Large Amount Of Testing Research Articles

Similarity analysis of chloride transport behavior in fly ash concrete under different environments aiding by machine learning method

To forecast chloride transport in site by applying accelerated test results, it is necessary to investigate the similarity of chloride diffusivity under different environments. However, limited by test period and cost expenses, a large amount of tests in field and simulated environment are hard to conduct, and a limit test data will make prediction on chloride diffusivity of concrete less precise. To solve this problem, machine learning methods are explored to predict chloride transport. Based on the existing chloride concentration test data of fly ash concrete in natural and accelerated simulated environment, this paper establishes a chloride concentration predictive model based on the one-dimensional convolutional neural network (1D-CNN) method at first, and verifies the stability and accuracy of the established model. Second, similarity models for concentration ratio and diffusion time ratio based on multivariate nonlinear model and based on theoretical Fick’s second law are constructed, and the model accuracy is also tested. Finally, the chloride concentration data predicted by the 1D-CNN model is applied to the constructed similarity models respectively, and the application results are compared and analyzed to determine the optimal similarity model. Results indicate that the concentration predicted by the 1D-CNN method has a strong robustness and a small prediction error. Besides, based on multivariate nonlinear model, the error of concentration predicted by the similarity models for concentration ratio and diffusion time ratio is less than 16 % and 30 %, respectively. Similarly, according to the theoretical model based on Fick’s second law, the error of concentration predicted by the similarity models for concentration ratio based on the time dependent chloride diffusion coefficient and time dependent peak chloride concentration is less than 21 % and 36 %, respectively. The similarity model for concentration ratio based on multivariate nonlinear model has the best prediction effect.

An approach to recognizing the working cycle stage with small sample data for energy conservation

Energy conservation is an important strategy for low-carbon development. With utilizing stage recognition and control strategies, engineering machinery can achieve energy savings, and reduce carbon emissions. The traditional machine learning approach requires a large amount of data to train models. In addition to many cases where the available data is limited, even if the data volume is sufficient, it often means that a large amount of testing is required, which is difficult for users who are not professionals and may cause further waste, it is necessary to develop methods that can achieve high accuracy even with limited data. This paper proposes an intelligent recognition method based on library for support vector machine (LIBSVM), which uses the relationship between working stages and pressure waveforms of working cycle. Initially, the pressure waveforms are carefully extracted and subjected to a robust filtering process to effectively eliminate noise artifacts. Subsequently, relevant features are extracted from the waveforms, which include important characteristics such as the amplitude and frequency of the pressure waves, then the extracted features are normalized using appropriate techniques. The LIBSVM model is carefully trained using the aforementioned extracted features and label data. Finally, to further enhance the accuracy of the proposed method, an intelligent calibration system is utilized. This system plays a crucial role in ensuring that the results obtained from the machine learning algorithm are effectively calibrated, thereby improving the overall accuracy of the method. A series of experiments have verified the accuracy of the proposed identification method, and the effectiveness of using this method for energy-saving design has been verified through real machine validation.

Stochastic model for determination of arbitrariness and completeness of information system

When evaluating information system reliability indicators, the necessary initial data for a priori probability calculations are often missing, and statistical evaluation is complicated by a large amount of tests, which can only be used to determine the estimates of the moments of the determining random variables of the process of functioning of the information system or its constituent parts (mathematical expectation and variance of failure time processing, recovery time, backup time, etc.). However, in this situation, it is necessary to justify some characteristics of the information system, for example, time reserve, guaranteed exact limits of the probability of fault-free operation of the system and availability coefficient. In this case, «justification» means the construction of precise upper and lower limits of the change of functionals, which characterize the efficiency of the functioning of the information system, in the conditions of incomplete a priori information about the distribution functions of the determining random variables. In this paper, an N-periodic set of information states with one risky and one risk-free process is considered, the arbitrariness and completeness of this set is investigated, and the entropy for the Radon-Nikodym derivative of the corresponding equivalent martingale measures is found. Conditions for the signal loss distribution function were also found, under which the set of signal states is arbitragefree and complete. It is shown that the use of the proposed signal change function made it possible to effectively investigate a set of information states for impartiality and completeness. The application of the obtained results to the determination of the fair price for the аsian arithmetic option with the initially specified payment function is considered.

