• Steel and Composite Structures
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• v.42 no.3
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• pp.353-361
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• 2022

In this study, an estimation regarding nonlocal shell model based on wave propagation approach has been considered for vibrational behavior of the double walled carbon nanotubes with distinct nonlocal parameters. Vibrations of double walled carbon nanotubes for chiral indices (8, 3) have been analyzed. The significance of small scale is being perceived by developing nonlocal Love shell model. The influence of changing mechanical parameter Poisson's ratio has been investigated in detail. The dominance of boundary conditions via nonlocal parameter is shown graphically. It is found that on increasing the Poisson's ratio, the frequencies increases. It is noted that the frequencies of clamped-clamped frequencies are higher than that of simply-supported and clamped-free edge conditions. The outcomes of frequencies are tested with earlier computations.

• Smart Media Journal
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• v.12 no.3
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• pp.112-119
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• 2023

Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

• Annual Conference of KIPS
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• 2020.11a
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• pp.1040-1043
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• 2020

본 논문에서는 골퍼의 자세 교정을 위해 레슨 프로 혹은 코치가 수행하는 교육을 담당하는 시스템을 구현한다. 이 시스템은 골프를 배우고자 하는 골퍼와 자세를 교정하고자 하는 골퍼를 대상으로 한다. 프로 골퍼의 스윙자세 영상을 촬영하고 딥러닝 라이브러리로 관절, 클럽의 위치를 디지털로 식별하여 표준 자세 정보를 입수한다. 그리고 사용자의 영상을 촬영하여 표준자세 정보와 비교 후 올바른 자세를 도표 및 시각적으로 제공 할 수 있도록 한다. 사람이 하는 방식 보다 객관적이고, 센서방식 보다 경제적인 시스템으로 골프교육산업의 활성화에 기여 할 수 있을 것이다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.417-419
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• 2022

감각대체기술은 손상된 감각 정보를 다른 감각으로 전환하여 전달하는 기술로써 기존의 시각장애인을 위한 시각 정보의 촉각 대체 기술은 주로 거리 정보나 물체의 윤곽선 정보를 전달하여 사용자가 주변 환경을 이해하는 데 어려움이 있었다. 이를 해결하기 위해 본 논문에서는 딥러닝을 사용하여 사용자 주변의 모션 정보를 분석하고, 이를 촉각 정보로 전달함으로써 사용자가 주변 상황 정보를 인지 할 수 있는 방법을 제안하였다. 제안 방법과 기존의 윤곽선 정보 전달 방법을 사용자 실험을 통하여 비교하였을 때, 제안 방법이 영상 속 물체의 움직임 정보를 이해하는 데에 더욱 효과적임을 확인하였다.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.154-157
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• 2015

Construction industry still requires a lot of laborers to perform a project despite of advance in technologies, and improving labor productivity is an important strategy for successful project management. Since repetitive construction works exhibits learning effect, understanding laborers' learning phenomenon therefore allows managers to have improved labor productivity. In this context, previous research efforts quantified individual laborer's learning effect, though numerous construction works are performed in group. In other words, previous research about labor learning assumed that sum of individual's productivity is same as group productivity. Also, managers in construction sites need understanding about group learning behavior for dealing with labor performance problem. To address these issues, the authors investigate what variables affect laborers' group level learning process and develop conceptual model as a basic tool of productivity estimation regarding group learning. Based on the result of this research, it is possible to understand forming mechanism of learning within the group level. Further, this research may contribute to maximizing laborers' productivity in construction sites.

• Smart Media Journal
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• v.13 no.2
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• pp.23-31
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• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.466-470
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• 2024

This study proposes a deep learning-based LSTM model to predict the state of charge (SOC) of lithium-ion batteries. The model was trained using data collected under various temperature and load conditions, including measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. The LSTM model effectively models temporal patterns in the data by learning long-term dependencies. Performance evaluation by epoch showed that the predicted SOC improved from 14.8400 at epoch 10 to 12.4968 at epoch 60, approaching the actual SOC value of 13.5441. The mean absolute error (MAE) and root mean squared error (RMSE) also decreased from 0.9185 and 1.3009 at epoch 10 to 0.2333 and 0.5682 at epoch 60, respectively, indicating continuous improvement in predictive performance. This study demonstrates the validity of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance battery management systems.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.587-598
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• 2016

In predicting an outcome of election using a variety of methods ahead of the election, non-response is one of the major issues. Therefore, to address the non-response issue, a variety of methods of non-response imputation may be employed, but the result of forecasting tend to vary according to methods. In this study, in order to improve electoral forecasts, we studied a model based method of non-response imputation attempting to apply the Monte Carlo Expectation Maximization (MCEM) algorithm, introduced by Wei and Tanner (1990). The MCEM algorithm using maximum likelihood estimates (MLEs) is applied to solve the boundary solution problem under the non-ignorable non-response mechanism. We performed the simulation studies to compare estimation performance among MCEM, maximum likelihood estimation, and Bayesian estimation method. The results of simulation studies showed that MCEM method can be a reasonable candidate for non-response model estimation. We also applied MCEM method to the Korean presidential election exit poll data of 2012 and investigated prediction performance using modified within precinct error (MWPE) criterion (Bautista et al., 2007).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.43-51
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• 2010

Attenuation coefficients of medical ultrasound not only reflect the pathological information of tissues scanned but also provide the quantitative information to compensate the decay of backscattered signals for other medical ultrasound parameters. Based on the frequency-selective attenuation property of human tissues, attenuation estimation methods in spectral domain have difficulties for real-time implementation due to the complexicity while estimation methods in time domain do not achieve the compensation for the diffraction effect effectively. In this paper, we propose the modified VSA method, which compensates the diffraction with reference phantom in time domain, using adaptive bandpass filters with decreasing center frequencies along depths. The adaptive bandpass filtering technique minimizes the distortion of relative echogenicity of wideband transmit pulses and maximizes the signal-to-noise ratio due to the random scattering, especially at deeper depths. Since the filtering center frequencies change according to the accumulated attenuation, the proposed algorithm improves estimation accuracy and precision comparing to the fixed filtering method. Computer simulation and experimental results using tissue-mimicking phantoms demonstrate that the distortion of relative echogenicity is decreased at deeper depths, and the accuracy of attenuation estimation is improved by 5.1% and the standard deviation is decreased by 46.9% for the entire scan depth.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.98-113
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• 2002

In this paper, we present advanced algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced multi-level successive elimination algorithms(AMSEA) are based on the Multi-level successive elimination algorithm(MSEA)[1]. The first algorithm is that when we calculate the sum of absolute difference (SAD) between the sum norms of sub-blocks in MSEA, we use the partial distortion elimination technique. By using the first algorithm, we can reduce the computations of MSEA further. In the second algorithm, we calculate SAD adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in SAD calculation can occur early. So, the computations of MSEA can be reduced. In the third algorithm, we can estimate the elimination level of MSEA. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is a very fast block matching algorithm with nearly 100% motion estimation accuracy. Experimental results show that AMSEA are very efficient algorithms for the estimation of motion vectors.