• Title/Summary/Keyword: performance parameters

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Hysteretic behaviors and calculation model of steel reinforced recycled concrete filled circular steel tube columns

  • Ma, Hui;Zhang, Guoheng;Xin, A.;Bai, Hengyu
    • Structural Engineering and Mechanics
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    • v.83 no.3
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    • pp.305-326
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    • 2022
  • To realize the recycling utilization of waste concrete and alleviate the shortage of resources, 11 specimens of steel reinforced recycled concrete (SRRC) filled circular steel tube columns were designed and manufactured in this study, and the cyclic loading tests on the specimens of columns were also carried out respectively. The hysteretic curves, skeleton curves and performance indicators of columns were obtained and analysed in detail. Besides, the finite element model of columns was established through OpenSees software, which considered the adverse effect of recycled coarse aggregate (RA) replacement rates and the constraint effect of circular steel tube on internal RAC. The numerical calculation curves of columns are in good agreement with the experimental curves, which shows that the numerical model is relatively reasonable. On this basis, a series of nonlinear parameters analysis on the hysteretic behaviors of columns were also investigated. The results are as follows: When the replacement rates of RA increases from 0 to 100%, the peak loads of columns decreases by 7.78% and the ductility decreases slightly. With the increase of axial compression ratio, the bearing capacity of columns increases first and then decreases, but the ductility of columns decreases rapidly. Increasing the wall thickness of circular steel tube is very profitable to improve the bearing capacity and ductility of columns. When the section steel ratio increases from 5.54% to 9.99%, although the bearing capacity of columns is improved, it has no obvious contribution to improve the ductility of columns. With the decrease of shear span ratio, the bearing capacity of columns increases obviously, but the ductility decreases, and the failure mode of columns develops into brittle shear failure. Therefore, in the engineering design of columns, the situation of small shear span ratio (i.e., short columns) should be avoided as far as possible. Based on this, the calculation model on the skeleton curves of columns was established by the theoretical analysis and fitting method, so as to determine the main characteristic points in the model. The effectiveness of skeleton curve model is verified by comparing with the test skeleton curves.

A study on frost prediction model using machine learning (머신러닝을 사용한 서리 예측 연구)

  • Kim, Hyojeoung;Kim, Sahm
    • The Korean Journal of Applied Statistics
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    • v.35 no.4
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    • pp.543-552
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    • 2022
  • When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

A Comparison of Analysis Methods for Work Environment Measurement Databases Including Left-censored Data (불검출 자료를 포함한 작업환경측정 자료의 분석 방법 비교)

  • Park, Ju-Hyun;Choi, Sangjun;Koh, Dong-Hee;Park, Donguk;Sung, Yeji
    • Journal of Korean Society of Occupational and Environmental Hygiene
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    • v.32 no.1
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    • pp.21-30
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    • 2022
  • Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

Proposal of a New Type of 4-Lane Soundproof Tunnel Girder and Structural Performance Evaluation (4차선급 신형식 방음터널 거더 제안 및 구조적 성능평가)

  • Goh, Won-Hui;Kim, Min-Jae;Ma, Chuan;Kang, Duck-Man;Zi, Goang-Suep
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.25 no.5
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    • pp.24-31
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    • 2021
  • The soundproof tunnels have been generally designed with H-beam girders, and the high weight of H-beam may cause the excessive design of the substructure. To solve this problem, this paper proposes a new soundproof tunnel girder design composed of pipes and discontinuous plates. First, the structural behavior of the straight girder according to the design parameters was examined through finite element analysis. The arrangement and shape of the plates were determined as the design parameter, to obtain the optimal design of girder. After then, the structural behavior and buckling stability of the arched girder were subsequently evaluated. As a result of the parameter analysis, it was confirmed that the axial force acting on the girder increased and the moment decreased as the ratio of unsupported sections decreased or the number of supporting plates increased. The stress concentration on the pipe member was relieved by increasing the long axis length of the elliptical plate. Arched girder analysis showed that the structural efficiency increase as the long axis of elliptical plate increase. As a result of the buckling evaluation, the buckling threshold load of the three connected girders was about 3.7 times higher than the design load. Consequently, it was confirmed that the proposed soundproof tunnel structure design satisfies both light weight and structural safety.

Effect of γ-Aminobutyric Acid and Probiotics on the Performance, Egg Quality and Blood Parameter of Laying Hens Parent Stock in Summer (γ-Aminobutyric Acid 및 생균제 급여가 여름철 산란 종계의 생산성, 계란 품질 및 혈액 성상에 미치는 영향)

