• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.98-108
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• 2008

Terrain reconstruction from images is an ill-posed, yet commonly desired Structure from Motion task when compositing visual effects into live-action photography. These surfaces are required for choreography of a scene, casting physically accurate shadows of CG elements, and occlusions. We present a novel framework for generating the geometry of landscapes from extremely noisy point cloud datasets obtained via limited resolution techniques, particularly optical flow based vision algorithms applied to live-action video plates. Our contribution is a new statistical approach to remove erroneous tracks ('outliers') by employing a unique combination of well established techniques-including Gaussian Mixture Models (GMMs) for robust parameter estimation and Radial Basis Functions (REFs) for scattered data interpolation-to exploit the natural constraints of this problem. Our algorithm offsets the tremendously laborious task of modeling these landscapes by hand, automatically generating a visually consistent, camera position dependent, thin-shell surface mesh within seconds for a typical tracking shot.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.87-96
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• 1993

Spectrum analysis of surface electromyogram (FMG) signals is an effective approach to the study of localized muscular fatigue during isometric contraction. Many investigators have con firmed the frequency of the EMG signals being lowered during sustained contaction. In this study, the cyclic loading tasks were performed, and a comparison was made for the median power frequency shift pattern of the EMG signals with the sustained contraction of the same load. The median power frequency shift of the EMG signals for the cyclic loading task was found to be a part of that for the sustained contraction. Based on this result, a new muscle fatigue index was computed by normalizing the duration of the sustained contraction. A fatigue index was obtained as a function of exertion level and the work/rest schedule. With the proposed fatigue index, it is possible to evaluate or predict the degree of muscular fatigue for a physically demanding task.

• ETRI Journal
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• v.42 no.1
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• pp.67-77
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• 2020

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.84-90
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• 2023

In this article the problem of computing floating-point reciprocal cube root functions is considered. Our new algorithms for this task decrease the number of arithmetic operations used for computing $1/{\sqrt[3]{x}}$. A new approach for selection of magic constants is presented in order to minimize the computation time for reciprocal cube roots of arguments with movable decimal point. The underlying theory enables partitioning of the base argument range x∈[1,8) into 3 segments, what in turn increases accuracy of initial function approximation and decreases the number of iterations to one. Three best algorithms were implemented and carefully tested on 32-bit microcontroller with ARM core. Their custom C implementations were favourable compared with the algorithm based on cbrtf(x) function taken from C ＜math.h＞ library on three different hardware platforms. As a result, the new fast approximation algorithm for the function $1/{\sqrt[3]{x}}$ was determined that outperforms all other algorithms in terms of computation time and cycle count.

• The Journal of Society for e-Business Studies
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• v.12 no.1
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• pp.73-87
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• 2007

To survive in a global competition, many companies are trying to standardize and visualize Business Process (BP) by implementing Business Process Management (BPM). Recently, enhancing business process efficiency has become one of critical success factors. In this paper, we introduce a two-phase perspective of BP efficiency: Process Engine Perspective (PEP) and Task Performer Perspective (TPP). The former is related to allocation function of BP engine; it is mainly concerned with efficient task allocation to users. The latter phase influences efficiency depending on how users execute tasks assigned to them. Instead of considering each phase separately, we develop a comprehensive method considering the two-phase together, which is more effective for the BP efficiency. We carry out simulation experiment to show the combinational effect of the two phases.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.365-368
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• 1992

This paper presents an approach to the design of real time task scheduling for a line controller of continuous manufacturing process automation. The line controller has multiprocessor-based architecture with shared memory and is operated by firmware. This firmware contains menu-driven software supporting real-time database management and fuction-block control language. The multitasking line control processor performs the following three functions: 1) interprets the function block control language by virtue of shared memory in the database; 2) invokes an interupt service routine as required by external hardware; 3) detects errors and notifies the user. We propose real time task scheduling method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.17-17
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• 1996

An experimental research is a useful approach for realizing autonomous mobile robots to work in real environment. We are developing an autonomous mobile robot platform named "Yamabico" as a tool for experimental real world robotics research. The architecture of Yamabico is based on the concept of centralized decision making and functionally modularization. Yamabico robot has two level structure with behavior and function levels, and its hardware and software are functionally distributed for providing incremental development and good maintenancibility. We are using many Yamabico robots in our laboratory to realize the robust navigation technology for autonomous robots. The methodology for experimental and task-oriented approach of mobile robotics will be presented. And some experimental results of real world navigation in indoor and outdoor environment will be shown. be shown.

• Speech Sciences
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• v.8 no.4
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• pp.119-129
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• 2001

Language is one aspect of cognition, along with attention and concentration, learning and memory, visuospatial abilities, and executive function. The purpose of this study was to determine the effect of language intervention by cognitive approach on language expressive performance in a patient with conduction aphasia. This study used several tasks such as Attention and concentration task, visual memory tasks, memory tasks, categorization, divergent thinking, self-monitoring and evaluate thinking. The effects of treatment were evaluated by periodic probing of both trained and untrained familiar words in three tasks; picture naming, answering to questions and telling stories. The results showed improvements both in trained and untrained words. Therefore, we concluded that expressive language performance of this aphasic patient is amenable to this intervention, and that cognitive therapy approach can be useful.