• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.677-680
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• 2003

Perceptual landmarks are an effective solution for a mobile robot realizing steady and reliable long distance navigation. But the prerequisite is those landmarks must be detected and recognized robustly at a higher speed under various lighting conditions. This made image processing more complicated so that its speed and reliability can not be both satisfied at the same time. Color based target detection technique can separate target color regions from non-target color regions in an image with a faster speed, and better results were obtained only under good lighting conditions. Moreover, in the case that there are other things with a target color, we have to consider other target features to tell apart the target from them. Such thing always happens when we detect a target with its single character. On the other hand, we can generally search for only one target for each time so that we can not make use of landmarks efficiently, especially when we want to make more landmarks work together. In this paper, by making use of the moment invariants of each landmark, we can not only search specified target from separated color region but also find multi-target at the same time if necessary. This made the finite landmarks carry on more functions. Because moment invariants were easily used with some low level image processing techniques, such as color based target detection and gradient runs based target detection etc, and moment invariants are more reliable features of each target, the ratio of target detection were improved. Some necessary experiments were carried on to verify its robustness and efficiency of this method.

• 설비공학논문집
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• 제15권12호
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• pp.1043-1048
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• 2003

An experimental investigation was executed to determine the capacity degradation due to non-uniform refrigerant distribution in a multi-path evaporator. In addition, the possibility of recovering the capacity reduction by controlling the refrigerant distribution among refrigerant paths was assessed. The finned-tube evaporator, which had a three-path and three-depth-row, was tested by controlling inlet quality, exit pressure, and exit superheat for each refrigerant path. The capacity reduction due to superheat unbalance between each path was as much as 30%, even when the overall evaporator superheat was kept at a target value of 5.6$^{\circ}C$. It may indicate that the internal heat transfer within the evaporator assembly caused the partial capacity drop. For the evaporator having air mal-distributions, the maximum capacity reduction was found to be 8.7%. A 4.5% capacity recovery was obtained by controlling refrigerant distribution to obtain the target superheat at the outlet of each path.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.187-193
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• 2021

In order to solve the problem of false terrains caused by environmental interferences and tunneling effect in the conventional multi-beam seafloor terrain detection, this paper proposed a seafloor topography detection method based on fast two-dimensional (2D) Censored Mean Level Detector-statistics Constant False Alarm Rate (CMLD-CFAR) method. The proposed method uses s cross-sliding window. The target occlusion phenomenon that occurs in multi-target environments can be eliminated by censoring some of the large cells of the reference cells, while the remaining reference cells are used to calculate the local threshold. The conventional 2D CMLD-CFAR methods need to estimate the background clutter power level for every pixel, thus increasing the computational burden significantly. In order to overcome this limitation, the proposed method uses a fast algorithm to select the Regions of Interest (ROI) based on a global threshold, while the rest pixels are distinguished as clutter directly. The proposed method is verified by experiments with real multi-beam data. The results show that the proposed method can effectively solve the problem of false terrain in a multi-beam terrain survey and achieve a high detection accuracy.

• 자동차안전학회지
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• 제11권3호
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• Journal of Computational Design and Engineering
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• 제1권2호
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• pp.88-95
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• 2014

Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.819-832
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• 2022

Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.

• 한국군사과학기술학회지
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• 제23권4호
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• 한국전산구조공학회논문집
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• 제32권3호
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• pp.141-147
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• 2019

본 논문에서는 체계적인 설계법을 통해 다중 빔 형성을 위한 빔 분배기의 설계를 소개한다. 본 연구의 목표는 산란하는 마이크로파를 다중 방향으로 진행하는 빔으로 변환시키는 빔 분배기를 설계하는 것이다. 기존의 이론 기반 접근법으로는 불특정 방향으로의 다중 빔 분배가 어렵다. 그러므로 본 연구에서는 기존의 이론 기반 접근법인 변환광학 이론이 아닌 체계적인 설계 방법인 페이즈 필드 설계법을 통해 최적의 빔 분배기 구조를 설계하였다. 목적함수는 각 방향으로 특정 지점의 전기장 세기의 표준값을 최대화로 설정하였다. 섬 형상의 구조를 피하고 하나의 연결된 구조를 얻기 위해 증강된 라그랑지안을 사용하여 체적 제약조건을 설정하였다. 목표 주파수는 X-band의 주파수 대역의 10GHz이다. 설계된 최적 형상의 빔 분배기는 다중 빔 형성 성능을 잘 보였고, 목표 영역에 전달되는 전기 에너지는 증가하였다. 또한 설계가 유효한 주파수 대역을 평가하기 위해 X-band 대역에 대해 주파수 대역 성능 평가를 수행하였다.

• 대한전자공학회논문지SP
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• 제37권5호
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• pp.94-104
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• 2000

차량 충돌 경보용 레이더 시스템의 개발에 있어 표적 추적의 정확도와 신뢰도는 매우 중요한 요소이다. 여러 표적을 동시에 추적할 때 중요한 것은 표적과 측정치와의 데이터 연관(data association) 이며, 부적절한 측정치가 어느 표적과 연관되면 그 표적은 트랙을 벗어나 추적능력을 잃어버릴 수 있고 심지어 다른 표적의 추적에도 영향을 줄 수 있다 지금까지 발표된 대부분의 데이터 연관 필터들은 근접하여 이동하는 표적들의 경우 이와 같은 문제점을 보여왔다 따라서, 현재 개발되고 있는 많은 알고리즘들은 이러한 데이터 연 관 문제의 해결에 초점을 맞추고 있다 본 논문에서는 순서통계(order statistics)를 이용한 새로운 다중 표적의 데이터 연관 방법에 대하여 서술하고자 한다 OSPDA와 OSJPDA로 불리는 제안된 방법은 각각 PDA 필터 또는 JPDA 필터에서 계산된 연관 확률을 이용하며 이 연관 확률을 결정 논리(dicision logic)에 의한 가중치로 함수화 하여 표적과 측정치 사이에 최적 혹은 최적 근처의(near optimal) 데이터 연관이 가능하도록 한 것이다 시뮬레이션 결과를 통해, 제안한 방법은 기존의 NN 필터, PDA 필터, 그리고 JPDA 필터의 성능과 비교 분석되었으며, 그 결과 제안한 OSPDA, OSJPDA 필터는 PDA, JPDA 필터보다 추적 정확도에 대해 각각 약 18%, 19% 이상으로 성능이 향상됨을 확인하였다 제안한 방법은 CAN을 통해 차량 엔진 등의 ECU와 통신하도록 개발된 DSP 보드를 이용하여 구현되었다