Multiple Scheme Research Articles

A Steffensen type optimal eighth order multiple root finding scheme for nonlinear equations

In this study, we introduce a novel weight function-based eighth order derivative-free method for locating repeated roots of nonlinear equations. It is a three-step Steffensen-type scheme with first order divided differences in place of the first order derivatives. It is noteworthy that so far only few eighth order derivative free multiple root finding scheme exist in literature. Different nonlinear standard and applications based nonlinear functions are used to demonstrate the applicability of the suggested approach and to confirm its strong convergence tendency. Drawing basins of attraction on the graphical regions demonstrates how the offered family of approaches converge.

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) Model is proposed for advancing the network-level movement-based traffic volume prediction task. The proposed model attempts to further improve the-state-of-art and practice algorithms in traffic prediction for arterial network adaptive signal control utilizing tradition traffic flow theory boosted deep-learning methodology. For precisely inferencing the movement-based demand at cycle-to-cycle level, the proposed model incorporates spatial graph convolution inferencing layer and temporal inferencing layer to explore both the intricate spatial temporal dependencies, respectively. A signal control refining module is contrived to deduce the controlled movement saturation flow and introduce the essential control inferences, which is of great significance but frequently neglected in the previous researches. Additionally, according to the real-time movement specified travel time, this paper creatively constructs an adjacent graph with dynamic order for more accurately capturing the ever-changing spatial relevancies. Field experiments with multiple signal schemes were conducted in the downtown area of Zhangzhou (China). The promising results demonstrated the state-of-the-art accuracy than other high-performance volume prediction algorithms. Implementing the proposed model enables to obtain accurate movement-based volume predictions, which would assist the traffic management agencies in adjusting signal timing adaptively and further improve the efficiency of signal intersection.

Hysteretic behavior and recoverability research on the shape memory effect of SMA bars under cyclic loading

To utilize the special shape memory effect of shape memory alloy (SMA) to develop energy dissipation devices with a good energy dissipation effect and easy recovery of residual deformation after an earthquake, six SMA bars with a diameter of 14 mm were tested under multiple loading schemes in this paper. Hysteretic performance indicators, such as the hysteresis loop, energy dissipation capacity, secant stiffness, equivalent damping ratio and deformation recovery rate, were analyzed. The results illustrated that all specimens exhibited the desired full and stable hysteresis loops and a high residual deformation recovery rate achieved by water-bath heating and cooling after being subjected to 6.0% tensile strain. Due to the characteristics of SMA material, excessive compressive strain caused the stress value to soar, and hysteresis loops contributed to the phenomenon of tension–compression stress imbalance and affected the deformation recovery rate. No significant differences were observed in the bearing capacity and energy dissipation capacity of the same specimen with a strain amplitude of 6.0% after repeated loadings, indicating that martensitic shape memory alloys (SMAs) have stable mechanical characteristics and favorable recycling performance. The tensile cyclic constitutive model and tension–compression cyclic constitutive model of the martensite shape memory effect were proposed. Based on MATLAB/Simulink software, numerical simulations were carried out. The simulation results and fitted the experimental results, which proved the theoretical support for utilizing the shape memory effect of martensite SMA.

Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming

With the increasing coupling of integrated electricity and gas systems, it's essential to implement a proper planning scheme. The planning of the integrated electricity and gas systems usually consists of multiple planning stages and various uncertainties, making the problem difficult to solve. In this paper, combining the approximate dynamic programming method and the alternating direction multiplier method, a distributed approximate dynamic programming based multiple stage stochastic planning scheme is proposed for the integrated electricity and gas systems. The multiple stage stochastic planning scheme realizes the coordination of new installation and expansion of power grid lines, natural gas pipelines, substations, city gates, photovoltaics, wind turbines, gas turbines and energy storage devices, while considering both the long-term and the short-term uncertainties. The multiple stage stochastic planning model is reformulated as a Markov decision process with a sequential decision strategy, in which the investment variables are decided stage by stage with long-term uncertainties gradually revealed in the planning period. The distributed approximate dynamic programming algorithm is used to solve the multiple stage stochastic planning model, which is in the form of the Markov decision process, by decoupling in both temporal and spatial dimensions. Numerical tests on the test system with a 24-node power grid and a 30-node natural gas network verify the effectiveness of the multiple stage stochastic planning model.

