• Composites Research
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• v.30 no.1
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• pp.52-58
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• 2017

This paper presents the optimum design methodology for composite wing structure which automatically calculates the safety margin using optimization framework integrating failure modes. Particularly, its framework is possible to optimize sizing procedure to prevent failure mode which has the greatest effect on reducing the sizing time of composite structure. The main failure mode was set as the first ply failure, buckling failure mode, and bolted joint stress field, and the margin was calculated to minimize the weight. The design variable is a laminate sequence database and the responses are strain, buckling, bolted joint stress field. The objective function is the mass of the wing structure. The results of buckling analysis were compared using the finite element model to verify the robustness and reliability of Composite Optimizer.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.443-449
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• 2017

Swarm of low-cost UAVs for search mission has benefit in the sense of rapid search compared to use of single high-end UAV. As the number of UAVs forming swarm increases, not only the time for the mission planning increases, but also the system to operate UAVs has excessive burden. This paper addresses a decentralized area search algorithm adequate for multiple UAVs which takes advantages of flexibility, robustness, and simplicity. To down the cost, it is assumed that each UAV has limited ability: close-communication, basic calculation, and limited memory. In close-communication, heath conditions and search information are shared. And collision avoidance and consensus of next search direction are then done. To increase weight on un-searched area and to provide overlapped search, the score function is introduced. Performance and operational characteristics of the proposed search algorithm and mission planning logic are verified via numerical simulations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.48-55
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• 2009

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.391-405
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• 2019

Compared to the ambient vibration test mainly identifying the structural modal parameters, such as frequency, damping and mode shapes, the impact testing, which benefits from measuring both impacting forces and structural responses, has the merit to identify not only the structural modal parameters but also more detailed structural parameters, in particular flexibility. However, in traditional impact tests, an impacting hammer or artificial excitation device is employed, which restricts the efficiency of tests on various bridge structures. To resolve this problem, we propose a new method whereby a moving vehicle is taken as a continuous exciter and develop a corresponding flexibility identification theory, in which the continuous wheel forces induced by the moving vehicle is considered as structural input and the acceleration response of the bridge as the output, thus a structural flexibility matrix can be identified and then structural deflections of the bridge under arbitrary static loads can be predicted. The proposed method is more convenient, time-saving and cost-effective compared with traditional impact tests. However, because the proposed test produces a spatially continuous force while classical impact forces are spatially discrete, a new flexibility identification theory is required, and a novel structural identification method involving with equivalent load distribution, the enhanced Frequency Response Function (eFRFs) construction and modal scaling factor identification is proposed to make use of the continuous excitation force to identify the basic modal parameters as well as the structural flexibility. Laboratory and numerical examples are given, which validate the effectiveness of the proposed method. Furthermore, parametric analysis including road roughness, vehicle speed, vehicle weight, vehicle's stiffness and damping are conducted and the results obtained demonstrate that the developed method has strong robustness except that the relative error increases with the increase of measurement noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• Journal of Animal Science and Technology
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• v.66 no.5
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• pp.981-998
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• 2024

This study was to investigate effects of different phytogenic feed additives (PFA) in grower finishing pigs with stressed by high stocking density. A total of 84 growing pigs ([Landrace × Yorkshire] × Duroc) with initial body weight (BW) of 28.23 ± 0.21 kg were used for 10 weeks (4 replicate pens with 3 pigs per pen). The dietary treatment consisted of basal diets in animal welfare density (positive control [PC]), basal diet in high stocking density (negative control [NC]), NC + 0.04% bitter citrus extract (PT1), NC + 0.01% microencapsulated blend of thymol & carvacrol (PT2), NC + 0.10% mixture of 40% bitter citrus extract and 10% microencapsulated blend of thymol and carvacrol (PT3), NC + 0.04% premixture of grape seed and grape marc extract, green tea and hops (PT4), and NC + 0.10% fenugreek seed powder (PT5). The reduction of space allowance significantly decreased (p < 0.05) growth performance (average daily gain, average daily feed intake, feed efficiency) and nutrient digestibility (dry matter, crude protein). Also, the fecal score of NC group increased (p < 0.05) compared with other groups. In blood profiles, lymphocyte decreased (p < 0.05), and neutrophil, cortisol, TNF- α increased (p < 0.05) when pigs were in high stocking density. Basic behaviors (feed intake, standing, lying) were inactive (p < 0.05) and singularity behavior (biting) were increased (p < 0.05) under high stocking density. However, PFA groups alleviated the negative effects such as reducing growth performance, nutrient digestibility, increasing stress indicators in blood and animal behavior. In conclusion, PFA groups improved the health of pigs with stressed by high stocking density and PT3 is the most effective.

• Korean Journal of Poultry Science
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• v.43 no.3
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• pp.177-189
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• 2016

This study was conducted to verify the relationships between the expression values of stress-related markers and their production performances in 25 strains of Korean domestic chicken breeds. For stress response markers, the amount of telomeric DNA; expression levels of heat shock protein (HSP)-70, $HSP-90{\alpha}$, and $HSP-90{\beta}$; and comet scores were analyzed. Production performances were measured by the survival rate, body weights, days at first egg laying, egg weight and hen housed egg production. The results showed that the production traits and values of stress-related markers showed significant differences between strains. In general, the stress response of pure bred chickens with heavy weights was relatively high, while that of hybrid chickens with light weights was relatively low. The correlation coefficients between telomere contents and body weights showed that there were weak negative relationships. However, the correlations of telomere content with the survival rate and egg production were weakly positive after 20 weeks old. The expression levels of HSP genes and DNA damage rate (comet scores) were positively correlated to body weight, but were negatively correlated to the survival rate and egg production. The results implied that increasing body weight was associated with increasing HSPs expression and the DNA damage rate was associated with decreasing telomere content. In addition, increasing HSPs expression and the DNA damage rate decreased the survival rate and egg production, but the relationships with the telomere content was the reverse. Correlations among the stress-related markers showed that there were significant correlation coefficients between all of the marker values. HSPs expression was negatively correlated to the telomere content, while it was positively correlated to the DNA damage rate. There was a highly negative correlation between the telomere content and DNA damage rate. In conclusion, increasing the HSP values and DNA damage rate can promote telomere reduction, which led to a decrease in disease resistance and robustness of the chicken. Thus, increasing the stress response was verified to adversely affect the laying performance and viability of chickens.