Journal articles on the topic 'Load Based Testing Validation'
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Ramakrishnan, Raghu, and Arvinder Kaur. "Little’s law based validation framework for load testing." Information and Software Technology 108 (April 2019): 88–98. http://dx.doi.org/10.1016/j.infsof.2018.11.007.
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Jeong, In Wha. "DC-Link Capacitor Voltage Balancing Control of a Five-Level Regenerative AC Electronic Load Using One-Cycle Control." Energies 14, no. 19 (September 24, 2021): 6101. http://dx.doi.org/10.3390/en14196101.
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High voltage electric power equipment requires rigorous regulation testing to specific standards which ensure proper and safe operation in the grid. Manufacturers conduct these tests in order to prove standard compliance and product liability. Variable linear or nonlinear loads are necessary for testing medium voltage (MV) high power AC power converters. Generally, those AC power converters or power supplies require performance validation, burn-in and/or lifetime testing under different load conditions, defined by the end-user or standards for the given applications. For flexible and efficient MV verification testing, this paper presents a five-level multilevel converter-based MV regenerative AC electronic load with one-cycle control (OCC), which is based on five-level diode-clamped multilevel converters with back-to-back structure and can emulate any impedance load. In this paper, especially the dc-link capacitor voltage balance of the proposed multilevel MV regenerative AC load is deeply analyzed. Simulation and experimental results are presented to verify the dc-link voltage balance performance of the proposed multilevel MV regenerative AC electronic load.
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Bokarev, A. I., V. A. Kulagin, and I. A. Nazarkov. "Development and validation of vehicle wheel load scaling method for formation of durability testing loading cycle." Journal of Physics: Conference Series 2061, no. 1 (October 1, 2021): 012028. http://dx.doi.org/10.1088/1742-6596/2061/1/012028.
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Abstract For a vehicle durability study, performed with simulation or laboratory testing, impact on the object should be described as set of time-domain load signals, which reflects accelerated service modes of a vehicle operation. For a newly developed vehicle these load signals can be based on previously prepared loading cycle of a similar vehicle with use of the load scaling theory. Importance and certainty of the load scaling theory is approved by active use among foreign car makers and engineering centers. However, mathematical description of the theory and scaling procedure are strictly classified, despite of being based on fundamentals of vehicle dynamics. In this paper the method of scaling of time-domain wheel and driveline loads is suggested; the method is based on proportions of size and mass properties of the original and the new vehicles. Assumed that previously recorded loading cycle corresponds to the service life of a vehicle. It is worth noting that level of loading and damage accumulation, shown with scaled loading cycle and after first prototypes testing on a proving ground, will be different because of impact of chassis stiffness and damping properties. Suggested scaling theory is being studied in FSUE “NAMI” and is on the validation and verification stage. First completed experiments showed that a multiaxial test rig could perform the vehicle suspension loading cycle, obtained with the theory, with a satisfactory accuracy. This makes utilization of the theory limited to early stages of vehicle development.
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Callaghan, Jack P., Samuel J. Howarth, and Tyson A. C. Beach. "Validation of occupational estimates of cumulative low-back load." Occupational Ergonomics 10, no. 3 (July 5, 2012): 113–24. http://dx.doi.org/10.3233/oer-2012-0196.
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In most genuine industrial settings, it is not yet feasible to directly measure in vivo tissue loads, nor is it practical to estimate dynamic load-time histories using biomechanical models. Thus, data extrapolation techniques are often used for obtaining occupational estimates of shift or daily cumulative low-back load exposures. These techniques are reliant on the assumption that the observed duty cycle of apparently stereotypical work is consistent over long working durations. This investigation evaluated the validity of this assumption using a controlled laboratory-based repetitive lifting task. Nine men performed 30-minutes of sagittal plane repetitive lifting tasks. Upper body kinematics were captured during the tasks, and a two-dimensional dynamic biomechanical model was used to generate peak and cumulative estimates of low-back loads. Over the course of the 30-minute testing sessions, kinematic adaptations at the elbow were responsible for an 8% reduction in duty cycle duration while peak low-back load magnitudes remained consistent. Combining reductions in duty cycle duration with negligible changes in peak loading contributed to a small decrease (⩽ 10%) in cumulative low-back load over the final 20-minutes of lifting. However, when data extrapolation was incorporated to estimate a shift exposure it was found that these changes could overestimate occupational cumulative low-back loading exposures by 10–27% inferences made regarding the risk of low-back pain or injury reporting associated with exposure.
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Dimech, Wayne, Liza Cabuang, Kylie Davies, and Giuseppe Vincini. "Implementation of Novel Quality Assurance Program for Hepatitis C Viral Load Point of Care Testing." Viruses 14, no. 9 (August 30, 2022): 1929. http://dx.doi.org/10.3390/v14091929.
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All patients should have access to accurate and timely test results. The introduction of point of care testing (PoCT) for infectious diseases has facilitated access to those unable to access traditional laboratory-based medical testing, including those living in remote and regional locations, or individuals who are marginalized or incarcerated individuals. In many countries, laboratory testing for infectious diseases, such as hepatitis C virus (HCV), is performed in a highly regulated environment. However, this is not the case for PoCT, where testing is performed by non-laboratory staff and quality controls are often lacking. An assessment of the provision of laboratory-based quality assurance to PoCT for infectious disease was conducted and the barriers to participation identified. A novel approach to providing quality assurance to PoCT sites, in particular those testing for HCV, was designed and piloted. This novel approach incudes identifying and validating sample types that are inactivated and stable at ambient temperature, creating cost-effective supply chains to facilitate logistics of samples, and the development of a smart phone-enabled portal for data entry and analyses. The creation and validation of this approach to quality assurance of PoCT removes the barriers to participation and acts to improve the quality and accuracy of testing, reduce errors and waste, and improve patient outcomes.
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Ngoc, Tran Thanh, Le Van Dai, and Dang Thi Phuc. "Grid search of multilayer perceptron based on the walk-forward validation methodology." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (April 1, 2021): 1742. http://dx.doi.org/10.11591/ijece.v11i2.pp1742-1751.
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Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework.
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Hughes, Jake Edmond, Yeesock Kim, Tahar El-Korchi, and David Cyganski. "Radar-based impact load prediction for damage mitigation of infrastructure." Journal of Vibration and Control 23, no. 12 (September 9, 2015): 1908–24. http://dx.doi.org/10.1177/1077546315603856.
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The application of smart control technology to both aging and new infrastructure is essential to extending service life, increasing life safety, and decreasing repair and replacement costs. One area of control technology research for civil engineering structures that has received little attention historically is that of high-impact loads, such as collision events. The dissipation of impact energy using smart control devices, such as magnetorheological (MR) dampers, leads to less plastic deformation and damage, and a lower likelihood of collapse in civil engineering structures. Due to the short duration and high variability in magnitude of potential impact loads, the issue of sub-optimal controller performance arises. In order to boost controller performance and improve the effectiveness of the control system, a radar-based impact load identifier is proposed. This radar-based impact load identifier will be used to estimate impact loads from imminent impacting objects, for example vessels and trucks, thus providing input information to the control system before the impact actually occurs. This paper presents the characterization and validation, through laboratory tests, of one part of the radar-based impact load identifier, the range and velocity estimation of the incoming moving objects. The range and velocity information are then used to direct structural control based on laboratory impact tests. An ultrawideband monostatic pulsed radar is used for range and velocity measurements of a laboratory-scale impacting vehicle. The range and velocity measurements obtained from the radar scans are verified using physical measurements and control testing. The tests showed great accuracy for both range and velocity with less than 3% error for each measurement and demonstrated structural control based on these measurements. It is shown from control system testing that the proposed approach is effective in reducing the structural impact responses by 11–30%, depending on the performance index, for pre-impact structural stiffening with passive control of MR dampers.
