• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.329-339
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• 2023

The objective of this study is to develop a Stability Evaluation System for retaining walls to assess their safety in real-time during excavation. A ground investigation is typically conducted before construction to gather information about the soil properties and predict wall stability. However, these properties may not accurately reflect the actual ground being excavated. To address this issue, the study employed a differential evolution algorithm to estimate the soil parameters of the actual ground. The estimated results were then used as input for an artificial neural network to evaluate the stability of the retaining walls. The study achieved an average accuracy of over 90% in predicting differential settlement, wall displacement, anchor force, and structural stability of the retaining walls. If implemented at actual excavation sites, this approach would enable real-time prediction of wall stability and facilitate effective safety management. Overall, the developed Stability Evaluation System offers a promising solution for ensuring the stability of retaining walls during construction. By incorporating real-time soil parameter analysis, it enhances the accuracy of stability predictions and contributes to proactive safety management in excavation projects.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Steel and Composite Structures
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• v.44 no.5
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• pp.691-705
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• 2022

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model (R² = 0.982, RMSE = 9.466 kN, MAE = 6.299 kN, µ = 1.018, and Cov = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.201-209
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• 2017

Facade structure system plays an important role in modern architecture and design. Many contractors start using Building Information Modeling (BIM) to help design and lay-out façade walls in recent years. However, there are still some users refuse to accept BIM on façade construction. Therefore, we employed Technology Acceptance Model (TAM) to assess the users acceptable of BIM work flow, with using a practical case of facade construction in Chongqing Wanda City. The factors that will affect the builder's decision of whether using BIM or not when construct façade, and the relationship among them will be found via this model. Through the analysis using TAM, this research found that the direct factors influencing the completely acceptance of BIM in façade construction is the BIM quality and Result Demonstrating, and the parameter impacting the intuition engendering is the Exterior Condition. Therefore, this paper proposes a more systemic model of BIM acceptance in curtain wall to analyze the user's acceptance. The solution can also offer a reference for future research and construct on façade structure. The acceptance model has the significance that it can help to analyze the reason why users refuse to use BIM in façade construction, thus to help users accept BIM.

• Steel and Composite Structures
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• v.50 no.1
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• pp.63-75
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• 2024

In this paper, the modified couple stress theory (MCST) and first order shear deformation theory (FSDT) are employed to investigate the free vibration and bending analyses of a three-layered micro-shell sandwiched by piezoelectric layers subjected to an applied voltage and reinforced graphene nanoplatelets (GPLs) under external and internal pressure. The micro-shell is resting on an elastic foundation modeled as Pasternak model. The mixture's rule and Halpin-Tsai model are utilized to compute the effective mechanical properties. By applying Hamilton's principle, the motion equations and associated boundary conditions are derived. Static/ dynamic results are obtained using Navier's method. The results are validated with the previously published works. The numerical results are presented to study and discuss the influences of various parameters on the natural frequencies and deflection of the micro-shell, such as applied voltage, thickness of the piezoelectric layer to radius, length to radius ratio, volume fraction and various distribution pattern of the GPLs, thickness-to-length scale parameter, and foundation coefficients for the both external and internal pressure. The main novelty of this work is simultaneous effect of graphene nanoplatelets as reinforcement and piezoelectric layers on the bending and vibration characteristics of the sandwich micro shell.

• Steel and Composite Structures
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• v.50 no.2
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• pp.159-181
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• 2024

In the present study, the effect of geometrical parameters of two different types of aluminum thin-walled structures on energy absorption under three-bending impact loading has been investigated experimentally and numerically. To evaluate the effect of parameters on the specific energy absorption (SEA), initial peak crushing force (IPCF), and the maximum crushing distance (δ), a design of experiment technique (DOE) with response surface method (RSM) was applied. Four different thin-walled structures have been tested under the low-velocity impact, and then they have simulated by ABAQUS software. An acceptable consistency between the numerical and experimental results was obtained. In this study, statistical analysis has been performed on various parameters of three different types of tubes. In the first and the second statistical analysis, the dimensional parameters of the cross-section, the number of holes, and the dimensional parameter of holes were considered as the design variables. The diameter reduction rate and the number of sections with different diameters are related to the third statistical analysis. All design points of the statistical method have been simulated by the finite element package, ABAQUS/Explicit. The final result shows that the height and thickness of tubes were more effective than other geometrical parameters, and despite the fact that the deformations of the cylindrical tubes were around forty percent greater than the rectangular tubes, the top desirability was relevant to the cylindrical tubes with reduced cross-sections.

• Advances in Computational Design
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• v.9 no.1
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• pp.39-52
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• 2024

If the governing differential equation arising from engineering problems is treated as an analytic, continuous and derivable function, it can be expanded by one point as a series of finite numbers. For the function to be zero for each value of its domain, the coefficients of each term of the same power must be zero. This results in a recursive relationship which, after applying the natural conditions or the boundary conditions, makes it possible to obtain the values of the derivatives of the function with acceptable accuracy. The elastoplastic analysis of an inhomogeneous thick sphere of metallic materials with linear variation of the modulus of elasticity, yield stress and Poisson's ratio as a function of radius subjected to internal pressure is presented. The Beltrami-Michell equation is established by combining equilibrium, compatibility and constitutive equations. Assuming axisymmetric conditions, the spherical coordinate parameters can be used as principal stress axes. Since there is no analytical solution, the natural boundary conditions are applied and the governing equations are solved using a proposed new method. The maximum effective stress of the von Mises yield criterion occurs at the inner surface; therefore, the negative sign of the linear yield stress gradation parameter should be considered to calculate the optimal yield pressure. The numerical examples are performed and the plots of the numerical results are presented. The validation of the numerical results is observed by modeling the elastoplastic heterogeneous thick sphere as a pressurized multilayer composite reservoir in Abaqus software. The subroutine USDFLD was additionally written to model the continuous gradation of the material.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.3 s.55
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• pp.217-224
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• 2009

Many engineering researches are performed to ensuring structural safety from earthquake. In this study, the damping capacity of LRB(lead rubber bearing) with high lead-plug area ratio was examined by hysteresis loop from experiments. The displacement controlled tests were performed for 12 specimens designed in 2 types by lead-plug area ratio as main parameter. Each coupled specimens were tested by 3 times sinusoidal loads with different loading velocities. From the experimental results, LRB with high lead-plug area ratio has sufficient damping ratio for reducing horizontal seismic load to structures.