• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.643-656
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• 2021

Recently, water supply management technology is highly developed, and a computer simulation model plays a critical role for estimating hydraulics and water quality in water distribution networks (WDNs). However, a simulation of complex large water networks is computationally intensive, especially for the water quality simulations, which require a short simulation time step and a long simulation time period. Thus, it is often prohibitive to analyze the water quality in real-scale water networks. In this study, in order to improve the computational efficiency of water quality simulations in complex water networks, a hierarchical water-quality-simulation technique was proposed. The water network is hierarchically divided into two sub-networks for improvement of computing efficiency while preserving water quality simulation accuracy. The proposed approach was applied to a large-scale real-life water network that is currently operating in South Korea, and demonstrated a spatiotemporal distribution of chlorine concentration under diverse chlorine injection scenarios.

• Journal of Periodontal and Implant Science
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• v.50 no.6
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• pp.392-405
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• 2020

Purpose: Titanium implants are widely used in the treatment of dentition defects; however, due to problems such as osseointegration failure, peri-implant bone resorption, and periimplant inflammation, their application is subject to certain restrictions. The surface modification of titanium implants can improve the implant success rate and meet the needs of clinical applications. The goal of this study was to evaluate the effect of the use of porous titanium with a chitosan/hydroxyapatite coating on osseointegration. Methods: Titanium implants with a dense core and a porous outer structure were prepared using a computer-aided design model and selective laser sintering technology, with a fabricated chitosan/hydroxyapatite composite coating on their surfaces. In vivo and in vitro experiments were used to assess osteogenesis. Results: The quasi-elastic gradient and compressive strength of porous titanium implants were observed to decrease as the porosity increased. The in vitro experiments demonstrated that, the porous titanium implants had no biological toxicity; additionally, the porous structure was shown to be superior to dense titanium with regard to facilitating the adhesion and proliferation of osteoblast-like MC3T3-E1 cells. The in vivo experimental results also showed that the porous structure was beneficial, as bone tissue could grow into the pores, thereby exhibiting good osseointegration. Conclusions: Porous titanium with a chitosan/hydroxyapatite coating promoted MC3T3-E1 cell proliferation and differentiation, and also improved osseointegration in vitro. This study has meaningful implications for research into ways of improving the surface structures of implants and promoting implant osseointegration.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.55-63
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• 2021

Recently, the incidence rate of malicious codes is over tens of thousands of cases, and it is known that it is almost impossible to detect/respond all of them. This study proposes a method for detecting multiple behavior patterns based on a graph database as a new method for dealing with malicious codes. Traditional dynamic analysis techniques and has applied a method to design and analyze graphs of representative associations malware pattern(process, PE, registry, etc.), another new graph model. As a result of the pattern verification, it was confirmed that the behavior of the basic malicious pattern was detected and the variant attack behavior(at least 5 steps), which was difficult to analyze in the past. In addition, as a result of the performance analysis, it was confirmed that the performance was improved by about 9.84 times or more compared to the relational database for complex patterns of 5 or more steps.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.167-174
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• 2021

Recently, deep learning technology is widely used in various computer vision applications, such as object recognition, classification, and image generation. In particular, the deep learning-based super-resolution has been gaining significant performance improvement. Fast super-resolution convolutional neural network (FSRCNN) is a well-known model as a deep learning-based super-resolution algorithm that output image is generated by a deconvolutional layer. In this paper, we propose an FPGA-based convolutional neural networks accelerator that considers parallel computing efficiency. In addition, the proposed method proposes Optimal-FSRCNN, which is modified the structure of FSRCNN. The number of multipliers is compressed by 3.47 times compared to FSRCNN. Moreover, PSNR has similar performance to FSRCNN. We developed a real-time image processing technology that implements on FPGA.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.51-62
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• 2021

This study developed the factors capable of systematic/comprehensive evaluation of the task performance in order to strengthen the performance management of the professional career personnel invitation program (PCPIP). To this end, a performance evaluation framework was developed by analyzing existing project evaluation studies based on boundary theory and Kirkpatrick's four-level evaluation model. Afterwords, through two Delphi surveys, evaluation factors that can measure performance in terms of individual and invitation institutions of PCP were derived and validated. With this procedure, five evaluation factors were finally selected: adaptability, connectivity, clarity, compatibility, and expandability. This study has implications suggesting a performance evaluation factors capable of hybrid quantitative/qualitative evaluation for the performance management of PCPIP operated by National Research Foundation of Korea Research since 1994.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.43-49
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• 2019

The wired and wireless Internet is a useful window to easily acquire various types of media data. On the other hand, the public can easily get the media data including the object to which the personal information is exposed, which is a social problem. In this paper, we propose a method to robustly detect a target object that has exposed personal information using a learning algorithm and effectively block the detected target object area. In the proposed method, only the target object containing the personal information is detected using a neural network-based learning algorithm. Then, a grid-like mosaic is created and overlapped on the target object area detected in the previous step, thereby effectively blocking the object area containing the personal information. Experimental results show that the proposed algorithm robustly detects the object area in which personal information is exposed and effectively blocks the detected area through mosaic processing. The object blocking method presented in this paper is expected to be useful in many applications related to computer vision.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.1
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• pp.71-79
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• 2019

Body surface potential map, an electric potential distribution on the body torso surface, enables us to infer the electrical activities of the heart. Therefore, observing electric potential projected to the torso surface can be highly useful for diagnosing heart diseases such as coronary occlusion. The BSPM for the heart of a patient show a higher level of sensitivity than 12-lead ECG. Relevant research has been mostly based on clinical statistics obtained from patients, and, therefore, a simulation for a variety of pathological phenomena of the heart is required. In this study, by using computer simulation, a body surface potential map was implemented according to various occlusion locations (distal, mid, proximal occlusion) in the left anterior descending coronary artery. Electrophysiological characteristics of the body surface during the ST segment period were observed and analyzed based on an ST isointegral map. We developed an integrated system that takes into account the cellular to organ levels, and performed simulation regarding the electrophysiological phenomena of the heart that occur during the first 5 minutes (stage 1) and 10 minutes (stage 2) after commencement of coronary occlusion. Subsequently, we calculated the bipolar angle and amplitude of the ST isointegral map, and observed the correlation between the relevant characteristics and the location of coronary occlusion. In the result, in the ventricle model during the stage 1, a wider area of ischemia led to counterclockwise rotation of the bipolar angle; and, during the stage 2, the amplitude increased when the ischemia area exceeded a certain size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.165-171
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• 2019

The study of the computer architecture has shown that major improvements in price-to-energy performance stems from domain-specific hardware development. This paper analyzes the tensor processing unit (TPU) ASIC which can accelerate the reasoning of the artificial neural network (NN). The core device of the TPU is a MAC matrix multiplier capable of high-speed operation and software-managed on-chip memory. The execution model of the TPU can meet the reaction time requirements of the artificial neural network better than the existing CPU and the GPU execution models, with the small area and the low power consumption even though it has many MAC and large memory. Utilizing the TPU for the tensor flow benchmark framework, it can achieve higher performance and better power efficiency than the CPU or CPU. In this paper, we analyze TPU, simulate the Python modeled OpenTPU, and synthesize the matrix multiplication unit, which is the key hardware.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.