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Sasikala, Dr K., Dr J. Jayakumar, Dr A. Senthil Kumar, Dr Shanty Chacko, and Dr Hephzibah Jose Queen. "Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 550–56. http://dx.doi.org/10.37391/ijeer.100324.
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The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.
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Maffiodo, Daniela, Raffaella Sesana, Stefano Gabetti, and Alessandro Colombo. "Innovative force sensor for indoor climbing holds – real-time measurements and data processing, design and validation." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 234, no. 4 (June 18, 2020): 298–311. http://dx.doi.org/10.1177/1754337120927122.
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In this article, a system to measure the evolution of load in time and space during indoor climbing is described. The system is based on a set of dedicated multiaxial load cells, which measure the load on each hold of an indoor climbing wall. When the climber hangs on a hold, the load signal is read and sent to a digital acquisition and processing system. Sensor design allows for measurement of the force components applied to the climbing holds, regardless of the application point of the force on the hold. Local deformations were measured through strain gauges. Based on the electrical configuration of the strain gauges, the values of the applied forces can be computed, making the contributions to the deformation due to bending moments and torsion negligible. The sensor was designed, assuming a maximum applicable load of 200 kg without plastic deformation. The design process was based on both analytical and finite element method analyses. An experimental calibration and testing campaign was performed to validate the sensor design.
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M.K., Chudasama, and Papil M. Gautam. "Testing and Performance Validation of Ordinary Evacuated Tube Based Parabolic Trough Collector for Low Enthalpy Process." ECS Transactions 107, no. 1 (April 24, 2022): 19335–65. http://dx.doi.org/10.1149/10701.19335ecst.
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In this paper, a parabolic trough collector is designed and fabricated, and its thermal performance is evaluated for hot water generation. Theparabolic trough collector was built with a 90o rim angle with a small concentration ratio of 5.5, and the collector aperture area is1.7 m2.Both side open ordinaryevacuatedglass tube and acrylic sheet were usedas a receiver and reflective surface,respectively.Siemens NX has been used to simulate the maximum wind load of 1000 N. The thermal output of the collector is evaluated for close and open circuits as per ASHRAE 93 1986 (RA 91) standard. The achieved maximum efficiencies
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AL-KINANI, ALI MAJID, and Mahmood D. Ahmed. "Comparison of Single and Group Bored Piles Settlement Based on Field Test and Theoretical Methods." Journal of Engineering 26, no. 2 (January 30, 2020): 144–58. http://dx.doi.org/10.31026/j.eng.2020.02.11.
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Bored piles settlement behavior under vertical loaded is the main factor that affects the design requirements of single or group of piles in soft soils. The estimation of bored pile settlement is a complicated problem because it depends upon many factors which may include ground conditions, validation of bored pile design method through testing and validation of theoretical or numerical prediction of the settlement value. In this study, a prototype single and bored pile group model of arrangement (1*1, 1*2 and 2*2) for total length to diameter ratios (L/D) is 13.33 and clear spacing three times of diameter, subjected to vertical axial loads. The bored piles model used for the test was 2000 mm in length, and 150 mm in diameter has been constructed in soft clayey soils. Furthermore, different theoretical methods have been used for the estimation of bored pile settlement, such as Poulos and Vesic's methods and then their comparison with the pile load test data based on the quick pile load test as presented in (ASTM-D1143, 2007). In general, the theoretical method for estimation the bored pile settlement by Poulos and Vesic's gives higher value of the settlement for the single and group bored pile compared to the pile settlement results obtained from field pile load test data. Therefore, it is not recommended to be used for soft clayey soils. On the other hand, Hansen’s 90% and Butler and Hoy’s results may be considered reliable interpretation method to compute the settlement of single and group bored pile.
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Pramono, Sholeh Hadi, Mahdin Rohmatillah, Eka Maulana, Rini Nur Hasanah, and Fakhriy Hario. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System." Energies 12, no. 17 (August 30, 2019): 3359. http://dx.doi.org/10.3390/en12173359.
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A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.
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Masood, Zaki, Rahma Gantassi, Ardiansyah, and Yonghoon Choi. "A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting." Energies 15, no. 7 (April 3, 2022): 2623. http://dx.doi.org/10.3390/en15072623.
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The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model.
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Moro, Adriano, Telma Pileggi Vinha, and Alessandra de Morais. "SCHOOL CLIMATE EVALUATION: DESIGNING AND VALIDATING MEASUREMENT INSTRUMENTS." Cadernos de Pesquisa 49, no. 172 (June 2019): 312–34. http://dx.doi.org/10.1590/198053146151.
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Abstract This study aims to present the process of designing, testing and validating measurement instruments to measure the school climate from the perspective of students from the 7th grade of primary education onwards, as well as their teachers and managers. We reviewed the national and international literature on the subject, built the concept, the reference framework formed by eight interrelated dimensions and the measurement instruments. Based on a sample of 11,516 respondents, we carried out the statistical and qualitative analyzes concerning the validation process: evidence content and construct validity. The dimensions and their respective items indicated good componential load and good reliability indices, which validates the measurement instruments.
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Susilo, Dwi Budi, Hari Wibawanto, and Anggraini Mulwinda. "Prototype Mesin Pengantar Barang Otomatis Menggunakan Load Cell Berbasis Robot Line Follower." Jurnal Teknik Elektro 10, no. 1 (June 20, 2018): 23–29. http://dx.doi.org/10.15294/jte.v10i1.12277.
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Overloaded condition in the product porter like hand pallet, hand stacker, forclift, and etc, can causes damage and terrible accident in the machine operator. This research aim to build a prototype of porter machine that can prevent overloaded condition through the reading of load cell and photo diode sensor to guide the line follower direction. Research and development methods are used with the systematic writing are background of study and problems, prototype design and validation, testing, taking and analysis of the data. Output of the research is a prototype of porter machine using load cell sensor based on line follower robot. The Result of the load cell reading is compared to SF-400 weigher, and found the difference amount 0.117%. The load testing data of automatic moving trigger is 1 gram until 2999 gram. The Overload condition warning is active while the load is more than 3000 gram. The data reading of the photo diode on the black line is running succesfully, however on the white line with orange color is found some error.
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Johnston, Tyler Robert, Jordan Liles, and Jonathan Riboh. "Anchor-Based Femoral Fixation for Physeal-Sparing Medial Patellofemoral Ligament Reconstruction: A Time-Zero Biomechanical Comparison With Tenodesis Screw Fixation." American Journal of Sports Medicine 48, no. 12 (September 10, 2020): 3021–27. http://dx.doi.org/10.1177/0363546520951523.
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Background: Open physes and trochlear/notch geometries in pediatric patients limit the safe corridor for femoral interference screw graft fixation during medial patellofemoral ligament (MPFL) reconstruction. Accordingly, interest is increasing in anchor-based fixation, but biomechanical validation is deficient. Purpose: To compare anchor-based and tenodesis screw femoral fixation of MPFL grafts in a time-zero biomechanical model. Study Design: Controlled laboratory study. Methods: Twenty-seven fresh-frozen porcine distal femurs were potted for testing in an electromechanical load frame, while bovine tendons were used for MPFL grafts. Reconstructions were performed with 1 of 3 femoral fixation strategies: 4.5-mm biocomposite double-loaded threaded anchor (DLA group), 3.9-mm biocomposite knotless threaded anchor (KA group), or traditional 7 × 23–mm biocomposite tenodesis screw (TS group). For testing, femoral specimens were oriented and secured in the mechanical testing apparatus such that actuator tensile pull re-created the normal MPFL trajectory. Specimens underwent 10 cycles of 5- to 15-N loading at 1-Hz preconditioning, followed by 1000 cycles of 10- to 50 N-loading at 1 Hz. After cyclic loading, all specimens were loaded to failure at 305 mm/min. The average cyclic construct stiffness, displacement, and load-to-failure data between the 3 groups were compared using analysis of variance (ANOVA) with the significance level set at P < .05. Results: Average cyclic construct stiffnesses were comparable across groups per repeated-measures ANOVA analysis: 68.3 ± 6.3, 71.4 ± 6.4, and 74.3 ± 7.9 N/mm for TS, DLA, and KA groups, respectively (at cycle 1000). Average construct displacements at cycles 100 and 1000 were significantly less in the anchor versus tenodesis screw groups per ANOVA and Tukey post hoc analysis: 7.7 ± 4.2 mm for the TS group versus 3.7 ± 0.4 and 4.3 ± 0.6 mm for the DLA and KA groups, respectively (at cycle 1000). There was no significant difference in ultimate failure loads between the anchor and tenodesis screw groups, but 3 of 9 TS constructs failed at loads below the average failure load of the native MPFL. Conclusion: Compared with the tenodesis group, anchor-based fixation produced constructs with equivalent cyclic stiffnesses, improved load-displacement characteristics, and had less failure load variability in the porcine cadaveric model. Clinical Relevance: Femoral fixation of the MPFL graft with a single anchor (4.5 or 3.9 mm threaded) is a viable alternative to traditional tenodesis screw fixation.
