• Title/Summary/Keyword: Performance Evaluation Method

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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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Verification of Validity on Awareness Tool of Business Continuity for Railway Organizations (철도기관을 대상으로 한 사업연속성 인식도구의 타당성 검증)

  • Jeong-ho Chang;Chong-soo Cheung
    • Journal of the Society of Disaster Information
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    • v.19 no.1
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    • pp.195-203
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    • 2023
  • Purpose: This study intends to check validity of tools for awareness of Business Continuity through measurement and analysis on the sub factors of Business Continuity by employees of railway-related organizations. Method: Based on the preceding study, sub factors of the awareness of Business Continuity are divided into 7 and the total of 29 questions were delivered to employees of railway-related organizations for investigation and analysis through the online survey tool. Result: According to EFA result, the number of factors of awareness of Business Continuity based on the theoretical ground was reduced to 7 and the total coefficient of determination was 82.616%. Checking the questions by factor, all the questions were loaded as intended. Conclusion: Validity of measurement tools of Business Continuity whose sub factors are the Context of Organization, Leadership, Planning, Operation, Support, Performance evaluation, and Improvement for railway organizations were secured through the Exploratory Factor Analysis of this study. As for the further tasks, studies on the structural relationship among internalization of business continuity, organization effectiveness, learning support environment, etc are required.

Study of Improved CNN Algorithm for Object Classification Machine Learning of Simple High Resolution Image (고해상도 단순 이미지의 객체 분류 학습모델 구현을 위한 개선된 CNN 알고리즘 연구)

  • Hyeopgeon Lee;Young-Woon Kim
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.1
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    • pp.41-49
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    • 2023
  • A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

Design and Structural Safety Evaluation of 1MW Class Tidal Current Turbine Blade applied Composite Materials (복합재료를 적용한 1MW급 조류 발전 터빈 블레이드의 설계와 구조 안전성 평가)

  • Haechang Jeong;Min-seon Choi;Changjo Yang
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.28 no.7
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    • pp.1222-1230
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    • 2022
  • The rotor blade is an important component of a tidal stream turbine and is affected by a large thrust force and load due to the high density of seawater. Therefore, the performance must be secured through the geometrical and structural design of the blade and the blade structural safety to which the composite material is applied. In this study, a 1 MW class large turbine blade was designed using the blade element momentum (BEM) theory. GFRP is a fiber-reinforced plastic used for turbine blade materials. A sandwich structure was applied with CFRP to lay-up the blade cross-section. In addition, to evaluate structural safety according to flow variations, static load analysis within the linear elasticity range was performed using the fluid-structure interactive (FSI) method. Structural safety was evaluated by analyzing tip deflection, strain, and failure index of the blade due to bending moment. As a result, Model-B was able to reduce blade tip deflection and weight. In addition, safety could be secured by indicating that the failure index, inverse reserve factor (IRF), was 1 or less in all load ranges excluding 3.0*Vr of Model-A. In the future, structural safety will be evaluated by applying various failure theories and redesigning the laminated pattern as well as the change of blade material.

Deep Learning-based Object Detection of Panels Door Open in Underground Utility Tunnel (딥러닝 기반 지하공동구 제어반 문열림 인식)

  • Gyunghwan Kim;Jieun Kim;Woosug Jung
    • Journal of the Society of Disaster Information
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    • v.19 no.3
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    • pp.665-672
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    • 2023
  • Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

Study on the On-Board Test of After-Treatment Systems to Reduce PM-NOx in Low-Speed Marine Diesel Engine (선박용 저속디젤엔진 적용을 위한 PM-NOx 동시저감 배출저감설비 해상실증 연구)

  • Dong-Kyun Ko;Suk-Young Jeong;In-Seob Kim;Gye-Won An;Youn-Woo Nam
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.5
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    • pp.497-504
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    • 2023
  • In this study, Selective catalytic reduction (SCR) + Diesel particulate filter (DPF) system was installed on a ship with a low-speed engine to conduct the on-board test. The target ship (2,881 gross tons, rated power 1,470 kW@240 rpm ×1) is a general cargo ship sailing in the coastal area. Drawing development, approvals and temporary survey of the ship were performed for the installation of the after-treatment system. For performance evaluation, the gaseous emission analyzer was used according to the NOx technical code and ISO-8178 method of measurement. The particulate matter analyzer used a smoke meter to measure black carbon, as discussed by the International Maritime Organization (IMO). Tests were conducted using MGO (0.043%) and LSFO (0.42%) fuels according to the sulfur content. The test conditions were selected by considering the engine rpm (130, 160 and 180). Gaseous emission and particulate matter (smoke) were measured according to the test conditions to confirm the reduction efficiency of the after treatment system. The results of NOx emission and particulate matter (smoke) revealed that reduction efficiency was more than 90%. The exhaust pressure met the allowable back pressure (less than 50 mbar). This study confirms the importance of the on-board test and the potential of SCR + DPF systems as a response technology for reducing nitrogen oxides and particulate matter.

Establishment of a Standard Procedure for Safety Inspections of Bridges Using Drones (드론 활용 교량 안전점검을 위한 표준절차 정립)

  • Lee, Suk Bae;Lee, Kihong;Choi, Hyun Min;Lim, Chi Sung
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.42 no.2
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    • pp.281-290
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    • 2022
  • In Korea, the number of national facilities for which a safety inspection is mandatory is increasing, and a safer safety inspection method is needed. This study aimed to increase the efficiency of the bridge safety inspection by enabling rapid exterior inspection while securing the safety of inspectors by using drones to perform the safety inspections of bridges, which had mainly relied on visual inspections. For the research, the Youngjong Grand Bridge in Incheon was selected as a test bed and was divided into four parts: the warren truss, suspension bridge main cable, main tower, and pier. It was possible to establish a five-step standard procedure for drone safety inspections. The step-by-step contents of the standard procedure obtained as a result of this research are: Step 1, facility information collection and analysis, Step 2, analysis of vulnerable parts and drone flight planning, Step 3, drone photography and data processing, Step 4, condition evaluation by external inspection, Step 5, building of external inspection diagram and database. Therefore, if the safety inspections of civil engineering facilities including bridges are performed according to this standard procedure, it is expected that these inspection can be carried out more systematically and efficiently.

