• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.243-252
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• 2024

Among the Foley sound generation models that have recently begun to be studied, a sound generation technique using the Vector Quantized-Variational AutoEncoder (VQ-VAE) structure and generation model such as Pixelsnail are one of the important research subjects. On the other hand, in the field of deep learning-based acoustic signal compression, residual vector quantization technology is reported to be more suitable than the conventional VQ-VAE structure. Therefore, in this paper, we aim to study whether residual vector quantization technology can be effectively applied to the Foley sound generation. In order to tackle the problem, this paper applies the residual vector quantization technique to the conventional VQ-VAE-based Foley sound generation model, and in particular, derives a model that is compatible with the existing models such as Pixelsnail and does not increase computational resource consumption. In order to evaluate the model, an experiment was conducted using DCASE2023 Task7 data. The results show that the proposed model enhances about 0.3 of the Fréchet audio distance. Unfortunately, the performance enhancement was limited, which is believed to be due to the decrease in the resolution of time-frequency domains in order to do not increase consumption of the computational resources.

• Design & Manufacturing
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• v.13 no.1
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• pp.55-60
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• 2019

Smart-phone in the recently released high-end applied to the camera module is equipped with the most features auto focusing camera module. Also, auto focusing camera module is divided into voice coil motor, encoder, and piezo according to type of motion mechanism. Auto focusing camera module is composed of voice coil motor (VCM) as an actuator and leaf spring as a guide and suspension. VCM actuator is made of magnet, yoke as a metal, and coil as a copper wire. Recently, the assembly as yoke and magnet is made by human resources. These process has a long process time and it is difficult to secure quality. Also, These process is not economical in cost, and productivity is reduced. Therefore, an automatic assembly as yoke and magnet is needed in the present process. In this paper, we have developed an automatic assembly device that can automatically assemble yoke and magnet, and performed verifying performance. Therefore, by using the developed automatic assembly device, it is possible to increase the productivity and reduce the production cost.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.71-72
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• 2006

In this study, automatic assembly and evaluation system fer phone camera module is conceptually designed. The designed core(Auto focus & UV curing, Image Test) equipments adopts a clustering mechanism and compactible structure using index table for minimum tact time. Using a ball screw actuator and absolute encoder in each axis, we can verifies the repeatability and position accuracy of system within ${\pm}3{\mu}m$. In result of simulation test, the proposed system is expected up to 30% in productivity than manual operation.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.424-428
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• 2008

On processing of the shipbuilding, Various steel plates are used as the important material in many fields including the shell plate, a structure, etc. Therefore, the proper steel plate management system like a warehousing, pile, delivery is very important. Presently Operators manage the steel plate by using the software program, but they manage many parts manually, so many problems are generated on the steel plate check, management, and operator safety. In order to solve this problem, we developed Auto Steel-Plate Piling System. Also this system automatically manages and traces the steel-plate from warehousing to delivery.

• Transactions of the Society of Information Storage Systems
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• v.9 no.2
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• pp.56-61
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• 2013

The recent increased use of mobile phone has resulted in a technical focusing on reliability issues related to drop performance. Since mobile phone may be dropped several times during their use, it is required to survive common drop accidents. The plastic injection parts such as base stopper and carrier in the encoder type actuator can be broken easily in the actual reliability test of 1.5m free drop. So, we analyzed the shock resistance characteristics of auto focus actuator with variables in the material properties using finite element method. By applying the new resin materials, we can decrease the breakage of plastic injection parts and improve the reliability of mobile phone.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.52-54
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• 2022

Aerial images are being one of the important aspects of satellite imagery, delivers effective information on landcovers. Their special characteristics includes the viewpoint from space which clarifies data related to land examining processes. Aerial images taken by satellites employed radio waves to wirelessly transmit images to ground stations. Due to transmission errors, images get distorted and unable to perform in landcover examining. This paper proposes an aerial image enhancement method using deep autoencoders. A properly trained autoencoder can enhance an aerial image to a considerable level of improvement. Results showed that the achieved enhancement is better than that was obtained from traditional image denoising methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.714-717
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• 2019

IOT는 복잡하고 이질적인 네트워크 환경이며 저전력 장치를 위한 새로운 라우팅 프로토콜의 존재로 인해 혁신적인 침입탐지 시스템이 필요하다. 특히 접근이 어려운 IOT 환경에서는 공격을 받았을 때 정확하고 빠른 탐지가 용이하여야 한다. 따라서 본 논문에서는 탐지의 정확성과 희소의 공격을 잘 탐지하기 위한 효과적인 특징 추출과 분류를 위한 SAR(Stacked Auto Encoder+Random Forest) 시스템을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1161-1170
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• 2016

In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.