scholarly journals Calibration of Electrical Resistance to Moisture Content for Beech Laminated Veneer Lumber “BauBuche S” and “BauBuche Q”

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 635
Author(s):  
Philippe Grönquist ◽  
Gianna Weibel ◽  
Claude Leyder ◽  
Andrea Frangi

Electrical resistance measurements are often employed for the purpose of nondestructive long-term monitoring of wood moisture content (MC) in timber structures. As a structural material for high-performance load-bearing applications in such structures, beech laminated veneer lumber (LVL) enjoys a growing popularity. However, due to the processing of beech LVL affecting physical properties, calibration curves for bulk beech wood cannot be used. In this study, resistance was measured on 160 beech LVL samples equilibrated in four different relative humidity (RH) climates. The results show a difference not only between the beech LVL products “BauBuche S” and “BauBuche Q”, but also between measurements at two different depths. For each data set, parameters for calibration models using two and using three model parameters were determined by regression analysis to MC determined by the gravimetric method.

2015 ◽  
Vol 19 (3) ◽  
pp. 1119-1129 ◽  
Author(s):  
Vangelce Mitrevski ◽  
Monika Lutovska ◽  
Vladimir Mijakovski ◽  
Ivan Pavkov ◽  
Mirko Babic ◽  
...  

The moisture adsorption isotherms of pear were determined at 15?C, 30?C and 45?C using the standard static gravimetric method over a range of water activity from 0.112 to 0.920. The experimental data were fitted with isotherm equations recommended in ASAE Standard D245.5. In order to find which equation gives the best results, large number of numerical experiments were performed. After that, several statistical criteria proposed in scientific literature for estimation and selection of the best sorption isotherm equations were used. For each equation and experimental data set, the average performance index was calculated and models were ranked afterwards. After that, some statistical rejection criteria were checked (D?Agostino-Pearson test of normality, single-sample run test and significance and precision of the model parameters). The performed statistical analysis shows that the Guggenheim-Anderson-de Boer (GAB) equation has the highest value of average performance index, but higher correlation between pair of parameters leads to lower precision of estimated parameters.


2014 ◽  
Vol 10 (4) ◽  
pp. 747-755
Author(s):  
Hamid Tavakolipour ◽  
Mohsen Mokhtarian

Abstract In this study, two intelligent tools of genetic algorithm (GA) and artificial neural network (ANN) were employed to use experimental data to predict equilibrium moisture content (EMC) of Persian pistachio powder. Initially the moisture sorption isotherms of pistachio powder were determined by gravimetric method at different temperatures (15, 25, 35 and 40°C) and constant relative humidity’s (0.11, 0.23, 0.36, 0.49, 0.62, 0.75 and 0.88 aw values) and then traditional mathematical models including BET, Iglesias and Chirife, GAB, Caurie and Freundlich were used to check the fitness of experimental data. Later the experimental data were compared with similar data obtained from GA and ANN models. The overall results showed that the Caurie model had high performance to predict EMC and revealed that GA model had greater accuracy to predict EMC of pistachio powder with very high R2 values (equal to 0.9996).


2019 ◽  
Vol 9 (17) ◽  
pp. 3619 ◽  
Author(s):  
Chang-Yu Cao ◽  
Jia-Chun Zheng ◽  
Yi-Qi Huang ◽  
Jing Liu ◽  
Cheng-Fu Yang

We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex weather conditions was used for training, and better model parameters were selected through tests and subsequent adjustments. The experimental results showed that, for different weather scenarios, the accuracy of the YOLO-UA was ~22% greater than that of the YOLO model before optimization, and the recall rate increased by about 21%. On both cloudy and sunny days, the accuracy, precision, and recall rate of the YOLO-UA model were more than 94% above the floating rate, which suggested that the precision and recall rate achieved a good balance. When used for video testing, the YOLO-UA model yielded traffic statistics with an accuracy of up to 100%; the time to count the vehicles in each frame was less than 30 ms and it was highly robust in response to changes in scenario and weather.


2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.


Author(s):  
C. Sauer ◽  
F. Bagusat ◽  
M.-L. Ruiz-Ripoll ◽  
C. Roller ◽  
M. Sauer ◽  
...  

AbstractThis work aims at the characterization of a modern concrete material. For this purpose, we perform two experimental series of inverse planar plate impact (PPI) tests with the ultra-high performance concrete B4Q, using two different witness plate materials. Hugoniot data in the range of particle velocities from 180 to 840 m/s and stresses from 1.1 to 7.5 GPa is derived from both series. Within the experimental accuracy, they can be seen as one consistent data set. Moreover, we conduct corresponding numerical simulations and find a reasonably good agreement between simulated and experimentally obtained curves. From the simulated curves, we derive numerical Hugoniot results that serve as a homogenized, mean shock response of B4Q and add further consistency to the data set. Additionally, the comparison of simulated and experimentally determined results allows us to identify experimental outliers. Furthermore, we perform a parameter study which shows that a significant influence of the applied pressure dependent strength model on the derived equation of state (EOS) parameters is unlikely. In order to compare the current results to our own partially reevaluated previous work and selected recent results from literature, we use simulations to numerically extrapolate the Hugoniot results. Considering their inhomogeneous nature, a consistent picture emerges for the shock response of the discussed concrete and high-strength mortar materials. Hugoniot results from this and earlier work are presented for further comparisons. In addition, a full parameter set for B4Q, including validated EOS parameters, is provided for the application in simulations of impact and blast scenarios.


