• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Journal of the Korean Geotechnical Society
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• v.39 no.8
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• pp.17-28
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• 2023

Laboratory-scale hydraulic fracturing experiments were conducted on granite specimens at various viscosities and injection rates of the fracturing fluid. A series of cross-sectional computed tomography (CT) images of fractured specimens was obtained via a three-dimensional X-ray CT imaging method. Pixel-level fracture segmentation of the CT images was conducted using a convolutional neural network (CNN)-based Nested U-Net model structure. Compared with traditional image processing methods, the CNN-based model showed a better performance in the extraction of thin and complex fractures. These extracted fractures extracted were reconstructed in three dimensions and morphologically analyzed based on their fracture volume, aperture, tortuosity, and surface roughness. The fracture volume and aperture increased with the increase in viscosity of the fracturing fluid, while the tortuosity and roughness of the fracture surface decreased. The findings also confirmed the anisotropic tortuosity and roughness of the fracture surface. In this study, a CNN-based model was used to perform accurate fracture segmentation, and quantitative analysis of hydraulic stimulated fractures was conducted successfully.

• Journal of Platform Technology
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• v.11 no.4
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• pp.50-61
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• 2023

The advancement of various big data technologies has had a tremendous impact on many industries. Diverse big data research studies have been conducted to process and analyze massive data quickly. Under these circumstances, new emerging technologies such as high-capacity persistent memory (PMEM) and Compute Express Link (CXL) have lately attracted significant attention. However, little investigation into a big data "search" platform has been made. Moreover, most big data software platforms have been still optimized for traditional DRAM-based computing systems. This paper first evaluates the basic performance of Intel Optane PMEM, and then investigates both indexing and searching performance of Elasticsearch, a widely-known enterprise big data search platform, on the PMEM-based computing system to explore its effectiveness and possibility. Extensive and comprehensive experiments shows that the proposed Optane PMEM-based Elasticsearch achieves indexing and searching performance improvement by an average of 1.45 times and 3.2 times respectively compared to DRAM-based system. Consequently, this paper demonstrates the high I/O, high-capacity, and nonvolatile PMEM-based computing systems are very promising for big data search platforms.

• Journal of Ginseng Research
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• v.47 no.3
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• pp.429-439
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• 2023

Background: The incidence and clinical importance of nonalcoholic fatty liver disease (NAFLD) has emerged. However, effective therapeutic strategies for NAFLD have yet to be found. Panax ginseng (P. ginseng) is a traditional herb in Eastern Asia with therapeutic effects in many chronic disorders. However, the precise effects of ginseng extract on NAFLD are currently unknown. In present study, the therapeutic effects of Rg3-enriched red ginseng extract (Rg3-RGE) on the progression of NAFLD were explored. Methods: Twelve-week-old C57BL/6 male mice were fed a chow or western diet supplemented with high sugar water solution with or without Rg3-RGE. Histopathology, immunohistochemistry, immunofluorescence, serum biochemistry, western blot analysis, and quantitative RT-PCR were used for in vivo experiment. Conditionally immortalized human glomerular endothelial cell (CiGEnC) and primary liver sinusoidal endothelial cells (LSECs) were used for in vitro experiments. Results: Eight weeks of Rg3-RGE treatment significantly attenuated the inflammatory lesions of NAFLD. Furthermore, Rg3-RGE inhibited the inflammatory infiltrate in liver parenchyma and the expression of adhesive molecules to LSECs. Moreover, the Rg3-RGE exhibited similar patterns on the in vitro assays. Conclusion: The results demonstrate that Rg3-RGE treatment ameliorates NAFLD progression by inhibiting chemotaxis activities in LSECs.

• Design Convergence Study
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• v.16 no.3
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• pp.69-84
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• 2017

Service blueprint is used to show the interaction between each service element at a glance and to understand the flow of the whole service centering on the customer at the stage of proposing a new service system. It was proposed in the 1980s before online business was developed. However, current services are changing in a way that provides various forms and channels, and the service blueprint seems to be not enough. To reflect this problem consciousness, we selected PSS among diversified service business models and propose a service blueprint type optimized for each business. After collecting 137 PSS cases to be used in the research, we made a business matrix and classified the cases and selected two representative cases to conduct two experiments. As a result, six types of service blueprint corresponding to the matrix could be derived: online service type, online remote support type, self rental type, online order type, traditional type, and offline support type. The validity of the proposed types of service blueprint was verified to confirm the suitability of those types.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.255-257
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• 2021