  • Ji Heon, Kim;Yoo Don, Ko;Ha Guyn, Sung
    • Korean Journal of Poultry Science
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    • v.49 no.4
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    • pp.239-246
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    • 2022
  • This study was conducted to investigate the effects of dietary γ-aminobutyric acid (GABA) and a probiotic mixture on egg production and quality, blood parameters, and stress levels (corticosterone) in Hy-Line parent stock during summer in Korea. A total of 105 Hy-Line parent stock aged 24 weeks were randomly divided into three groups, each containing thirty-five birds: control, γ-aminobutyric acid (GABA), and probiotics (1 × 108/g Bacillus licheniformis, 1 × 107/g Lactobacillus plantarum, and 1 × 107/g Corynebacterium butyricum). The hens were fed a diet containing 50 ppm GABA or 0.1% probiotics for 6 weeks. Compared with the control group, the hen-day egg production, egg mass, and feed conversion ratio over the total period were significantly higher in the probiotic group (P<0.05). In contrast no significant differences were detected among groups with respect to egg weight, albumen height, Haugh units, yolk color, shell thickness or shell strength. Similarly, no significant difference were observed among groups with regards to biochemical profile (total cholesterol, triglyceride, glucose, total protein, aspartate aminotransferase, alanine aminotransferase, albumin, and inorganic phosphorus). However, compared with the control group, we did detect significant reductions in corticosterone levels in the GABA and probiotics groups (P<0.05). On the basis of our findings in this study, it would appear that dietary GABA and probiotics can alleviate heat stress in Hy-Line parent stock, with probiotics in particular being found to promote significant improvements in the hen-day egg production, egg mass, and feed conversion of laying hens during the summer season in Korea.

A Study on Non-Fungible Token Platform for Usability and Privacy Improvement (사용성 및 프라이버시 개선을 위한 NFT 플랫폼 연구)

  • Kang, Myung Joe;Kim, Mi Hui
    • KIPS Transactions on Computer and Communication Systems
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    • v.11 no.11
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    • pp.403-410
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    • 2022
  • Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

THE USE OF NEAR INFRARED REFLECTANCE SPECTROSCOPY(NIRS) TO PREDICT CHEMICAL COMPOSITION ON MAIZE SILAGE

  • D.Cozzolino;Fassio, A.;Mieres, J.;Y.Acosta
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1610-1610
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    • 2001
  • Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

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Running Safety and Ride Comfort Prediction for a Highspeed Railway Bridge Using Deep Learning (딥러닝 기반 고속철도교량의 주행안전성 및 승차감 예측)

  • Minsu, Kim;Sanghyun, Choi
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.35 no.6
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    • pp.375-380
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    • 2022
  • High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

Acoustic outputs from clinical extracorporeal shock wave lithotripsy devices (임상에서 사용중인 체외충격파쇄석기의 음향 출력 분포)

  • Jong Min Kim;Oh Bin Kwon;Jin Sik Cho;Sung Joung Jeon;Ki Il Nam;Sung Yong Cho;Min Joo Choi
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.5
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    • pp.469-490
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    • 2023
  • Survey was carried out on the acoustic outputs from 12 shock wave fields produced by the 10 extracorporeal shock wave lithotriptors whose technical documents are available, among the 33 devices approved by the Ministry of Food & Drug Safety (MFDS).The results show that the acoustic outputs (P+, P-, efd, and E), critical to the therapeutic efficacy and the patient safety, are largely different between the devices. The maximum values of P+, P-, efd, and E vary up to 2.08, 3.72, 3.89, and 15.98 times, respectively. The acoustic output parameters are not thoroughly provided in the technical documents, and some of data (eg. efd) are suspected to be abnormal outside usual ranges. The large device to device differences in the shock wave outputs are likely to undermine equivalence between the ESWL devices approved for the same indication. To verify the reliability of the data in the technical documents of the approved devices and to confirm if the acoustic outputs from the devices in clinical use are the same as those in their technical documents, an authorized test laboratory should be available. A postapproval monitoring led by the regulatory agency is suggested to maintain the acoustic outputs from the ESWL devices that suffer from degrading in performance due to aging.

Probiotics Increase Intramuscular Fat and Improve the Composition of Fatty Acids in Sunit Sheep through the Adenosine 5'-Monophosphate-Activated Protein Kinase (AMPK) Signaling Pathway

  • Yue Zhang;Duo Yao;Huan Huang;Min Zhang;Lina Sun;Lin Su;LiHua Zhao;Yueying Guo;Ye Jin
    • Food Science of Animal Resources
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    • v.43 no.5
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    • pp.805-825
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    • 2023
  • This experiment aims to investigate the impact of probiotic feed on growth performance, carcass traits, plasma lipid biochemical parameters, intramuscular fat and triglyceride content, fatty acid composition, mRNA expression levels of genes related to lipid metabolism, and the activity of the enzyme in Sunit sheep. In this experiment, 12 of 96 randomly selected Sunit sheep were assigned to receive the basic diet or the basic diet supplemented with probiotics. The results showed that supplementation with probiotics significantly increased the loin eye area, and decreased plasma triglycerides and free fatty acids, increasing the content of intramuscular fat and triglycerides in the muscle and improving the composition of the fatty acids. The inclusion of probiotics in the diet reduced the expression of adenosine 5'-monophosphate-activated protein kinase alpha 2 (AMPKα2) mRNA and carnitine palmitoyltransferase 1B (CPT1B) mRNA, while increasing the expression of acetyl-CoA carboxylase alpha (ACCα) mRNA, sterol regulatory element-binding protein-1c (SREBP-1c) mRNA, fatty acid synthase mRNA, and stearoyl-CoA desaturase 1 mRNA. The results of this study indicate that supplementation with probiotics can regulate fat deposition and improves the composition of fatty acids in Sunit sheep through the signaling pathways AMPK-ACC-CPT1B and AMPK-SREBP-1c. This regulatory mechanism leads to an increase in intramuscular fat content, a restructuring of muscle composition of the fatty acids, and an enhancement of the nutritional value of meat. These findings contribute to a better understanding of the food science of animal resources and provide valuable references for the production of meat of higher nutritional value.