2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.


Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Łukasz Warguła ◽  
Dominik Wojtkowiak ◽  
Mateusz Kukla ◽  
Krzysztof Talaśka

This article presents the results of experimental research on the mechanical properties of pine wood (Pinus L. Sp. Pl. 1000. 1753). In the course of the research process, stress-strain curves were determined for cases of tensile, compression and shear of standardized shapes samples. The collected data set was used to determine several material constants such as: modulus of elasticity, shear modulus or yield point. The aim of the research was to determine the material properties necessary to develop the model used in the finite element analysis (FEM), which demonstrates the symmetrical nature of the stress distribution in the sample. This model will be used to analyze the process of grinding wood base materials in terms of the peak cutting force estimation and the tool geometry influence determination. The main purpose of the developed model will be to determine the maximum stress value necessary to estimate the destructive force for the tested wood sample. The tests were carried out for timber of around 8.74% and 19.9% moisture content (MC). Significant differences were found between the mechanical properties of wood depending on moisture content and the direction of the applied force depending on the arrangement of wood fibers. Unlike other studies in the literature, this one relates to all three stress states (tensile, compression and shear) in all significant directions (anatomical). To verify the usability of the determined mechanical parameters of wood, all three strength tests (tensile, compression and shear) were mapped in the FEM analysis. The accuracy of the model in determining the maximum destructive force of the material is equal to the average 8% (for tensile testing 14%, compression 2.5%, shear 6.5%), while the average coverage of the FEM characteristic with the results of the strength test in the field of elastic-plastic deformations with the adopted ±15% error overlap on average by about 77%. The analyses were performed in the ABAQUS/Standard 2020 program in the field of elastic-plastic deformations. Research with the use of numerical models after extension with a damage model will enable the design of energy-saving and durable grinding machines.


2017 ◽  
Vol 37 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Haluk Ay ◽  
Anthony Luscher ◽  
Carolyn Sommerich

Purpose The purpose of this study is to design and develop a testing device to simulate interaction between human hand–arm dynamics, right-angle (RA) computer-controlled power torque tools and joint-tightening task-related variables. Design/methodology/approach The testing rig can simulate a variety of tools, tasks and operator conditions. The device includes custom data-acquisition electronics and graphical user interface-based software. The simulation of the human hand–arm dynamics is based on the rig’s four-bar mechanism-based design and mechanical components that provide adjustable stiffness (via pneumatic cylinder) and mass (via plates) and non-adjustable damping. The stiffness and mass values used are based on an experimentally validated hand–arm model that includes a database of model parameters. This database is with respect to gender and working posture, corresponding to experienced tool operators from a prior study. Findings The rig measures tool handle force and displacement responses simultaneously. Peak force and displacement coefficients of determination (R2) between rig estimations and human testing measurements were 0.98 and 0.85, respectively, for the same set of tools, tasks and operator conditions. The rig also provides predicted tool operator acceptability ratings, using a data set from a prior study of discomfort in experienced operators during torque tool use. Research limitations/implications Deviations from linearity may influence handle force and displacement measurements. Stiction (Coulomb friction) in the overall rig, as well as in the air cylinder piston, is neglected. The rig’s mechanical damping is not adjustable, despite the fact that human hand–arm damping varies with respect to gender and working posture. Deviations from these assumptions may affect the correlation of the handle force and displacement measurements with those of human testing for the same tool, task and operator conditions. Practical implications This test rig will allow the rapid assessment of the ergonomic performance of DC torque tools, saving considerable time in lineside applications and reducing the risk of worker injury. DC torque tools are an extremely effective way of increasing production rate and improving torque accuracy. Being a complex dynamic system, however, the performance of DC torque tools varies in each application. Changes in worker mass, damping and stiffness, as well as joint stiffness and tool program, make each application unique. This test rig models all of these factors and allows quick assessment. Social implications The use of this tool test rig will help to identify and understand risk factors that contribute to musculoskeletal disorders (MSDs) associated with the use of torque tools. Tool operators are subjected to large impulsive handle reaction forces, as joint torque builds up while tightening a fastener. Repeated exposure to such forces is associated with muscle soreness, fatigue and physical stress which are also risk factors for upper extremity injuries (MSDs; e.g. tendinosis, myofascial pain). Eccentric exercise exertions are known to cause damage to muscle tissue in untrained individuals and affect subsequent performance. Originality/value The rig provides a novel means for quantitative, repeatable dynamic evaluation of RA powered torque tools and objective selection of tightening programs. Compared to current static tool assessment methods, dynamic testing provides a more realistic tool assessment relative to the tool operator’s experience. This may lead to improvements in tool or controller design and reduction in associated musculoskeletal discomfort in operators.


2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Hyeon W. Park ◽  
Jae W. Park ◽  
Won B. Yoon

AbstractNovel algorithm to determine the least cost formulation of a surimi blend was developed using linear programming (LP). Texture properties and the unit cost of surimi blend at the target moisture content were used as constraint functions and the objective function, respectively. The mathematical models to describe the moisture content dependence of the ring tensile properties were developed using critical moisture content, and the model parameters were used for the least cost LP (LCLP) model. The LCLP model successfully predicted the quality of surimi blend. Sensitivity analysis was used to obtain an additional information when the perturbations of design variables are provided. A standard procedure to determine the least cost formulation for blending surimi with varied moisture contents was systematically developed.


2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.


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