• Journal of Korean Society of Rural Planning
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• v.10 no.4 s.25
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• pp.19-28
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• 2004

In order to revitalize rural areas fundamentally through multifunctional utilization of their resources, it should be necessary to prepare the rational development plan to the areal characteristics and conditions, and the first priority of its planning works should be given to spatial planning. The space syntax method, a powerful objective and quantitative analysis tool on the relationship between social and spatial characteristics, was introduced in this study. Five Comprehensive Rural Clustered Villages Development Areas in the Jeonnam-province were selected as case study areas, of which total area's and included villages' spatial variables were measured and analyzed. Rural villages analyzed in this study have the spatial structure badly systematized and much complicated, which results from low integration and deep spatial depth of them. And, by virtue of relatively many axial lines, there should be few differences between villages in terms of local integration, connectivity and control, while being significant difference in terms of global integration showing the whole areal characteristics. Intelligibility, the correlation coefficient between connectivity(local variable) and integration(global one) is low, which means that the spatial structure of the study areas is difficult for visitors to understand the area or village well. Spatial configuration analysis results in the case study areas showed that each development area has a unique spatial structure and is differentiated in terms of not only local spatial variables but also global spatial variables. Therefore, global and local characteristics should be considered in spatial analysis of development areas.

• Journal of the Korean earth science society
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• v.28 no.6
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• pp.707-719
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• 2007

Teachers play key roles in classroom instruction. The perceptions and orientations of teachers about teaching have substantial effect on the practical context of science teaching. Analyzing science teacher's perceptions and orientations about Earth Systems Education (ESE) offers an opportunity to figure out how the goals of ESE might be dealt with. In this study, lesson plans developed by and in-depth interview results with two teachers were analyzed in terms of ESE perceptions. ESE orientations were also investigated in terms of teaching orientations and integration orientations. Research results showed that the teacher's deep understandings about 'Global Scientific Literacy (GSL)', the ultimate goal of ESE, precede the sound ESE teaching in the classroom. To enhance teachers' GSL, exemplary aspects of various integration, including networked integration, should be provided specifically to teachers. Also, the institutionalized approaches to developing ESE curriculum could help classroom teachers activate ESE teaching in their classroom.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1415-1429
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• 2019

Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• International Journal of Automotive Technology
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• v.1 no.1
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• pp.35-41
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• 2000

The crashworthiness of vehicles with finite element methods depends on the geometry modeling and the material properties. The vehicle body structures are generally composed of various members such as frames, stamped panels and deep-drawn parts from sheet metals. In order to ensure the impact characteristics of auto-body structures, the dynamic behavior of sheet metals must be examined to provide the appropriate constitutive relation. In this paper, high strain-rate tensile tests have been carried out with a tension type split Hopkinson bar apparatus specially designed for sheet metals. Experimental results from both static and dynamic tests with the tension split Hopkinson bar apparatus are interpolated to construct the Johnson-Cook and a modified Johnson-Cook equation as the constitutive relation, that should be applied to simulation of the dynamic behavior of auto-body structures. Simulation of auto-body structures has been carried out with an elasto-plastic finite element method with explicit time integration. The stress integration scheme with the plastic predictor-elastic corrector method is adopted in order to accurately keep track of the stress-strain relation for the rate-dependent model accurately. The crashworthiness of the structure with quasi-static constitutive relation is compared to the one with the rate-dependent constitutive model. Numerical simulation has been carried out for frontal frames and a hood of an automobile. Deformed shapes and the Impact energy absorption of the structure are investigated with the variation of the strain rate.

• Korean Journal of Computational Design and Engineering
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• v.7 no.2
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• pp.110-120
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• 2002

This paper presents the method that allows easy and rapid integration of legacy resources within the company and between departments. The proposed system can easily construct a distributed environment for collaborative design between departments in the companies. It supports knowledge-based integration system, which allows designers to develop product with deep knowledge about product design. For the purpose, DOME (Distributed Object-based Modeling Environment)-which has been developed through various studies-was used in this paper. To overcome its problems and insufficiency, the Web-Integrator is proposed. The Web-Integrator is very suitable for an Internet environment because it uses HTTP (Hyper Text Transfer Protocol) and XML (extensible Markup Language) as its main communication method. By supporting the remote object access via URL (Uniform Resource Locator), the implementation of the integrated system makes the Web-Integrator systematic and intuitive. All the functions and resources provided by DOME could be used with the interface that enables bi-directional communication with the DOME system. Web-Integrator provides full web-based environments for the general designers, who do not have a full design knowledge and experience, and the proposed system allows design operations to happen at any place and anytime. Also it provides XML-RPC(Remote Procedure Call) based web service framework, which allows other systems to use easily the service that the DOME system supplies regardless the location and the platform.