Nondestructive Detection of Ceramic Products Based on Tapping Sound Signal Feature Recogition

Recent studies on the test of ceramic non-destructive testing are mainly based on high cost technologies, image processing and so on, these method possesses some drawback of low efficiency, high cost and so on. What’s more, detecting whether the ceramic products by human through listening to sound of tapping is also effectless. This paper proposed a non-destructive method for ceramic products to solve this problem. This non-destructive method consists of a tapping device and a signal processing module. The tapping device will be applied to generate the tapping sound signal and the signal processing system will be applied to analysis signal. After the process of signal analysis, sample length and peak of spectrum 2 parameters is extracted, then use these parameters to train SVM, the results will be compared with BP neural network (BPNN). The result of experiment shows that SVM with different kernels of linear, poly, rbf, sigmoid respectively reach the accuracy of 96.29%, 96.29%, 46.29%, 93.82%, while BPNN reaches the accuracy of 93.21%. This result proves that SVM can effectively complete the task of identifying defective ceramics, and its performance is better than BPNN.

Novel Model for Automatic Classification of the Epileptic Seizures Using Fast Fourier Series-Haar Wavelet Transform

The disorder based on neurological can be considered as epilepsy that leads to the recurrent seizures in occurrence. The electronic characteristics of brain can be monitor by the electroencephalogram (EEG). It is most commonly used in the medical application. The function monitoring records can be non linear as well as non stationary functioning. The present work produce a novel methodology, it is depend on Fast Fourier series (FFS) and wavelet transform based on Haar. These methods are used for the various kinds of epileptic seizure the electroencephalogram based signal. The detection of boundary is occur by the representation of scale-space and it also adapted to the image segmentation of the spectrum depends on the FBSE that can be obtained with the electroencephalogram based signal and the purpose of the EWT is also used to attain the narrow sub band based signals. These image segmentation and classification process implementation by FPGA based microprocessor and systems. The FFS-HMT can produce the sub band signal from the Hilbert marginal spectrum it is represented as HMS. The HMS can be used to compute the line length and the entropy characteristics due to the corresponding various kinds of the level based oscillatory of the electroencephalogram signal. Here we apply the selected feature extraction depends on the ranking parallel vector. With the use of an electroencephalogram signal, the robust random forest is utilized to classify selected feature extraction in normal and epileptic participants. The assessment of performance based on classification can be measured in FPGA microprocessor the term of classification accuracy for different sample length of EEG. The current methodology aids neurologists in distinguishing between healthy and epileptic people using electroencephalogram signals.

Language Variation in the Writing of African American Students: Factors Predicting Reading Achievement

Purpose This study aims to examine the predictive relation between measures obtained from African American students' written narrative language samples and reading achievement, as measured by standardized academic assessments. Method Written language samples were elicited from 207 African American students in Grades 1–8. The samples were examined for morphosyntactic variations from standardized written Generalized American English (GAE). These variations were categorized as either (a) specific to African American English (AAE) or (b) neutral across AAE and standardized written GAE (i.e., considered ungrammatical both in AAE and in standardized written GAE). Structural equation modeling was employed to then examine the predictive relation between the density of AAE-specific forms in students' writing and their performance on standardized assessments of literacy and reading vocabulary. This relation was examined while accounting for the density of dialect-neutral morphosyntactic forms, reported family income, age, and written sample length. Results The written samples were highly variable in terms of morphosyntax. Younger students and those from lower income homes tended to use AAE-specific forms at higher rates. However, the density of AAE-specific forms did not significantly predict standardized literacy scores or reading vocabulary after accounting for dialect-neutral variations, income, and sample length. Conclusions These results support the ongoing need to better understand the language, literacy, and overall academic development of students from all backgrounds. It may be essential to focus on dialect-neutral language forms (i.e., morphosyntactic forms that are consistent across both AAE and standardized written GAE) in written samples to maximize assessment validity across students who speak varying dialects of English. Supplemental Material https://doi.org/10.23641/asha.16879558

Preliminary Evaluation of Potential Impacts Associated with Small Cetacean Remote Biopsy Sampling by Controlled Testing on Stranded Common Bottlenose Dolphins (Tursiops truncatus)

