Colored pattern recognition with a neural network model

An algorithm for multicolored pattern recognition is proposed. A scheme for recognizing patterns encoded in three basic colors, i.e., red, green, and blue, is presented. This scheme can be implemented optically with grating structures. Another advantage of this scheme is its capability of pattern recognition with gray levels. This can be accomplished by coding gray levels with different colors.     

Neural network pattern recognition of thermal-signature spectra for chemical defense

We treat infrared patterns of absorption or emission by nerve and blister agent compounds (and simulants of this chemical group) as features for the training of neural networks to detect the compounds' liquid layers on the ground or their vapor plumes during evaporation by external heating. Training of a four-layer network architecture is composed of a backward-error-propagation algorithm and a gradient-descent paradigm. We conduct testing by feed-forwarding preprocessed spectra through the network in a scaled format consistent with the structure of the training-data-set representation. The bestperformance weight matrix (spectral filter) evolved from final network training and testing with software simulation trials is electronically transferred to a set of eight artificial intelligence integrated circuits (ICs') in specific modular form (splitting of weight matrices). This form makes full use of all input-output IC nodes. This neural network computer serves an important real-time detection function when it is integrated into pre- and postprocessing data-handling units of a tactical prototype thermoluminescence sensor now under development at the Edgewood Research, Development, and Engineering Center.     

Micro gas sensor array with neural network for recognizing combustible leakage gases

A micro gas sensor array, consisting of four porous tin oxide thin films added with noble metal catalysts on a micro-hotplate, was designed and fabricated. The micro-hotplate was designed to obtain a uniform thermal distribution along with a low-power consumption and fast thermal response. The sensing properties of the sensors toward certain combustible gases, i.e., propane, butane, LPG, and carbon monoxide, were evaluated. A multilayer neural network was then used to classify the gas species. The results demonstrated that the proposed micro sensor array, plus multilayer neural network employing a backpropagation learning algorithm, was very effective in recognizing specific kinds and concentration levels of combustible gas below their respective threshold limit values.     

Zinc oxide thin-film chemical sensors in conjunction with neural network pattern recognition for trimethylamine and dimethylamine gases

Transient response curves for exposure to several gases are observed using zinc oxide (ZnO) thin-film gas sensors. It is found that an aluminium-doped ZnO (ZnO:Al) sensor exhibits a high sensitivity and an excellent selectivity for amine gases. In order to discriminate between gas species such as trimethylamine (TMA), dimethylamine (DMA) and other gases pattern recognition analysis using a neural network is carried out using parameters which characterize the transient responses of the sensor for exposure to gases. The recognition probability of the neural network is 90% for TMA and DMA with constant concentration and is 100% for TMA and DMA with different concentrations, except for a concentration of 1 p.p.m.     

An optical-fiber sensor for use in water systems utilizing digital signal processing techniques and artificial neural network pattern recognition

An optical-fiber sensor is reported which is capable of detecting ethanol in water. A single optical-fiber sensor was incorporated into a 1-km length of 62.5-/spl mu/m core diameter polymer-clad silica optical fiber. In order to maximize sensitivity, a U-bend configuration was used for the sensor where the cladding was removed and the core exposed directly to the fluid under test. The sensor was interrogated using optical time domain reflectrometry, as it is intended to extend this work to multiple sensors on a single fiber. In this investigation, the sensor was exposed to air, water, and alcohol. The signal processing technique has been designed to optimize the neural network adopted in the existing sensor system. In this investigation, a discrete Fourier transform, using a fast Fourier transform algorithm, is chosen and its application leads to an improvement in efficiency of the neural network i.e., minimizing the computing resources. Using the Stuttgart neural network simulator, a feed-forward three-layer neural network was constructed with the number of input nodes corresponding to the number of points required to represent the sensor frequency domain response.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Bidirectional multiple‐valued neural network for pattern recognition and associative recall

