Array Optimization Method Research Articles

Novel magnetic field array optimization method for the chemical magnetorheological finishing of Ti-6Al-4V alloy with SiO2 abrasive and Fe3O4 magnetic particles

Novel magnetic field array optimization method for the chemical magnetorheological finishing of Ti-6Al-4V alloy with SiO2 abrasive and Fe3O4 magnetic particles

Electronic sensing combined with machine learning models for predicting soil nutrient content

Traditional methods for detecting soil nutrient content usually involve laborious and time-consuming experimental procedures, hindering the efficiency of soil analysis and making them less suitable for large-scale soil testing. Therefore, there exists a pressing need to develop innovative solutions. This study aimed to investigate the potential relationship between soil pyrolysis gas and soil nutrient content by establishing a model that combined electronic sensing and machine learning to predict soil nutrient content. The initial data sets were derived from utilizing pyrolysis gas response curves of soil samples acquired from 10 distinct gas sensors. Afterward, we innovatively proposed a sensor array optimization (SAO) method based on the dynamic feature importance (DFI) of the Random Forest-Pearson Correlation Coefficient (RF-PCC) to identify the optimal sensor combinations for four soil nutrients. Furthermore, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Multilayer Perceptron-Random Forest (MLP-RF) models were employed to predict the soil nutrient content based on the electronic sense to address the challenges associated with developing a unified model. The MLP-RF models showed superior performance compared with the other three models, with the coefficient of determination (R2) for the four soil nutrients ranging from 0.54 to 0.94.Overall, our research not only provides approximately quantitative predictive outcomes but also contributes to a better understanding ofa new approach for detecting soil nutrient content.

A Quantitative Array Optimization Method for the Electronic Nose System Based on Edge Computing and MEMS Sensors

A Quantitative Array Optimization Method for the Electronic Nose System Based on Edge Computing and MEMS Sensors

A Low-Cost Wideband Dual Circularly Polarized Aperiodic 2-D Phased Array

In this article, an aperiodic wideband high isolation dual circularly polarized (CP) scanning array is introduced. A novel aperiodic array optimization method based on the perturbation principle is proposed to solve the grating lobes of large spacing scanning array and thinning the number of array elements, thereby saving energy and reducing cost. The impedance bandwidth (IBW) of the proposed array element is 58%. Meanwhile, the bandwidth of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert S_{21}\vert &lt; -15$ </tex-math></inline-formula> dB is 42.5%, and the bandwidth of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert S_{21}\vert &lt; -20$ </tex-math></inline-formula> dB is 27.5%, which is better than previous reports. The 3 dB axial ratio bandwidth (ARBW) of both the left-hand circular polarization and the right-hand circular polarization is 55.4%. Furthermore, the formed 16-element aperiodic array can achieve a 2-D low side lobe level (SLL) with ±45° beam scanning across the entire operating band, improving the system’s ability to avoid interference. Compared with a 25-element compact uniform array of the same aperture, 36% transmitter and receiver components are saved. The proposed scanning array not only has high scanning performance but also has engineering versatility due to its low-cost implementation scheme.

Design and Optimization of Electronic Nose Sensor Array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants

Traditional vehicle exhaust pollutant detection methods, such as bench test and remote sensing detection, have problems such as large volume, high cost, complex process, long waiting time, etc. In this paper, according to the main components of vehicle exhaust pollutants, an electronic nose with 12 gas sensors was designed independently for real-time and rapid detection of vehicle exhaust pollutants. In order to verify that the designed electronic nose based on machine learning classification method can accurately identify the exhaust pollutants from different engines or different concentration levels from the same engine. After feature extraction of the collected data, Random Forest (RF) was used as the classifier, and the average classification accuracy reached 99.92%. This result proved that the designed electronic nose combined with RF method can accurately and sensitively judge the concentration level of vehicle exhaust pollutants.. Then, in order to enable the electronic nose to be vehicle-mounted and to achieve real-time and rapid detection of vehicle exhaust pollutants. We used Recursive Feature Elimination with Cross Validation (RFECV), Random Forest Feature Selector (RFFS) and Principal Component Analysis (PCA) to optimize the sensor array. The results showed that these methods can effectively simplify the sensor array while ensuring the RF classifier’s classification recognition rate. After using RFECV and RFFS to optimize the sensor array, the RF classifier’s classification recognition rate of the optimized sensor arrays for vehicle exhaust pollutants reached 99.77% and 99.44%, respectively. The numbers of sensors in the optimized sensor arrays were six and eight respectively, which achieved the miniaturization and low-cost of the electronic nose. With the limitation of six sensors, RFECV is the best sensor array optimization method among the three methods.

Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

Proposal of novel analytical wake model and GPU-accelerated array optimization method for oscillating wave surge energy converter

Oscillating wave surge converter (OWSC) is widely utilized to exploit energy from waves. Due to the wake effects, the layout of OWSCs will significantly affect the energy output of an OWSC farm. However, up to now the layout of OWSC farm was only determined empirically, and there was no quantitative layout optimization method for OWSC farm. The quantitative layout optimization needs analytical wake model for OWSC to conduct fast optimization iterations. However, there was still no analytical wake model for OWSC. Therefore, in this study, we proposed an analytical wake model regarding regular waves, based on which a quantitative method for optimizing the layout of OWSC farm was also proposed. It was found that the total energy outputs of the optimized layouts using the proposed method are significantly larger than those of the empirical ones, and increasing the spacing between the OWSCs in the bi-direction of wave propagation can greatly increase the total energy output. The proposed OWSC analytical wake model can predict the wave height changes with an accuracy of 94.73%. Innovatively, GPU-acceleration technology was utilized, and the GPU-based codes can run at least 1479 times faster than the CPU-based ones. Those implemented codes are opened for other researchers.

Multi-response Characteristics Optimization of a Paper-based Composite using Taguchi Method and Particle Swarm Optimization

Our dependency towards wood has reduced the number of forests especially in Indonesia. Hence, many people tried to use another material as a replacement of wood. One of them is composite. Department of Industrial Engineering Universitas Sebelas Maret has developed paper-based composite using newspaper waste in Praktikum Perancangan Teknik Industri (PPTI) II. Unfortunately, the quality of composite is not so good. This paper tried to understand the effect of factors to responses and discover the best combination of factors to obtain the best responses. The responses of this experiment are tensile strength, bending strength and impact value. The factors of this experiment are paper pulp, PVAc glue and water. Taguchi method is used to find the appropriate orthogonal array and Particle Swarm Optimization method to find the optimum combination of the factors. Orthogonal array used in this experiment is L9 (34). Through analysis, all of the factors are significant to tensile and bending strength, but they are not significant to impact value. The best combination of factors is 175 grams paper pulp, 130 grams PVAc glue and 175 ml water. Optimum results obtained for tensile strength is 0.7595 N/mm2, bending strength is 4.873 N/mm2 and impact value is 0.0138 J/mm2.

Array Optimization for Maximum Beam Collection Efficiency to an Arbitrary Receiving Plane in the Near Field

In this article, an array optimization method for maximizing beam collection efficiency (BCE) to an arbitrary receiving plane in the near field is proposed. The proposed method considers each element pattern in an array; thus, optimization with mutual coupling is possible by using active element patterns. Besides, the proposed method can consider the polarization direction of a receiving antenna since the optimization problem is calculated in vector form. The problem of maximization of BCE boils down to the generalized eigenvalue problem, and the best solution can be obtained mathematically. The results obtained via numerical analysis in various calculation models indicate the validity of the proposed method. Optimization is then performed using the active element patterns obtained from electromagnetic simulation. Based on the comparison of the results without considering mutual coupling in the optimization, it is confirmed that the proposed method yields better results, such as lower reflection and antenna losses and higher BCE and transmission efficiency, by using active element patterns.

A conceptual study of utilizing compressive-sensing-based fan noise mode detection for aeroengine prognostic and health management.