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He, Xi, Heng Dong, Wanli Yang, and Jun Hong. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring." Energies 15, no. 6 (March 21, 2022): 2290. http://dx.doi.org/10.3390/en15062290.
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Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.
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Dai, Dong, Du Chen, Shumao Wang, Song Li, Xu Mao, Bin Zhang, Zhenyu Wang, and Zheng Ma. "Compilation and Extrapolation of Load Spectrum of Tractor Ground Vibration Load Based on CEEMDAN-POT Model." Agriculture 13, no. 1 (January 2, 2023): 125. http://dx.doi.org/10.3390/agriculture13010125.
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Effective indoor testing on a four-poster rig of agricultural machinery should mimic the real-world environment while the machinery is in farming and transportation. The emerging load spectrum extrapolation and compilation are capable of speeding up the use of the indoor vibration testing of tractors. However, most ground load spectrum studies consider a particular type of ground rather than all the rough farmland conditions that tractors experience daily. Such a problem inevitably raises doubts about validating the load spectrum-based fatigue durability and reliability testing of tractors. For a remedy, this article proposes a CEEMDAN-POT (Peak Over Threshold) model to comprehensively build a full life-cycle ground load spectrum of tractor vibration with six ground conditions and different field operations. The study acquires the real four vertical acceleration signals from the front and rear axles of the tractor to collect representative vibration load data. Furthermore, the study refines the measured load data by proposing a CEEMDAN-Wavelet threshold method, which is proved to be effective for the load signal decomposition and denoising. Lastly, the study presents a time domain extrapolation method, integrating the sample principle of the POT model, the proper parameter estimation with the Generalized Pareto Distribution (GPD) function, and the POT super-threshold model. The statistical analysis implies that the produced load spectrum fully preserves the statistical features observed in the original one. After extrapolation, the overall distribution of the rainflow matrix becomes more consistent while the mean and amplitude of the spectrum data increase. This study unifies a load spectrum of tractors operating and transporting on various farm ground conditions, providing the real load data of the indoor four-poster rig test.
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Illampas, Rogiros, Rui A. Silva, Dimos C. Charmpis, Paulo B. Lourenço, and Ioannis Ioannou. "Validation of the repair effectiveness of clay-based grout injections by lateral load testing of an adobe model building." Construction and Building Materials 153 (October 2017): 174–84. http://dx.doi.org/10.1016/j.conbuildmat.2017.07.054.
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Tanta, Mohamed, Jose Cunha, Luis A. M. Barros, Vitor Monteiro, José Gabriel Oliveira Pinto, Antonio P. Martins, and Joao L. Afonso. "Experimental Validation of a Reduced-Scale Rail Power Conditioner Based on Modular Multilevel Converter for AC Railway Power Grids." Energies 14, no. 2 (January 18, 2021): 484. http://dx.doi.org/10.3390/en14020484.
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Rail power conditioner (RPC) has the ability to improve the power quality in AC railway power grids. This power conditioner can increase the loading capacity of traction substations, balance the active power between the feeder load sections, and compensate for reactive power and current harmonics. At present, there is increasing use of multilevel converter topologies, which provide scalability and robust performance under different conditions. In this framework, modular multilevel converter (MMC) is emerging as a prominent solution for medium-voltage applications. Serving that purpose, this paper focuses on the implementation, testing, and validation of a reduced-scale laboratory prototype of a proposed RPC based on an MMC. The developed laboratory prototype, designed to be compact, reliable, and adaptable to multipurpose applications, is presented, highlighting the main control and power circuit boards of the MMC. In addition, MMC parameter design of the filter inductor and submodule capacitor is also explained. Experimental analysis and validation of a reduced-scale prototype RPC based on MMC topology, are provided to verify the power quality improvement in electrified railway power grids. Thus, two experimental case studies are presented: (1) when both of the load sections are unequally loaded; (2) when only one load section is loaded. Experimental results confirm the RPC based on MMC is effective in reducing the harmonic contents, solving the problem of three-phase current imbalance and compensating reactive power.
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Chen, Yu-Tung, Eduardo Piedad, and Cheng-Chien Kuo. "Energy Consumption Load Forecasting Using a Level-Based Random Forest Classifier." Symmetry 11, no. 8 (July 29, 2019): 956. http://dx.doi.org/10.3390/sym11080956.
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Energy consumers may not know whether their next-hour forecasted load is either high or low based on the actual value predicted from their historical data. A conventional method of level prediction with a pattern recognition approach was performed by first predicting the actual numerical values using typical pattern-based regression models, hen classifying them into pattern levels (e.g., low, average, and high). A proposed prediction with pattern recognition scheme was developed to directly predict the desired levels using simpler classifier models without undergoing regression. The proposed pattern recognition classifier was compared to its regression method using a similar algorithm applied to a real-world energy dataset. A random forest (RF) algorithm which outperformed other widely used machine learning (ML) techniques in previous research was used in both methods. Both schemes used similar parameters for training and testing simulations. After 10-time cross training validation and five averaged repeated runs with random permutation per data splitting, the proposed classifier shows better computation speed and higher classification accuracy than the conventional method. However, when the number of its desired levels increases, its prediction accuracy seems to decrease and approaches the accuracy of the conventional method. The developed energy level prediction, which is computationally inexpensive and has a good classification performance, can serve as an alternative forecasting scheme.
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Abbas, Rabiya, Zainab Sultan, and Shahid Nazir Bhatti . "Comparative Study of Load Testing Tools: Apache JMeter, HP LoadRunner, Microsoft Visual Studio (TFS), Siege." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 2 (December 31, 2017): 102. http://dx.doi.org/10.30537/sjcms.v1i2.24.
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Software testing is the process of verifying and validating the user’s requirements. Testing is ongoing process during whole software development. Software testing is characterized into three main types. That is, in Black box testing, user doesn’t know domestic knowledge, internal logics and design of system. In white box testing, Tester knows the domestic logic of code. In Grey box testing, Tester has little bit knowledge about the internal structure and working of the system. It is commonly used in case of Integration testing.Load testing helps us to analyze the performance of the system under heavy load or under Zero load. This is achieved with the help of a Load Testing Tool. The intention for writing this research is to carry out a comparison of four load testing tools i.e. Apache JMeter, LoadRunner, Microsoft Visual Studio (TFS), Siege based on certain criteria i.e. test scripts generation , result reports, application support, plug-in supports, and cost . The main focus is to study these load testing tools and identify which tool is better and more efficient . We assume this comparison can help in selecting the most appropriate tool and motivates the use of open source load testing tools.
