Conventional Square Research Articles

Study on effect of boundary conditions on simulation of wind flow in mountainous area

The simulation analysis of wind flow in mountainous area can assist the design of the transmission line. The conventional square computational domain is not conducive for the condition of different wind directions. In this paper, a circular computational domain is proposed for the solution of wind flow in mountainous area in the condition of different wind directions. A square computational domain and a circular one are used to solve the wind flow around the standard hill model. The simulation accuracy of the circular computational domain is verified by the wind profiles at the windward slope, the top of the slope and the leeward slope.

Joint Model and Data-Driven Receiver Design for Data-Dependent Superimposed Training Scheme With Imperfect Hardware

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise.

Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils

This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R2 = 1.00 for model and R2 = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.

Development of a new method for pattern synthesizing of the concentric ring arrays with minimum number of rings

In this paper, a new method is proposed for pattern synthesizing of the concentric ring arrays with minimum number of rings. The method is established based on a hybrid combination of the conventional least square method and the Richardson technique. Using a sampling function, a system of linear equations is created. After that, using the QR factorization and Richardson techniques, the complex excitation coefficients of the array elements are obtained. Furthermore, it is shown that using a simple try and error process, the number of array rings can be reduced. To verify the accuracy and the advantages of the proposed technique, a few well-known patterns are investigated and the obtained results are compared.

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.

Demonstration of 10-channel mode- and polarization-division multiplexed free-space optical transmission with successive interference cancellation DSP.

We experimentally demonstrate 10-channel mode-division multiplexed free-space optical transmission with five spatial modes, each carrying 19.6925-Gbaud dual-polarization quadrature phase shift keying signals. Strong inter-mode cross talk is observed in our commercially available photonic lantern based system when using a complete orthogonal mode set as independent channels. A successive interference cancellation based multiple-input multiple-output digital signal processing (DSP) algorithm is first applied to mitigate the inter-mode cross talk in mode-division multiplexed systems. The DSP also supports unequal transmit and receive channel numbers to further improve the cross talk resiliency. Compared to the conventional minimum mean square error DSP, the required optical signal-to-noise ratio of the successive interference cancellation DSP is decreased by approximately 5 dB at the hard-decision forward error correction limit. As a result, this system demonstrates a record-high independent channel number of 10 and spectral efficiency of 13.7 b/s/Hz in mode-division multiplexed free-space optical systems.

Generalized de-homogenization via sawtooth-function-based mapping and its demonstration on data-driven frequency response optimization

De-homogenization is becoming an effective method to significantly expedite the design of high-resolution multiscale structures, but existing methods have thus far been confined to simple static compliance minimization problems. There are two critical issues in accommodating general design cases: enabling the design of unit-cell orientation and using free-form microstructures. In this paper, we propose a generalized de-homogenization method to address these two issues, significantly increasing its applicability in various multiscale design cases. Instead of using conventional square cells with rectangular holes, we devise a parameterized microstructure composed of bars in different directions to provide more diversity in stiffness while retaining geometrical simplicity. The microstructural geometry-property relationship is then surrogated by a multi-layer neural network to avoid costly homogenization analysis during optimization. A Cartesian representation of the rotation angle is incorporated into homogenization-based optimization to design the unit-cell orientation. Corresponding high-resolution multiscale structures are obtained from the homogenization-based designs through a conformal mapping constructed with sawtooth function fields. This allows us to morph complex microstructures into an oriented and compatible tiling pattern, while preserving the local homogenized properties. To demonstrate our method with a specific application, we optimize the frequency response of structures under harmonic excitations within a given frequency range. It is the first time that the de-homogenization framework, enhanced by the sawtooth function, is applied for complex design scenarios beyond static compliance minimization. The examples illustrate that high-resolution multiscale structures can be generated with high efficiency and much better dynamic performance compared with the macroscale-only optimization. Beyond frequency response design, our proposed framework can be applied to general static and dynamic problems.

Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy.

In order to reduce the uncertainty of the genetic algorithm (GA) in optimizing the near-infrared spectral calibration model and avoid the loss of spectral information of the unselected variables, a strategy of fusing consensus models is proposed to measure the soluble solids content (SSC) in peaches. A total of 266 peach samples were collected at four arrivals, and their interactance spectra were scanned by an integrated analyzer prototype, and then an internal index of SSC was destructively measured by the standard refractometry method. The near-infrared spectra were pre-processed with mean centering and were selected successively with a genetic algorithm (GA) to construct the consensus model, which was integrated with two member models with optimized weightings. One was the conventional partial least square (PLS) optimized with GA selected variables (PLSGA), and the other one was the derived PLS developed with residual variables after GA selections (PLSRV). The performance of PLSRV models showed some useful spectral information related to peaches’ SSC and someone performed close to the full-spectral-based PLS model. Among these 10 runs, consensus models obtained a lower root mean squared errors of prediction (RMSEP), with an average of 1.106% and standard deviation (SD) of 0.0068, and performed better than that of the optimized PLSGA models, which achieved a RMSEP of average 1.116% with SD of 0.0097. It can be concluded that the application of fusion strategy can reduce the fluctuation uncertainty of a model optimized by genetic algorithm, fulfill the utilization of the spectral information amount, and realize the rapid detection of the internal quality of the peach.

