• 한국가시화정보학회:학술대회논문집
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• 2006.12a
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• pp.109-110
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• 2006

Osteoporosis is a deadly bone disease. Early detection of this disease is an important issue for better treatment. There is for a novel technique to identify the disease at early stage. Tremendous research is going on in this aspect. However, more work is required to be done to develop cheap and reliable early detection techniques. In the present study new optical technique has been explored using optical studies.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.391-395
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• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.712-713
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• 2015

In recent years, the bid-rigging in public construction markets has been treated as a critical issue in Fair Trade Commission. The investigation revealed that the collusion was implemented extensively in every area from the material supply to the construction service of general contractors. This study reviewed the causes of the bid-rigging in public construction projects, and proposed the improvement plan to eradicate bad practices. Firstly, the causes and purposes of bid-rigging were categorized into two types of internal factors from construction companies and external environment factors influencing business activities. Secondly, the system development method was explained to detect the signs of bid-rigging based on the technical proposal documents in open tender. The detection systems of repetitive public owner also provide the function of sharing data on the companies and cases to violate the fair trade regulation. In addition, the problems and improvement direction of public procurement policies were discussed.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.577-586
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• 2012

The smartphone emerged due to the rapid development of the Internet has brought greater convenience to life in a positive manner. Recently, however, because of unconstrained exposure to harmful video, reckless use of smart phones has become a domestic issue in our society. In this paper, a service which detects harmful videos by using the acoustic information is designed and implemented on the Android OS. In order to implement the service of Android OS-based detection of the harmful movie, the speed of existing sound-based detection method for harmful videos is improved. The GMM(Gaussian Mixture Model) was used for classifier and the number of Gaussian Mixture was 18. The implemented service shows a detection rate of 97.02% for a total of 1,210 data files (approximately 687 hours) which comprises 669 general videos files (about 424 hours) and 541 harmful video files (about 263 hours). It's speed is 5.6 times faster than the traditional methods whitout reducing the detection rate.

• Journal of IKEEE
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• v.22 no.3
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• pp.723-728
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• 2018

This paper proposes a signal detection method for IoT door lock system which is a new application field of VLC (Visible Light Communication). This paper describes the signal detection technique for user recognition that needs to be overcome in order to apply VLC to door lock system which has a demand for new technology due to security issue. This system has security and high signal detection characteristics because it uses existing infrastructure to communicate with visible light. In order to detect the signal using FFT, the signal of the user who accesses the authentication channel based on the pilot signal is detected, and the performance of the false alarm probability and detection probability is shown in the channel model.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.329-332
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• 2023

3D Object Detection using deep neural network has been developed a lot for obstacle detection in autonomous vehicles because it can recognize not only the class of target object but also the distance from the object. But in the case of 3D Object Detection models, the detection performance for distant objects is lower than that for nearby objects, which is a critical issue for autonomous vehicles. In this paper, we introduce a technique that increases the performance of 3D object detection models, particularly in recognizing distant objects, by generating virtual 3D vehicle data and adding it to the dataset used for model training. We used a spherical point tracing method that leverages the characteristics of 3D LiDAR sensor data to create virtual vehicles that closely resemble real ones, and we demonstrated the validity of the virtual data by using it to improve recognition performance for objects at all distances in model training.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.28 no.5
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• pp.277-284
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• 2015

The humans are under attack involving the hazardous environment and pathogen/biotoxin aerosol that is realistic concerned. A portable, fast, reliable, and cheap Pathogen and Biotoxin Aerosol threat Detection(PBAD) trigger is an important technology for detect-to-protect and detect-to-treat system because the man-made biological terror is a fast and lethal infection. The ultraviolet C(UVC) wavelengths light source is key issue for PBAD that is sensitive because of strong fluorescence cross section from fluorescent amino acids in proteins such as tryptophan and tyrosine. The UVC-light emitting diode(LED) is emerging light source technology as alternative to laser or lamps as they offer several advantages. This paper discussed about the design consideration of UVC-LED for the PBAD system. The UVC-LED and PBAD technology, currently available or in development, are also discussed.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.345-351
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• 2014

This paper presents pedestrian recognition to improve performance for vehicle safety system or surveillance system. Pedestrian detection method using HOG (Histograms of Oriented Gradients) has showed 90% recognition rate. But if someone takes a picture in the rain, the image may be distorted by rain streaks and recognition rate goes down by 62%. To solve this problem, we applied image decomposition method using MCA (Morphological Component Analysis). In this case, rain removal method improves recognition rate from 62% to 70%. However, it is difficult to apply conventional image decomposition method using MCA on vehicle safety system or surveillance system as conventional method is too slow for real-time system. To alleviate this issue, we propose a rain removal method by using low-pass filter and DCT (Discrete Cosine Transform). The DCT helps separate the image into rain components. The image is removed rain components by Butterworth filtering. Experimental results show that our method achieved 90% of recognition rate. In addition, the proposed method had accelerated processing time to 17.8ms which is acceptable for real-time system.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.607-614
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• 2019

It has been an issue among researchers that the tsunamis that occurred on the west coast of Japan in 1983 and 1993 damaged the coastal cities on the east coast of Korea. In order to predict and reduce the damage to the Korean Peninsula effectively, it is necessary to install offshore tsunami observation instruments as part of the system for the early detection of tsunamis. The purpose of this study is to recommend the optimal deployment of tsunami observation instruments in terms of the higher probability of tsunami detection with the minimum equipment and the maximum evacuation and warning time according to the current situation in Korea. In order to propose the optimal location of the tsunami observation equipment, this study will analyze the tsunami propagation phenomena on the east sea by considering the potential tsunami scenario on the west coast of Japan through numerical modeling using the COrnell Multi-grid COupled Tsunami (COMCOT) model. Based on the results of the numerical model, this study suggested the optimal deployment of Korea's offshore tsunami observation instruments on the northeast side of Ulleung Island.