In this paper, a concept about the aeroengine fan health monitoring approach is proposed based on the compressive-sensing-based acoustic mode detection method. Utilizing only a few acoustic sensors, possible accidents inducing the change of the fan noise mode spectrum can be inferred. To enable such a concept, the array design strategy and optimization method are first studied by maximizing the incoherence of the so-called sensing matrix. The performance of the designed array is examined in both simulation and experimental studies. Then, the idea of fan noise monitoring is conceptually demonstrated in wind tunnel tests by taking into account possible accidental scenarios with foreign body intrusions. The simulation and experimental results suggest that under such circumstances remarkable changes appear in the azimuthal mode spectrum from fan noise. Finally, it is demonstrated that the fan noise variation can be successfully detected by the compressive sensing method with just six sensors. In this way, the foreign body intrusion can be further diagnosed through the combination of compressive sensing and mode detection. Overall, the results confirm the potential capability of the proposed concept for future aeroengine health monitoring applications.

High Throughput Screening of Surface Modificated In2O3 for VOC Gas Sensing Array Optimization

Gas sensor array & pattern recognition is the most promising method to improve the anti-interference performance of metal oxide gas sensor. How to choose the initial gas sensor array is very important in gas sensor array optimization. In this paper, an initial array optimization method based on combinatorial and high-throughput (CHT) method is proposed. In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> samples were synthesized by non-aqueous sol-gel method. The metal ion modified In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> gas sensing film was deposited by a gas-sensing film parallel synthesis platform using a combinatorial and high-throughput (CHT) experiment process. A high-throughput gas sensing unmanned testing platform was used to characterize the gas sensing properties. Volatile organic compounds (VOCs), including benzene, toluene, xylene, ethanol, acetone and formaldehyde, were tested at constant temperature and under temperature programmed cooling. And the In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> -based gas sensing film with Ho, Ir, Ru, Mn, V and Mo surface modification were screened out with good cross-selectivity to different VOCs. All the results in this work form a comprehensive and detailed database, providing a reference for choosing the initial gas sensor array.

An effective gas sensor array optimization method based on dynamic feature importance

Gas sensor array optimization is a key problem in the field of electronic noses, and it is also a special feature selection problem. In this paper, we propose a novel measure of sensor (or feature) importance, named dynamic feature importance, based on feature correlation and feature importance. Also, we propose an effective electronic nose sensor array optimization algorithm SAO_DFI based on the dynamic feature importance. We analyze the effects of repeated sensors, sensor (or feature) importance, sensor (or feature) correlation, and sensor characteristic parameters, based on the proposed SAO_DFI algorithm using data collected in two different gas environments. The optimization results demonstrate the effectiveness, robustness, and interpretability of the array optimization algorithm.

An Optimization Method of MIMO Radar Array Based on Genetic Algorithm

Since the traditional radar imaging takes too much time, it is necessary to find new imaging radar which can image fast and conserve resources. MIMO radar has waveform diversity or spatial diversity. So it can realize high resolution snapshot imaging under the same condition. However, the imaging performance of MIMO radar is tightly related to the antenna array design. This paper proposes a MIMO array optimization method based on the genetic algorithm (GA). The GA is used to directly calculate the position of the transceiver element and at meantime the fitness function based on MIMO array design is proposed. Experiments show that this method not only can reduce the number of array elements, but also has certain optimization ability in beam pattern performance

Design and optimization of sensor array for magnetic gradient tensor system

PurposeThe differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array.Design/methodology/approachThe authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array.FindingsThis paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance.Originality/valueIn this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios.

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

Methods for Assessing and Optimizing Solar Orientation by Non-Planar Sensor Arrays.

Non-planar sensor arrays are used to determine solar orientation based on the orientation matrix formed by orientation vectors of the sensor planes. Solar panels or existing photodiodes can be directly used without increasing the size or mass of the spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies with the lack of an assessment-based approach. A formulation was developed for the supremum (i.e., the least upper bound) of orientation error of an arbitrary orientation matrix in terms of its influencing factors. The new formulation offers a way to evaluate the supremum of orientation error considering interference with finite energy and interference with infinite energy but finite average energy. For a given non-planar sensor array, a sub-matrix of the full orientation matrix would reach the optimal accuracy of orientation if its supremum of orientation error is the least. Principles for designing an optimal sensor array relate to the configuration of the orientation matrix, which can be pre-determined for a given number of sensors. Simulations and field experiment tested and validated the methods, showing that our sensor array optimization method outperforms the existing methods, while providing a way of assessment and optimization.

A Fast Terahertz Imaging Method Using Sparse Rotating Array.

