• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.427-439
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• 2006

The SBF (Simple Boundary Follower) is a boundary-following algorithm, and is used mainly for image recognition and presentation. The SBF is very popular because of its simplicity and efficiency in tracing the boundary of an object from an acquired binary image; however, it does have two drawbacks. First, the SBF cannot consistently process inner or inner-outer corners according to the follower's position and direction. Second, the SBF requires movement operations for the non-boundary pixels that are connected to boundary pixels. The MSBF (Modified Simple Boundary Follower) has a diagonal detour step for preventing inner-outer corner inconsistency, but is still inconsistent with inner-corners and still requires extra movement operations on non-boundary pixels. In this paper, we propose the ISBF (Improved Simple Boundary Follower), which solves the inconsistencies and reduces the extra operations. In addition, we have classified the tour maps by paths from a current boundary pixel to the next boundary pixel and have analyzed SBF, MSBF, and ISBF. We have determined that the ISBF has no inconsistency issues and reduces the overall number of operations.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.273-291
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• 2018

Gestures accompanied by scientific discourses play an important role in constructing mental models and making model-based inferences. According to embodied cognition literature, gestures can be a source of recognition of the mental models of students and help them in changing naive beliefs about science. This study intends to compare the gestures of scientists with that of middle school students in explaining scientific phenomena and to explore the relationship between gestures and scientific discourse. In the study, 10 scientists and 10 middle school students participated in clinical interviews and the tests of knowledge and self-efficacy. Participants engaged in one-on-one clinical interviews with semi-structured questions about three tasks regarding the molecular movement and the state change of matter. Four researchers carried out open coding and applied a constant comparison method in order to analyze video-recorded gestures. This study found four themes (feature of gesture, use of gesture, content of gesture, function of gesture) about the differences of gestures between scientists and middle school students. Scientists used more diverse and elaborate gestures systematically and frequently in the interview. Although students used gestures in their scientific talk and reasoning, the gestures of students were not well grounded on scientific knowledge and had different functions from those of scientists. The findings revealed that gestures can represent underlying cognition and strengthen scientific thinking. We should encourage students to use gestures as a tool to understand scientific concepts and make inferences.

• Journal of Chest Surgery
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• v.31 no.2
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• pp.208-211
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• 1998

Reflex sympathetic dystrophy is an important clinical entity that is characterized by excessive or prolonged pain, vasomotor and other autonomic disturbances, delayed recovery of function, and trophic changes. This syndrome is among the most frequently encountered problems in clinical medicine, and proper diagnosis and therapy are critical. Accidental or surgical trauma or one of a variety of disease states may become a precipitating factor. Proper recognition and treatment result in rapid elimination of symptoms and complete recovery. A 56-years old male accidented total amputation of the proxomal part of the left index finger in May, 1996. Emergently, complete replantation procedure was successfully performed in the department of reconstructive surgery, medical center, Korea University. Afterward, he began to suffer from uncontrolled, prolonged pain in left index finger, proximally spreading pain to the left upper extremity and limited joint movement of the left shoulder. Although many treatments were used for this syndrom, not all of them were effective. Furthermore, the treatments which proved effective had detrimental side effects. However, thoracoscopic left thoracic sympathectomy was performed in our department. This therapy successfully relieved the pain and improved shoulder joint movement.

• Journal of KIISE:Information Networking
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• v.37 no.6
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• pp.459-465
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• 2010

Mobile IPv6(MIPv6) features have several defects such as overloading of nodes, loss of wireless signals, packet loss, movement problem and so forth. Proxy Mobile IPv6 (PMIPv6) got over the limit of MIPv6 problems. MIPv6 features have several defects such as overloading of nodes, loss of wireless signals, packet loss, movement problem and so forth. Research on PMIPv6, which features network-based mobility is actively in progress in order to resolve these issues. PMIPv6 is emerging as a new paradigm that can overcome the limitations of the existing MIPv6. Nevertheless, such PMIPv6 also incurs problems during hand-over. While it offers a solution to node-based problems, it does, too, create long delay times during hand-over. Since MN (Mobile Node) has been sensing its own movements on MIPv6, fast handover can be done easily. However it can't apply fast handover like MIPv6, as it can't apply fast handover like MIPv6 In this paper, the author solved hand-over problem on MIPv6. MAG knows location information of MN and if MN moves into other MAG's area, Location Server gives MN information to the MAG. Therefore, this mechanism makes hand-over process easier.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.619-624
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• 2014

Nowadays there are various smart devices and development with the development of smart phones and that can be attached to the human body wearable computing device has been in the spotlight. In this paper, we proceeded developing wearable devices in watch type which can detect user's movement and developing a system which connects the wearable devices to smart TVs, or smart phones so that users can save and manage their physical information in those devices. Health care wearable devices already existing save information by connecting their systems to smart phones. And, smart TV health applications usually include motion detecting systems using cameras. However, there is a limit when connecting smart phone systems to different devices from various companies. Also, in case of smart TV, because some devices may not have cameras, there can be a limit for users who wants to connect their devices to smart TVs. Wearable device and user information collected by using the smart phone and when it is possible to exercise and manage anywhere. This information can also be confirmed by the smart TV applications. By using this system will be able to take advantage of the study of the behavior of the future work of the user more accurately be measured in recognition technology and other devices.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.