Novel probability neural network

This paper presents a novel probability neural network (PNN) that can classify the data for both continuous and categorical input data types. A mixture model of continuous and categorical variables is proposed to construct a probability density function (PDF) that is the key part for the PNN. The proposed PNN has two advantages compared to conventional algorithms such as the multilayer perceptron (MLP) neural network. One is that the PNN can produce better results compared to the MLP neural network when the input data set includes both continuous and categorical data types, even using the normalised input variables. Normally, the normalised input variables generate a better result than the non-normalised input variables for the MLP neural network. The second advantage is that the PNN does not need the cross-validation data set and does not produce over-training like the MLP neural network. These advantages have been proven in our experimental study. The proposed PNN can also be used to perform unsupervised cluster analysis. The superiority of the PNN, compared to the MLP neural network, Radical Basis Function (RBF) neural network, C4.5 and Random Forest decisions trees, are demonstrated by applying them to two real-life data sets, the Heart Disease and Trauma data sets, which include both continuous and categorical variables.     

Multiclass support vector machines for EEG-signals classification.

In this paper, we proposed the multiclass support vector machine (SVM) with the error-correcting output codes for the multiclass electroencephalogram (EEG) signals classification problem. The probabilistic neural network (PNN) and multilayer perceptron neural network were also tested and benchmarked for their performance on the classification of the EEG signals. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and the Lyapunov exponents and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the EEG signals and the multiclass SVM and PNN trained on these features achieved high classification accuracies.     

基于多网络的矿井智能化监管系统设计

提出一种矿井智能化监管系统设计方案,该系统包括无线传感器网络（WSN）、工业以太网络、人员定位卡、井下视频手机、监管服务器、监控计算机、数据库、中心交换机、智能化监管信息平台。系统将工业以太网络和无线传感器网络结合,构建井下有线／无线的混合通信系统进行数据传输,井上利用智能化信息监管平台实现与井下作业人员、井上管理人员、井上其他人员的数据通信和资源共享;该系统具有井下人员实时定位、井下与井上实时视频通信、矿井环境监测、智能化信息处理、矿井信息分发、资源调度、异常情况报警等功能,矿井环境监测包括对瓦斯、CO、电化学氧气、硫化氢、风速、矿压、温湿度、矿尘、噪音等环境参数的监测。     

Low Cost Low Power Smart Helmet for Real-Time Remote Underground Mine Environment Monitoring

Underground mining production process is vulnerable and highly dynamic in nature. Among the various causes of accidents in underground mine, major one is presence of flammable and noxious gases. Though many existing safety gadgets are there but they could not work reliably because of the typical nature of mines structure and production variability. Wireless data and communication network is also not successful because wireless communication in underground mine is significantly more challenging than through air. This work introduces the application of mobile wireless sensor network in order to monitor a variety of parameters in underground mines which have life threatening effects towards them. Each node of the network placed over the safety gear (helmet wore statutorily by every miner) comprises of various sensors depending on the requirement with microcontroller unit and other low power accessories. The proposed work has a unique feature that it will make the personnel aware about the situation of the gases present and surrounding by automatically generating different alarms and different light indicators. Other function of this device will be to transmit the data sensed by the sensors in the device to the control room wirelessly so that the responsible person would be aware of the situation. This work is focused on the design of such a prototype model for the underground mines with the aforementioned specification.     

Automated diagnostic systems with diverse and composite features for Doppler ultrasound signals

In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.     

物联网在煤矿生产上的应用

中国煤矿资源丰富,但煤矿安全事故频发。针对煤矿安全生产问题,结合物联网技术,提出了一种矿井物联网的构建方案。以2．4GHz RFID射频读卡器作为感知末梢,感知并采集电子标签携带的员工身份码信息,校验正确后,再将身份码数据经工业总线网上传至地面监控计算机并由VB编程分析处理。实现了煤矿员工基本信息的可查询以及井下作业员...     

兆位电路用高纯水,气和化学试剂的质量控制

本文阐述了高纯水、高纯气和高纯化学试剂的质量对兆位电路的影响,研究了高纯水中颗粒、金属、非金属、细菌以及总有机炭对VLSI性能的影响,研究了半导体器件工艺中氯化氢、氨、氮及硅烷气体中氧、水、二氧化碳、总碳氢化物等杂质对VLSI工艺的影响;研究了化学试剂中杂质对VLSI工艺的影响。     

基于超窄线宽激光器的多气体光声光谱检测研究

针对多组分痕量气体检测中精确度差、灵敏度低等问题,设计了基于光源波长扫描技术的光声光谱式多组分痕量气体检测系统。系统采用单一超窄线宽半导体激光器作光源,设计了一阶纵向共振式多光程吸收光声池,通过正弦信号对光源波长进行调制,并结合二次谐波信号检测技术和超窄线宽激光特性,有效地消除了吸收池内的背景噪声和光源抖动的影响,实现...     

Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences

A novel artificial neural network (NN)-based technique is proposed for enabling smart sensors to operate in harsh environments. The NN-based sensor model automatically linearizes and compensates for the adverse effects arising due to nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental variables. To show the potential of the proposed NN-based technique, we have provided results of a smart capacitive pressure sensor (CPS) operating under a wide range of temperature variation. A multilayer perceptron is utilized to transfer the nonlinear CPS characteristics at any operating temperature to a linearized response characteristics. Through extensive simulated experiments, we have shown that the NN-based CPS model can provide pressure readout with a maximum full-scale error of only 1.5% over a temperature range of 50 to 200 with excellent linearized response for all the three forms of nonlinear dependencies considered. Performance of the proposed technique is compared with a recently proposed computationally efficient NN-based extreme learning machine. The proposed multilayer perceptron based model is tested by using experimentally measured real sensor data, and found to have satisfactory performance.     

基于二元偶极子阵的边射端射可重构RFID阅读器天线设计

基于射频识别应用场景中存在的一些T型结构，如楼内、地下矿井等存在的一些T型通道，为做好这些场景中的信号覆盖，本文提出了一种新颖的边射端射可重构的RFID阅读器天线。天线由可重构的馈电网络和两个相距半波长的偶极子组成，天线边射和双向端射的可重构特性通过控制馈电网络中的开关状态实现。为了验证天线性能，对其进行了加工测试。测试结果表明，天线在边射和端射状态下的阻抗带宽和增益分别为2.7%(900～925 MHz),4.15 dBi和1.9%(905～923 MHz),2.10 dBi,与仿真结果基本一致。该天线具有结构简单、小型化、低剖面等优势，在射频识别系统中有一定的应用价值。     

车载有毒气体CO痕量检测报警系统研究

实时CO浓度检测报警是基于单片机AT89S52系统。使用对一氧化碳敏感的TGS2600-B00传感器对空气中的CO进行采样检测,通过传感器电阻值的变化反映出浓度的变化,再经模数转换器ADC0809转换后由单片机进行数据处理,并在LED上实时显示出当前CO的浓度,在超过30×10^-6 时报警。该系统具有灵敏度高、结构简单、体积小、携带与安装方便等特点,具有较高的应用价值。     

基于STM32的矿用电化学一氧化碳传感器的设计

针对煤矿安全生产中对一氧化碳气体浓度的检测与报警,提出了一种矿用电化学一氧化碳传感器的设计方案。该方案以STM32F103c8单片机为处理核心,利用CitYTechnology生产的4cn1一氧化碳敏感元件、微弱信号放大器与Ds18820温度传感器、nRF24L01等器件,实现了矿用一氧化碳传感器的软硬件设计。采集的一氧化碳浓度数据经滤波、校标后由RS485输出给上位机,用于显示与报警,传感器参数可以通过RS485通讯或者无线通讯设定。实际测试表明,该一氧化碳传感器精度高、稳定性好、参数设定方便,适宜于矿井下需要监测一氧化碳的场所使用．．     

Contextual image labelling with a neural network

A neural network with a multilayer perceptron architecture is shown to be capable of labelling the visible objects in colour images of urban and rural outdoor scenes. The two problems of segmentation and recognition are separated by using `ideal' segmentations, allowing the performance of the recognition method to be studied independently of the effects of using an imperfect real segmentation process. A label clustering transformation is proposed and shown to cause a significant increase in the expected classification accuracy of the network. The deletion of the contextual features from the feature vector is shown to degrade the performance of the network. Measurements of the generalisation performance on unseen test data show that, on average, the system correctly recognises approximately 72% of the area of these images     

Fault detection and diagnosis of permanent-magnet DC motor based onparameter estimation and neural network

In this paper, fault detection and diagnosis of a permanent-magnet DC motor is discussed. Parameter estimation based on block-pulse function series is used to estimate the continuous-time model of the motor. The electromechanical parameters of the motor can be obtained from the estimated model parameters. The relative changes of electromechanical parameters are used to detect motor faults. A multilayer perceptron neural network is used to isolate faults based on the patterns of parameter changes. Experiments with a real motor validate the feasibility of the combined use of parameter estimation and neural network classification for fault detection and isolation of the motor     

基于超低功耗单片机MSP430的矿用一氧化碳报警器设计

本文设计实现了一款基于MSP430F4250超低功耗单片机的一氧化碳报警器.报警器采用电化学一氧化碳传感器输出浓度信号,通过4位数码管实时显示CO气体浓度值,精度可达到1PPM(百万分之一).同时,报警器可预设报警值,当浓度值超过报警值后,发出报警信号.文章最后对报警器进行了实际校准,并对检测结果进行了数据分析.     