Design and first implementation of wireless square-shaped transmission line resonators in 1H MRI for small animal studies

This work is dedicated to the development of a novel design for wireless transmission line resonators (TLRs). The TLRs are often considered as circular-shaped coils made up of two conductive circuits separated by a dielectric layer. We propose a square-shaped TLR design, wherein the coil has two square turns with two symmetrical gaps on each of the conductive layers, and the latter are rotated relative to each other by 90°. The calculation error of the resonant frequency of the square-shaped TLRs is no more than ∼3% of the measured value. The effectiveness of the square-shaped TLR design was evaluated in comparative 1H MRI studies to conventional wireless square loop of the same resonant frequency and with the same-sized inner square of the TLR. The Bruker birdcage was used as a transceiver and as inductively coupled with the wireless coils. We found that the performance of the square-shaped TLR and the square loop is comparable, but the B1+-field generated by the TLR has a wider distribution profile. It was reflected in rat brain studies, when some structures of rat head were not captured by the square loop. Comparative experiments with a standard circular-shaped TLR showed that a signal is predominantly concentrated inside the inner turn of the TLRs. The proposed TLR design can be a promising path to be explored, especially for scanning small objects of study, when the scan area is comparable to the size of the rigid lumped capacitors.

Axial mechanical properties and robust optimization of foam-filled hierarchical structures

In this study, two novel foam-filled hierarchical structures are proposed, namely foam-filled square hierarchical tubes (FSHT) and foam-filled circular hierarchical tubes (FCHT). First, dynamic FEA models of the foam-filled hierarchical tubes are established by LS-DYNA and validated against experimental data. The mechanical behaviors of the foam-filled hierarchical tubes under axial load are investigated. The results show that novel foam-filled hierarchical tubes have better crashworthiness performances than conventional foam-filled square tubes (FST) and foam-filled circular tubes (FCT). Then, the theoretical solution of energy absorption is developed, and its accuracy is validated. Next, the design parameters (foam density and thin-wall thicknesses) of FSHT and FCHT are analyzed by orthogonal array design, and the effects of design variables on crashworthiness are studied. Finally, to obtain the optimal design parameters of FSHT and FCHT, the robust optimization method considering the manufacturing uncertainty is implemented by employing the interval uncertainty model (IUM) and the feedforward neural network (FNN). Structures obtained with the proposed robust optimization method are of more stable performance compared with the traditional deterministic optimization counterparts. The findings provide a hybrid design combining hierarchical tubes with foam fillers with superior crashworthiness performance for energy-absorbing devices, and the robust optimization method can be used to design efficient lightweight crashworthy structures.

Behavior of circular-steel-tube-confined square CFST short columns under axial compression

This paper proposes circular-steel-tube-confined square concrete-filled steel tube (CFST) to improve the mechanical property of the newly built and existing deficient square CFST. The external adding circular steel tube does not enter the beam-column connection area and mainly functions as lateral confinement to the inner square CFST. Twelve circular-steel-tube-confined square CFST short column specimens and twelve conventional square CFST short column specimens were designed and tested under axial compressive load. The main variables of the test include column type, steel ratio, outer circular steel tube size, and concrete strength. The overall failure modes, axial load-displacement curves, macroscopic deformation characteristics, and the inner and outer circular steel tube confinement were analyzed and compared systematically. The study revealed that the outer circular steel tube yielded horizontally in tensile strength and provided effective confinement to the inner square CFST. Therefore, the bearing capacity of the square stub column was improved significantly, and the brittle failure in the confined area was prevented. Due to the effective confinement by the outer circular steel tube, the stress state in the square steel tube was much more uniform. The column configuration led to an effective but uneven confinement in the outer circular steel tube, concentrating near the square steel tube corner. Based on the superposition method, a suitable design method is proposed to predict the compressive strength of circular-steel-tube-confined square CFST, and the theoretical predictions are in good agreement with the test results of this paper.

