采用人工神经网络技术改善光学模式识别系统的识别效率

在采用光学模式识别技术,ＳＤＦ滤波技术进行实际场景中的三维目标畸变不变识别的时候,由于面对的晨在量的非训练像的相关识别,加上场景图像中的不同噪声,背景的干扰,以及硬件识别系统的各种非理想特征等因素,均不可避免带来相关平面的Ｓ／Ｎ的严重退化,从而使按通常的阈值技术进行相关信号分割的在而大大降低了ＯＰＲ系统的识别效率。     

An artificial neural network for SPECT image reconstruction

An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then backprojected to form an image as the network output. The learning algorithm adjusts the weighting coefficients using a backpropagation algorithm which minimizes the mean squared error between the ideal training image and the reconstructed training image. The response of the trained network to an impulse projection resembles the ramp filter typically used with backprojection, and reconstructed images are similar to filtered backprojection images.     

Saliency-directed color image interpolation using artificial neural network and particle swarm optimization

In this study, a saliency-directed color image interpolation approach using artificial neural network (ANN) and particle swarm optimization (PSO) is proposed. First, a high-quality saliency map of a color image to be interpolated is generated by a modified block-based visual attention model in an effective manner. Then, based on the saliency map, bilinear interpolation and ANN-PSO interpolation are employed for non-saliency (non-ROI) and saliency (ROI) blocks, respectively, to obtain the final color interpolation results. In the proposed ANN-PSO interpolation scheme, ANN is used to determine the orientation of each 5 × 5 image pattern (block), whereas PSO is employed to determine the weights in 5 × 5 interpolation filtering masks. The proposed approach is applicable to image interpolation with arbitrary magnification factors (MFs). Based on the experimental results obtained in this study, the color interpolation results by the proposed approach are better than those by five comparison approaches.     

Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network

In order to automate routine fecal examination for parasitic diseases, we propose in this study a computer processing algorithm using digital image processing techniques and an artificial neural network (ANN) classifier. The morphometric characteristics of eggs of human parasites in fecal specimens were extracted from microscopic images through digital image processing. An ANN then identified the parasite species based on those characteristics. We selected four morphometric features based on three morphological characteristics representing shape, shell smoothness, and size. A total of 82 microscopic images containing seven common human helminth eggs were used. The first stage (ANN-1) of the proposed ANN classification system isolated eggs from confusing artifacts. The second stage (ANN-2) classified eggs by species. The performance of ANN was evaluated by the tenfold cross-validation method to obviate the dependency on the selection of training samples. Cross-validation results showed 86.1% average correct classification ratio for ANN-1 and 90.3% for ANN-2 with small variances of 46.0 and 39.0, respectively. The algorithm developed will be an essential part of a completely automated fecal examination system.     

Atrial activity enhancement by Wiener filtering using an artificial neural network

This paper describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with nine input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.     

Multiscale image segmentation using fractal and neural network

Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper, the multifractal dimensions are chosen as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. SOM (Self-Organizing Map) network is applied to cluster the segmentation feature parameters. The experiment shows that the performance of the presented algorithm is very good.     

激光冲击区表面质量的人工神经网络研究

大量的实验表明,经激光冲击处理后,材料受冲击区的表面质量与材料的疲劳寿命有着明显的关系。因此,表面质量是判断激光冲击强化效果的重要手段。将人工神经网络技术用于激光冲击处理后试件的表面质量分析,建立了激光参数与激光冲击处理后试件的表面质量之间的联系,并用其实现了对冲击处理后的试件表面质量的预测。研究及实验表明,该方法不仅具有准确及稳定性好等特点,而且这种预测能力在实际应用中还具有不断提高的智能特性。     

Multiple contour extraction from graylevel images using an artificial neural network.

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of an edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.     

基于人工神经网络的图像检索平台的设计与开发

用户描述图像的高层抽象语义与图像内在的底层特征之间存在差异,此时仅依靠图像内容特征进行检索的系统无法准确完成用户的检索任务。针对以上问题,提出了使用神经网络进行图像的匹配计算方法,通过样例自动学习和用户反馈学习两种学习方式,形成图像底层特征到图像分类的正确映射,学习后的神经网络可以进行图像的自动分类及检索。该方法结合了图像的底层特征描述及用户的高层语义反馈,有效地弥补了语义鸿沟。最后,系统通过整合Web前端、图像提取模块、神经网络模块及数据库模块,实现了神经网络学习及图像检索的完整流程。     

Surface roughness determination using spectral correlations ofscattered intensities and an artificial neural network technique