A cooperative security monitoring mechanism aided by optimal multiple slave cluster heads for UASNs

As each cluster head (CH) sensor node is used to aggregate, fuse, and forward data from different sensor nodes in an underwater acoustic sensor network (UASN), guaranteeing the data security in a CH is very critical. In this paper, a cooperative security monitoring mechanism aided by multiple slave cluster heads (SCHs) is proposed to keep track of the data security of a CH. By designing a low complexity "equilateral triangle algorithm (ETA)", the optimal SCHs (named as ETA-based multiple SCHs) are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve large-scale data security monitoring. In addition, by analyzing the entire monitoring process, the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced. The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes. In addition, the numerical simulation results are consistent with the theoretical analysis results, thus verifying the effectiveness of the proposed scheme.

Comprehensive review on control schemes and stability investigation of hybrid AC-DC microgrid

The utility grid integration of distributed generation units is seen as one of the most accomplished through the use of microgrid. Control methods and stability analyses in the microgrid are continuously being investigated in the field of hybrid microgrid. The most reviewed control method is hierarchical control, consisting of primary, secondary, and tertiary layers. This paper reviews multiple schemes of primary control for current or voltage regulation, secondary control for voltage or current error correction, power-sharing in microgrid, and tertiary control for power flow and energy management in a hybrid microgrid system. Additionally, this study examines the benefits and drawbacks of several control architectures that function as distributed, centralized, and decentralized controls. The principle of operation and effectiveness of each microgrid architecture control method have been discussed. Specific control factors are investigated in this paper, including mode transition and coordinated control between numerous interlinking converter and energy storage systems. System stability is the main concern of microgrid for system engineer thorough knowledge of control strategies is the upmost requirement. Thus, this paper highlights control strategies, rotor angle, voltage and frequency instability in power systems, and methods for improving stability in microgrid are also reviewed.

Enhanced security using multiple paths routine scheme in cloud-MANETs

Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Application of Algorithm for Inventive Problem Solving (ARIZ) for the Heat Dissipation of Energy Storage Supply System for High-Power Locomotive

With the increasing demand for the energy density of battery system in railway vehicles, the ambient temperature of the battery system is increased. This means that the heat dissipation efficiency and battery service life are reduced, thus reducing the reliability of the battery. Contraposing the problem of the heat dissipation of energy storage batteries, the full deployment of the ARIZ algorithm has been provided for applications of problem-solving processes in this investigation. The advantage of using the ARIZ algorithm is that while the engineering system is kept unchanged or less changed, the effective heat dissipation of the energy storage battery with increasing energy density can be satisfied. During the process of the ARIZ algorithm, a mini problem is first defined. Moreover, the ideal final result and physical contradiction are defined. Following this, expanded material field resources are used, and finally, 22 innovative designs are obtained using the problem-solving tool in TRIZ theory. The final design scheme is provided by combining multiple schemes, and the heat compensator dispersion of a partially improved system in the optimized scheme has been simulated. The results show that the heat compensator dispersion of the air conditioning cooling system for battery modules has reached the design goals in this investigation, in which the maximum temperature of the module’s surface has been reduced to below 35.68 °C, and the maximum temperature difference of the module has reduced to below 5.6 °C.