A new neural network algorithm based on the counter‐propagation network (CPN) architecture, named MVL‐CPN, is proposed in this paper for bidirectional mapping and recognition of multiple‐valued patterns. The MVL‐CPN is capable of performing a mathematical mapping of a set of multiple‐valued vector pairs by self‐organization. The use of MVL‐CPN reduces considerably the number of nodes required for the input layers as well as the number of synaptic weights compared to the binary CPN. The training of the network is stable because all synaptic weights are monotonically nonincreasing. The bidirectional mapping and associative recall features of the MVL‐CPN are tested by using various sets of quaternary patterns. It is observed that the MVL‐CPN can converge within three or four iterations. The high‐speed convergence characteristics of the network can lead to the possibility of using this architecture for real‐time applications. An important advantage of the proposed type of neural network is that it can be implemented in VLSI with reduced number of neurons and synaptic weights when compared to a larger binary network needed for the same application. © 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 125–129, 2000     

基于粗糙集和神经网络的联合模式识别系统

提出了一种划分属性离散区间的新方法．针对这种划分，提出一种约简和去噪的方法．随后，建立了粗糙集和LVQ神经网络的联合模式识别系统．最后，比较了用该系统和仅用神经网络进行识别的效果，证明了该方法的有效性．     

Visual pattern recognition network: its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition.     

Improved neural network model for reverse engineering

While conventional engineering transforms engineering concepts into real parts, in reverse engineering real parts are transformed into engineering models. The construction of a surface from three-dimensional (3D) measuring data points is an important problem in reverse engineering. This paper presents a reconstruction method for the sculptured surfaces from the 3D measuring data points. The surface reconstruction scheme is presented based on a neural network. The reconstruction of the existing surfaces is realized by training the network. A series of measuring points from existing sculptured surfaces is used as a training set. Once the neural network has been trained, it serves as a geometric model to generate all the points that are needed. However, the learning rate for the neural network is relatively slow, and the learning accuracy is often unacceptably low. In this paper, to improve the performance of the neural network, a pre-processor is proposed before the input layer. The pre-processor maps the input into the larger space by generating a set of linearly independent values. The effect of the pre-processor is to increase modelling accuracy, and reduce learning time. Based on this method, experimental results are given to show that the reconstructed surfaces are faithful to the original data points. The proposed scheme is useful for regular or irregular digitized data.     

Radial basis probabilistic neural network for differential protection of power transformer

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB.     

无线传感网络中能量和距离改良的LEACH分簇算法

LEACH算法作为经典分簇算法在无线传感器网络中有着广泛应用,但由于没有考虑簇头数量及监测区域等因素,使得网络消耗巨大,大大缩减了网络的生命周期.针对这一缺陷,在Warneke的最优覆盖定理的基础上,提出CDE-LEACH算法,通过在基站中预构建“数据表”存储最优覆盖理想簇头位置坐标,结合保证网络能量消耗最小这一目标来选取最优的簇头,改善LEACH算法随机选择簇头的弊端.在Matlab 7.0实验仿真平台下对提出的CDELEACH算法进行仿真,与LEACH算法结果对比发现,网络能量消耗大大减少,并且延长了网络生命周期.     

Application of probabilistic neural network for differential relaying of power transformer

Investigations towards the applicability of probabilistic neural networks (PNNs) as core classifiers to discriminate between magnetising inrush and internal fault of power transformer are made. An algorithm has been developed around the theme of conventional differential protection of transformer. It makes use of the ratio of the voltage-to-frequency and the amplitude of differential current for the detection of the operating condition of the transformer. The PNN has a significant advantage in terms of a much faster learning capability because it is constructed with a single pass of exemplar pattern set and without any iteration for weight adaptation. For the evaluation of the developed algorithm, transformer modelling and simulation of fault are carried out in power system computer-aided designing PSCAD/EMTDC. The operating condition detection algorithm is implemented in MATLAB     

Incorporating pattern recognition capability in a flexible vibratory bowl feeder using a neural network