An artificial neural network (ANN) technique is applied to the determination of the RMS height and the correlation distance of one-dimensional rough surfaces. The surface is illuminated by a beam wave, and the intensity correlations of the scattered wave at two wavelengths in the specular and backward directions are used to determine the roughness parameters. Scattered intensity correlations calculated by Monte Carlo simulations are used to train the ANN, and two methods, the explicit inversion method and the iterative constrained inversion method, are used to perform the inversion. The inversion values are compared with the target values, and the iterative constrained method is shown to give smaller errors, but it requires longer computer CPU time     

Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach

Pneumatic Artificial Muscle (PAM) actuator has been widely used in medical and rehabilitation robots, owing to its high power-to-weight ratio and inherent safety characteristics. However, the PAM exhibits highly non-linear and time variant behavior, due to compressibility of air, use of elastic-viscous material as core tube and pantographic motion of the PAM outer sheath. It is difficult to obtain a precise model using analytical modeling methods. This paper proposes a new Artificial Neural Network (ANN) based modeling approach for modeling PAM actuator. To obtain higher precision ANN model, three different approaches, namely, Back Propagation (BP) algorithm, Genetic Algorithm (GA) approach and hybrid approach combing BP algorithm with Modified Genetic Algorithm (MGA) are developed to optimize ANN parameters. Results show that the ANN model using the GA approach outperforms the BP algorithm, and the hybrid approach shows the best performance among the three approaches.     

Hardware implementation of an artificial neural network using fieldprogrammable gate arrays (FPGA's)

In this paper, the authors present a hardware implementation of a fully digital multilayer perceptron artificial neural network using Xilinx Field Programmable Gate Arrays (FPGAs). Each node is implemented with two XC3042 FPGAs and a 1 K×8 EPROM. Training is done offline on a PC. The authors have tested successfully the performance of the network     

An application of an artificial neural network to reliability screen classification from noise measurement

In this paper, we suggest that the reliability screen classification of BJTs from noise measurement belongs in statistical pattern recognition, then the multilayer artificial neural network is used as reliability screen classifier. The structure of a multilayer neural network (MLNN) with a back-propagation algorithm for training weights of the MLNN is discussed. This method can obtain optimal decision regions and the minimum summed squared error. Finally, an application of a neural network to the reliability screen classification of 100 BJTs is given, the results show that the MLNN is a feasible reliability screen classifier.     

Prognostics of aluminum electrolytic capacitors using artificial neural network approach

In this work, an effort is being made to monitor the condition of in-circuit aluminum electrolytic capacitor using artificial neural network (ANN). Recent industrial surveys on the reliability of power electronic systems shows that most of faults occur due to the wear out of aluminum electrolytic capacitors and thermal stress is the major cause for its parametric degradation. The condition of target capacitors can be estimated by monitoring variation in equivalent series resistance (ESR) from the initial pristine state value. ANN is used to estimate ESR of pristine and weak target capacitors at the test conditions. The data set for training and testing of proposed back-propagation trained artificial neural network are experimentally obtained from the developed test bed. Using the test bed, target capacitors are subjected to different operating frequency and temperature in the output section of DC/DC buck converter circuit to determine the effect of variation in electrical and thermal stress on ESR value. After off-line training, the proposed ANN is implemented using National Instruments LabVIEW software. A low cost microcontroller is programmed for real time data acquisition of target capacitors and the serial transmission of acquired dataset to the LabVIEW software installed at host computer. The performance of the proposed method is evaluated in real time by comparing the resulting ESR with the experimental values of in-circuit target capacitors. The proposed ANN, once trained properly, can be used for different circuits and in different operating conditions because of its generalization capability.     

利用改进的人工神经网络优选激光切割工艺参数

在激光切割中,工艺参数的优化搭配直接影响切割质量.为了更好的选择出最优化的工艺参数搭配,本文利用人工神经网络分析方法,建立一个用遗传算法改进的人工神经网络模型,并在实际的应用中选出大量实际实验数据对其加以训练和验证.实验证明,该模型将遗传算法和神经网络的优点相结合,既克服了以往正交实验中存在的选出工艺参数准确度的问题,同时也克服了传统神经网络中易出现的局部最优和收敛速度较慢的问题,从而有效地解决了激光切割中各参数优化搭配的问题.     

Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm²), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (–40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.     

Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network

Analog Integrated Circuits and Signal Processing - Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging...     

人工神经网络应用于企业管理信息系统的研究

结合人工神经网络和信息技术，提出一种构建平台式企业管理信息系统的方法。该系统便于在不同企业以及不同行业之间相互移植，满足企业发展的动态需要，具备可扩展性，符合数据高度集成和实施成本低的要求。     

An artificial neural network model for effective dielectricconstant of microstrip line

A backpropagation network structure is presented for the calculation of the effective dielectric constant (ϵ_eff) of microstrip lines. Results of the network are compared with those of the spectral-domain (SD) technique     

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.