• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1301-1306
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• 2018

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as 'superintelligence-based systems and infrastructures', 'superconnection-based IoT and big data systems', 'analysis of IoT-based data and big data', 'infrastructure design for IoT and big data', 'artificial intelligence applications', and 'superconnection-based IoT devices'.

• International Journal of Oral Biology
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• v.44 no.2
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• pp.37-42
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• 2019

Oral squamous cell carcinoma (OSCC) is the most common oral malignancy and an increasing global public health problem. OSCC frequently invades the jaw bone. OSCC-induced bone invasion has a significant impact on tumor stage, treatment selection, patient outcome, and quality of life. A number of studies have shown that osteoclast-mediated bone resorption is a major step in the progression of bone invasion by OSCC; however, the molecular mechanisms involved in OSCC bone invasion are not yet clear. In this review, we present the clinical types of OSCC bone invasion and summarize the role of key molecules, including proteases, cytokines, and growth factors, in the sequential process of bone invasion. A better understanding of bone invasion will facilitate the discovery of molecular targets for early detection and treatment of OSCC bone invasion.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.967-985
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• 2019

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

• Smart Structures and Systems
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• v.24 no.6
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• pp.683-692
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• 2019

Traditional damage detection methods for nonlinear structures are often based on simplified models, such as the mass-spring-damper and shear-building models, which are insufficient for predicting the vibration responses of a real structure. Conventional global nonlinear finite element model updating methods are computationally intensive and time consuming. Thus, they cannot be applied to practical structures. A decentralized approach for identifying the nonlinear material parameters is proposed in this study. With this technique, a structure is divided into several small zones on the basis of its structural configuration. The unknown material parameters and measured vibration responses are then divided into several subsets accordingly. The structural parameters of each subset are then updated using the vibration responses of the subset with the Newton-successive-over-relaxation (SOR) method. A reinforced concrete and steel frame structure subjected to earthquake loading is used to verify the effectiveness and accuracy of the proposed method. The parameters in the material constitutive model, such as compressive strength, initial tangent stiffness and yielding stress, are identified accurately and efficiently compared with the global nonlinear model updating approach.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of Multimedia Information System
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• v.7 no.4
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• pp.257-262
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• 2020

In this paper, a method of detecting the emergency situations such as body fall is proposed by using color images. We detect body areas and key parts of a body through a pre-learned Mask R-CNN in the images captured by a camera. Then we find the centerline of the body through the joint points of both shoulders and feet. Also, we calculate an angle to the center line and then calculate the amount of change in the angle per hour. If the angle change is more than a certain value, then it is decided as a suspected fall. Also, if the suspected fall state persists for more than a certain frame, then it is determined as a fall situation. Simulation results show that the proposed method can detect body fall situation accurately.

• Mass Spectrometry Letters
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• v.12 no.1
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• pp.26-30
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• 2021

The impurity concentration is a crucial parameter for semiconductor thin films. Evaluating the impurity distribution in silicon thin film is another challenge. In this study, we have investigated the doping concentration of boron in silicon thin film using time of flight secondary ion mass spectrometry in dynamic mode of operation. Boron doped silicon film was grown on i) p-type silicon wafer and ii) borosilicate glass using hot wire chemical vapor deposition technique for possible applications in optoelectronic devices. Using well-tuned SIMS measurement recipe, we have detected the boron counts 101~104 along with the silicon matrix element. The secondary ion beam sputtering area, sputtering duration and mass analyser analysing duration were used as key variables for the tuning of the recipe. The quantitative analysis of counts to concentration conversion was done following standard relative sensitivity factor. The concentration of boron in silicon was determined 1017~1021 atoms/㎤. The technique will be useful for evaluating distributions of various dopants (arsenic, phosphorous, bismuth etc.) in silicon thin film efficiently.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.105-110
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• 2022

Traffic sign recognition plays an important role in solving traffic-related problems. Traffic sign recognition and classification systems are key components for traffic safety, traffic monitoring, autonomous driving services, and autonomous vehicles. A lightweight model, applicable to portable devices, is an essential aspect of the design agenda. We suggest a lightweight convolutional neural network model with residual blocks for traffic sign recognition systems. The proposed model shows very competitive results on publicly available benchmark data.

• Animal Bioscience
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• v.36 no.2_spc
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• pp.374-384
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• 2023

Skeletal muscle metabolism regulates homeostatic balance in animals. The metabolic impact persists even after farm animal skeletal muscle is converted to edible meat through postmortem rigor mortis and aging. Muscle metabolites resulting from animal growth and postmortem storage have a significant impact on meat quality, including flavor and color. Metabolomics studies of postmortem muscle aging have identified metabolisms that contain signatures inherent to muscle properties and the altered metabolites by physiological adaptation, with glycolysis as the pivotal metabolism in postmortem aging. Metabolomics has also played a role in mining relevant postmortem metabolisms and pathways, such as the citrate cycle and mitochondrial metabolism. This leads to a deeper understanding of the mechanisms underlying the generation of key compounds that are associated with meat quality. Genetic background, feeding strategy, and muscle type primarily determine skeletal muscle properties in live animals and affect post-mortem muscle metabolism. With comprehensive metabolite detection, metabolomics is also beneficial for exploring biomarker candidates that could be useful to monitor meat production and predict the quality traits. The present review focuses on advances in farm animal muscle metabolomics, especially postmortem muscle metabolism associated with genetic factors and muscle type.

• Annual Conference of KIPS
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• 2009.11a
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• pp.285-286
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• 2009

본 논문은 실시간 이미지 매칭을 위한 빠른 BLoG 특징점 검출방법을 제안하고 이미지 크기, 회전변화등 다양한 실험을 통하여 기존 방법과 속도와 연산량 그리고 검출 성능에 대하여 비교하고 앞으로 나아갈 방향에 대하여 제시한다.