To explore the potential macroscopic tissue effects of select remote biopsy tools to common bottlenose dolphins (Tursiops truncatus), carcasses were darted and their traumatic effects on the anatomy in target and non-target areas of the body were described. In total, 87 samples were collected (target area, n = 19; non-target area, n = 68) within standardized grid partitions from five carcasses of sub-adult to adult age classes with a range of body condition scores. We broadly classified impacts penetrating completely through the blubber into muscle or deeper internal tissues as over-penetrations (n = 51/87, 59%). For samples collected in the defined target area, there was a low number of over-penetrations (n = 5/51; 10%). However, for samples collected in the defined, non-target areas, a much higher number of over-penetrations occurred (n = 45/51 88%). A visual examination of some samples indicated that sample length and appearance may not be reliable guides to assess the penetration depth of the wounds. These preliminary results suggest samples collected in non-targeted areas could pose much higher risk to the individual. We encourage other researchers considering the use of remote biopsy tools to conduct similar assessments prior to field sampling to better understand the potential consequences of misplaced samples with a view towards continually improving remote biopsy tools and techniques for the benefit of cetacean welfare.

Deep Ensemble Learning for Automatic Modulation Classification

Automatic modulation classification (AMC) plays an increasingly vital role in cognitive radio (CR), cognitive electronic warfare, and other areas. It aims at classifying the modulated modes of the received signals accurately and provides a guarantee for the subsequent detailed parameter identification. Deep learning (DL) methods allow the computer to automatically learn the pattern features and integrate features into the process of building the model, thereby reducing the incompleteness caused by artificial design features. At the same time, the DL methods have been applied in the AMC field as its powerful ability to process complex data and have achieved excellent performance in recent years. In this paper, we propose a deep ensemble learning AMC network, which uses a multi-model ensemble method to fuse multiple DL features. Specifically, different DL models are integrated by ensemble learning, which enhances the learning ability of the single model. With the proposed ensemble model trained on a measured wireless signal dataset, we conclude that the ensemble structure of Inception and CLDNN can fuse spatial features and temporal features, and achieve state-of-the-art performance in AMC tasks. Besides, the impact of the inphase/quadrature (I/Q) sample-length on wireless signals is further investigated, and find that the classification accuracy of the deep ensemble model is improved by 0.7% to 10% compared to the single model under various sample-length. Simultaneously, we visualize convergence clustering with t-distributed stochastic neighbor embedding (t-SNE), and the visualization results prove that the deep ensemble model has a stronger clustering ability than a single model.

Determining Deformation Behavior of AISI 9310 Steel Varying Temperature and Strain Rate for Aerospace Applications

Determination of flow stress behavior of materials is a critical aspect of understanding and predicting behavior of materials during manufacturing and use. However, accurately capturing the flow stress behavior of a material at different strain rates and temperatures can be challenging. Non-uniform deformation and thermal gradients within the test sample make it difficult to match test results directly to constitutive equations that describe the material behavior. In this study, we have tested AISI 9310 steel using a Gleeble 3500 physical simulator and Digital Image Correlation system to capture transient mechanical properties at elevated temperatures (300°C – 600°C) while controlling strain rate (0.01 s-1 to 0.1 s-1). The data presented here illustrate the benefit of capturing non-uniform plastic strain of the test specimens along the sample length, and we characterize the differences between different test modes and the impact of the resulting data that describe the flow stress behavior.

Fire Spread Characteristics of Metal-Polyethylene Sandwich Panels

An experimental study was conducted to determine the characteristics of the flame spread and droplets of metal-polyethylene (PE) sandwich panels during combustion. The mass-loss rate, average flame height, temperature, and fire spread rate were investigated. The results showed that the fire spread rate, mass change of the droplets, average flame height, and temperature increased with an increase in the sample length, except for the mass loss rate of the 40 cm-long sample. The time interval between the droplets decreased, and the flame pulsation frequency increased. The relationship between the flame height and sample length was determined. During the combustion process, bending deformation and top flame phenomena occurred due to the shrinkage of the PE, which increased the fire risk. The distance between the outer surface of the expanded metal aluminum layer and the insulation panel increased with an increase in the panel length. A schematic diagram of the fire spread of the metal sandwich panel was established based on the observations and theoretical analysis. The mechanism and combustion behavior of the metal sandwich panels were determined to provide references for the construction of metal sandwich panels of exterior walls.