A novel triaxial apparatus to evaluate the effect of high temperature nitrogen injection concept and design

High temperature nitrogen, injection into coal seams is supposed to improve the per?meability and thus maintain the safety of underground mining. A novel triaxial appa?ratus is recently developed, aiming at providing the effective method to evaluate the effect of high temperature nitrogen injection. The main feature of this novel appara?tus is its high confining pressure, gas injection with high pressure as well as the high temperature. This new device can be either used for natural coal samples (e.g. intact or fractured) or the synthetic coal samples. A series of leakage tests were conducted to verify the feasibility of this instrument, the results of which have confirmed that the maximum pressure (i.e. 10 MPa) can be reached. In addition, the high temperature and pressure of nitrogen gas can also be sustain at the requested level. Based on the preliminary tests on the instrument, a large amount of tests were carried out to eval?uate the effect of nitrogen injection in enhancing the permeability of coking coal from the Pingdingshan coalfield, China, and the influence of high temperature nitrogen injection on mechanical parameters of coal was obtained.

Experimental approach for the study of wood strength at fracture in mode II by using a modified prototype test specimen and confrontation of the results with those of the 4ENF test. Application: Eucalyptus Grandis

Wood is a material used since the beginning of history by Man, It has proved very practical in various fields, and we quote mainly the sector of building and furniture. Known to be anisotropic, cellular and very complex. Its structural properties have a major influence on the mechanical behavior of the material. The interest of this present work is to further enrich our knowledge of this material through the study of its behavior in the face of external stresses that generate the mode II fracture. To do this, the choice of a specimen to study is imperative, knowing that there is no standardized test for the study of this phenomenon; we then began by studying the different prototypes of specimens mentioned in the literature and identified their advantages and disadvantages. We concluded that each test presents many difficulties of use and a great dispersion of the results caused on the one hand, by the nature of the material but mainly by the instability of the propagation of the crack. Based on this state of the art and the various experimental devices available to us, we have developed a test specimen prototype based on a tensile test for the study of the mode II of fracture. To carry out this work, in order to apprehend the studied material, we began by carrying out a complete characterization of the studied essence: Eucalyptus Grandis that includes the two aspects: mechanical and physical.. The dispersive nature of the material, the tests of the feasibility of the prototype developed and the number of notches require the reproduction of the test, hence the large amount of testing. During this test, the wood has adopted a fragile character, which gives it a linear behavior until it reaches a break with a stable propagation of the crack. This has been noticed for all notch lengths. The microscopic analysis that we carried out enabled us to validate the mode of fracture, to do this we studied the fracture facies of each length of notch using the scanning electron microscope. To determine the GII energy mode II initiation fracture toughness, we used the global approach using the convenience method and the theory of IRWIN KIES, we then deduced the KII constraint factor. We wanted to compare the results obtained with those of the literature but this operation can hardly be feasible except for the case, if we compare that order of magnitude because the tests are not done under the same conditions and using even the species. This led us to reproduce the most used test in the research and which is more accurate compared to other experiments in this case the test 4ENF.

Model-based testing as a service

The quality of Web services is an important factor for businesses that advertise or sell their services in the Internet. Failures can directly lead to fewer costumers or security problems. However, the testing of complex Web services that are organized in service-oriented architectures is a difficult and complex problem. Model-based testing (MBT) is one solution to deal with the complexity of the testing. With MBT, testers do not define the tests directly, but rather specify the structure and behavior of the System Under Test using models. Then, a test strategy is used to derive test cases automatically from the models. However, MBT yields a large amount of tests for complex systems which require lots of resources for their execution, thereby limiting its potential. Within this article, we discuss how cloud computing can be used to provide the required resources for scaling up test campaigns with large amounts of test cases derived using MBT.

