• Journal of the Korean Society of Physical Medicine
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• v.5 no.2
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• pp.165-172
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• 2010

Purpose : The purpose of this study was to investigate curriculums of physical therapy education. Methods : We identified the curriculums of physical therapy education by internet. Results : In Korea, education on physical therapy started as a two-year program in 1963, and recently reorganized as three or four years programs. Currently some ten schools offer physical therapy programs for master's and doctor's degrees. The member countries of the World Confederation for Physical Therapy (WCPT) provide approximately ten types of education programs that take two to six years. In Korea, the same programs have three or four years of education periods. The American Physical Therapist Association (APTA) launched on the doctor of physical therapy (DPT) course in the late 1990s, encouraging physical therapists to acquire a doctorate degree. In addition, the U.S. Vision 2020 envisions that all physical therapists acquire DPT by the year 2020. As the medical field becomes more professional and specialized, physical therapists are expected to supplement and even replace works of doctors, instead of merely assisting them. It is necessary to reinforce education programs and improve related school systems to enhance status of physical therapist in accordance with the changing social needs and to provide quality service to patients. Conclusion : We suggest to change the curriculum of Korea. It is more accurately reflected the scope, depth, breadth, and rigor of the high-quality education preparation needed for current and future practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3315-3337
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• 2012

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.1-7
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• 2019

In this paper, we propose an approach to design a practical filter and a mathematical modeling for images. In the areas of signal processing, including high-dimensional image processing, the filtering process has been fundamental and crucial in diverse practical applications such as image processing, computer vision, and pattern recognition. In general, the ideal filter is modeled as circular-shaped in the 2D frequency domain as the rectangular shape is ideal for the 1D frequency domain. This paper proposes an approach to modeling practical and efficient image filter in the 2D frequency domain. Instead of employing a circular-shaped filter, this study proposes a polygon-shaped filter inspired by the concept of a hexagon cellular system for frequency reuse in wireless communication systems. By employing the concept of frequency reuse, bandwidth efficiency is also achieved in the frequency domain. To substantiate the proposed approach, quantitative evaluation is performed using PSNR.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.299-305
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• 2019

The purpose of study was to investigate the effects of career decision efficacy, learning Flow, and academic achievement on department adaptation of dental hygiene students. The subjects of the study are self-administered surveys of 200 students who indicated their intention to participate. 181 questionnaires were analyzed using SPSS 18.0. The results of this study show that career decision efficacy, learning flow, academic achievement affect to major satisfaction, major confidences, major attachment. The results of this study show that career decision efficacy, academic achievement affect to major vision. Some dental hygienists students need support systems such as learning methods and educational environment that can improve academic achievement and learning commitment in order to help students adapt to their department. In addition, education and career support for career decision efficacy should be continuously maintained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.421-424
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• 2014

Currently, car black box, and CCTV products, such as image processing are prevalent on the market giving convenience to users.In particular, the black box of the driver driving a vehicle accident that occurred at the time to help identify the cause of the accident is gaining. Black box, the front or rear of the vehicle can check the image only. Because of the angle of view of the driver's vision or the black box can not determine a non-scene. In order to solve this problem by a more advanced system, the black box AVM (Around-View Monitoring) systems have been developed. AVM system to the vehicle's top-view images obtained before and after, left and right of the image, ie, $360^{\circ}$ image of the vehicle can be secured. AVM system must be installed on the vehicle, a desktop that you can acquire images Cling conditions. In this paper, we propose an Android-based tablet using the AVM system of the vehicle can achieve a $360^{\circ}$ image you want to design the system.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.85-116
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• 2019

This research examines the characteristics of the newly emerging 'transformative innovation policy' and discusses the current status and issues of the Korean social problem solving R & D policy. Transformative innovation policy is a new paradigm that aims to transform socio-technical systems to address societal challenges. In this study, we analyzed 'the policy plan for solving social problems based on science and technology'. In the "Policy Plan", efforts are being made to establish new direction of science and technological innovation activities such as emphasis on social values, network formation of innovation actors, and spreading of social impact. But in this "Policy Plan", the perspective of transformative innovation policy is weakly reflected. The Policy Plan refers to system improvement that adds new elements to existing system, but it is not discussing system transformation. In order to develop social problem solving R & D policy from the viewpoint of the transformative innovation policy, it is necessary to construct the innovation platform deliberating vision and prospect for the socio-technical transformation.