• Geomechanics and Engineering
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• 제20권6호
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• pp.527-536
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• 2020

The paper represents an optimization algorithm for reinforced concrete retaining wall design. The proposed method, called Rao-3 optimization algorithm, is a recently developed algorithm. The total weight of the steel and concrete, which are used for constructing the retaining wall, were chosen as the objective function. Building Code Requirements for Structural Concrete (ACI 318-05) and Rankine's theory for lateral earth pressure were considered for structural and geotechnical design, respectively. Number of the design variables are 12. Eight of those express the geometrical dimensions of the wall and four of those express the steel reinforcement of the wall. The safety against overturning, sliding and bearing capacity failure were regarded as the geotechnical constraints. The safety against bending and shear failure, minimum and maximum areas of reinforcement, development lengths of steel reinforcement were regarded as structural constraints. The performance of proposed algorithm was evaluated with two design examples.

• Structural Engineering and Mechanics
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• 제81권3호
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• pp.367-378
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• 2022

In this study, combined size and shape optimization of spatial truss tower structures are presented by using new optimization algorithms named Rao-1, and Rao-2. The nodal displacements, allowable stress and buckling for compressive members are taken into account as structural constraints for truss towers. The discrete and continuous design variables are used as design variables for size and shape optimization. To show the efficiency of the proposed optimization algorithm, 25-bar, and 39-bar 3D truss towers are solved for combined size and shape optimization. The 72-bar, and 160-bar 3D truss towers are solved only by size optimization. The optimal results obtained from this study are compared to those given in the literature to illustrate the efficiency and robustness of the proposed algorithm. The structural analysis and the optimization process are coded in MATLAB programming.

• Geomechanics and Engineering
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• 제24권3호
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• 한국항행학회논문지
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• 제24권1호
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• pp.47-52
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• 2020

도래각추정은 표적으로부터 생성, 혹은 반사된 신호를 분석하여 표적의 방향을 추정하는 것으로 다양한 분야에 활용되고 있다. 인공신경망은 생물의 신경망을 모방한 기계학습의 한 분야로 패턴인식에서 좋은 성능을 보인다. 이러한 인공신경망을 도래각 추정에 활용하는 연구가 진행되어왔으나, 다양한 신호대잡음비 환경에 대응하는데에 제한이 있는 상황이다. 본 논문에서는 도래각 추정을 위한 3단계 인공신경망 알고리듬을 제안한다. 제안하는 알고리듬은 잡음제거과정을 통해 단일 신호대잡음비 환경에서 학습한 모델을 다양한 환경에 적용해도 성능감소를 최소화할 수 있다. 또한 도래각 시프트 과정을 통해 학습 난이도를 낮출 수 있고 효율적인 추정이 가능하다. 우리는, 제안하는 알고리듬과 다른 부공간 기법, Cramer-Rao bound (CRB)와의 성능 비교를 통해 제안하는 알고리듬이 낮은 신호대잡음비 환경, 표적들의 도래각이 가까운 환경 등 특정한 열악한 관측환경에서 타 기법에 비해 좋은 성능을 보이는 것을 확인하였다.

• 한국음향학회지
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• 제36권3호
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• pp.218-227
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• 2017

본 논문에서는 배열센서에서 DOA(Direction Of Arrival)를 수행하는 경우 크래머 라오 하한을 이용하여 표적신호가 수신되는 방위오차의 최소분산을 계산하고, 탐지 방위오차 및 위치추정 거리오차를 추정하는 방안을 제시한다. 신호 대 잡음비는 DOA의 정확도 즉, 표적의 탐지 방위오차 및 위치추정 거리오차를 결정한다. 일반적으로 신호대 잡음비는 음원준위, 소음준위, 전달손실, 배열센서의 형상, 빔 조향 방위에 따라 달라진다. 표적의 공간상 상대적 위치와 소음준위가 달라지는 경우, 신호 대 잡음비의 변화에 따른 탐지 방위오차 및 위치추정 거리오차를 확률적으로 추정하는 몬테카를로 시뮬레이션을 수행함으로써, 제안된 방법을 검증하였다.

• The Journal of the Acoustical Society of Korea
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• 제17권3E호
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• pp.22-26
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• 1998

This paper proposes a simple method to measure system's performance in target tracking problems. Essentially employing the Cramer-Rao Lower Bound (CRLB) on tracking accuracy, an algorithm of predicting system's performance under various scenarios is developed. The input data is a collection of measurements over tim from sensors embedded in Gaussian noise. The target of interest may not maneuver over the processing time interval while the own ship observing platform may maneuver in an arbitrary fashion. The proposed approach is demonstrated and discussed through simulation results.

• ETRI Journal
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• 제45권3호
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• pp.492-504
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• 2023

Researchers have recently shown an increased interest in estimating the direction-of-arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace-based methods. An off-grid sparse recovery-based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal-to-noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer-Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권9호
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• pp.1632-1652
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• 2011

This paper considers the object tracking problem in three dimensional (3-D) space when the azimuth and elevation of the object are available from the passive acoustic sensor. The particle filtering technique can be directly applied to estimate the 3-D object location, but we propose to decompose the 3-D particle filter into the three planes' particle filters, which are individually designed for the 2-D bearings-only tracking problems. 2-D bearing information is derived from the azimuth and elevation of the object to be used for the 2-D particle filter. Two estimates of three planes' particle filters are selected based on the characterization of the acoustic sensor operation in a noisy environment. The Cramer-Rao Lower Bound of the proposed 2-D particle filter-based algorithm is derived and compared against the algorithm that is based on the direct 3-D particle filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3721-3737
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• 2015

3D reconstruction based on binocular data is significant to machine vision. In our method, we propose a new and high efficiency 3D reconstruction approach by using a consumer camera aiming to: 1) address the configuration problem of dual camera in the binocular reconstruction system; 2) address stereo matching can hardly be done well problem in both time computing and precision. The kernel feature is firstly proposed in calibration stage to rectify the epipolar. Then, we segment the objects in the camera into background and foreground, for which system obtains the disparity by different method: local window matching and kernel feature-based matching. Extensive experiments demonstrate our proposed algorithm represents accurate 3D model.

• Applied Microscopy
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• 제51권
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• pp.4.1-4.12
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• 2021

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.