Resting-state EEG Predicts Cognitive Impairment in Parkinson’s Disease (P6-11.015)

<h3>Objective:</h3> To develop and evaluate an EEG-based assay to predict cognitive functions in Parkinson’s disease (PD) using resting-state EEG data. <h3>Background:</h3> Cognitive dysfunction is a prominent feature of PD. PD related cognitive impairment is usually diagnosed by neuropsychological testing; however, these tests are complex, cannot capture cognitive fluctuations in PD patients, and are not readily compatible with repeated measurements, real-time feedback, or closed-loop neuromodulation therapies. <h3>Design/Methods:</h3> We recorded a few minutes of resting-state EEG data from a few electrodes. Our approach optimized a data-driven, machine-learning technique called ‘Linear predictive coding EEG Algorithms for Parkinson’s disease’ (LEAD-PD) to predict cognitive measures from 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal Cognitive Assessment (MOCA), as well as with different cognitive domains from NIH Toolbox, using multiple cross-validation schemes and randomization tests. We also investigated the relationship between them with multiple regression models, compared our approach with traditional EEG-based features and measured robustness with data truncation tests. <h3>Results:</h3> We found a strong correlation (rho = 0.69, <i>p</i> value &lt; 0.001) between our proposed LEAD-PD cognitive index and the MOCA score using data from only 8 EEG electrodes. LEAD-PD index also strongly predicted other cognitive domains from the NIH toolbox (Dimensional Change Card Sort, Flanker Attention and Inhibitory Control, Picture Vocabulary Task, Pattern Completion Speed Processing, and the Picture Sequence Memory tests). Our approach was consistently more predictive than traditional EEG spectral analysis, yielded 80.44% accuracy (0.90 AUC, 79.52 % sensitivity, and 81.14% specificity) in detecting cognitive impairment, and extended to both PD and control participants. The relationship between our approach and the MOCA was nonlinear with an R² of 0.46 for quadratic regression models. <h3>Conclusions:</h3> LEAD-PD approach using resting-state EEG is computationally-efficient and capable of providing continuous real-time control and feedback, facilitating the diagnosis and monitoring of cognitive impairment in PD. <b>Disclosure:</b> An immediate family member of Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an officer or member of the Board of Directors for American Board of Pediatrics. Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. The institution of Dr. Uc has received research support from Department of Veterans Affairs. The institution of Dr. Uc has received research support from Department of Defense. The institution of Dr. Uc has received research support from Michael J. Fox Foundation. The institution of an immediate family member of Dr. Uc has received research support from NIH. Dr. Anjum has nothing to disclose. Dr. Dasgupta has nothing to disclose. Dr. Narayanan has nothing to disclose.

Energy efficient multiply-accumulate unit using novel recursive multiplication for error-tolerant applications

The primary requirement for multimedia applications is energy efficient design along with computational efficiency of portable devices. This work proposes an energy efficient MAC with error compensating novel approximate adder and approximate multiplier. The proposed approximate hierarchical adder (AXHA – N) has the advantage of zero carry propagation and consists of only two gate levels which deliver very high speed and very low energy consumption along with the near to zero mean error distances. The proposed approximate 4 - bit multiplier also has no carry propagation from Least Significant Bit (LSB) to Most Significant Bit (MSB), which leads to high speed with very low energy consumption and acceptable error profiles. Higher bit size multipliers can be built using the proposed modified recursive multiplication scheme. This work proposes three variants AXMAC1, AXMAC2, AXMAC3 basing on one or both of adder and multiplier being approximate. The proposed AXMAC3 with any k, has energy savings ranging from 40% to 60% for different AxL when compared to accurate with Mean Error Distance between −358 and 4305. The MED for AXMAC3 with k=8, AXL=4 is only 0.7% of the maximum possible value with 60% energy savings. The MED for AXMAC3 with k=12s, AXL=4 is only 0.8% of the maximum possible value with 57% energy savings. Mean Relative Error Distance (MRED) and Normalised Mean Error Distance (NMED) are also evaluated and presented in the paper for wholesome evaluation of AXMAC. The (Energy savings, MRED, MED) tuple 8−bit MAC for design of Adams et al., (2019) with k=8 is (26.9%, 0.1, −15048), for proposed AXMAC3 with similar configuration has (57%, 0.01, 3663).

