• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.689-712
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• 2019

Time-series models like AR-ARX and ARMAX, provide a robust way to capture the dynamic properties of structures, and their residuals can be effectively used as features for damage detection. Even though several research papers discuss the implementation of AR-ARX and ARMAX models for damage diagnosis, they are basically been exploited so far for detecting the time instant of damage and also the spatial location of the damage. However, the inverse problem associated with damage quantification i.e. extent of damage using time series models is not been reported in the literature. In this paper, an approach to detect the extent of damage by combining the ARMAX model by formulating the inverse problem as a multi-constrained optimization problem and solving using a newly developed hybrid adaptive differential search with dynamic interaction is presented. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighborhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics and also the overall performance of the technique. The multi-constrained optimization formulations of the inverse problem, associated with damage quantification using time series models, attempted here for the first time, can considerably improve the robustness of the search process. Numerical simulation studies have been carried out by considering three numerical examples to demonstrate the effectiveness of the proposed technique in robustly identifying the extent of the damage. Issues related to modeling errors and also measurement noise are also addressed in this paper.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.80-86
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• 2006

The wireless communication based on LDPC and adaptive spatial-subcarrier coded modulation using MQAM for orthogonal frequency division multiplexing (OFDM) wireless transmission by using instantaneous channel state information and employing multiple antennas at both the transmitter and the receiver. Adaptive coded modulation is a promising idea for bandwidth-efficient transmission on time-varying, narrowband wireless channels. On power limited Additive White Gaussian Noise (AWGN) channels, low density parity check (LDPC) codes are a class of error control codes which have demonstrated impressive error correcting qualities, under some conditions performing even better than turbo codes. The paper demonstrates OFDM with LDPC and adaptive modulation applied to Multiple-Input Multiple-Output (MIMO) system. An optimization algorithm to obtain a bit and power allocation for each subcarrier assuming instantaneous channel knowledge is used. The experimental results are shown the potential of our proposed system.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1996.06b
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• pp.285-309
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• 1996

Non oxide ceramics such as nitrides of transition metals have shown significant potential for future economic impact, in diverse applications in ceramic, aerospace and electronic industries, as refractory products, abrasives and cutting tools, aircraft components, and semi-conductor substrates amid others. Combustion synthesis has become an attractive alternative to the conventional furnace technology to produce these materials cheaply, faster and at a higher level of purity. However he process os highly exothermic and manifests complex dynamics due to its strongly non-linear nature. In order to develop an understanding of this process and to study the effect of operational parameters on the final outcome, numerical modeling is necessary, which would generated essential knowledge to help scale-up the process. the model is based on a system of parabolic-hyperbolic partial differential equations representing the heat, mass and momentum conservation relations. The model also takes into account structural change due to sintering and volumetric expansion, and their effect on the transport properties of the system. The solutions of these equations exhibit steep moving spatial gradients in the form of reaction fronts, propagating in space with variable velocity, which gives rise to varying time scales. To cope with the possibility of extremely abrupt changes in the values of the solution over very short distances, adaptive mesh techniques can be applied to resolve the high activity regions by ordering grid points in appropriate places. To avoid a control volume formulation of the solution of partial differential equations, a simple orthogonal, adaptive-mesh technique is employed. This involves separate adaptation in the x and y directions. Through simple analysis and numerical examples, the adaptive mesh is shown to give significant increase in accuracy in the computations.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.103-112
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• 2012

We demonstrate the preliminary results of ship detection application using synthetic aperture radar (SAR) and automatic identification system (AIS) together. Multi-frequency and multi-temporal SAR images such as TerraSAR-X and Cosmo-SkyMed (X-band), and Radarsat-2 (C-band) are acquired over the West Sea in South Korea. In order to compare with SAR data, we also collected an AIS data. The SAR data are pre-processed considering by the characteristics of scattering mechanism as for sea surface. We proposed the "Adaptive Threshold Algorithm" for classification ship efficiently. The analyses using the combination of the SAR and AIS data with time series will be very useful to ship detection or tracing of the ship.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1142-1149
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• 2004

