• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.1
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• pp.23-41
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• 2023

Coupled thermo-hydraulic-mechanical (THM) processes are essential for the long-term performance of deep geological disposal of high-level radioactive waste. In this study, a numerical sensitivity analysis was performed to analyze the effect of rock properties on THM responses after the execution of the heater test at the Kamaishi mine in Japan. The TOUGH-FLAC simulator was applied for the numerical simulation assuming a continuum model for coupled THM analysis. The rock properties included in the sensitivity study were the Young's modulus, permeability, thermal conductivity, and thermal expansion coefficients of crystalline rock, rock salt, and clay. The responses, i.e., temperature, water content, displacement, and stress, were measured at monitoring points in the buffer and near-field rock mass during the simulations. The thermal conductivity had an overarching impact on THM responses. The influence of Young's modulus was evident in the mechanical behavior, whereas that of permeability was noticed through the change in the temperature and water content. The difference in the THM responses of the three rock type models implies the importance of the appropriate characterization of rock mass properties with regard to the performance assessment of the deep geological disposal of high-level radioactive waste.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.1
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• pp.43-53
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• 2023

Sulfide concentrations critically affect worker safety and the integrities of underground facilities, such as deep geological repositories for spent nuclear fuel. Sulfide is highly sensitive to oxygen, which can oxidize sulfide to sulfate. This can hinder precise measurement of the sulfide concentration. Hence, a literature review was conducted, which revealed that two methods are commonly used: the methylene blue and sulfide ion-selective electrode (ISE) methods. Inductively coupled plasma optical emission spectroscopy (ICP-OES) was used for comparison with the two methods. The sulfide ISE method was found to be superior as it yielded results with a higher degree of accuracy and involved fewer procedures for quantification of the sulfide concentration in solution. ICP-OES results can be distorted significantly when sulfide is present in solution owing to the formation of H₂S gas in the ICP-OES nebulizer. Therefore, the ICP-OES must be used with caution when quantifying underground water to prevent any distortion in the measured results. The results also suggest important measures to avoid problems when using ICP-OES for site selection. Furthermore, the sulfide ISE method is useful in determining sulfide concentrations in the field to predict the lifetime of disposal canisters of spent nuclear fuel in deep geological repositories and other industries.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.4
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• pp.451-463
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• 2023

With South Korea increasingly focusing on nuclear energy, the management of spent nuclear fuel has attracted considerable attention in South Korea. This study established a novel procedure for selecting safety-relevant radionuclides for long-term safety assessments of a deep geological repository in South Korea. Statistical evaluations were performed to identify the design basis reference spent nuclear fuels and evaluate the source term for up to one million years. Safety-relevant radionuclides were determined based on the half-life criteria, the projected activities for the design basis reference spent nuclear fuel, and the annual limit of ingestion set by the Nuclear Safety and Security Commission Notification No. 2019-10 without considering their chemical and hydrogeological properties. The proposed process was used to select 56 radionuclides, comprising 27 fission and activation products and 29 actinide nuclides. This study explains first the determination of the design basis reference spent nuclear fuels, followed by a comprehensive discussion on the selection criteria and methodology for safety-relevant radionuclides.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.4
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• pp.489-502
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• 2023

This article presents the crucial role played by the French underground research laboratory (URL) in initiating the deep geological repository project Cigéo. In January 2023, Andra finalized the license application for the initial construction of Cigéo. Depending on Government's decision, the construction of Cigéo may be authorized around 2027. Cigéo is the result of a National program, launched in 1991, aiming to safely manage high-level and intermediate level long-lived radioactive wastes. This National program is based on four principles: 1) excellent science and technical knowledge, 2) safety and security as primary goals for waste management, 3) high requirements for environment protection, 4) transparent and open-public exchanges preceding the democratic decisions and orientations by the Parliament. The research and development (R&D) activities carried out in the URL supported the design and the safety demonstration of the Cigéo project. Moreover, running the URL has provided an opportunity to gain practical experience with regard to the security of underground operations, assessment of environmental impacts, and involvement of the public in the preparation of decisions. The practices implemented have helped gradually build confidence in the Cigéo project.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.84.1-84.1
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• 2015

Recent years in particular in Korea see intensive interests in a deep geothermal engineering and its application in different uses as far as from direct uses to power generation sectors, that are achieved by harnessing hot energy sources from the earth. For instance widespread interest has been generated because the geothermal energy is the source that one extracts it for more than 20 hours per day and for about 30 years of an operation of the plant, which enables to give base load as for heating as well as an electric generation. In retrospect, shallow geothermal energy using heat pumps is commonplace in Korea while the deep geothermal is in the early stage of the development. Geothermal energies in view of the way of extracting heat are mainly categorized into several types such as a single well system, a hydrothermal system, an enhanced geothermal system (EGS) etc. In this talk, this speaker focuses on the thermo-fluid engineering of the single well system by introducing the modeling in order to harness hot fluid that is thermally balanced with the fluid of an injection well, which provides a challenge to assess the life time of the well. To avoid the loss of the temperature in producing the hot fluid, a specialized pipe or a borehole heat exchanger has been designed, and its concept is introduced. On the other hand, a binary system or an organic Rankine cycle, which provides the methodology to convert the heat into an electricity, is briefly introduced. Some experimental results of the binary system which has been constructed in our lab will be presented. Lastly as for the future direction, some comments for the industrialization of the deep geothermal energy in this country will be discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Journal of the Korean Society of Food Culture
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• v.11 no.4
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• pp.527-538
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• 1996

The contents of articles on nutrition in life cycle, health and disease in the Korean daily newspapers were analyzed for the evaluation of the trends in nutrition information in mass media. Among 922 articles pressed from January 1960 to June 1996, articles on nutrition in life cycle were most frequently appeared, which is followed by articles on nutrition in disease, health foods and other related food and nutrition informations. There was a deep contrast in that the proportion of articles on nutrition in life clyle decreased from 58% in the 60's to 33% in the 90's, and those of nutrition in disease, and health foods increased from 23% and 5% in 60's to 34% and 18% in 90's, respectively.