• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.1-8
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• 2004

This study investigated the effect of visual learning preference levels in computer assisted instruction (CAI) using static graphics and animations on students' conceptual understandings, application abilities and learning motivations. Fifty-nine seventh graders were selected from a middle school in seoul, and they were taught about the motion of molecule for 4 class hours. Two-way ANCOVA results revealed that the scores of the conception test of the animations group, regardless of student's visual learning preference levels, were significantly higher than those of the static graphics group. However, there were no differences between the two groups in the scores of application test and learning motivation test.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3270-3294
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• 2020

Some things come easily to humans, one of them is the ability to navigate around. This capability of navigation suffers significantly in case of partial or complete blindness, restricting life activity. Advances in the technological landscape have given way to new solutions aiding navigation for the visually impaired. In this paper, we analyze the existing works and identify the challenges of path selection, context awareness, obstacle detection/identification and integration of visual and nonvisual information associated with real-time assisted mobility. In the process, we explore machine learning approaches for robotic path planning, multi constrained optimal path computation and sensor based wearable assistive devices for the visually impaired. It is observed that the solution to problem is complex and computationally intensive and significant effort is required towards the development of richer and comfortable paths for safe and smooth navigation of visually impaired people. We cannot overlook to explore more effective strategies of acquiring surrounding information towards autonomous mobility.

• The Journal of Korean Association of Computer Education
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• v.8 no.2
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• pp.33-40
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• 2005

It has been found that the learning method based on conventional CAI(Computer Assisted Instruction) to be inadequate and inefficient as it is designed without considering the individual learning characteristics of the learners. In order to rectify and remedy the problem, the development of an ITS(Intelligent Tutoring System) that is adequately equipped with an artificial intelligence that successfully interprets the individual learning ability characteristics through accumulated individual data is in order. This study attempts to verify the individual acquisition ability and the possible error committed by learners in the process of learning in order to present the elements to be considered for designing a successful student module that enables the effective learning through the 'learner ability grouping' for learning Electronic Calculator Architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.232-248
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• 2014

A series of kernel regression (KR) algorithms, such as the classic kernel regression (CKR), the 2- and 3-D steering kernel regression (SKR), have been proposed for image and video super-resolution. In existing KR frameworks, a single algorithm is usually adopted and applied for a whole image/video, regardless of region characteristics. However, their performances and computational efficiencies can differ in regions of different characteristics. To take full advantage of the KR algorithms and avoid their disadvantage, this paper proposes a kernel regression framework for video super-resolution. In this framework, each video frame is first analyzed and divided into three types of regions: flat, non-flat-stationary, and non-flat-moving regions. Then different KR algorithm is selected according to the region type. The CKR and 2-D SKR algorithms are applied to flat and non-flat-stationary regions, respectively. For non-flat-moving regions, this paper proposes a similarity-assisted steering kernel regression (SASKR) algorithm, which can give better performance and higher computational efficiency than the 3-D SKR algorithm. Experimental results demonstrate that the computational efficiency of the proposed framework is greatly improved without apparent degradation in performance.

• ETRI Journal
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• v.45 no.5
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• pp.874-886
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• 2023

A joint resource-optimization scheme is investigated for nonorthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)-assisted radio-access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality-maximization problem is formulated as a mixed-integer nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment-association, spectrum-partition, and UAV transmit-power-control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine-tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal-to-noise ratio, aggregate videoreception rate, and spectrum utilization over various baselines.

• Clinical Endoscopy
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• v.57 no.3
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• pp.302-308
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• 2024

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.3
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• pp.351-361
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• 2012

The leading surgical method for correcting the misalignment of the varus and valgus in the knee joint is the high tibial osteotomy (HTO). In the opening wedge HTO (OWHTO), there is no concern about damaging the peroneal nerve on the lateral tibia of the proximal fibula. OWHTO has been the preferred choice, as the opening of the correction angle can be modulated during the operation. The correction of the varus and valgus on the coronal plane are performed adroitly. Nevertheless, there have been numerous reports of unintended changes in the medial tibial plateau and posterior slope angle (PSA). The authors have developed an HTO method using computer-assisted surgery with the aim of addressing the abovementioned problems from an engineer's perspective. CT images of the high tibia were reconstructed three-dimensionally, and a virtual osteotomy was performed on a computer. In addition, this study recommends a surgical method that does not cause changes in the PSA after OWHTO. The results of the study are expected to suggest a clear relationship between the anteromedial cortex oblique angle of each patient and the PSA, and an optimal PSA selection method for individuals.

• Archives of Plastic Surgery
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• v.40 no.4
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• pp.320-326
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• 2013

Robotically assisted microsurgery or telemicrosurgery is a new technique using robotic telemanipulators. This allows for the addition of optical magnification (which defines conventional microsurgery) to robotic instrument arms to allow the microsurgeon to perform complex microsurgical procedures. There are several possible applications for this platform in various microsurgical disciplines. Since 2009, basic skills training courses have been organized by the Robotic Assisted Microsurgical and Endoscopic Society. These basic courses are performed on training models in five levels of increasing complexity. This paper reviews the current state of the art in robotically asisted microsurgical training.

• Transactions of Materials Processing
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• v.12 no.8
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• pp.738-743
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• 2003

The major problems of a plastic injection mould can be devided into two kinds, one is in mould another in injected parts. Most of defects of the former comes from mould design, such as, structure and working. The latter are weld line, warpage, sink mark, burning, flow mark, scratching, shading, black hole and so on. Most of problems in injection molding are difficult to find the reason because of complexity. The purpose of this paper is to improve the quality of plastic injection mould and parts with inquiring the counter plan and analizing troubles of the part of Flat TV Front Cover by flow control method and gas-assisted injection moulding. For minimizing defects of the injection moulded parts, computer aided simulation method for injection mould filling was used. Based on these numerical results, the guidelines of mould design and injection processing condition were established. As a result, the improvement of quality, such as minimizing surface defects of injection moulding parts and troubles in mould was achieved.