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Grüber, Bernd, Werner Hufenbach, Robert Gottwald, Martin Lepper, Niels Modler, and Bingquan Zhou. "Novel Testing Device for the Experimental Stress Concentration Analysis of Multilayer Textile Composites under In-Plane Compressive Load." Key Engineering Materials 601 (March 2014): 7–11. http://dx.doi.org/10.4028/www.scientific.net/kem.601.7.
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An analytically based calculation tool for the stress-strain analysis of multilayer composite plate structures with cut-outs is developed which offers high potential to be used in a sense as an analytical sub-model in combination with FE-Analysis Systems. For its validation extensive numerical and experimental investigations have to be carried out. Especially the examination of structures loaded by in-plane compression does not seem to be realizable by conventional testing techniques. That is why a novel testing device for the experimental stress concentration analysis of notched thin plate structures under in-plane compressive load is developed and its application in combination with an adapted optical measurement system is demonstrated.
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Al-Kahwati, Kammal, Wolfgang Birk, Evert Flygel Nilsfors, and Rune Nilsen. "Experiences of a Digital Twin Based Predictive Maintenance Solution for Belt Conveyor Systems." PHM Society European Conference 7, no. 1 (June 29, 2022): 1–8. http://dx.doi.org/10.36001/phme.2022.v7i1.3355.
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Availability of belt conveyor systems is essential in production and logistic lines to safeguard production and delivery targets to customers. In this paper, experiences from commissioning, validation, and operation of an interactive predictive maintenance solution are reported. The solution and its development is formerly presented in Al-Kahwati et.al. (Al-Kahwati, Saari, Birk, & Atta, 2021), where the principles to derive a digital twin of a typical belt conveyor system comprising component-level degradation models,estimation schemes for the remaining useful life and the degradation rate, and vision-based hazardous object detection. Furthermore, the validation approach of modifying the belt conveyor and thus exploiting the idler misalignment load (IML) for the degradation predictions for individual components (including long-lasting ones) together with the actionable insights for the decision support is presented and assessed. Moreover, the approach to testing and validation of the object detection and its performance is assessed and presented in the same manner. An overall system assessment is then given and concludes the paper together with lessons learned. As pilot site for the study a belt conveyor system at LKAB Narvik in northern Norway is used.
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Pratama, Destra Andika, Masayu Anisah, and Kabul Andi Setiyadi. "The Three Phase Induction Motor Test Using MATLAB 2021b/SIMULINK at Bukit Energi Servis Terpadu, Ltd." International Journal of Research in Vocational Studies (IJRVOCAS) 1, no. 4 (January 29, 2022): 60–65. http://dx.doi.org/10.53893/ijrvocas.v1i4.84.
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The purpose of the paper is to investigate the performance characteristics squirrel-cage induction motor available. This paper discusses determination parameters for three-phase induction motor based on experimental data, which the standard test data is not completely available in the end-users. Alternatively, equivalent circuit parameters should be estimated based on the released performance data of the induction motor. This paper also describes MATLAB 2021b/Simulink implementation of three phases of induction motor testing, which includes the no-load, full load, and locked rotor tests were performed to identify the equivalent circuit parameters. The value calculated of parameters simulation results has been compared to experimental values as verification and validation techniques. The deviation of measured values was diagnosed between 0.13% and 1.31%.
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Gabl, Roman, Samuel Draycott, Ajit C. Pillai, and Thomas Davey. "Experimental Data of Bottom Pressure and Free Surface Elevation including Wave and Current Interactions." Data 6, no. 10 (September 30, 2021): 103. http://dx.doi.org/10.3390/data6100103.
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Force plates are commonly used in tank testing to measure loads acting on the foundation of a structure. These targeted measurements are overlaid by the hydrostatic and dynamic pressure acting on the force plate induced by the waves and currents. This paper presents a dataset of bottom force measurement with a six degree-of-freedom force plate (AMTI OR6-7 1000, surface area 0.464 m × 0.508 m) combined with synchronised measurements of surface elevation and current velocity. The data cover wave frequencies between 0.2 to 0.7 Hz and wave directions between 0∘ and 180∘. These variations are provided for current speeds of 0 and 0.2 m/s and a variation of the current in the absence of waves covering 0 to 0.45 m/s. The dataset can be utilised as a validation dataset for models predicting bottom pressure based on free surface elevation. Additionally, the dataset provides the wave- and current-induced load acting on the specific load cell at a fixed water depth of 2 m, which can subsequently be removed to obtain the often-desired measurement of structural loads.
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Czop, Piotr, and Damian Slawik. "Validation of Fatigue Model of a Hydraulic Shock Absorber Equipped with Shim Stack Valves." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012057. http://dx.doi.org/10.1088/1742-6596/2184/1/012057.
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Abstract The aim of the paper is to formulate and validate the fatigue model of a shock absorber equipped with shim-based clamped valves. The fatigue model has application potential in a virtual valve system pre-selection process during the shock absorber design and configuration stage. It allows the required testing capacity at shock absorber manufacturers to be significantly reduced, i.e. the number of long-term and expensive fatigue tests performed on servo-hydraulic load frame testers. The shock absorber fatigue model is a combination of the previously developed finite element model [1] and analytical routines using the experimentally derived Wöhler characteristics of shim materials [2]. The fatigue model validation process was conducted with a high-performance servo-hydraulic load frame tester. Six shock absorbers were subjected to the same long-cycle kinematic sine-load and tested until initial valve damage symptoms appeared. The paper reports the fatigue calculation process and validation results. The obtained model accuracy in the range of ±30% error of shock absorber life-time prediction. This result is accurate enough for the model to be recommended as a quick-and-dirty engineering tool saving shock absorber development costs.
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Ren, Shuang, Huijuan Shi, Zhenlong Liu, Jiahao Zhang, Hanjun Li, Hongshi Huang, and Yingfang Ao. "Finite Element Analysis and Experimental Validation of the Anterior Cruciate Ligament and Implications for the Injury Mechanism." Bioengineering 9, no. 10 (October 21, 2022): 590. http://dx.doi.org/10.3390/bioengineering9100590.
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This study aimed to establish a finite element model that vividly reflected the anterior cruciate ligament (ACL) geometry and investigated the ACL stress distribution under different loading conditions. The ACL’s three-dimensional finite element model was based on a human cadaveric knee. Simulations of three loading conditions (134 N anterior tibial load, 5 Nm external tibial torque, 5 Nm internal tibial torque) on the knee model were performed. Experiments were performed on a knee specimen using a robotic universal force/moment sensor testing system to validate the model. The simulation results of the established model were in good agreement with the experimental results. Under the anterior tibial load, the highest maximal principal stresses (14.884 MPa) were localized at the femoral insertion of the ACL. Under the external and internal tibial torque, the highest maximal principal stresses (0.815 MPa and 0.933 MPa, respectively) were mainly concentrated in the mid-substance of the ACL and near the tibial insertion site, respectively. Combining the location of maximum stress and the location of common clinical ACL rupture, the most dangerous load during ACL injury may be the anterior tibial load. ACL injuries were more frequently loaded by external tibial than internal tibial torque.
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Deshmukh, Atul R., Gopalan Venkatachalam, and M. R. Saraf. "Fatigue life prediction of joggle weld joint using virtual strain gauge and its validation through experiments." Mechanics & Industry 19, no. 6 (2018): 604. http://dx.doi.org/10.1051/meca/2018047.
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This paper highlights fatigue life prediction and interactions between weld design parameters and their effect on joggle weld joint using the virtual strain gauging and its validation through experimental testing. Based on the concept of linear elastic fracture mechanics, the effects of weld geometry, load conditions and the boundary constraints on the fatigue strength of joggle weld joint are investigated using the finite element analysis. Response surface methodology is used to evaluate the influences of three weld joint parameters: plate thickness (Pt), root gap (Rg) and load (Ld) on normal stress (St) and fatigue life (Lf). Main effect plot shows that the plate thickness and the load are important parameters affecting the normal strain and fatigue life, whereas root gap influences the fatigue life only up to 2 mm, but no influence on fatigue life is observed after 3 mm of root gap. Experimental results show that the presence of secondary bending affects the experimental values of fatigue life. Contour plot and regression equation are used to study the effects of weld joint parameters on normal stress and fatigue life.