During the winter season, when electricity usage increases rapidly every year, fires are frequent due to short circuits in aging electrical facilities in multi-use facilities such as traditional markets and jjimjilbangs, apartments, and multi-family houses. Most of the causes of such fires are caused by excessive loads applied to aging wires, causing the wire covering to melt and being transferred to surrounding ignition materials. In this study, we implement a system that measures the overload and overheating of the wire through a composite sensor, detects the toxic gas generated there, and logs it to the server through the gateway. Based on this, we will develop a platform that can predict, alarm and block electric fires in real time through big data analysis, and a simulator that can simulate fire occurrence experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.57-62
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• 2022

Recently, fast storage media such as phage-change memory (PCM) emerge, and memory management policies for slow disk storage need to be revisited. In this paper, we propose a new page replacement policy that makes use of PCM as a swap device of virtual memory systems. The proposed policy aims at reducing write traffic to the swap device as well as reducing the number of page faults pursued by traditional page replacement policies. This is because a write operation in PCM is slow and PCM has limited write endurances. Specifically, the proposed policy focuses on the reduction of page faults when the memory load of the system is high, but it aims at reducing write traffic to storage when free memory space is sufficient. Simulation experiments with various memory reference traces show that the proposed policy reduces write traffic to PCM without performance degradations.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2656-2661
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• 2023

The project of China initiative Accelerator Driven Subcritical (CiADS) system has been started to construct in southeast China's Guangdong province since 2019, which is expected to be checked and accepted in the year 2025. In order to make the students in University of Chinese Academy of Sciences (UCAS) better understand the main characteristic and the operation condition in the subcritical nuclear facility, the training platform for CiADS has been developed based on the Cave Automatic Virtual Environment (CAVE) in the Institute of Modern Physics Chinese Academy of Sciences (IMPCAS). The CAVE platform is a kind of non-head mounted virtual reality display system, which can provide the immersive experience and the alternative training platform to substitute the dangerous operation experiments with strong radioactivity. In this paper, the CAVE platform for the training scenarios in CiADS system has been presented with real-time simulation feature, where the required devices to generate the auditory and visual senses with the interactive mode have been detailed. Moreover, the three dimensional modeling database has been created for the different operation conditions, which can bring more freedom for the teachers to generate the appropriate training courses for the students. All the user-friendly features will offer a deep realistic impression to the students for the purpose of getting the required knowledge and experience without the large costs in the traditional experimental nuclear reactor.

• Progress in Superconductivity and Cryogenics
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• v.25 no.4
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• pp.60-64
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• 2023

The manufacturing process of antibody drugs comprises two main stages: the upstream process for antibody cultivation and the downstream process for antibody extraction. The domestic bio industry has excellent technology for the upstream process. However, it relies on the technology of foreign countries to execute downstream process such as affinity chromatography. Furthermore, there are no domestic companies capable of producing the equipment for affinity chromatography. High gradient magnetic separation technology using a high temperature superconducting magnet as a novel antibody separation and purification technology is introduced to substitute for the traditional technology of affinity chromatography. A specially designed magnetic filter was equipped in the bore of the superconducting magnet enabling the continuous magnetic separation of nano-sized paramagnetic beads that can be used as affinity magnetic nano beads for antibodies. To optimize the magnetic filter that captures superparamagnetic nanoparticles effectively, various shapes and materials were examined for the magnetic filter. The result of magnetic separation experiments show that the maximum separation and recovery ratio of superparamagnetic nanoparticles are 99.2 %, and 99.07 %, respectively under magnetic field (3 T) and flow rate (600 litter/hr).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2550-2572
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• 2023

Epidemiological survey is an important means for the prevention and control of infectious diseases. Due to the particularity of the epidemic survey, 1) epidemiological survey in epidemic prevention and control has a wide range of people involved, a large number of data collected, strong requirements for information disclosure and high timeliness of data processing; 2) the epidemiological survey data need to be disclosed at different institutions and the use of data has different permission requirements. As a result, it easily causes personal privacy disclosure. Therefore, traditional access control technologies are unsuitable for the privacy protection of epidemiological survey data. In view of these situations, we propose a black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data. Firstly, a black box-assisted multi-attribute authority management mechanism without a trusted center is established to avoid authority deception. Meanwhile, the establishment of a master key-free system not only reduces the storage load but also prevents the risk of master key disclosure. Secondly, a sensitivity classification method is proposed according to the confidentiality degree of the institution to which the data belong and the importance of the data properties to set fine-grained access permission. Thirdly, a hierarchical authorization algorithm combined with data sensitivity and hierarchical attribute-based encryption (ABE) technology is proposed to achieve hierarchical access control of epidemiological survey data. Efficiency analysis and experiments show that the scheme meets the security requirements of privacy protection and key management in epidemiological survey.