• East Asian Economic Review
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• v.27 no.4
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• pp.303-325
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• 2023

This paper examines how APEC has transformed itself into an international forum for the vision of regional integration. It aims to quantify the documentation produced by the international organization and provide quantifiable evidence that aligns with prior knowledge rather than relying solely on intuition. For this purpose, I use various text mining techniques to extract multi-dimensional features from the text of APEC Leaders' Declarations from 1993 to 2023. In terms of interest and expectations for APEC as a forum, it is found that members have experienced two major peaks and troughs over the last three decades. It is found that the change point coincides with the Asian financial crisis of 1997 and the tensions between the United States and China since 2017. To explore more various aspects of economic integration in the Asia-Pacific region, this study also considers how consistently APEC has been an international forum for addressing issues, which members are active, and how members have clustered based on their views of APEC.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.59-67
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• 2020

This study aims to develop the fuzzy risk assessment model of the debris flow to verify the accuracy of risk assessment in order to help related organizations reduce losses caused by landslides. In this study, actual cases of landslides that occurred are utilized as the database. The established models help us assess the occurrence of debris flows using computed indicators, and to verify the model errors. In addition, comparisons are made between the models to determine the best one to use in practical applications. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.368-373
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• 2010

A new indoor mobile robot localization method is presented. Robot recognizes well designed single color landmarks on the ceiling by vision system, as reference to compute its precise position. The proposed likelihood prediction based method enables the robot to estimate its position based only on the orientation of landmark.The use of single color landmarks helps to reduce the complexity of the landmark structure and makes it easily detectable. Edge based optical flow is further used to compensate for some landmark recognition error. This technique is applicable for navigation in an unlimited sized indoor space. Prediction scheme and localization algorithm are proposed, and edge based optical flow and data fusing are presented. Experimental results show that the proposed method provides accurate estimation of the robot position with a localization error within a range of 5 cm and directional error less than 4 degrees.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.55-61
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• 2008

A real-time three-dimensional vision is a passive system, which would support various applications including collision avoidance, home networks. It is a good alternative of active systems, which are subject to interference in noisy environments. In this paper, we designed a SAD correlator with respect to resource usage for a real-time three-dimensional vision system. Regular structures, linear data flow and abundant parallelism make the correlation algorithm a good candidate for a reconfigurable hardware. We implemented two versions of SAD correlator in HDL and synthesized them to determine resource requirements and performance. From the experiment we show that the SAD correlator fits into reconfigurable hardware in marginal cost and can handle about 30 frames/sec with $640{\times}480$ images.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.699-708
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• 2021

In this paper, a steering control and speed control algorithm was presented for autonomous driving based on two vision sensors and road speed sign board. A car speed control algorithm was developed to recognize the speed sign by using TensorFlow, a deep learning program provided by Google to the road speed sign image provided from vision sensor B, and then let the car follows the recognized speed. At the same time, a steering angle control algorithm that detects lanes by analyzing road images transmitted from vision sensor A in real time, calculates steering angles, controls the front axle through PWM control, and allows the vehicle to track the lane. To verify the effectiveness of the proposed algorithm's steering and speed control algorithms, a car's prototype based on the Python language, Raspberry Pi and OpenCV was made. In addition, accuracy could be confirmed by verifying various scenarios related to steering and speed control on the test produced track.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.753-758
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• 2003

The purpose of the present study was to establish the mechanism of the generation of atherosclerosis by analyzing the hemodynamic variables in the coronary artery where atherosclerosis occurs frequently. From the previous results, the stenosis phenomena due to atherosclerosis were related to not only biochemical reaction between blood and blood vessel but also the hemodynamic factors like flow separation and oscillatory wall shear stress. The present study aimed to investigate the causes of the generation and progression of atherosclerosis in the coronary artery. This study also aimed to develop the softwares which generate automatically three dimensional vascular models obtained by the angiogram images and the computer vision techniques. In the present study, the flow patterns for full three-dimensional hemodynamic characteristics were analyzed. To understand the three-dimensional hemodynamic characteristics, the wall shear stress distributions and secondary flows were investigated quantitatively.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.557-557
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• 2000

Tracking of the moving object, which is realized by the computer vision, is used for military and industrial fields. It is the application technique with imply complicated processing for understanding the input images. But, in these days, the most moving object tracking algorithms have many difficult problems. A typical problem is the increase of calculation time depending on target number. For this reason, there are many studies to solve real time processing problems and errors for background environmental change. In this paper, we used optical flow which is one of moving object tracking algorithms. It represents vector of the moving object. Optical flow estimation based on the regularization method depends on iteration method but it is very sensitive the noise. We proposed a new method using the Combinatorial Hough Transform (CHT) and Voting Accumulation in order to find optimal constraint lines. Also, we used the logical operation in order to release the operation time. The proposed method can easily and accurately extract the optical flow of moving object area and the moving information. We have simulated the proposed method using the test images. This images are included the noise. Experimental results show that the proposed method get better flow and estimate accurately the moving information.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.270-273
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• 2008

The integrated power system combining a tidal power plant and two ocean current power parks is suggested. It is characterized by the set up of an ocean current power park in the lake side by installing a number of ocean current turbines generating electricity by using sea water flow discharged into the lake side from the turbine generator of a tidal power plant and an ocean current power park in the sea side by installing a number of ocean current turbines generating electricity by using sea water flow exiting into the sea side through the sluice gate from the lake side. The vision of the integrated power system is demonstrated by the simple theory and simulation results of the SIWHA Tidal Power Plant. And it is shown that the newly proposed integrated power system combining tidal power and ocean current power can produce very high economical benefits.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.348-357
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• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.