Evaluation of Removal Efficiency of Pollutants in Constructed Wetlands for Controlling Nonpoint Sources in the Daechung Reservoir Watershed (대청호 유역 비점오염원 제어를 위한 생태습지의 오염물질 제거효율 평가)

  • Pyeol-Nim Park;Young-Cheol Cho
    • Korean Journal of Ecology and Environment
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    • v.56 no.2
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    • pp.127-139
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    • 2023
  • Daechung Reservoir has been suffering from severe cyanobacterial blooming periodically due to the water pollutants from the watershed, especially nutrients from nonpoint sources. As a countermeasure, an artificial wetland was constructed to mitigate the pollutant load from the watershed by utilizing the vegetation. We investigated the water quality of the influent and outflow of the wetland during years 2014~2020 to evaluate the performance of pollutant removal through the wetland. Major pollutants (e.g. BOD, COD, SS, T-N, and T-P) were largely reduced during the retention in the wetland while nutrients removal was more efficient than that of organic matters. Pollutant removal efficiency for different inflow concentrations was also investigated to estimate the wetland's capability as a way of managing nonpoint sources. The efficiency of water treatment was significantly higher when inflow concentrations were above 75th percentile for all pollutant, implying the wetland can be applied to the pre-treatment of high pollution load including initial rainfall runoff. Furthermore, the yearly variation of removal efficiency for seven years was analyzed to better understand long-term trends in water treatment of the wetland. The annual treatment efficiency of T-P was very high in the early stages of vegetation growth with high concentration of inflow water. However, it was confirmed that the concentration of inflow water decreased, vegetation stabilized, and the treatment efficiency gradually decreased as the soil was saturated. The findings of the study suggest that artificial wetlands can be an effective method for controlling harmful algal blooms by alleviating pollutant load from the tributaries of Daechung Reservoir.

A Study on Development of Digital Curation Maturity Models and Indicators: Focusing on KISTI (디지털 큐레이션 성숙도 모델 및 지표 개발에 관한 연구: 한국과학기술정보연구원 디지털큐레이션센터를 중심으로)

  • Seonghun, Kim;Suelki, Do;Sangeun, Han;Jayhoon, Kim;Seokjong, Lim;Jinho, Park
    • Journal of the Korean Society for information Management
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    • v.39 no.4
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    • pp.269-306
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    • 2022
  • This study aimed to develop indicators that can measure the digital transformation performance of science and technology information construction and sharing systems by utilizing the Digital Curation Maturity Models. For digital transformation, it is necessary to consider not only simple service improvement but also organizational and business changes. In this study, we aimed to develop a model for measuring the digital transformation of KISTI, Korea's representative science and technology information service organization. KISTI has already carried out BPR work for digital transformation and borrowed the concept of a maturity model. However, in BPR, there is no method to measure the result. Therefore, in this paper, we developed an index to measure digital transformation based on the maturity model. Indicator development was carried out in two ways: model development and evaluation. Cases for model construction were made through a comprehensive review of existing KISTI and various domestic and foreign cases. The models before verification were technology (37), data (45), strategy (18), organization (36), and (social)influence (14) based on the major categories. After verification using confirmatory factor analysis, the model is classified as technology (20 / 17 indicators dropped), data (36 / 9 indicators dropped), strategy (18 / maintenance), organization(30 / 6 indicators dropped), and (social) influence (13 indicators / 1 indicator dropped).

Detecting Vehicles That Are Illegally Driving on Road Shoulders Using Faster R-CNN (Faster R-CNN을 이용한 갓길 차로 위반 차량 검출)

  • Go, MyungJin;Park, Minju;Yeo, Jiho
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.1
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    • pp.105-122
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    • 2022
  • According to the statistics about the fatal crashes that have occurred on the expressways for the last 5 years, those who died on the shoulders of the road has been as 3 times high as the others who died on the expressways. It suggests that the crashes on the shoulders of the road should be fatal, and that it would be important to prevent the traffic crashes by cracking down on the vehicles intruding the shoulders of the road. Therefore, this study proposed a method to detect a vehicle that violates the shoulder lane by using the Faster R-CNN. The vehicle was detected based on the Faster R-CNN, and an additional reading module was configured to determine whether there was a shoulder violation. For experiments and evaluations, GTAV, a simulation game that can reproduce situations similar to the real world, was used. 1,800 images of training data and 800 evaluation data were processed and generated, and the performance according to the change of the threshold value was measured in ZFNet and VGG16. As a result, the detection rate of ZFNet was 99.2% based on Threshold 0.8 and VGG16 93.9% based on Threshold 0.7, and the average detection speed for each model was 0.0468 seconds for ZFNet and 0.16 seconds for VGG16, so the detection rate of ZFNet was about 7% higher. The speed was also confirmed to be about 3.4 times faster. These results show that even in a relatively uncomplicated network, it is possible to detect a vehicle that violates the shoulder lane at a high speed without pre-processing the input image. It suggests that this algorithm can be used to detect violations of designated lanes if sufficient training datasets based on actual video data are obtained.