• Advances in nano research
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• v.12 no.1
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.395-400
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• 2020

Coronavirus disease 2019 (COVID-19) was identified in December 2019 in China and has spread globally, resulting in an ongoing pandemic. Because COVID-19 is spread mainly from person to person, every person is required to wear a facemask in public. On the other hand, many people are still not wearing facemasks despite official advice. This paper proposes a method to predict whether a human subject is wearing a facemask or not. In the proposed method, two eye regions are detected, and the mask region (i.e., face regions below two eyes) is predicted and extracted based on the two eye locations. For more accurate extraction of the mask region, the facial region was aligned by rotating it such that the line connecting the two eye centers was horizontal. The mask region extracted from the aligned face was fed into a convolutional neural network (CNN), producing the classification result (with or without a mask). The experimental result on 186 test images showed that the proposed method achieves a very high accuracy of 98.4%.

• Journal of Auto-vehicle Safety Association
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• v.13 no.2
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• pp.35-41
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• 2021

In this study, improved path tracking control algorithm based on pure pursuit algorithm is newly proposed by using improved lane detection algorithm through real time post-processing with interpolation methodology. Since the original pure pursuit works well only at speeds below 20 km/h, the look-ahead distance is implemented as a sigmoid function to work well at an average speed of 45 km/h to improve tracking performance. In addition, a smoothing filter was added to reduce the steering angle vibration of the original algorithm, and the stability of the steering angle was improved. The post-processing algorithm presented has implemented more robust lane recognition system using real-time pre/post processing method with deep learning and estimated interpolation. Real time processing is more cost-effective than the method using lots of computing resources and building abundant datasets for improving the performance of deep learning networks. Therefore, this paper also presents improved lane detection performance by using the final results with naive computer vision codes and pre/post processing. Firstly, the pre-processing was newly designed for real-time processing and robust recognition performance of augmentation. Secondly, the post-processing was designed to detect lanes by receiving the segmentation results based on the estimated interpolation in consideration of the properties of the continuous lanes. Consequently, experimental results by utilizing driving guidance line information from processing parts show that the improved lane detection algorithm is effective to minimize the lateral offset error in the diverse maneuvering roads.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Wind Energy
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• v.13 no.2
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• pp.42-52
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• 2022

In this paper, we propose a time-series generation methodology using a generative adversarial network (GAN) for long-term prediction of wind and sea conditions, which are information necessary for operations and maintenance (O&M) planning and optimal plans for offshore wind farms. It is a "Conditional TimeGAN" that is able to control time-series data with monthly conditions while maintaining a time dependency between time-series. For the generated time-series data, the similarity of the statistical distribution by direction was confirmed through wave and wind rose diagram visualization. It was also found that the statistical distribution and feature correlation between the real data and the generated time-series data was similar through PCA, t-SNE, and heat map visualization algorithms. The proposed time-series generation methodology can be applied to monthly or annual marine weather prediction including probabilistic correlations between various features (wind speed, wind direction, wave height, wave direction, wave period and their time-series characteristics). It is expected that it will be able to provide an optimal plan for the maintenance and optimization of offshore wind farms based on more accurate long-term predictions of sea and wind conditions by using the proposed model.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• The Journal of Information Systems
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• v.28 no.1
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• pp.97-114
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• 2019

Purpose The purpose of this study is to deduct the factors for explaining the economic behavior of an Internet user who provides personal information notwithstanding the concern about an invasion of privacy based on the Information Privacy Calculus Theory and Communication Privacy Management Theory. Design/methodology/approach This study made a design of the research model by integrating the factors deducted from the computation theory of information privacy with the factors deducted from the management theory of communication privacy on the basis of the Dual-Process Theory. Findings According to the empirical analysis result, this study confirmed that the Privacy Concern about forms through the Perceived Privacy Risk derived from the Disposition to value Privacy. In addition, this study confirmed that the behavior of an Internet user involved in personal information offering occurs due to the Perceived Benefits contradicting the Privacy Concern.