Programmable parts feeders that can orientate most of the parts of one or more part families, with short changeover times from one part to the next, are highly sought after in batch production. This study investigates a suitable neural-network-based pattern recognition algorithm for the recognition of parts in a programmable vibratory bowl feeder. Three fibre-optic sensors were mounted on a vibratory bowl feeder to scan the surface of each feeding part. The scanned signatures were used as the input for the different neural network models. The performances of ARTMAP, ART2 and backpropagation neural network models were compared. The results showed that, among the three models, ARTMAP is deemed to be superior, based on the criteria of learning speed, high generalization and flexibility. The better performance obtained with the ARTMAP neural network is mainly the result of its online training and supervised learning capabilities.     

基于改进双流卷积递归神经网络的RGB-D物体识别方法

为了提高基于图像的物体识别准确率,提出一种改进双流卷积递归神经网络的RGB-D物体识别算法(Re-CRNN).将RGB图像与深度光学信息结合,基于残差学习对双流卷积神经网络(CNN)进行改进:增加顶层特征融合单元,在RGB图像和深度图像中学习联合特征,将提取的RGB和深度图像的高层次特征进行跨通道信息融合,继而使用So...     

Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements

The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed in this paper. The structure, as well as the learning algorithm of the neuro-fuzzy network, is presented and tested on the example of estimation of the concentration of gas components in the gaseous mixture (so-called artificial nose problem). The results of numerical experiments are presented and discussed.     

一种改进的红外焦平面非均匀性校正算法

针对红外焦平面探测器现有非均匀性校正算法在实际应用中存在的问题,结合实际系统的开发,提出了一种改进算法——一点加两点校正算法。先求两点校正算法的校正增益和校正偏置,再求一点校正算法的校正偏置,求取一点校正算法的偏置参数时的图像数据来自前面求得的两点校正之后的数据,即最后的校正结果是两点校正后的再校正。理论分析和应用表明该算法与目前流行的算法相比具有实时性好、误差小、处理效果好等特点。     

一种基于矢量拖曳阵的拖船干扰抵消算法

拖曳阵中的拖船干扰常常是限制目标的观测范围和检测性能的重要因素之一。在浅海环境中,由于拖船干扰存在严重的多途效应,采用传统的自适应干扰抵消方法,效果并不理想。为了抵消矢量拖曳阵的拖船干扰,利用矢量拖曳阵中振速通道正交于阵艏的分量对拖船干扰不敏感的特点,提出一种基于矢量阵的拖船干扰抵消算法。该算法抗多途干扰效果好,可以有效地抑制拖船干扰,实验数据处理结果验证了该算法的有效性,可望在实际的声纳工程中得到应用。     

Seismic vulnerability assessment of chemical plants through probabilistic neural networks

A chemical industrial plant represents a sensitive presence in a region and, in case of severe damage due to earthquake actions, its impact on social life and environment can be devastating. From the structural point of view, chemical plants count a number of recurrent elements, which are classifiable in a discrete set of typological families (towers, chimneys, cylindrical or spherical or prismatic tanks, pipes etc.). The final aim of this work is to outline a general procedure to be followed in order to assign a seismic vulnerability estimate to each element of the various typological families.In this paper, F.E. simulations allowed to create a training set, which has been used to train a probabilistic neural system. A sample application has concerned the seismic vulnerability of simple spherical tanks.     

凸多边形星图识别算法

为解决星敏感器中较大视场快速、可靠的星图识别,提出了以凸多边形为基元、完全不依赖于星等的星图识别算法。对给定的视场,挑选其中较亮的恒星,依其坐标排序,然后采用由平面上的点生成凸多边形的算法,就能得到唯一的、以恒星为顶点的凸多边形。为验证星图识别算法的有效性,建立了导航星数据库,其储存单元为凸多边形的边和相邻边的夹角,共有3832个边数不等的凸多边形。在CPU为33MHz 的PC104上仿真结果表明:在任意视场中,生成凸多边形的时间小于5ms,基于凸多边形的星图识别成功率高于99%,并具有较强的鲁棒性。