• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.709-719
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• 2023

In general, most of the electrical equipment responsible for control within power plants is housed in self-standing cabinets. These cabinets are typically fixed to a slab using post-installed anchors. Although the fixation method of using post-installed anchors provides stability, there is a risk of conductor failure due to external forces, including moments. However, the performance assessment of current anchors is only evaluated through uniaxial material tests. Therefore, the primary purpose of this study is to compare the static performance of post-installed anchors, considering on-site installation conditions, with their performance in material tests and to analyze the behavioral characteristics of the anchors. While conducting experiments using actual cabinets would be ideal, practical and spatial constraints make this approach difficult. As an alternative, experiments were conducted using a test specimen consisting of a steel column and a support. As a result, the pull-out performance of anchors reflecting on-site installation conditions was measured to be about 10% higher than that observed in material tests. The trends in load reduction and the point of maximum performance for the anchors also differed. To verify the reliability of the experimental study, a 3D FEM analysis was performed, which will provide predictive information on the loads transferred to the post-installed anchors for structural performance evaluations of electrical cabinets using shaking table test in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Korean Journal of Optics and Photonics
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• v.35 no.1
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• pp.9-17
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• 2024

A plenoptic optical system for microscopy comprises an objective lens, tube lens, microlens array (MLA), and an image sensor. Numerical aperture (NA) matching between the tube lens and MLA is used for optimal performance. This paper extends performance predictions from NA matching to unmatching cases and introduces a computational technique for plenoptic configurations using optical analysis software. Validation by fabricating and experimenting with two sample systems at 10× and 20× magnifications resulted in predicted spatial resolutions of 12.5 ㎛ and 6.2 ㎛ and depth of field (DOF) values of 530 ㎛ and 88 ㎛, respectively. The simulation showed resolutions of 11.5 ㎛ and 5.8 ㎛, with DOF values of 510 ㎛ and 70 ㎛, while experiments confirmed predictions with resolutions of 11.1 ㎛ and 5.8 ㎛ and DOF values of 470 ㎛ and 70 ㎛. Both formula-based prediction and simulations yielded similar results to experiments that were suitable for system design. However, regarding DOF values, simulations were closer to experimental values in accuracy, recommending reliance on simulation-based predictions before fabrication.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.519-533
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• 2023

In this study, the linear cutting tests of pick cutters were carried out on a granitic rock with the average compressive strength over 100 MPa. From the tests, the correlation between the cutter acting force and the temperature measured by infrared thermal imaging camera during rock cutting was analyzed. In every experimental condition, the maximum temperature was measured at the rock surface where the chipping occurred, and the temperature generated in the rock was closely correlated with the cutter acting force. On the other hand, the temperature of a pick cutter increased up to only 36℃ above the ambient temperature, and the correlation with the cutter force was not obvious. This can be attributed to the short cutting distance under laboratory conditions and the high thermal conductivity of the tungsten carbide inserts. However, the relatively high temperature of the tungsten carbide inserts was found to be maintained. Therefore, it is recommended that a reinforcement between the insert and the head of a pick cutter or the quality improvement of silvering brazing in the production of a cutter is necessary to maintain the high cutting performance of a pick cutter.

• Journal of the Korean Institute of Gas
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• v.27 no.3
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• pp.98-107
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• 2023

This study presents a well spacing design for coalbed methane(CBM) reservoirs using the experimental results of methane gas adsorption measurement of coal samples obtained from North Kalimantan Island, Indonesia. The gas productivity analysis shows that the cumulative gas production increases as the Langmuir volume increases. This indicates that the maximum gas adsorption directly affects the gas production. In addition, the maximum gas production increases with the increase of reservoir permeability, and the dewatering period is shortened. In particular, the cumulative gas production increases as the production influence area increases. However, when comparing productivity per unit well, the maximum cumulative gas production is found between 2,000 ft of depth and 80-160 acres of the influence area. When reservoir depth and production influence area are considered simultaneously, the results of the appropriate well depth and spacing calculations show that gas productivity is highest between 600-2,000 ft. In this case, it is appropriate to design well spacing in the range of 80-160 acres. Therefore, well spacing design considering coalbed depth in undeveloped CBM reservoirs can be accomplished using gas sorption test results from coal samples.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.263-272
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• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.161-172
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• 2023

Autonomous driving vehicles recognize the surroundings through various sensors mounted on the vehicle and control the vehicle based on the collected information. The level of autonomous driving technology is improving due to the development of sensor technology and algorithms that process collected data, but the implementation of perfect autonomous driving technology has not been achieved. To overcome these limitations, through autonomous cooperative driving centered on infrastructure. In this study, developed a position correction sign that provides a reference for positioning of autonomous vehicles. First of all, an analysis was performed on the current status of positioning technology for autonomous driving. And measure the number of point clouds for the 1st sample consisting of two square reflective surfaces and 2nd sample that increased the vertical length of each reflective surface. Experimental results show that both primary and secondary products are installed at least 15 m apart It could be recognized as a sensor, and it was confirmed that the secondary production that increased the length of the top and bottom had a higher number of point clouds than the primary production and better expressed the shape of the facility.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.955-967
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• 2023

Climate change is expected to amplify the future flooding risks in rural areas which could have devastating implications for the sustainability of the agricultural sector and food security in South Korea. In this study, spatially disaggregated and statistically bias-corrected outputs from three global circulation models (GCMs) archived in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) were used to project the future climate by 2100 under medium and extreme scenarios. A hydrological model was developed to simulate the flood phenomena at the Shindae experimental site located in the Chungcheongbuk Province, South Korea. Hourly rainfall, inundation depth, and discharge data collected during the two extreme events that occurred in 2021 and 2022 were used to calibrate and validate the hydrological model. Probability analysis of extreme rainfall data suggested a higher likelihood of intense and unprecedented extreme rainfall events, which would be particularly notable during 2051-2100. Consequently, the flooded area under an inundation depth of >700 mm increased by 13-36%, 54-74%, and 71-90% during 2015-2030, 2031-2050, and 2051-2100, respectively. Severe flooding probability was notably higher under extreme CMIP6 scenarios than under their CMIP5 counterparts.