• Journal of Korean Neurosurgical Society
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• v.59 no.4
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• pp.385-391
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• 2016

Objective : The purpose of this study was to evaluate long-term follow-up radiologic/clinical outcomes of patients who underwent anterior cervical discectomy and inter-body fusion (ACDF) with stand-alone cages (SAC) in a single academic institution. Methods : Total 99 patients who underwent ACDF with SAC between February 2004 and December 2012 were evaluated retrospectively. A total of 131 segments were enrolled in this study. Basic demographic information, radiographic [segmental subsidence rate, fusion rate, C2-7 global angle, and segmental angle changes)/clinical outcomes (by Odom's criteria and visual analog score (VAS)] and complications were evaluated to determine the long-term outcomes. Results : The majority were males (55 vs. 44) with average age of 53.2. Mean follow-up period was 62.9 months. The segmental subsidence rate was 53.4% and fusion rate was 73.3%. In the subsidence group, anterior intervertebral height (AIH) had more tendency of subsiding than middle or posterior intervertebral height (p=0.01). The segmental angle led kyphotic change related to the subsidence of the AIH. Adjacent segmental disease was occurred in 18 (18.2%) patients. Total 6 (6%) reoperations were performed at the index level. There was no statistical significance between clinical and radiological outcomes. But, overall long-term clinical outcome by Odom's criteria was unsatisfactory (64.64%). The neck and arm VAS score were increased by over time. Conclusion : Long-term outcomes of ACDF with SAC group were acceptable but not satisfactory. For optimal decision making, more additional comparative long-term outcome data is needed between ACDF with SAC and ACDF with plating.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.58-63
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• 2017

Emergencies and disasters can happen any time without any warning, and things can change and escalate very quickly, and often it is swift and decisive actions that make all the difference. It is a responsibility of the building facility management to ensure that a proven evacuation plan in place to cover various worst scenario to handled automatically inside the facility. To mapping out optimal safe escape routes is a straightforward undertaking, but does not necessarily guarantee residents the highest level of protection. The emergency evacuation navigation approach is a state-of-the-art that designed to evacuate human livings during an emergencies based on real-time decisions using live sensory data with pre-defined optimum path finding algorithm. The poor decision on causalities and guidance may apparently end the evacuation process and cannot then be remedied. This paper propose a cloud connected emergency evacuation system model to react dynamically to changes in the environment in emergency for safest emergency evacuation using IoT based emergency exit sign system. In the previous researches shows that the performance of optimal routing algorithms for evacuation purposes are more sensitive to the initial distribution of evacuees, the occupancy levels, and the type and level of emergency situations. The heuristic-based evacuees routing algorithms have a problem with the choice of certain parameters which causes evacuation process in real-time. Therefore, this paper proposes an evacuee routing algorithm that optimizes evacuation by making using high computational power of cloud servers. The proposed algorithm is evaluated via a cloud-based simulator with different "simulated casualties" are then re-routed using a Dijkstra's algorithm to obtain new safe emergency evacuation paths against guiding evacuees with a predetermined routing algorithm for them to emergency exits. The performance of proposed approach can be iterated as long as corrective action is still possible and give safe evacuation paths and dynamically configure the emergency exit signs to react for real-time instantaneous safe evacuation guidance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.54-57
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• 2013

최근 다양한 방송 및 영상 분야에서 사람의 행동을 인식하여는 연구들이 많이 이루어지고 있다. 영상은 다양한 형태를 가질 수 있기 때문에 제약된 환경에서 유용한 템플릿 방법들보다 특징점에 기반한 연구들이 실제 사용자 환경에서 더욱 관심을 받고 있다. 특징점 기반의 연구들은 영상에서 움직임이 발생하는 지점들을 찾아내어 이를 3차원 패치들로 생성한다. 이를 이용하여 영상의 움직임을 히스토그램에 기반한 descriptor(서술자)로 표현하고 학습기반의 판별기(classifier)로 최종적으로 영상 내에 존재하는 행동들을 인식하였다. 그러나 단일 판별기를 이용한 다양한 영상 인식을 수용하기에는 힘들다. 최근에 이를 개선하기 위하여 다중 판별기를 활용한 연구들이 영상 판별 및 물체 검출 영역에서 사용되고 있다. 따라서 본 논문에서는 행동 인식을 위하여 support vector machine과 spare representation을 이용한 decision-level fusion 방법을 제안하고자 한다. 제안된 논문의 방법은 영상에서 특징점 기반의 descriptor를 추출하고 이를 각각의 판별기를 통하여 판별 결과들을 획득한다. 이 후 학습단계에서 획득된 가중치를 활용하여 각 결과들을 융합하여 최종 결과를 도출하였다. 본 논문에 실험에서 제안된 방법은 기존의 융합 방법보다 높은 행동 인식 성능을 보여 주었다.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of Information Technology Applications and Management
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• v.28 no.3
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• pp.49-58
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• 2021

Due to the increasing interest in wellness aroused by the aging population and the pursuing feature of active old age, Korean elderly set importance on long life with their healthy condition. Following the change in the paradigm of the medical delivery system from hospital-oriented, treatment-oriented to personal-centered and self-care, Service design application of Smart Healthcare for the elderly became valuable. Smart Healthcare is a healthcare service provided through the fusion of ICT technologies including mobile/wearable devices, IoT, big data, and information technology, and it is utilized to prevent diseases managing abundant health information and living habits. As a methodology for delivering such Smart Healthcare to the elderly, Service design can be adopted. Therefore, this study would like to present the perquisites of Smart Healthcare design for the elderly through analyzing the results from in-depth interview methods between the elderly and medical staff. As a result of this study, guidelines for Service design application of health vulnerability management for the elderly utilizing smart phones were presented. Therefore, this study presented four prerequisites composed of 'high level of supplementation and ethical decision making', 'improvement of inequality in accessibility and experience', 'resolving problems in policy implementation' and 'user-friendliness' for the Smart Healthcare service design for the elderly. Overall, Service design is expected to play an innovative role in improving the quality of life for the elderly through the process of collecting and delivering information on Smart Healthcare centered on the experience of the elderly.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.209-212
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• 2013

The Nuri process of emphasis by the Ministry of Education to promote is standardized curriculum at the national level for the education and care. It is to improve the quality of pre-school education and Ensure a fair starting line early in life and It emphasizes character education in all areas of the window. Nuri the process of development of a the insect robot for the Creativity education Increased the interesting and educational effects. Assembly and the effect on learning of educational content using a VR educational robot using the existing floor assembly using the online website to help assemble and learning raised. Order to take advantage of information technology in the information-based society requires the active interest and motivation in learning, creative learning toddlers learning robot are also needed. A three-dimensional model of the robot, and augmented by linking through the marker, the target marker and the camera relative to the coordinate system of augmented reality, seeking to convert the marker to be used in augmented reality marker patterns within a pre-defined patternto be able to make a decision on what of. The fusion of a smart education through training and reinforcement the educational assembly of the robot in the real world window that is represented by a virtual environment in this paper to present a new form of state-of-the-art smart training, you will want to lay the foundation of the nation through the early national talent nurturing talent.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.