Evaluating the Variability of Simulated Raindrop Size Distributions in the “21·7” Henan Extremely Heavy Rainfall Event

AbstractSignificant variability of raindrop size distributions (DSDs) has been observed in the “21·7” Henan extremely heavy rainfall event (the “21·7” Henan EHR event), while the capability of model to reproduce such complicated heavy rainfall DSDs is yet unclear. This study primarily evaluates the simulated DSDs of multiple microphysics schemes by comparing with the observations from a network of 50 disdrometers. Constrained DSD variability is identified in most schemes that the simulated raindrop mean sizes are gradually restricted around specific values as the growth of heavy rainfall intensity. The schemes are also incapable of reproducing the different raindrop mean sizes from deep convection and shallow convection. Moreover, simulations show unrealistic evolutions of raindrop mean size standard deviations as height declines. By investigating the empirical formula and performing sensitivity experiment, the constrained DSD variability in heavy rainfall is largely blamed on the insufficient parameterizations of the self‐collection (breakup) processes.

Flexible terahertz phase modulation profile via all-silicon metasurfaces

Metasurfaces represent a group of artificially designed structures capable of efficiently manipulating the transmission properties of incident waves, have attracted considerable attention in terahertz (THz) radiation, and gradually overcome the complexity of bulky optical systems. Although the introduction of meta-optics has made great progress in full-span phase modulation schemes within orthogonal polarization channels, flexible phase functions can still effectively alleviate the design complexity of wavefront engineering. Here, we experimentally demonstrate several designs of general-purpose all-silicon THz metasurfaces applied in different scenarios. By combining geometric phase and propagation phase, switchable multiple phase modulation in orthogonal circularly polarized (CP) channels can be realized by switching the handedness of the incident wave. The advantage of the proposed scheme is that not only a locked phase modulation profile can be generated within the cross-polarized channels with opposite handedness, but also the phase distribution within each channel can be arbitrarily tailored to break the locking, demonstrating the flexible wavefront manipulation capability of the spin-decoupled phase control. Two different metalens are experimentally demonstrated to verify the feasibility of the multiple phase modulation scheme, mainly exploiting the topological charge and type of the focused beams. This work allows the metasurface to show a higher degree of freedom in wavefront manipulation by using a simpler phase-composite algorithm.

Alignment scheme optimization when gyro accuracy inconsistency for inertial navigation system

Rotation modulation technology can average out constant errors in fiber optic gyro (FOG) rotational inertial navigation system (RINS). As a result, angle random walk (ARW) of FOGs plays a significant role in the precision of initial alignment. Using different gyros as horizontal gyros gradually transitioned from theory to practical application with the advancement of design philosophy. The theoretical formula between azimuth alignment accuracy and random walk coefficients (RWCs) of two horizontal gyros is deduced in this paper. A completely new multiple rotations and stationary alignment scheme is suggested. A recursive least-squares filter with azimuth variation in body reference frame and accelerometer outputs in navigation frame as measurements is constructed. The efficiency of this method is demonstrated through simulation and experimentation. The findings demonstrate that using this formula and alignment scheme, azimuth alignment accuracy could be greatly increased, which can be improved by 10.85% compared to common continuous rotation alignment scheme.

Design of LTE-Wi-Fi Aggregation with Multiple Wi-Fi APs for Heterogenous Network

The growth in mobile data traffic is forcing network operators to find ways to deliver higher bandwidth, better coverage, and better quality of service (QoS) at lower development costs. One method that can be used to meet this need is the aggregation technique. Aggregation techniques that run on several different technologies are called heterogeneous networks (HetNet). Recently, some advanced technologies have been proposed to aggregate different long-term evolution (LTE) carriers or different radio access technologies (RAT) to generate higher bandwidth. Many efforts have been made to improve the throughput of heterogeneous networks. One of the factors that affects the maximum LTE-WLAN aggregation (LWA) throughput is the Wi-Fi access point (AP) signal strength. Since the power of small cells is very low, the LWA range is not very large, so the LWA range needs to be increased. This paper proposes the solution that can be implemented by using multiple Wi-Fi Aps scheme. From the experiments, it found that LWA with multiple Wi-Fi APs has better signal strength than LWA with a single Wi-Fi AP. LWA with multiple Wi-Fi APs further increase the coverage of the network.