Performance Analysis of Neural Network Detectors in DS/CDMA Systems

In this paper, we consider neural networks as the detectors of signals of users in DS/CDMA systems. We apply multilayer perceptron neural network with back propagation learning algorithm in AWGN and multipath fading channels. Our analysis results in significant reduction in the receiver complexity over the previous studies. We compare the performance of neural network with the conventional and suboptimal detectors in AWGN channel and with the RAKE and single user lower bound receivers in fading channels. We also apply different criterion for training the network such as the decision based, fuzzy decision, discriminative learning, minimum classification, and entropy neural networks in AWGN channels and compare their performance. Further, we propose modified decision based network which improves the performance of the decision based network. A comparison between multilayer perceptron and Hopfield neural detectors is presented.     

基于改进YOLOv5的煤矿井下目标检测研究

针对煤矿井下环境多利用红外相机感知周边环境温度成像,但形成的图像存在纹理信息少、噪声多、图像模糊等问题,该文提出一种可用于煤矿井下实时检测的多尺度卷积神经网络(Ucm-YOLOv5)。该网络是在YOLOv5的基础上进行改进,首先使用PP-LCNet作为主干网络,用于加强CPU端的推理速度;其次取消Focus模块,使用shuffle_block模块替代C3模块,在去除冗余操作的同时减少了计算量;最后优化Anchor同时引入H-swish作为激活函数。实验结果表明,Ucm-YOLOv5比YOLOv5的模型参数量减少了41%,模型缩小了86%,该算法在煤矿井下具有更高的检测精度,同时在CPU端的检测速度达到实时检测标准,满足煤矿井下目标检测的工作要求。     

Performance evaluation of neural network and linear predictors for near-lossless compression of EEG signals.

This paper presents a comparison of the performances of neural network and linear predictors for near-lossless compression of EEG signals. Three neural network predictors, namely, single-layer perceptron (SLP), multilayer perceptron (MLP), and Elman network (EN), and two linear predictors, namely, autoregressive model (AR) and finite-impulse response filter (FIR) are used. For all the predictors, uniform quantization is applied on the residue signals obtained as the difference between the original and the predicted values. The maximum allowable reconstruction error delta is varied to determine the theoretical bound delta 0 for near-lossless compression and the corresponding bit rate rp. It is shown that among all the predictors, the SLP yields the best results in achieving the lowest values for delta 0 and rp. The corresponding values of the fidelity parameters, namely, percent of root-mean-square difference, peak SNR and cross correlation are also determined. A compression efficiency of 82.8% is achieved using the SLP with a near-lossless bound delta 0 = 3, with the diagnostic quality of the reconstructed EEG signal preserved. Thus, the proposed near-lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes.     

四针状ZnO晶须(T-ZnO)的掺杂及其气敏特性研究

采用高温固溶工艺制备了Al3+,Fe3+和Ag+掺杂的T-ZnO气敏材料,并制作了烧结型厚膜气敏元件,测试了元件对H2S,NH3,C2H5OH和H2的敏感特性,研究了掺杂剂、掺杂工艺和材料形貌结构对T-ZnO材料气敏特性的影响规律。结果显示,T-ZnO材料对H2S和C2H5OH气体灵敏度较高,对H2和NH3等气体灵敏度较差;经过H2气氛热处理,掺物质的量百分数为0.1%Al3+的T-ZnO对气体表现出很高的灵敏度,在268.5℃时,对体积分数为10-4的H2S的灵敏度达160;同时,Al3+掺杂工艺改善了材料对H2S和C2H5OH的恢复-响应特性。在Fe3+掺杂ZnO样品中,出现第二相(ZnFe2O4)可以提高对气体的灵敏度。     

Methodologies for characterizing ultrasonic transducers usingneural network and pattern recognition techniques

System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial neural network techniques provide far better classification results than the pattern recognition techniques. A multilayer backpropagation neural network which provides a classification accuracy of 94% is developed. Two other multilayer neural networks-sum-of-products and a newly devised neural network called hybrid sum-of-products-have a classification accuracy of 90% and 93%, respectively. The most successful pattern recognition technique for this application is found to be the perceptron, which provides a classification accuracy of 77%