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Yoo, Yeong, Yousef Al-Shawesh, and Alain Tchagang. "Coordinated Control Strategy and Validation of Vehicle-to-Grid for Frequency Control." Energies 14, no. 9 (April 28, 2021): 2530. http://dx.doi.org/10.3390/en14092530.
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The increased penetration of renewable energy sources (RES) and electric vehicles (EVs) is resulting in significant challenges to the stability, reliability, and resiliency of the electrical grid due to the intermittency nature of RES and uncertainty of charging demands of EVs. There is a potential for significant economic returns to use vehicle-to-grid (V2G) technology for peak load reduction and frequency control. To verify the effectiveness of the V2G-based frequency control in a microgrid, modeling and simulations of single- and multi-vehicle-based primary and secondary frequency controls were conducted to utilize the integrated components at the Canadian Centre for Housing Technology (CCHT)-V2G testing facility by using MATLAB/Simulink. A single-vehicle-based model was validated by comparing empirical testing and simulations of primary and secondary frequency controls. The validated conceptual model was then applied for dynamic phasor simulations of multi-vehicle-based frequency control with a proposed coordinated control algorithm for improving frequency stability and facilitating renewables integration with V2G-capable EVs in a microgrid. This proposed model includes a decentralized coordinated control of the state of charge (SOC) and charging schedule for five aggregated EVs with different departure times and SOC management profiles preferred by EV drivers. The simulation results showed that the fleet of 5 EVs in V2B/V2G could effectively reduce frequency deviation in a microgrid.
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Liu, Guo, and Han. "Load Transfer of Offshore Open-Ended Pipe Piles Considering the Effect of Soil Plugging." Journal of Marine Science and Engineering 7, no. 9 (September 8, 2019): 313. http://dx.doi.org/10.3390/jmse7090313.
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Open-ended pipe piles have been increasingly used as the foundations for offshore structures. Considering the soil plugging effect, a novel analytical model is proposed in this paper to study the load transfer mechanism of open-ended pipe piles. A trilinear model for the external shaft friction was introduced, while a rigid plastic model was adopted to describe the load transfer at the pile-plug interface. Furthermore, an equilibrium equation of the soil plug was proposed, based on the hypothesis of a trilinear distribution of lateral earth pressure. The pile end resistance was analyzed by dividing it into two parts, i.e., the soil plug and pile annulus, the behaviors of which were described by the double broken line model. A calculation example was carried out to analyze the load transfer properties of the open-ended pipe piles. As a validation, similar load transfer processes of the open-ended pile were also captured in a newly built discrete element method model, mimicking the 100g centrifuge testing conditions.
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Rao, Stephen M., Genna Losinski, Lyla Mourany, David Schindler, Bernadett Mamone, Christine Reece, Danielle Kemeny, et al. "Processing speed test: Validation of a self-administered, iPad®-based tool for screening cognitive dysfunction in a clinic setting." Multiple Sclerosis Journal 23, no. 14 (January 12, 2017): 1929–37. http://dx.doi.org/10.1177/1352458516688955.
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Background: Cognitive dysfunction is common in multiple sclerosis (MS) patients and has important consequences for daily activities, yet, unlike motor function, is not routinely assessed in the clinic setting. We developed the Processing Speed Test (PST), a self-administered iPad®-based tool to measure MS-related deficits in processing speed. Objective: To determine whether the PST is valid for screening cognitive dysfunction by comparing it to the paper-and-pencil Symbol Digit Modalities Test (SDMT). Methods: We assessed PST test–retest reliability, sensitivity of PST and SDMT in discriminating MS patients from healthy controls (HC), convergent validity between PST and SDMT, correlations between T2 lesion load and PST and SDMT, and PST performance with and without technician present during administration. Results: PST had excellent test–retest reliability, was highly correlated with SDMT, was slightly more sensitive than SDMT in discriminating MS from HC groups, and correlated better with cerebral T2 lesion load than did SDMT. Finally, PST performance was no different with or without a technician in the testing environment. Conclusion: PST has advantages over SDMT because of its efficient administration, scoring, and potential for medical record or research database integration. PST is a practical tool for routine screening of processing speed deficits in the MS clinic.
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Rehman, Attique Ur, Tek Tjing Lie, Brice Vallès, and Shafiqur Rahman Tito. "Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques." Inventions 5, no. 4 (November 23, 2020): 57. http://dx.doi.org/10.3390/inventions5040057.
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The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.
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Cherkaoui, Dounia, Judith Heaney, Da Huang, Matthew Byott, Benjamin S. Miller, Eleni Nastouli, and Rachel A. McKendry. "Clinical Validation of a Rapid Variant-Proof RT-RPA Assay for the Detection of SARS-CoV-2." Diagnostics 12, no. 5 (May 19, 2022): 1263. http://dx.doi.org/10.3390/diagnostics12051263.
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The COVID-19 pandemic has unveiled a pressing need to expand the diagnostic landscape to permit high-volume testing in peak demand. Rapid nucleic acid testing based on isothermal amplification is a viable alternative to real-time reverse transcription polymerase chain reaction (RT-PCR) and can help close this gap. With the emergence of SARS-CoV-2 variants of concern, clinical validation of rapid molecular tests needs to demonstrate their ability to detect known variants, an essential requirement for a robust pan-SARS-CoV-2 assay. To date, there has been no clinical validation of reverse transcription recombinase polymerase amplification (RT-RPA) assays for SARS-CoV-2 variants. We performed a clinical validation of a one-pot multi-gene RT-RPA assay with the E and RdRP genes of SARS-CoV-2 as targets. The assay was validated with 91 nasopharyngeal samples, with a full range of viral loads, collected at University College London Hospitals. Moreover, the assay was tested with previously sequenced clinical samples, including eleven lineages of SARS-CoV-2. The rapid (20 min) RT-RPA assay showed high sensitivity and specificity, equal to 96% and 97%, respectively, compared to gold standard real-time RT-PCR. The assay did not show cross-reactivity with the panel of respiratory pathogens tested. We also report on a semi-quantitative analysis of the RT-RPA results with correlation to viral load equivalents. Furthermore, the assay could detect all eleven SARS-CoV-2 lineages tested, including four variants of concern (Alpha, Beta, Delta, and Omicron). This variant-proof SARS-CoV-2 assay offers a significantly faster and simpler alternative to RT-PCR, delivering sensitive and specific results with clinical samples.
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Hussain, Fazal, Shayan Ali Khan, Rao Arsalan Khushnood, Ameer Hamza, and Fazal Rehman. "Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete." Sustainability 15, no. 1 (December 30, 2022): 641. http://dx.doi.org/10.3390/su15010641.
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Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightweight aggregate concrete is finding an optimized mix per requirements. However, the conventional material design of this composite is quite costly, time-consuming, and iterative. This research proposes a simplified methodology for the mix designing of structural and non-structural lightweight aggregate concrete by incorporating machine learning. For this purpose, five distinct machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), decision tree (DT), Gaussian process of regression (GPR), and extreme gradient boosting tree (XGBoost) algorithms, were investigated. For the training, testing, and validation process, a total of 420 data points were collected from 43 published journal articles. The performance of models was evaluated based on statistical performance indicators. Overall, 11 input parameters, including ingredients of the concrete mix and aggregate properties were entertained; the only output parameter was the compressive strength of lightweight concrete. The results revealed that the GPR model outperformed the remaining four machine learning models by attaining an R2 value of 0.99, RMSE of 1.34, MSE of 1.79, and MAE of 0.69. In a nutshell, these simplified modern techniques can be employed to make the design of lightweight aggregate concrete easy without extensive experimentation.