Optimization and assessment method for total energy system retrofit in the petrochemical industry considering clean energy substitution for fossil fuel

Carbon emission peak and neutrality prospect has expedited the revolution of conventional energy utilization mode, particularly in highly energy-intensive industries. The petrochemical industry bears great responsibility for energy conservation and carbon emission reduction. TES (Total Energy System) retrofit schemes in the petrochemical industry classified as REST (Retrofitting through Energy-Saving Techniques) and RSFC (Retrofitting through Substituting Fossil fuel with Clean energy) have emerged in large numbers recently. It is challenging for energy managers to optimize and assess TES retrofit schemes simultaneously and globally, once clean energy supply is involved. State-of-the-art assessment and optimization method for TES retrofitting is urgently needed. This work proposes a novel optimization and assessment method based on the exergy conversion and consumption processes to figure out the most cost-effective retrofit schemes in view of exergy analysis. The method features with the simulation of TES, the clean energy substitution for fossil fuel, the optimization of the subsystems, and the exergy efficiency assessment of retrofit schemes. The double-parallel-loop application framework is then presented, which deals with the optimization and assessment of RSFC and REST simultaneously. Combined plants of continuous catalytic reforming and aromatic extraction plants along with their matched TES are investigated. A MINLP optimization model is formulated and programmed with GAMS software. The optimal results show that the total exergy consumption decreased by 3200 kW with clean energy substitution. Sensitivity analysis explores the energy conservation benefits and exergy efficiency improvement when clean energy with different qualities and quantities substituting for fossil fuel, which provides valuable information for feasibly implementing retrofit schemes. Comparison of scenarios reveals the interacting influence between the implementation of RSFC and REST, which is significant for decision-making when multiple retrofit schemes are alternatives. Real-world case investigations demonstrate the validation and efficacy of the proposed method. The main findings of this study are significant and quantitative references for petrochemical industrial practice, which would contribute to energy conservation and carbon emission reduction in the future.

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport.

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.

Laser repointing scheme for octahedral spherical hohlraums on the SGIII laser facility

The novel octahedral spherical hohlraum can provide an ideal and practical approach for the next generation of laser systems to support both direct and indirect drive to achieve predictable and reproducible fusion gain via multiple schemes. To demonstrate its advantage in a naturally high symmetry at a cylindrically configured laser facility, it requires to repoint the laser beams to approach as close as possible the ideal octahedral beam configuration with an injection angle (the angle between a beam and the normal direction of its laser entrance hole (LEH)) ranging from 50° to 60°. We report our investigation and experiment on the optimum repointing scheme at the SGIII facility, which uses 32 beams, with 8 beams entering each polar LEH at 49.5° and 55°, and 4 beams entering each equatorial LEH at 61.5° and 62.1°. It contains residual imbalance between the polar and equatorial beams, leading to an asymmetry dominated by the spherical harmonic Y20 mode, which can be remarkably reduced by the stronger backscatters of equatorial beams. Our experiment demonstrated the feasibility of the 32-beam optimum repointing scheme and generation of 175 eV under 86 kJ inside a 2.4-mm-radius octahedral hohlraum with 0.7-mm-radius LEHs, which provided a strong support for the later experiment on proof-of-concept of octahedral spherical hohlraum [Lan et al., Phys. Rev. Lett. 127, 245001 (2021)]. 2D simulations on LEH closure agree well with the observations. This work opens a novel way of realization of a quasi-spherical irradiation at a cylindrically configured laser facility without supplementary symmetry control.