Octagonal Three-Dimensional Shim Coils Structure and Design in Atomic Sensors for Magnetic Field Detection

Triaxial uniform coils installed in the magnetically shielded cylinder are essential components of an atomic magnetometer to achieve ultra-sensitive measurements and are used to compensate for the ambient magnetic remanence in the magnetometer’s operating area. This paper proposes the new three-dimensional octagonal coils configured with a two-pair octagonal coil and two double-nested octagonal coils, which differs from the conventional square or circular shim coils. The spatially homogeneous distribution of the magnetic field of the three-dimensional shim coils based on this configuration is simulated and evaluated, and then the fabrication and experimental tests are completed. The theoretical calculation results show that the magnetic field uniformity of the z-direction two-pair octagonal coil is improved by about one order of magnitude compared with that of the conventional coil such as the Lee-Whiting coil and the Rubens coil. The magnetic field uniformity of x-direction or y-direction double nested octagonal coil is promoted by about two orders of magnitude compared with that of saddle coil. The experimental magnetic field distribution is consistent with the theoretical value. This configuration can reduce the outer diameter of the coil under the premise of meeting the requirement of setting the magnetic field uniform zone, and it is easier to install than the cylindrical coil assemblies.

Robust classification via clipping-based kernel recursive least lncosh of error

Classification as a supervised machine learning method predicts the class label from the features distribution and the training process. The traditional classifier algorithms are not robust in the presence of outliers and noisy features. In this study, we suggest a novel robust classifier using kernel recursive least lncosh (RC-KRLL), in which the clipping concept is used to ignore the effect of large noises. Instead of the conventional mean square error cost function, the suggested RC-KRLL method is derived from the lncosh loss function, being more suitable for non-Gaussian noise. The mean, mean-square convergence, and learning curve of the RC-KRLL are discussed theoretically. The simulation results show that the proposed method outperforms the other robust state-of-the-art classification algorithms in the presence of synthetic non-Gaussian noise over UCI data sets, such as the mixture of Gaussian noise with different variances, impulse, and Alpha–Beta noises. The obtained results over 500px data sets on social media with real outliers and mislabeled samples confirmed the acceptable performance of the proposed method.

RSS-Based Localization of Multiple Radio Transmitters via Blind Source Separation

This letter proposes a methodology for counting and locating the nodes of an uncooperative wireless network using power measurements collected by sensors. The approach is blind, allowing the detection and localization of the nodes without knowing the network&#x2019;s specific features (i.e., the number of nodes, modulation type, and medium access control (MAC)). Because the signals captured by the radio-frequency (RF) sensors are additively mixed, blind source separation (BSS) is used to separate transmitted power profiles. Then, received signal strength (RSS) is extracted from the reconstructed signals and localization is performed through conventional least square (LS) and maximum likelihood (ML) techniques. Numerical results reveal that the BSS-ML approach reaches a rather low localization error in mild shadowing regimes, even when the ratio between the number of RF sensors and nodes, <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>, is close to 1. Finally, it is shown how the performance degradation introduced by the imperfect BSS is slight and that the root mean square error (RMSE) approaches the Cram&#x00E9;r-Rao lower bound (CRLB) when increasing <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>.

Behavior of reinforced concrete columns with double spirals under axial and eccentric loads

To provide reliable lateral confinement in square reinforced concrete columns, a double spiral reinforcement was developed in this study. Sixteen column specimens were tested under axial and eccentric loads to investigate the structural behavior. The test parameters were the concrete strength, spacings of circular spirals, spacings of square spirals, and eccentricity. The failure modes and load-displacement relationships of the specimens were examined. The structural performances including the load-carrying capacity, stiffness, and ductility were analyzed. Results showed that the double spirals were fully developed and provided satisfactory confinement in square reinforced concrete columns under axial and low eccentric loads. The load-carry capacity, stiffness, and ductility of double spirals confined concrete columns were increased compared to those of conventional square reinforced concrete columns. A set of design formulas for the axial and eccentric load-carrying capacity of the double spirals confined concrete columns was proposed. The method can provide satisfactory predictions of the load-carrying capacity.

Channel Estimation Based on Complex-Valued Neural Networks in IM/DD FBMC/OQAM Transmission System

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is a promising candidate for 5G mobile fronthaul system. Due to the inherent imaginary interference of FBMC/OQAM signals, an accurate channel estimation is particularly indispensable. In this paper, an efficient method is proposed based on complex-valued neural networks (CVNN) for an accurate channel estimation of FBMC/OQAM signals with low computational complexity and pilot overhead. Here, the channel frequency response (CFR) is first calculated for training the CVNN. Next, the CFR estimated from the extracted pilots is exploited as the input of the trained CVNN to yield the CFR of data symbols. We experimentally demonstrate a 12.5-GBd intensity modulation direct detection (IM/DD) FBMC/OQAM transmission system over 30-km and 50-km standard single mode fibers (SSMF). The experimental results show that the proposed method achieves 3-dB and 1-dB receiver sensitivity improvement at the bit error ratio (BER) of 3.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> with only 5% pilot overhead respectively, compared to the conventional least square (LS) and linear minimum mean error (LMMSE) methods. When the pilot overhead decreases from 10% to 1%, its BER performance is always better than LS and LMMSE. For the computational complexity, its complexity is the same order of magnitude as LS and lower than LMMSE. On the other hand, the CVNN requires less hidden neurons to keep the similar BER performance as real-valued neural networks (RVNN). Meanwhile, it has excellent resilience to fiber chromatic dispersion over 30-km and 50-km SSMF.

