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.     

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.     

Active MEMS-based flow control using artificial neural network

These last years several research works have studied the application of Micro-Electro-Mechanical Systems (MEMS) for aerodynamic active flow control. Controlling such MEMS-based systems remains a challenge. Among the several existing control approaches for time varying systems, many of them use a process model representing the dynamic behavior of the process to be controlled. The purpose of this paper is to study the suitability of an artificial neural network first to predict the flow evolution induced by MEMS, and next to optimize the flow w.r.t. a numerical criterion. To achieve this objective, we focus on a dynamic flow over a backward facing step where MEMS actuators velocities are adjusted to maximize the pressure over the step surface. The first effort has been to establish a baseline database provided by computational fluid dynamics simulations for training the neural network. Then we investigate the possibility to control the flow through MEMS configuration changes. Results are promising, despite slightly high computational times for real time application.     

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.     

改进的神经网络算法在垃圾邮件过滤中的应用

针对垃圾邮件威胁信息安全而又屡禁不止的现状,如何从技术上增加对垃圾邮件的控制,维护网络安全成为一个研究的热点问题。人工神经网络具有自适应的特点,在处理变化多端的垃圾邮件问题上有显著优势,但传统算法存在效率低下的问题。现结合模糊理论遗传算法提出了一种改进的BP神经网络算法,在一定程度上提高了算法的效率。通过对中文邮件分类的实验分析,结果表明,本算法的效率优于传统算法,并有较高的识别准确率。     

Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction

This paper describes a novel method for enhancing optical images using a weighted guided trigonometric filter and the camera’s spectral properties in turbid water. Absorption, scattering, and artificial lighting are three major distortion issues in underwater optical imaging. Absorption permanently removes photons from the imaging path. Scattering is caused by large suspended particles found in turbid water, which redirect the angle of the photon path. Artificial lighting results in footprint effects, which cause vignetting distortion in the captured image. Our contributions include a novel deep-sea imaging method that compensates for the attenuation discrepancy along the propagation path, and an effective underwater scene enhancement scheme. The recovered images are characterized by a reduced noise level, better exposure of dark regions, and improved global contrast such that the finest details and edges are significantly enhanced. Our experiments showed that the average Peak Signal to Noise Ratio (PSNR) improved by at least 1 dB when compared with state-of-the-art-methods.     

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     

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。     

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     

人工神经网络在医学统计预测中的应用

当用于预测的指标和被预测指标间是一种复杂的多元非线性关系时,可运用人工神经网络的方法进行预测,本文探讨了人工神经网络理论在医学统计预测领域中的应用方法。     

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.     

人工神经网络概述

近年来,随着人工神经网络在信息、自动化、医学、经济等领域的广泛应用和杰出表现,使得人工神经网络又开始得到广泛关注和重新重视。基于需求,本文对人工神经网络做了简单概述。     

Motion artifact cancellation in NIR spectroscopy using Wiener filtering

We present a Wiener filtering based algorithm for the elimination of motion artifacts present in Near Infrared (NIR) spectroscopy measurements. Until now, adaptive filtering was the only technique used in the noise cancellation in NIR studies. The results in this preliminary study revealed that the proposed method gives better estimates than the classical adaptive filtering approach without the need for additional sensor measurements. Moreover, this novel technique has the potential to filter out motion artifacts in functional near infrared (fNIR) signals, too.     

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...     

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.     

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.     

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.     

基于神经网络的图像识别

神经网络图像识别技术是随着当代计算机技术、图像处理、人工智能、模式识别理论等发展起来的一种新型图像识别技术。在进行图像识别之前需要利用数字图像处理技术进行图像预处理以及特征提取。本文选取字符图像0~9作为识别目标,对图像预处理过程进行了叙述,并在此基础上选取字符图像矩阵每行的与每列的黑色像素点之和以及图像欧拉数这两个特征作为BP神经网络的输入样本。经实验仿真表明图像的平均识别率为89%,这表明图像预处理的结果和提取的特征是合适的、有效的,设计的BP网络也较好的完成了模式分类识别工作。     

Inception neural network for human activity recognition using wearable sensor

The experience from computer vision was learned,an innovative neural network model called InnoHAR (inception neural network for human activity recognition) based on the inception neural network and recurrent neural network was put forward,which started from an end-to-end multi-channel sensor waveform data,followed by the 1×1 convolution for better combination of the multi-channel data,and the various scales of convolution to extract the waveform characteristics of different scales,the max-pooling layer to prevent the disturbance of tiny noise causing false positives,combined with the feature of GRU helped to time-sequential modeling,made full use of the characteristics of data classification task.Compared with the state-of-the-art neural network model,the InnoHAR model has a promotion of 3% in the recognition accuracy,which has reached the state-of-the-art on the dataset we used,at the same time it still can guarantee the real-time prediction of low-power embedded platform,also with more space for future exploration.     

Pattern synthesis for uniform linear array using genetic algorithm and artificial neural network

Multidimensional Systems and Signal Processing - In this paper, we propose a new amplitude-only method for pattern synthesis of uniform linear array (ULA) based on genetic algorithm (GA) and...