Distortion Compensation for Orbital Angular Momentum Beams: From Probing to Deep Learning

Beams carrying orbital angular momentum (OAM), has attracted wide interests recently, due to their favorable potential in communications, optical tweezers, remote detection, etc. However, distortion will come once OAM beams propagate through inhomogeneous media like atmospheric turbulence. Such distortion will broaden the orbital angular momentum spectrum, thus introduce interchannel crosstalk. These phenomena will adversely affect practical applications, for instance, the rising bit-error-rate in OAM-based data transmission. Therefore, distortion correction for OAM beams, an important means to improve the robustness of OAM beams when propagating in atmospheric turbulence, must be developed. In this paper, we overview recent advances on distortion compensation of OAM beams, including the influence of atmospheric turbulence on OAM beams, and multiple wavefront correction schemes. In addition, toward the engineering realization of OAM based free space data-transmission, we propose to apply TSVMD-AR model to predict atmospheric turbulence, and expect to further improve the distortion correction efficiency.

Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are “Fractal Dimension” (FD), “Higher Order Spectra” (HOS), “Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the “Naïve Bayes” (NB), “Support Vector Machine” (SVM), “Random Forest” (RF), and “K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.

Identification of tomato leaf diseases based on LMBRNet

Tomato disease image identification plays a very important role in the field of agricultural production. Aiming at the problems of large intraclass differences, small inter-class differences and difficult feature extraction of some tomato leaf diseases, this paper proposes an identification of tomato leaf diseases based on LMBRNet. Firstly, a comprehensive grouped differentiated residual (CGDR) is built,The multi-branch structure of CGDR is used to capture the diversified feature information of tomato leaf diseases in different dimensions and receptive fields. then, a multiple residual connection scheme is adopted,using residuals to connect all layers, to ensure the maximum information transmission between layers in the network and to solve the problems of network degradation and gradient disappearance in the network training process. Secondly,the visual enhancement effectively fuses the results obtained by three different downsampling strategies using average pooling, max pooling, and 1*1 convolution. Avoid the loss of information caused by downsampling and improve the accuracy of the network. Moreover, deep separable convolution is used to optimize the network structure, reduce the amount of model parameters and reduce the computational resources occupied by training and deploying the model.we found that the depthwise separable convolution with a kernel size of 1*1 can slightly improve the efficiency of the model under the premise that it has little effect on the number of model parameters. The application results of more than 8000 images show that the overall identification accuracy is about 99.7%,higher than ResNet50(97.48%),GoogleNet(98.96%) etc. conventional models. The parameter amount of LMBRNet is 4.1M. Less than ResNet50(23M),GoogleNet(5.7M) etc. conventional models. It is worth noting that the accuracy of LMBRNet(99.7%) is similar to that of InceptionResNetV2(99.68%), but the amount of parameters of LMBRNet(4.1M) is much lower than that of InceptionResNetV2(54M). Moreover, the parameter amount of LMBRNet (4.1M) is slightly lower than that of MobileNetV2(2.2M), but the accuracy rate of LMBRNet(99.7%) is higher than that of MobileNetV2(97.87%). LMBRNet was tested on RS, SIW, Plantvillage-corn public datasets, all obtained high recognition accuracy, 82.32% on RS, 88.37% on SIW and 97.25% on Plantvillage-corn, indicating that LMBRNet has good generalization. Compare LMBRNet with advanced methods. In four different classification tasks, the performance of LMBRNet is similar to ResMLP12 and DCCAM-MRNet, and the difference of recognition accuracy between LMBRNet and ResMLP12 and DCCAM-MRNet is not more than 1%. However, the parameters of LMBRNet (4.1M) are lower than ResMLP12 (14.94M) and DCCAM MRNet (22.8M). LMBRNet is compared with MobileNetV3, an advanced lightweight classification model. LMBRNet(88.37% on SIW,82.32% ON RS) is used on certain datasets and performs better than MobileNetV3S(83.76% on SIW,75 on RS) and MobileNetV3L(84.34 on SIW,73.39 on RS). The parameters of LMBRNet(4.1M) are lower than MobileNetV3L(5.4M) and slightly higher than MobileNetV3S(2.9M). This indicates that LMBRNet has good generality even though it has a small number of parameters.