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (&gt;27%) and a high missing ratio (&gt;33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD &gt; 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.

Parametric Assessment of Spinal Cord Stimulation on Bladder Pain—Like Responses in Rats

ObjectivesSpinal cord stimulation (SCS) for the treatment of pelvic visceral pains has been understudied and underused. The goal of the current study was to examine multiple stimulation parameters of SCS to determine optimal settings for the inhibition of responses to urinary bladder distension (UBD) in animal models of bladder pain as a guide for human studies. Materials and MethodsAdult, female isoflurane/urethane-anesthetized rats underwent a T13/L1 mini-laminectomy sufficient to implant an SCS paddle lead for neuromodulation. Silver wire electrodes were inserted into the external oblique musculature. A 22-gauge angiocatheter was placed transurethrally into the bladder and used to deliver phasic, air UBDs at pressures of 10 to 60 mm Hg and visceromotor (abdominal contractile) electromyographic responses to UBD measured in the presence and absence of SCS. Electromyographic activity was quantified using standard differential amplification and rectification. Parameter settings for SCS included both conventional (10, 50, 100 Hz) and high frequency (1,000, 5,000, and 10,000 Hz) biphasic square wave pulses with 50 to 200 μs durations. To create states of hypersensitivity, pretreatment of adult rats included an intravesical zymosan infusion 24 hours before testing with and without a preceding episode of neonatal bladder inflammation. ResultsLow frequency (10, 50, and 100 Hz) 200 μs biphasic pulses at submotor thresholds demonstrated inhibition of visceromotor responses (VMRs) to UBD in rats made hypersensitive to UBD by a protocol that included neonatal cystitis. Onset of inhibitory effects occurred within 20 minutes of beginning SCS. Otherwise, SCS at all other parameters studied and in other tested rat models produced either no significant effect or augmentation of VMRs. ConclusionsDemonstration of inhibitory effects of SCS in a clinically relevant model of bladder pain suggests the potential utility of this therapy in patients with painful bladder disorders.

Ultrasonic Doppler Based Silent Speech Interface Using Perceptual Distance

Moderate performance in terms of intelligibility and naturalness can be obtained using previously established silent speech interface (SSI) methods. Nevertheless, a common problem associated with SSI has involved deficiencies in estimating the spectrum details, which results in synthesized speech signals that are rough, harsh, and unclear. In this study, harmonic enhancement (HE), was used during postprocessing to alleviate this problem by emphasizing the spectral fine structure of speech signals. To improve the subjective quality of synthesized speech, the difference between synthesized and actual speech was established by calculating the distance in the perceptual domains instead of using the conventional mean square error (MSE). Two deep neural networks (DNNs) were employed to separately estimate the speech spectra and the filter coefficients of HE, connected in a cascading manner. The DNNs were trained to incrementally and iteratively minimize both the MSE and the perceptual distance (PD). A feasibility test showed that the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility measure (STOI) were improved by 17.8 and 2.9%, respectively, compared with previous methods. Subjective listening tests revealed that the proposed method yielded perceptually preferred results compared with that of the conventional MSE-based method.

Development of an allocation method to synthesis of unequally spaced arrays with minimum number of elements and mutual coupling considerations

A method is proposed to synthesize unequally spaced arrays so that it has minimum number of elements. Also, it is shown that the proposed method can reduce the total number of elements of equally spaced arrays. The advantage of the proposed method is that it is very fast and straightforward. In this method, the locations of array elements are calculated by considering the mutual coupling between two adjacent array elements, first. After determining the elements locations, two scenarios may be existed. In the first scenario, conventional least square method is proposed. In the second scenario and to increase the accuracy of the final design, recursive least square estimation algorithm is suggested. Mean square error between the desired and synthesized pattern is considered to measure the performance of the method. Several arrays are examined to check the performance of the proposed method. Since, the matrix pencil method (MPM) method has been widely used in the design of nonuniformly spaced arrays, in all examples, the results of MPM technique are considered in comparison process. The obtained results show that the proposed method has a good performance both in reducing the number of array elements and in the accuracy of the synthesized pattern, especially for arrays with beam-shaped pattern such as flat-top and cosecant radiation patterns.