For fast and standoff personal screening, a novel terahertz imaging scheme using a sparse rotating array is developed in this paper. A linearly sparse array is designed to move along a circular path with respect to an axis perpendicular to the imaging scenario. For this new scheme, a modified imaging algorithm is proposed based on the frequency-domain reconstruction method in circular synthetic aperture radar. To achieve better imaging performance, an optimization method of the sparse array is also proposed, according to the distribution of the spectral support. Theoretical and numerical analysis of the point spread function (PSF) is provided to demonstrate the high-resolution imaging ability of the proposed scheme. Comprehensive simulations are carried out to validate the feasibility and effectiveness of the array optimization method. Finally, the imaging results of a human-scattering model are also obtained to further demonstrate the good performance of this new imaging scheme and the effectiveness of the array optimization approach. This work can facilitate the design and practice of terahertz imaging systems for security inspection.

Investigation of Low-Sidelobe Beampattern Controlling Methods for Acoustic Transmitting Array of Underwater Vehicles

Abstract In underwater unmanned vehicles, complex acoustic transducer arrays are always used to transmitting sound waves to detect and position underwater targets. Two methods of obtaining low-sidelobe transmitting beampatterns for acoustic transmitting arrays of underwater vehicles are investigated. The first method is the boundary element model optimization method which used the boundary element theory together with the optimization method to calculate the driving voltage weighting vector of the array. The second method is the measured receiving array manifold vector optimization method which used the measured receiving array manifold vectors and optimization method to calculate the weighting vector. Both methods can take into account the baffle effect and mutual interactions among elements of complex acoustic arrays. Computer simulation together with experiments are carried out for typical complex arrays. The results agree well and show that the two methods are both able to obtain a lower sidelobe transmitting beampattern than the conventional beamforming method, and the source level for each transmitting beam is maximized in constraint of the maximum driving voltage of array elements being constant. The effect of the second method performs even better than that of the first method, which is more suitable for practical application. The methods are very useful for the improvement of detecting and positioning capability of underwater unmanned vehicles.

Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach

Mobile Electronic Nose (MoLen) is a prospective concept for Sensing as a Service (S2aaS) development. Furthermore, gas sensor array is a substantial part of MoLen. This work treats about two issues related with gas sensor array. First, commonly used resistive sensor e.g. MOS (Metal-Oxide Semiconductor) gas sensor is highly contaminated with noise. Second, a poor combination of sensor array leads to features redundancy issue. These problems cause significant performance degradation on classifier. It will get worse if the classifier runs on S2aaS environment. To deal with these issues, this study proposes the robust sensor array optimization method based on Wavelet Transform to handle the noise and the modified Fast Correlation-Based Filter (FCBF) to find the best combination of sensor array with minimizing feature redundancy. This study has the following contribution: i) reducing the noise from gas sensor array that generated irrelevant data; ii) finding the best sensor array for beef quality classification to improve the quality of input to classifier. The experimental results show that the proposed method successfully reduces the noise power at maximum 14.41% and it is able to determine the best combination of sensors in sensor array with the 16% of improvement of General Resolution Factor (GRF)that is associated with larger classification rate.

Computation of optimized arrays for 3-D electrical imaging surveys

3-D electrical resistivity surveys and inversion models are required to accurately resolve structures in areas with very complex geology where 2-D models might suffer from artefacts. Many 3-D surveys use a grid where the number of electrodes along one direction (x) is much greater than in the perpendicular direction (y). Frequently, due to limitations in the number of independent electrodes in the multi-electrode system, the surveys use a roll-along system with a small number of parallel survey lines aligned along the x-direction. The ‘Compare R’ array optimization method previously used for 2-D surveys is adapted for such 3-D surveys. Offset versions of the inline arrays used in 2-D surveys are included in the number of possible arrays (the comprehensive data set) to improve the sensitivity to structures in between the lines. The array geometric factor and its relative error are used to filter out potentially unstable arrays in the construction of the comprehensive data set. Comparisons of the conventional (consisting of dipole-dipole and Wenner–Schlumberger arrays) and optimized arrays are made using a synthetic model and experimental measurements in a tank. The tests show that structures located between the lines are better resolved with the optimized arrays. The optimized arrays also have significantly better depth resolution compared to the conventional arrays.