• Structural Engineering and Mechanics
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• 제84권3호
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.256-261
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• 2005

In this research, we propose a path-planning algorithm for an autonomous mobile robot using geographical information, under the condition that the robot moves in unknown environment. All image inputted by camera at every sampling time are analyzed and geographical elements are recognized, and the geographical information is embedded in environmental map. The geographical information was transformed into 1-dimensional evaluation value that expressed the difficulty of movement for the robot. The robot goes toward the goal searching for path that minimizes the evaluation value at every sampling time. Then, the path is updated by integrating the exploited information and the prediction on unexploited environment. We used a sensor fusion method for improving the mobile robot dead reckoning accuracy. The experiment results that confirm the effectiveness of the proposed algorithm on the robot's reaching the goal successfully using geographical information are presented.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.31-36
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• 2020

Most of the background subtraction algorithms show good performance in static scenes. In the case of dynamic scenes, they frequently cause false alarm to "temporal clutter", a repetitive motion within a certain area. In this paper, we propose a robust technique for the multiple interval pixel sampling (MIS) algorithm to handle highly dynamic scenes. An adaptive threshold scheme is used to suppress false alarms in low-confidence regions. We also utilize multiple background models in the foreground segmentation process to handle repetitive background movements. Experimental results revealed that our approach works well in handling various temporal clutters.

• 대한전자공학회논문지SP
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• 제46권5호
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• pp.25-31
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• 2009

Compressed sensing은 기존의 Nyquist sampling 이론에 기반을 두었던 dynamic MRI에서의 시 공간 해상도의 제한을 획기적으로 향상시킴으로써, 최근 몇 년 사이, MR reconstruction 분야에서 가장 큰 이슈가 되고 있는 연구주제이다. Dynamic MRI 는 대부분 시간방향의 redundancy 가 매우 크므로, 쉽게 sparse 변환이 가능하다. 따라서 sparsity를 기본 조건으로 하는 compressed sensing은 거의 모든 dynamic MRI 에 대해 효과적으로 적용될 수 있다. 본 review 페이퍼에서는 최근 compressed sensing 에 기반을 두거나 영상의 sparsity를 이용하여 개발된 dynamic MR imaging algorithm 들을 간략히 소개하고, 비교 분석함으로써, compressed sensing과 같은 새로운 접근 방식의 dynamic MRI가 실제 임상에서 가져다 줄 발전 가능성을 제시한다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제10권6호
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• pp.730-740
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• 2018

Hydrofoil is the direct component to generate thrust for underwater glider. It is significant to improve propulsion efficiency of hydrofoil. This study optimizes the shape of a hydrofoil using Free-Form Deformation (FFD) parametric approach and Surrogate-based Optimization (SBO) algorithm. FFD approach performs a volume outside the hydrofoil and the position changes of control points in the volume parameterize hydrofoil's geometric shape. SBO with adaptive parallel sampling method is regarded as a promising approach for CFD-based optimization. Combination of existing sampling methods is being widely used recently. This paper chooses several well-known methods for combination. Investigations are implemented to figure out how many and which methods should be included and the best combination strategy is provided. As the hydrofoil can be stretched from airfoil, the optimizations are carried out on a 2D airfoil and a 3D hydrofoil, respectively. The lift-drag ratios are compared among optimized and original hydrofoils. Results show that both lift-drag-ratios of optimized hydrofoils improve more than 90%. Besides, this paper preliminarily explores the optimization of hydrofoil with root-tip-ratio. Results show that optimizing 3D hydrofoil directly achieves slightly better results than 2D airfoil.

• 사상체질의학회지
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• 제22권4호
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• pp.20-29
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• 2010

1. Objectives Our goal is to observe the feasibility of constitution discrimination from computing quantitative roughness index from dynamic friction coefficients and their gradients with the measurement device of skin friction with 3-Axis load cell sensor. 2. Methods In the traditional Korean medicine, skin diagnosis is one of the examination methods to discriminate Sasang constitution since it was known that Tae-eumin has rough skin, and Soyangin has smooth one. It is based on the skin roughness on the back of one's hand for the discrimination. The measurement device of skin friction with 3-axis load cell sensor has been developed in order to provide quantitative skin roughness through dynamic friction coefficients. The effective interval of the coefficients is obtained from the automatic sampling algorithm to use their curvature and slope. Then, Fisher's discriminant function of them makes the discrimination. 3. Results The success rate of extracting the effective interval was about 90% and the discriminant accuracy between Tae-eumin and Soyangin was 70% and 68% for men and women, respectively. The entire methods showed the possibility to distinguish between Tae-eumin and Soyangin by using stochastic properties of roughness index, which can make the entire system to include the measurement, the computation of the roughness index and the discrimination of constitution automatical. 4. Conclusions The measurement device, the automatic sampling algorithm of dynamic friction coefficients and the constitution discrimination algorithm were developed, respectively, and their combination can become the serial and automatic procedure for quantitative and objective skin diagnosis, which mimics the movement of the Oriental medical doctors' skin diagnosis. It can be applied to healthcare as well as the diagnosis of constitution in a u-Health system soon.

• 대한임베디드공학회논문지
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• 제14권2호
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4987-5005
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• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.870-881
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• 2020

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (kNN) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.