as fellows. 1. On effectiveness of home nursing careThere were n significant differences between experimental and control group in incidence of abnormal symptoms and any complication. The number of taking a bath [POD #5 P=0.001, #6 P=0.0003, #7 P=0.001] and the degree of appetite [POD #5 P=0.03, #6 P=0.02, #7 P=0.013] were significantly higher in experimental group than in control group. Contrary to author's expectation, the degree of the activities of daily living in experimental group was not higher than that of control group. All of the experimental group said they were satisfied with the home nursing care. 2. Cost analysis 1) Hospital charge of experimental group was lower than that of control group. ［P=0.009］ By taking home nursing care, average period of hospitalization was shortened to 3.1 days, and family members could save 22.8 hours. Total amount of money saved by early discharge was 3,443,093 Won. It is estimated that total amount of money saved by early discharge in a year will be 40,398,956 Won. 2) Home nursing care charge of 15 mothers was 1,781,633 Won. It is estimated that total amount of money Saved by it in a year Will be 20,904,493 Won. It was lower altogether than hospital charge of the three days which is 5th, 6th, 7th day of operation. The average cost of single home visit was calculated 10,940 Won. It took 87 minutes per round and it costed 1,017.3 Won. The average hour of home care was 39.0 minutes. 3) It is expected that early discharge can bring forth the increase of hospital income. On the condition that the rate of running bed is 100%, the expected increase of hospital income will be 202,374, 026 Won in a year. Suggestions for further study and nursing practice are as follows : 1. For the welfare of patients and the increased rates of running bed, home nursing care system should be included in the hospital nursing care system. 2. Studies to test effect of home nursing care on the patients with other diseases are needed. 3. Establishment of law on the practice of home nursing care is strongly recommended.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.