The Research on Flame Retardant Wind Turbines Nacelle Made through VARTM Process

During the process of Vacuum Assisted Resin Transfer Molding（VARTM）making flame retardant wind turbines nacelle, the viscosity of unsaturated polyester resin（UPR）with reactive flame retardant groups is too large to be used in VARTM process. In this paper, a large amount of tests were conducted by the way of adding flame retardant of Dimethyl methyl phosphate(DMMP) so as to make the resin fit for VARTM processing properties requirements as well as increase its flame retardant properties to a higher level. The results showed that, when the content of DMMP was 12%, the resin could be provided with lower viscosity with a reduction of 50.3%, making it possible for the resin to be used in vacuum import process. When the content of DMMP reached 12%, LOI of the casting body reached 31.6, with LOI of glass fiber reinforced plastic of 37.0%, the vertical burning classification of which reached V-0 level without significant drop on mechanical properties of fiberglass reinforced plastics(GFRP). Meet the requirements and provide a good material for flame-retardant wind turbines nacelle.

The Research on Flame Retardant Wind Turbines Nacelle Made through VARTM Process

During the process of Vacuum Assisted Resin Transfer Molding（VARTM）making flame retardant wind turbines nacelle, the viscosity of unsaturated polyester resin（UPR）with reactive flame retardant groups is too large to be used in VARTM process. In this paper, a large amount of tests were conducted by the way of adding flame retardant of Dimethyl methyl phosphate(DMMP) so as to make the resin fit for VARTM processing properties requirements as well as increase its flame retardant properties to a higher level. The results showed that, when the content of DMMP was 12%, the resin could be provided with lower viscosity with a reduction of 50.3%, making it possible for the resin to be used in vacuum import process. When the content of DMMP reached 12%, LOI of the casting body reached 31.6, with LOI of glass fiber reinforced plastic of 37.0%, the vertical burning classification of which reached V-0 level without significant drop on mechanical properties of fiberglass reinforced plastics(GFRP). Meet the requirements and provide a good material for flame-retardant wind turbines nacelle.

Recollections of Early Microplate Automation

History is always fascinating. How did things start compared to what they are today? The microplate, which is such an integral part of today’s Drug Discovery programs, had its inception back in the early 1960s. It originated with Dr. Takatsky in Hungary. He conceived a tulip shaped loop (Figure 1), that would mix and transfer a set amount of liquid from one well to another, to be used in serial dilution testing. Dr. John Sever, who was working at the NIH, recognized the concept as a means of mechanizing the large amount of testing required for vaccine testing.

Verification of accuracy of rules in a rule based system

Verification of Rule Based Systems has largely concentrated on checking the consistency, conciseness and completeness of the rulebase. However, the accuracy of rules vis-à-vis the knowledge that they represent, is not addressed, with the result that a large amount of testing has to be done to validate the system. For any reasonably-sized rulebase it becomes difficult to know the adequacy and completeness of the test-cases. In case a particular test-case is omitted the chances of an inaccurate rule remaining undetected increases. We discuss this issue and define a notion of accuracy of rules. We take the view that a rule represents a concept of the domain and in the scenario of Formal Concept Analysis, works on objects and attribute-value space. We then present a mechanism to measure the level of accuracy using the Rough Set Theory. In this framework, accuracy can be computed as a ratio of the objects definitely selected by the rule (the lower approximation) to the objects possibly selected by the rule (the upper approximation) with respect to the concept that it encodes. Our algorithm and its implementation for PROLOG clauses is discussed.

Semiautomatic installation for testing ac ammeters and milliammeters

The principle of two calibrators first applied in an equipment for testing high-precision ac voltmeters [1] can also be used in the equipment for testing ammeters. This principle serves to obtain a high precision with a high productivity. Despite its relative complexity this equipment proved to be adequately reliable in use. Specialized installations for testing voltmeters and ammeters are more economical than universal ones for a large amount of testing. The principles on which the design of the installations for testing audio-frequency instruments are based [1] can also be used for constructing efficient high-frequency test installations.