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Rośkowicz, Marek, Michał Jasztal, Piotr Leszczyński, and Szymon Wszelaki. "Geometrical Optimisation of a Wing Strut Joint. Part I." Journal of KONBiN 49, no. 4 (December 1, 2019): 1–26. http://dx.doi.org/10.2478/jok-2019-0072.
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Abstract This paper provides the result of the geometrical optimisation of a wing strut joint of an aircraft. The objective of the geometrical optimisation was to modify the geometry of the wing strut joint components to meet an optimisation criterion defined as yield strength determined by static tensile testing. The geometrical optimisation was processed on a computer model of the wing strut joint using FEM (finite element method). The design variables assumed in this geometrical optimisation were the load option and boundary conditions of interaction between the wing strut joint components. An analysis carried out as part of the geometrical optimisation was based on proposing modifications to the geometry of the joint features at their maximum stress levels. The geometry optimisation results will be applied in the preparation and performance of validation strength testing of the wing strut joint assembly.
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Marsalek, Pavel, Martin Sotola, David Rybansky, Vojtech Repa, Radim Halama, Martin Fusek, and Jiri Prokop. "Modeling and Testing of Flexible Structures with Selected Planar Patterns Used in Biomedical Applications." Materials 14, no. 1 (December 30, 2020): 140. http://dx.doi.org/10.3390/ma14010140.
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Flexible structures (FS) are thin shells with a pattern of holes. The stiffness of the structure in the normal direction is reduced by the shape of gaps rather than by the choice of the material based on mechanical properties such as Young’s modulus. This paper presents virtual prototyping of 3D printed flexible structures with selected planar patterns using laboratory testing and computer modeling. The objective of this work is to develop a non-linear computational model evaluating the structure’s stiffness and its experimental verification; in addition, we aimed to identify the best of the proposed patterns with respect to its stiffness: load-bearing capacity ratio. Following validation, the validated computational model is used for a parametric study of selected patterns. Nylon—Polyamide 12—was chosen for the purposes of this study as an appropriate flexible material suitable for 3D printing. At the end of the work, a computational model of the selected structure with modeling of load-bearing capacity is presented. The obtained results can be used in the design of external biomedical applications such as orthoses, prostheses, cranial remoulding helmets padding, or a new type of adaptive cushions. This paper is an extension of the conference paper: “Modeling and Testing of 3D Printed Flexible Structures with Three-pointed Star Pattern Used in Biomedical Applications” by authors Repa et al.
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Darwin, Darwin, and Nova Eka Budiyanta. "RANCANG BANGUN SISTEM PEMINJAMAN DAN MANAJEMEN ASET LABORATORIUM BERBASIS IMPLEMENTASI RFID DAN APLIKASI WEB." Jurnal Edukasi Elektro 5, no. 2 (November 30, 2021): 80–90. http://dx.doi.org/10.21831/jee.v5i2.43472.
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ABSTRACT:Laboratories in university are one of the most essential facilities needed for academic activities. However, because of the Covid-19 pandemic, there is now restriction to access university laboratories. Students have lost their access to laboratory equipment. Based on this problem, a web application for equipment booking and laboratory asset management is made. The web application uses RFID technology with NodeMCU ESP8266 as its microcontroller, and CodeIgniter 4 framework. The web application is evaluated by feature validation by the laboratory assistant head, white-box test and black-box test. White-box test uses Apache JMeter 5.3 to do load testing. The black-box test produces a functionality percentage of 82%, and from the white-box and feature validation we can conclude that the web application works well and is ready to be used.ABSTRAK:Laboratorium universitas merupakan salah satu fasilitas yang penting dalam kegiatan akademik. Namun dengan adanya pandemi Covid-19, akses laboratorium pada universitas dibatasi dan mahasiswa kehilangan akses penggunaan alat laboratorium. Berdasarkan masalah ini, aplikasi web peminjaman alat secara online dan manajemen laboratorium dibuat. Aplikasi berbasis web dilengkapi dengan RFID tapping tag pada alat, NodeMCU ESP8266 sebagai mikrokontroler dan CodeIgniter 4 untuk framework pengembangan aplikasi web tersebut. Sistem diuji dengan white-box test menggunakan Apache JMeter 5.3 sebagai load testing tool dan black-box test untuk menguji kelayakan aplikasi web seperti aspek fungsionalitas dan tampilan aplikasi, Hasil dari black-box test disimpulkan dari persentase kelayakan fungsional sistem yang diperoleh sebesar 82%. Dari hasil pengujian dapat dilihat bahwa sistem website peminjaman dapat bekerja dengan baik dan layak untuk digunakan.
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Larsen, Kevin, Bilikis Akindele, Henry Head, Rick Evans, Purvi Mehta, Quinn Hlatky, Brendan Krause, Sydney Chen, and Dominic King. "Developing a User-Centered Digital Clinical Decision Support App for Evidence-Based Medication Recommendations for Type 2 Diabetes Mellitus: Prototype User Testing and Validation Study." JMIR Human Factors 9, no. 1 (January 18, 2022): e33470. http://dx.doi.org/10.2196/33470.
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Background Closing the gap between care recommended by evidence-based guidelines and care delivered in practice is an ongoing challenge across systems and delivery models. Clinical decision support systems (CDSSs) are widely deployed to augment clinicians in their complex decision-making processes. Despite published success stories, the poor usability of many CDSSs has contributed to fragmented workflows and alert fatigue. Objective This study aimed to validate the application of a user-centered design (UCD) process in the development of a standards-based medication recommender for type 2 diabetes mellitus in a simulated setting. The prototype app was evaluated for effectiveness, efficiency, and user satisfaction. Methods We conducted interviews with 8 clinical leaders with 8 rounds of iterative user testing with 2-8 prescribers in each round to inform app development. With the resulting prototype app, we conducted a validation study with 43 participants. The participants were assigned to one of two groups and completed a 2-hour remote user testing session. Both groups reviewed mock patient facts and ordered diabetes medications for the patients. The Traditional group used a mock electronic health record (EHR) for the review in Period 1 and used the prototype app in Period 2, while the Tool group used the prototype app during both time periods. The perceived cognitive load associated with task performance during each period was assessed with the National Aeronautics and Space Administration Task Load Index. Participants also completed the System Usability Scale (SUS) questionnaire and Kano Survey. Results Average SUS scores from the questionnaire, taken at the end of 5 of the 8 user testing sessions, ranged from 68-86. The results of the validation study are as follows: percent adherence to evidence-based guidelines was greater with the use of the prototype app than with the EHR across time periods with the Traditional group (prototype app mean 96.2 vs EHR mean 72.0, P<.001) and between groups during Period 1 (Tool group mean 92.6 vs Traditional group mean 72.0, P<.001). Task completion times did not differ between groups (P=.23), but the Tool group completed medication ordering more quickly in Period 2 (Period 1 mean 130.7 seconds vs Period 2 mean 107.7 seconds, P<.001). Based on an adjusted α level owing to violation of the assumption of homogeneity of variance (Ps>.03), there was no effect on screens viewed and on perceived cognitive load (all Ps>.14). Conclusions Through deployment of the UCD process, a point-of-care medication recommender app holds promise of improving adherence to evidence-based guidelines; in this case, those from the American Diabetes Association. Task-time performance suggests that with practice the T2DM app may support a more efficient ordering process for providers, and SUS scores indicate provider satisfaction with the app.
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Andriani, Siwi, Abdul Rakhman, and Suroso Suroso. "Design and Build Automatic Rice Winnowing and Weighing Equipment IoT (Internet of Things) Based." Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) 9, no. 2 (September 24, 2022): 107–14. http://dx.doi.org/10.33019/jurnalecotipe.v9i2.3147.