Evaluating worker noise exposure levels in the presence of complex noise

Recent research into the assessment of worker noise exposure has demonstrated that the combination of impulsive noise and continuous noise creates an additional risk of developing noise-induced hearing loss (NIHL). Zhang et al (2021) demonstrated that workers exposed to non-Gaussian noise accumulated NIHL at a faster rate over their careers than worker exposed to Gaussian noise. The kurtosis statistic of the sound pressure distribution provides a means to adjust the estimated risk of hearing loss between exposure groups exposed to different types of noise. This paper will review the results from our recent studies of kurtosis and exposure level. Some unanswered questions involve the selection of a suitable sample length to estimate kurtosis, the selection of a compensation factor to apply, and understanding the differences exhibited in short (less than 10 years) and long-term exposures and kurtosis.

Information and algorithmic support of a multi-level integrated system for the investment strategies formation

The article summarizes the accumulated practical experience of the authors in the development of algorithms for the formation of investment strategies. For this purpose, the optimization of the studied parameters, information support of investment activities, verification, monitoring and adjustment in the testing mode and the subsequent practical application of the described tools are considered. The system is based on the main provisions of the Markowitz portfolio theory. The analytical block of the Information System Portfolio Investor includes Profitability-Risk model; empirical models of optimal complexity; hybrid predictive model systems; the principle of combining (integrating) both models and forecasts, as well as decision rules; optimization of the training sample length (modified Markowitz model); optimization of the frequency of monitoring and adjusting the composition of the investment portfolio. The principles of design and development of the information block of the system, its replenishment and functioning are described in detail. All the above listed components of the algorithmic content of the investment decision making system are described sequentially. The system modules have been successfully tested on a wide class of financial instruments: ordinary shares, preferred shares, government and corporate bonds, exchange commodities, stock, commodity, industry and bond indices, exchange-traded investment funds and real estate funds. The implemented Markowitz model with a dynamic database of historical data can significantly increase the efficiency of investment decisions, which is facilitated by taking into account the characteristics of both the markets under study and the corresponding financial instruments.

Model of the object of temperature control by electrostimulating action parameters

Technologies for pressure treatment of metal workpieces using powerful current pulses are becoming increasingly widespread both in Russia and abroad. Unique electromechanical processes are studied and improved in laboratory and production conditions. The process of applying an electric current to the workpiece is accompanied by a change in its physical properties as a result of the so-called electroplastic effect (EPE). At the same time, the temperature of the workpiece in the deformation zone increases. For high-quality and reliable operation of the drawing mill with electrostimulated drawing (ESW), it is necessary to use an automatic system for regulating the force and temperature. In order to implement the temperature control circuit, it is necessary to synthesize the transfer function of the control object – steel wire processed by pressure (rolling or drawing). Synthesis and analysis of parameters of the model of temperature control object are considered. The known relations are used: dependence of the pulse generator power on the calculated parameters (initial temperature, diameter, specific weight and electrical resistance of the workpiece, pulse duration); dependence of the RMS current of the generator on the amplitude and frequency of pulse reproduction; dependence of the magnetic permeability of the workpiece on its temperature; and dependence of the specific electrical resistance of the conductor material on temperature. In MATLAB – Simulink medium, a model of the temperature control object is synthesized as a function of the parameters of generator of high-power current pulses (amplitude and frequency), as well as the parameters of the workpiece to be processed (diameter, sample length, linear velocity, initial temperature, and resistivity at the initial temperature). The model is analyzed, and transients under different operating modes are presented. Using the developed model, the dependences of the temperature, power, and equivalent resistance on parameters of the generator and the workpiece are obtained for different generator pulse frequencies and workpiece diameters. The developed model can be used for laboratory studies of the electroplastic effect, as well as in production in auto-control systems with electrostimulated drawing in order to implement the object of regulation in the form of a model.