Motion Estimation(ME) has been developed to reduce temporal redundancy in digital video signals and increase data compression ratio. ME is an Important part of video encoding systems, since it can significantly affect the output quality of encoded sequences. However, ME requires high computational complexity, it is difficult to apply to real time video transmission. for this reason, motion estimation algorithms with low computational complexity are viable solutions. In this paper, we present an efficient method with low computational complexity based on spatial and temporal correlations of motion vectors. The proposed method uses temporally and spatially correlated motion information, the motion vector of the block with the same coordinate in the reference frame and the motion vectors of neighboring blocks around the current block in the current frame, to decide the search pattern and the location of search starting point adaptively. Experiments show that the image quality improvement of the proposed method over MVFAST (Motion Vector Field Adaptive Search Technique) and PMVFAST (Predictive Motion Vector Field Adaptive Search Technique) is 0.01～0.3(dB) better and the speedup improvement is about 1.12～l.33 times faster which resulted from lower computational complexity.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.99-113
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.119-127
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• 2008

Transmission of video over Internet or wireless network requires coded stream capable of adapting to dynamic network conditions instantly. To meet this requirement, various scalable video coding schemes have been developed, among which the Scalable Video Coding (SVC) extension of the H.264/AVC is the most recent one. In comparison with the scalable profiles of previous video coding standards, the SVC achieves significant improvement on coding efficiency performance. For adapting to dynamic network bandwidth, the SVC employs inter-layer switching between different temporal, spatial or/and fidelity layers, which is currently supported with instantaneous decoding refresh (IDR) access unit. However, for real-time adaptability, the SVC has to frequently employ the IDR picture, which dramatically decreases the coding efficiency. Therefore, an extension of SP picture from the AVC to the SVC for an efficient inter-layer switching is investigated and presented in this paper. Simulations regarding the adaptability to dynamic network bandwidth are implemented. Results of experiment show that the SP picture added SVC provides an average 1.2 dB PSNR enhancement over the current SVC while providing similar adaptive functionality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.849-858
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• 2019

Recent developments in computer graphics technology and image processing technology have increased interest in point cloud technology for inputting real space and object information as three-dimensional data. In particular, point cloud technology can accurately provide spatial information, and has attracted a great deal of interest in the field of autonomous vehicles and AR (Augmented Reality)/VR (Virtual Reality). However, in order to provide users with 3D point cloud contents that require more data than conventional 2D images, various technology developments are required. In order to solve these problems, an international standardization organization, MPEG(Moving Picture Experts Group), is in the process of discussing efficient compression and transmission schemes. In this paper, we provide a region division transfer method of 3D point cloud content through extension of existing MPEG-DASH (Dynamic Adaptive Streaming over HTTP)-SRD (Spatial Relationship Description) technology, quality parameters are further defined in the signaling message so that the quality parameters can be selectively determined according to the user's request. We also design a verification platform for ROUTE (Real Time Object Delivery Over Unidirectional Transport)/DASH based heterogeneous network environment and use the results to validate the proposed technology.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.63-77
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• 1994

This paper describes the real-time system which, through analyzing a sequence of images, can extract motional information on a moving object and can contol servo equipment to always locate the moving object at the center of an image frame. An image is a vast amount of two-dimensional signal, so it takes a lot of time to analyze the whole quantity of a given image. Especially, the time needed to load pixels from a memory to processor increase exponentially as the size of an image increases. To solve such a problem and track a moving object in real-time, this paper addresses how to selectively search the spatial and time domain. Based on the selective search of spatial and time domain, this paper suggests various types of techniques which are essential in implementing a real-time tracking system. That is, this paper describes how to detect an entrance of a moving object in the field of view of a camera and the direction of the entrance, how to determine the time interval of adjacent images, how to determine nonstationary areas formed by a moving object and calculated velocity and position information of a moving object based on the determined areas, how to control servo equipment to locate the moving object at the center of an image frame, and how to properly adjust time interval(${\Delta}$t) to track an object taking variable speed.