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Technological developments are growing rapidly without exception in the agricultural sector. Food needs are increasing every year in Indonesia, rice is a staple food for most Indonesian people before being distributed, it is necessary to process rice including cleaning and weighing so that it can reach the hands of buyers. This research was conducted using the R&D (Research and Development) method with a prototype development model. development procedures include the stages of needs analysis, system design and design, prototyping, testing and validation. The design of this rice winnowing and weighing device uses the telegram application, Arduino ide which is connected to the internet as software and hardware NodeMCU ESP 8266, Arduino Uno as a controller, hx711 load cell sensor as a weighing sensor, power supply as a power supplier, relay module, LCD 16 x 20, buzzer, MG996 servo motor, and AC fan that can be controlled remotely using IoT (Internet of Things).
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Kahraman, Hasan, and Edmund Haberstroh. "DIRECTION-DEPENDENT AND MULTIAXIAL STRESS-SOFTENING BEHAVIOR OF CARBON BLACK–FILLED RUBBER." Rubber Chemistry and Technology 87, no. 1 (March 1, 2014): 139–51. http://dx.doi.org/10.5254/rct.13.87910.
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ABSTRACT The mechanical behavior of filled rubbers depends on the maximum stretch previously reached and consequently on the induced stress softening. This softening effect is referred to as the Mullins effect. Current investigations point out that the Mullins effect exhibits a significant directional dependence, which calls for an anisotropic material model. But for the formulation and validation of anisotropic material models, there is still a lack of suitable experimental data. For this the purpose, experiments based on chloroprene rubber (CR) are reported. To trace the anisotropic Mullins effect, the standard test method for characterization of the isotropic mechanical behavior must be extended. The appropriate type of specimen enables us to perform multiple load steps with alternating load directions. After repeated stretching in the same direction, a subsequent first uniaxial loading in any other direction is characterized by a stiffer stress–strain behavior compared with the stabilized curve of the previous primary load. Hence, the experimental results confirm the deformation-induced anisotropy. To identify the multiaxial material behavior after the prestretching in one direction, a biaxial tensile-testing machine is developed. A specific property of the biaxial tensile-testing machine is the independent control of both the loading axes. Thus, the rubber material can be subjected to arbitrary loading histories. Therefore, a cross-shaped specimen with four arms is used. Multiple slits parallel to the sides on each arm ensures the homogenous uniaxial load condition in the primary load. In the secondary load step, the loading axis, which was previously inactive, is moved in a uniform manner as the master axis or in any arbitrary defined ratio. The experimental results confirm the deformation-induced anisotropy of the Mullins effect. In summary, the material behavior significantly results from the deformation mode and the loading direction applied in the loading history.
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Oliviero, S., M. Roberts, R. Owen, G. C. Reilly, I. Bellantuono, and E. Dall’Ara. "Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models." Biomechanics and Modeling in Mechanobiology 20, no. 3 (February 1, 2021): 941–55. http://dx.doi.org/10.1007/s10237-021-01422-y.
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AbstractNew treatments for bone diseases require testing in animal models before clinical translation, and the mouse tibia is among the most common models. In vivo micro-Computed Tomography (microCT)-based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experimental data. Different modelling techniques can be used to estimate the bone properties, and the accuracy associated with each is unclear. The aim of this study was to evaluate the ability of different microCT-based microFE models to predict the mechanical properties of the mouse tibia under compressive load. Twenty tibiae were microCT scanned at 10.4 µm voxel size and subsequently compressed at 0.03 mm/s until failure. Stiffness and failure load were measured from the load–displacement curves. Different microFE models were generated from each microCT image, with hexahedral or tetrahedral mesh, and homogeneous or heterogeneous material properties. Prediction accuracy was comparable among models. The best correlations between experimental and predicted mechanical properties, as well as lower errors, were obtained for hexahedral models with homogeneous material properties. Experimental stiffness and predicted stiffness were reasonably well correlated (R2 = 0.53–0.65, average error of 13–17%). A lower correlation was found for failure load (R2 = 0.21–0.48, average error of 9–15%). Experimental and predicted mechanical properties normalized by the total bone mass were strongly correlated (R2 = 0.75–0.80 for stiffness, R2 = 0.55–0.81 for failure load). In conclusion, hexahedral models with homogeneous material properties based on in vivo microCT images were shown to best predict the mechanical properties of the mouse tibia.
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PENG, XIONGQI, YU WANG, ZAOYANG GUO, and SHAOQING SHI. "NUMERICAL VALIDATION OF A FIBER-REINFORCED HYPERELASTIC CONSTITUTIVE MODEL FOR HUMAN INTERVERTEBRAL DISC ANNULUS FIBROSUS." Journal of Mechanics in Medicine and Biology 11, no. 01 (March 2011): 163–76. http://dx.doi.org/10.1142/s0219519410003691.
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This paper validates a constitutive model for human intervertebral disc annulus fibrosus via numerical simulations on a lumber spine motion segment. This anisotropic hyperelastic fiber-reinforced constitutive model was previously developed by the authors. Based on three-dimensional (3D) lumbar spine segments that are constructed from CT scanning images, a detailed and anatomically accurate human lumbar spine finite element (FE) model for L3–L4 motion segment is developed. The FE model includes vertebral bodies, intervertebral disc, and various ligaments. Numerical simulations are carried out by using commercial CAE software package ABAQUS/Standard. The loading cases considered in the numerical analysis are set to be consistent with sets-up of cadaveric specimen testing available in the literature. Numerical results such as load–displacement curves and nucleus pressure are compared with experimental data. Simulation results show good consistency with cadaveric experimental data, and have good biomechanical fidelity. The constitutive model can be used for human intervertebral disc modeling and biomechanical analysis of human spine column.
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Oganian, E. S., S. V. Chunin, D. M. Fazliakhmetov, E. V. Muravlev, and O. A. Lobachev. "Evaluating the strength of a beam-type electric switch mechanism housing." Dependability 22, no. 4 (November 21, 2022): 3–7. http://dx.doi.org/10.21683/1729-2646-2022-22-4-3-7.
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Aim. Choice of method and its validation for the purpose of confirming the feasibility of safe operation of a switch mechanism housing submitted to rolling stock axle loads of 30 tf. Method. As of today, there are neither regulations regarding the strength parameters of load-carrying elements of electric switch mechanisms, nor verification procedures. Given the above, when evaluating the strength of electric switch mechanism housing, the authors used the method used for the purpose of evaluating the strength of load-carrying elements of locomotives per GOST R 55513-2013. That method has a long history of application as part of calculation and testing of motive power and has shown good results. According to the method, strength is evaluated by comparing the safety factor of fatigue strength n with the allowed value for steel structures [n] = 2.0. Result. The activities resulted in the validation of the proposed method of strength estimation by the criterion of fatigue strength. The value of fatigue endurance of electric switch mechanism housing was estimated based on the results of fatigue benchmark tests of three items. Each was stressed by stepping up the amplitude of the applied force after the base number of cycles had been reached. At the first stage, the loading was equivalent to the operational value. After the base number of functions had been reached for each item, the following step was initiated. The tests continued until cracks have been found. In the process of testing, the amplitude of stress was recorded at each level of loading. For the purpose of identifying the fatigue strength characteristics, the reduced fatigue strength of the switch mechanism housing was calculated under the hypothesis of linear addition of fatigue damage subject to the condition of deterministic loading and subsequent processing of the findings using statistical methods. The value of fatigue limit has been obtained for the housing of a tie-type electric switch mechanism equal to 48.4 MPa. The safety factor of fatigue strength was found to be equal to 2.86, which is above the minimal allowed value. Conclusion. It has been shown that the housing of a tie-type electric switch mechanism has a sufficient strength as regards the operational static and dynamic loads. Its safe operation when exposed to axle loads of 30 tf has also been confirmed.
