• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.93-102
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• 2007

A serious problem to fight DDoS attacks is that attackers use incorrect or spoofed IP addresses in the attack packets. Due to the stateless nature of the internet, it is a difficult problem to determine the source of these spoofed IP packets. The most of previous studies to prevent and correspond to DDoS attacks using the traceback mechanism have been accomplished in IPv4 environment. Even though a few studies in IPv6 environment were introduced, those have no detailed mechanism to cope with DDoS attacks. The mechanisms for tracing the origin of attacks in IPv6 networks have so many differences from those of IPv4 networks. In this paper we proposed a lightweight IP traceback mechanism in IPv6 network environment. When marking for traceback is needed, the router can generate Hop-by-Hop option and transmit the marked packet. We measured the performance of this mechanism and at the same time meeting the efficient marking for traceback.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.79-84
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• 2021

The most important device of the AGV is the guide sensor, and the typical function of this sensor is high accuracy and extraction of the road. If the accuracy of the guide sensor is low or the sensor device is extracted the wrong track, this causes the problems such as the AGV collision, track-out, the load falling due to AGV swing. In order to improve these problems, this study is proposed a signal processing method of the guide sensor based on multi-maskings and the center of gravity method, and evaluated its performance. As a result, the proposed method showed that the mean error of absolute value is 2.32[mm] and it showed performance improvement of 27[%] than the center of gravity method of existence. Therefore, when the proposed signal processing method is applied, It is thought that the posture control and driving stability of the AGV will be improved.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.79-85
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• 2022

Internet of Things (IoT) connects devices with various platforms, computing power, and functions. Due to the diversity of networks and the ubiquity of IoT devices, demands for security and privacy are increasing. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, while at the same time effective enough to be implemented in devices with long-range specifications. In this paper, we present the performance and memory limitations of modern cryptographic primitives and schemes for different types of devices that can be used in IoT. In addition, detailed performance evaluation of the performance of the most commonly used encryption algorithms in low-spec devices frequently used in IoT networks is performed. To provide data protection, the binary ring uses encryption asymmetric fully homomorphic encryption and symmetric encryption AES 128-bit. As a result of the experiment, it can be seen that the IoT device had sufficient performance to implement a symmetric encryption, but the performance deteriorated in the asymmetric encryption implementation.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.167-175
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• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.481-487
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• 2023

Recently, interest in UAM (Urban Air Mobility) has surged as a critical solution to urban traffic congestion and air pollution issues. However, efficient UAM operation requires accurate 3D Point Cloud data processing, particularly in separating the ground and objects. This paper proposes and validates a method for effectively separating ground and objects in a UAM environment, taking into account its dynamic and complex characteristics. Our approach combines attitude information from MEMS sensors with ground plane estimation using RANSAC, allowing for ground/object separation that isless affected by GPS errors. Simulation results demonstrate that this method effectively operates in UAM settings, marking a significant step toward enhancing safety and efficiency in urban air mobility. Future research will focus on improving the accuracy of this algorithm, evaluating its performance in various UAM scenarios, and proceeding with actual drone tests.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.321-331
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• 2023

This study aimed to develop an engineering design-based science education program for high school students and to investigate its effects on students' science-related attitudes and engineering design abilities. "Creating healthy shoes for maintaining proper posture program" was developed as an activity to come up with creative solutions using the methodology of an engineering design process to solve problems in daily life. The creating healthy shoes for maintaining proper posture program consisted of the following five stages: "defining the problem," "ingathering information related to the problem," "generating the solution," "implementing the best solution," and "evaluating the solution & reflecting." As a result of applying this engineering design-based science education program, it improved the high school students' science-related attitudes and engineering design abilities. Therefore, the program developed in this study has a positive effect on enhancing high school students' science-related attitudes and engineering design abilities. These findings imply that the continuous operation and expansion of activities incorporating an engineering design process in science education are necessary as a teaching and learning strategy for cultivating future science talents.

• Journal of Preventive Medicine and Public Health
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• v.27 no.1 s.45
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• pp.145-158
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• 1994

This study was performed to investigate the knowledge and attitude of workers in small scale industries on health management, and to provide the basic data for more effective service by the group occupational health service system. The knowledge and attitude of 247 workers and 46 health personnel in the industries scattered around Incheon were investigated from December 1992 to February 1993. The results were summerized as follows ; 1. There were significant differences between the workers and the health personnel by age, sex, marriage status, job-position and education level. 2. The recognition level of the workers to contract work related disease was significantly higher than that of health personnel, and recognition level of the workers on environmental hazards and on the utility of measuring hazards were lower than that of health personnel. 3. The recognition level on the content of the group occupational health service system was significantly different between workers and health personnel, 72.6% of the responses from the workers answered that they did not know what the group occupational healthe service system was, but 82.2% of the responses from the health personnel answered that they knew well what it was. And 79.0% of all respondents thought it was necessary for worker's health. 4. Seventy three percent of the respondents from the workers indicated that they had never taken health education. However, 93.0% of all respondents answered positively for the need of health education to promote their health. 5. Current health service system was judged to be insufficient for the demand of workers for better health. Most of the respondents prefered a formal but flexible health service system and they wanted the periodic health examination to be followed up. It was revealed that despite of poor knowledge, the demand of workers for health service was higher than the current supply. Therefore, this study suggests that educating both health personnel and workers to obtain correct knowledge on the hazards to work enviroment and health management is needed for effective occupational health service.

• Journal of IKEEE
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• v.26 no.3
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• pp.408-415
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• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.17-24
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• 2019

Purpose: The present study aims to assess the level of coherency and the accuracy of Point dose of the Isocenter of VERO, a linear accelerator developed for the purpose of the Stereotactic Body Radiation Therapy(SBRT). Materials and Method: The study was conducted randomly with 10 treatment plans among SBRT patients in Kyungpook National University Chilgok Hospital, using VERO, a linear accelerator between June and December, 2018. In order to assess the equipment's power stability level, we measured the output constancy by using PTW-LinaCheck, an output detector. We also attempted to measure the level of accuracy of the equipment's Laser, kV(Kilo Voltage) imaging System, and MV(Mega Voltage) Beam by using Tofu Phantom(BrainLab, Germany) to assess the accuracy level of geometrical Isocenter. We conducted a comparative analysis to assess the accuracy level of the dose by using an acrylic Phantom($30{\times}30{\times}20cm$), a calibrated ion chamber CC-01(IBA Dosimetry), and an Electrometer(IBA, Dosimetry). Results: The output uniformity of VERO was calculated to be 0.66 %. As for geometrical Isocenter accuracy, we analyzed the error values of ball Isocenter of inner Phantom, and the results showed a maximum of 0.4 mm, a minimum of 0.0 mm, and an average of 0.28 mm on X-axis, and a maximum of -0.4 mm, a minimum of 0.0 mm, and an average of -0.24 mm on Y-axis. A comparison and evaluation of the treatment plan dose with the actual measured dose resulted in a maximum of 0.97 % and a minimum of 0.08 %. Conclusion: The equipment's average output dose was calculated to be 0.66 %, meeting the ${\pm}3%$ tolerance, which was considered as a much uniform fashion. As for the accuracy assessment of the geometric Isocenter, the results met the recommended criteria of ${\pm}1mm$ tolerance, affirming a high level of reproducibility of the patient's posture. The difference between the treatment plan dose and the actual measurement dose was calculated to be 0.52 % on average, significantly less than the 3 % tolerance, confirming that it obtained predicted does. The current study suggested that VERO equipment is suitable for SBRT, and would result in notable therapeutic effect.