Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Identification of pulse-like ground motions using artificial neural network

For more than 20 years,the concept of near-fault pulse-like ground motion has been a topic of great interest due to its distinct characteristics,particularly due to directivity or fling effects,which are hugely influenced by the rupture mechanism.These unexpected characteristics,along with their effective frequency,energy rate,and damage indices,create a near-fault,pulse-like ground motion capable of causing severe damage to structures.One of the most common approaches for identifying these ground motions is done by conducting wavelet decomposition of the ground motion time history to extract a pulse signal and eventually categorize an earthquake by comparing the original signal to the residual one.However,to overcome the intensive calculations required in this approach,this study proposes using artificial neural networks to identify pulse-like ground motions through classification to predict their pulse period by means of regression analysis.Furthermore,the study is intended to evaluate the reliability and accuracy of various artificial neural networks in identifying pulse-like ground motions and predicting their pulse periods.In general,the results of the study have shown that the artificial neural network can identify pulse-like earthquakes and reliably predict their pulse period.

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflects the subjects’intention in moving their limbs.Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches.However,these methods have drawbacks that limit their usability.Different from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the Artificial Neural Networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,impossible.The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals.The presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric study.All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system.The RBFNN has the best performance over the others with a 79.80%mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67%in some joints.Interestingly,the highest accuracy over individual joints is 93.46%,which is achieved via the GRNN.The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

Fingerprint Identification by Artificial Neural Network

Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

Study of Synthesis Identification in Cutting Process with Fuzzy Neural Network

With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

Neural network-based estimation of lower limb joint kinematics:A minimally intrusive approach for gait analysis

The establishment of a quantitative gait analysis system holds paramount importance,particularly in the context of functional rehabilitation of the lower limbs.Clinicians emphasize the imperative for sensors to be portable,compact,integrated,and non-intrusive,crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care protocols.This study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network,thus concentrating on the joint kinematics of the lower limbs.The primary objective is to estimate the flexion-extension angles of the hip,knee,and ankle during walking,utilizing data collected by two sensors positioned on the subject's legs.Initially,the neural network undergoes training with calculated data(leg tilt angles and angular velocities)sourced from the OpenSim database,followed by further refinement with experimental data obtained from a subject walking on a treadmill,wherein leg tilt angles and angular velocities are measured.The significance of this research is underscored by the demonstrated capability,through conducted tests,of the implemented networks to efficiently fuse data from a minimal set of sensors.Consequently,the proposed approach emerges as both practical and minimally intrusive,facilitating a robust evaluation of gait kinematic parameters.

Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera- ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup- plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith- ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.

Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment

The decision-making under complex urban environment become one of the key issues that restricts the rapid development of the autonomous vehicles. The difficulty in making timely and accurate decisions like human beings under highly dynamic traffic environment is a major challenge for autonomous driving. Car-following has been regarded as the simplest but essential driving behavior among driving tasks and has received extensive attention from researchers around the world. This work addresses this problem and proposes a novel method RSAN(rough-set artificial neural network) to learn the decisions from excellent human drivers. A virtual urban traffic environment was built by Pre Scan and driving simulation was conducted to obtain a broad set of relevant data such as experienced drivers' behavior data and surrounding vehicles' motion data. Then, rough set was used to preprocess these data to extract the key influential factors on decision and reduce the impact of uncertain data and noise data. And the car-following decision was learned by neural network in which key factor was the input and acceleration was the output. The result shows the better convergence speed and the better decision accuracy of RSAN than ANN. Findings of this work contributes to the empirical understanding of driver's decision-making process and it provides a theoretical basis for the study of car-following decision-making under complex and dynamic environment.

Particle identification using artificial neural networks at BESⅢ

A multilayered perceptrons＇ neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples.

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Writer identification(WI)based on handwritten text structures is typically focused on digital characteristics,with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles.Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke.As a result of the complexity of Arabic handwriting,segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes.In this work,we propose new texture features for WI based on text.The histogram of oriented gradient(HOG)features are modified to extract good features on the basis of the histogram of the orientation for different angles of texts.The fusion of these features with the features of convolutional neural networks(CNNs)results in a good vector of powerful features.Then,we reduce the features by selecting the best ones using a genetic algorithm.The normalization method is used to normalize the features and feed them to an artificial neural network classifier.Experimental results show that the proposed augmenter enhances the results for HOG features and ResNet50,as well as the proposed model,because the amount of data is increased.Such a large data volume helps the system to retrieve extensive information about the nature of writing patterns.The affective result of the proposed model for whole paragraphs,lines,and sub words is obtained using different models and then compared with those of the CNN and ResNet50.The whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different words.The HOG and CNN features achieve 94.2%accuracy for whole paragraphs with augmentation,83.2%of accuracy for lines,and 78%accuracy for sub words.Thus,this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help identify writers on the basis of their sentences,words,and sub words.

Optofluidic identification of single microorganisms using fiber-optical-tweezer-based Raman spectroscopy with artificial neural network

Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects,it holds the potential to rapidly identify microorganisms in a single step.To stabilize the microorganism for spectrum collection and to increase the accuracy of real-time identification,we propose an optofluidic method for single microorganism detection in microfluidics using optical-tweezing-based Raman spectroscopy with artificial neural network.A fiber optical tweezer was incorporated into a microfluidic channel to generate op-tical forces that trap different species of microorganisms at the tip of the tweezer and their Raman spectra were simultaneously collected.An artificial neural network was designed and employed to classify the Raman spectra of the microorganisms,and the identification accuracy reached 94.93%.This study provides a promising strategy for rapid and accurate diagnosis of mi-crobial infection on a lab-on-a-chip platform.

On－Line Identification of Grinding Burn and Wheel Wear Based on Grinding Chips Thermal Flow

The theoretical basis of the grinding chips thermal flow being regarded as the characteristic signal of on line identification is summarized. And on line identification of grinding burn and wheel wear based on the grinding chips thermal flow is introduc

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight （TOF） system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network （ANN） trained to recognize protons from ∧^0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron （assigned one proton mass） and a negative hadron （assigned one π^- mass） the reconstructed invariant mass lies within the ∧^0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero. The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.

Design and Control of a DC Boost Converter for Fuel-Cell-Powered Marine Vehicles

Economic factors along with legislation and policies to counter harmful pollution apply specifically to maritime drive research for improved power generation and energy storage.Proton exchange membrane fuel cells are considered among the most promising options for marine applications.Switching converters are the most common interfaces between fuel cells and all types of load in order to provide a stable regulated voltage.In this paper,a method using artificial neural networks(ANNs)is developed to control the dynamics and response of a fuel cell connected with a DC boost converter.Its capability to adapt to different loading conditions is established.Furthermore,a cycle-mean,black-box model for the switching device is also proposed.The model is centred about an ANN,too,and can achieve considerably faster simulation times making it much more suitable for power management applications.