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Revell, Andrew D., Dechao Wang, Maria-Jesus Perez-Elias, Robin Wood, Dolphina Cogill, Hugo Tempelman, Raph L. Hamers, et al. "2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings." Journal of Antimicrobial Chemotherapy 76, no. 7 (April 1, 2021): 1898–906. http://dx.doi.org/10.1093/jac/dkab078.
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Abstract Objectives With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. Methods Twelve sets of random forest models were trained using very large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral load in response to a new regimen (absolute models) following virological failure. Two ‘standard’ models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. Results The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78–0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral load with a mean absolute error of 0.65 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65–0.75 log10 copies HIV RNA/mL. All models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. All models were significantly better predictors of treatment response than genotyping with rules-based interpretation. Conclusions These latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.
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Zalameda, Joseph, and William Winfree. "Detection and Characterization of Damage in Quasi-Static Loaded Composite Structures using Passive Thermography." Sensors 18, no. 10 (October 20, 2018): 3562. http://dx.doi.org/10.3390/s18103562.
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Real-time nondestructive evaluation is critical during composites load testing. Of particular importance is the real time measurement of damage onset, growth, and ultimate failure. When newly formed damage is detected, the loading is stopped for further detailed characterization using ultrasound inspections or X-ray computed tomography. This detailed inspection data are used to document failure modes and ultimately validate damage prediction models. Passive thermography is used to monitor heating from damage formation in a hat-stiffened woven graphite epoxy composite panel during quasi-static seven-point load testing. Data processing techniques are presented that enable detection of the small transient thermographic signals resulting from damage formation in real time. It has been observed that the temperature rise due to damage formation at the surface is composed of two thermal responses. The first response is instantaneous and conforms to the shape of the damage. This heating is most likely due to irreversible thermoelastic, plastic deformation, and microstructural heating. The second response is a transient increase in temperature due to mechanical heating at the interface of failure. Two-dimensional multi-layered thermal simulations based on quadrupole method are used to investigate the thermal responses. In particular, the instantaneous response is used as the transient response start time to determine damage depth. The passive thermography measurement results are compared to ultrasonic measurements for validation.
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Ma, Xiaoge, and Xujie Yan. "Monitoring of Maximum Oxygen Intake of Breathing and Heart Rate in Exercise Training Based on Regression Equations." Journal of Healthcare Engineering 2022 (March 7, 2022): 1–5. http://dx.doi.org/10.1155/2022/5025615.
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In order to provide a new reference method and basis for the physical testing study in healthy adults aged 40∼and 49, this paper proposes a regression equation-based monitoring study of exercise training breathing and heart rate. Sixty four subjects (30 males and 34 females), aged 40∼and 49 years, were selected. First, the subjects were screened by relevant health test and medical questionnaire to exclude the subjects with exercise contraindications and high-intensity exercise; then, the incremental load test was directly measured by gas analysis, and then, the subjects who completed the maximum oxygen intake test were tested twice, the corresponding heart rate value was recorded, and walking time were averaged for calculation. We show that the regression analysis of each index yielded regression equations and cross-validation regression equations show correlation statistics for measured and inferred maximum oxygen intake of RLOOCV = 0.826 and SEELOOCV = 0.378 (L/min). The equation can effectively speculate on the maximum oxygen intake of 40∼in healthy adults aged 49, with the advantages of efficient, low cost, fast, and convenient.
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Nguyen, Tu Trung, and Kien Dinh. "Prediction of bridge deck condition rating based on artificial neural networks." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 13, no. 3 (August 31, 2019): 15–25. http://dx.doi.org/10.31814/stce.nuce2019-13(3)-02.
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An accurate prediction of the future condition of structural components is essential for planning the maintenance, repair, and rehabilitation of bridges. As such, this paper presents an application of Artificial Neural Networks (ANN) to predict future deck condition for highway bridges in the State of Alabama, the United States. A library of 2572 bridges was extracted from the National Bridge Inventory (NBI) database and used for training, validation, and testing the ANN model, which had eight input parameters and one output being the deck rating. Specifically, the eight input parameters are Current Bridge Age, Average Daily Traffic, Design Load, Main Structure Design, Approach Span Design, Number of main Span, Percent of Daily Truck Traffic, and Average Daily Traffic Growth Rate. The results indicated the obtained ANN model can predict the condition rating of the bridge deck with an accuracy of 73.6%. If a margin error of ±1 was used, the accuracy of the proposed model reached a much higher value of 98.5%. Besides, a sensitivity analysis was conducted for individual input parameters revealed that Current Bridge Age was the most important predicting parameter of bridge deck rating. It was followed by the Design Load and Main Structure Design. The other input parameters were found to have neglectable effects on the ANN’s performance. Finally, it was shown that the obtained ANN can be used to develop the deterioration curve of the bridge deck, which helps visualize the condition rating of a deck, and accordingly the maintenance need, during its remaining service life.
Keywords:
condition rating; bridge deck; deterioration curve; artificial neural networks; sensitivity analysis.
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Hastuti, Tri Ani, Soegiyanto Soegiyanto, Wawan Sundawan Suherman, Setya Rahayu, and Nur Sita Utami. "Improving the pedagogic competence of physical education teachers." Jurnal Cakrawala Pendidikan 41, no. 2 (May 29, 2022): 377–87. http://dx.doi.org/10.21831/cp.v41i2.48231.
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The competencies possessed by teachers have not been as expected, as evidenced by the average value of pedagogic competencies, which are still low compared to the results of professional competence scores on the Teacher Competency Test. This study aimed to develop a model for improving the pedagogic competence of Physical Education teachers which was arranged into a module that contains how to develop a learning implementation plan, how to create and use the learning media, how to create and use the teaching materials, and how to prepare and implement the learning assessments. The development tests that were carried out were: 1) alpha test, 2) beta test, and 3) final product validation. The alpha test is a product validation test by three experts, while the beta test is a load and product suitability test involving 27 teachers and 3 supervisors, followed by a final product validation stage involving 84 teachers who are divided into two groups, namely experimental and control. The research instruments used were a questionnaire and a competency improvement scale. The data analysis techniques used are quantitative descriptive data analysis, gain score data analysis, and t-test. Based on the results of the assessment at the alpha test, beta test, and program validation stages, the resulting product was declared suitable for use as a teaching material set. Furthermore, through the process of testing the effectiveness of the product, the model of increasing pedagogic competence based on teaching materials is proven to be effective and can significantly improve the pedagogic competence of Physical Education teachers.
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Rai, Gopal L. "Advanced Active Prestressed CFRP in RCC Structures." Advanced Materials Research 1129 (November 2015): 290–97. http://dx.doi.org/10.4028/www.scientific.net/amr.1129.290.
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. The need for rehabilitation of reinforced concrete structures is rapidly increasing. Fibre reinforced polymer (FRP) composite materials for concrete structures have high strength-to-weight ratios that can provide high prestressing forces while adding minimal additional weight to a structure. They also have good fatigue properties and exhibit low relaxation losses, both of which can increase the service lives and the load carrying capacities of reinforced concrete structures. Carbon fiber reinforced polymer (CFRP) composite system is integrated system based on carbon fibres and epoxy resins. By prestressing the CFRP laminates, the material is used more efficiently as a part of its tensile capacity is utilised and it contributes to the load bearing capacity under both service and ultimate load condition. This is an ideal technique as it combines the advantage of using noncorrosive and lightweight advanced composite material in the form of FRP laminates with high efficiency offered by external prestressing. An innovative mechanical anchorage system was developed to prestress the FRP laminates directly by jacking and reacting against the RCC structure.This paper describes the use of Prestressed CFRP laminates for strengthening of RCC structures including practical applications on slabs and bridges. Also it elucidates the post strengthening testing carried out for the validation of this technique.