Increase of Indicator Values of Identification Systems Quality on the Recognition of Human Face on the Basis of Photo Portraits

In this paper, algorithms of automatic identification of persons on the basis of their photographs are considered. For identification of persons, the comparative analysis of control systems by bases of images created in the different periods is carried out and their applied possibilities are shown.

Application of Computer Vision Technique to Maize Variety Identification

Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.

Research on Rice Leaf Disease Recognition Based on BP Neural Network

To solve the problem of mistake recognition among rice diseases, automatic recognition methods based on BP(back propagation) neural network were studied in this paper for blast, sheath blight and bacterial blight. Chose mobile terminal equipment as image collecting tool and built database of rice leaf images with diseases under threshold segmentation method. Characteristic parameters were extracted from color, shape and texture. Furthermore, parameters were optimized using the single-factor variance analysis and the effects of BP neural network model. The optimization would simplify BP neural network model without reducing the recognition accuracy. The finally model could successfully recognize 98%, 96% and 98% of rice blast, sheath blight and white leaf blight, respectively.

Accurate P2P traffic identification based on data transfer behavior

Peer-to-Peer (P2P) technology is one of the most popular techniques nowadays, and accurate identification of P2P traffic is important for many network activities. The classification of network traffic by using port-based or payload-based analysis is becoming increasingly difficult when many applications use dynamic port numbers, masquerading techniques, and encryption to avoid detection. A novel method for P2P traffic identification is proposed in this work, and the methodology relies only on the statistics of end-point, which is a pair of destination IP address and destination port. Features of end-point behaviors are extracted and with which the Support Vector Machine classification model is built. The experimental results demonstrate that this method can classify network applications by using TCP or UDP protocol effectively. A large set of experiments has been carried over to assess the performance of this approach, and the results prove that the proposed approach has good performance both at accuracy and robustness.

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities,such as face,voice,fingerprint,gait,etc.Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems,or jointly with two or more as in multimodal systems.However,multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels.Despite this enhancement,in real-life applications some factors degrade multimodal systems’performance,such as occlusion,face poses,and noise in voice data.In this paper,we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal biometrics.The proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition performance.The proposed dynamic fusion was achieved using face and voice biometrics,where face features were extracted using principal component analysis(PCA),and Gabor filters separately,whilst voice features were extracted using Mel-Frequency Cepstral Coefficients(MFCCs).Here,the facial data quality assessment of face images is mainly based on the existence of occlusion,whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio(SNR)as per the existence of noise.To evaluate the performance of the proposed algorithms,several experiments were conducted using two combinations of three different databases,AR database,and the extended Yale Face Database B for face images,in addition to VOiCES database for voice data.The obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using standard fusion methods.

Human Face Identification

Automatic face identification poses an exacting problem in feature extraction, pattern analysis and recognition. Owing to its wide potential for applications efforts have been made and some systems have been established catering for some aspects of the problem. The-state-of -the-art is summarized. The author's work on face feature analysis and extraction is also briefly introduced. These features, which are extracted from one monochrome frontal view image of the face, include the profile feature, the shape of the face contour, the eye region statistics and the generic measures across the face. Then the problems to be solved and the prospects of futher work and applications of automatic face identification are concluded.

FeatureCAM在车铣复合机床上同步加工的应用案例

本文通过一个典型零件在车铣复合机床上的编程过程，简要介绍了英国Delcam公司FeatureCAM软件在产品加工领域的智能化“特征”识别技术（AFR）的应用，车铣复合同步操作编程的便捷性，以及通过该软件比较完美地解决制造部门工艺知识库的标准化思路。

Radar Target Recognition Algorithm Based on RCS Observation Sequence——Set-Valued Identification Method

This paper studies the problem of radar target recognition based on radar cross section(RCS)observation sequence.First,the authors compute the discrete wavelet transform of RCS observation sequence and extract a valid statistical feature vector containing five components.These five components represent five different features of the radar target.Second,the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target.By set-valued identification method,the authors can estimate the system parameter,based on which the recognition criteria is given.In order to illustrate the efficiency of the proposed recognition method,extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed.The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.

Peripheral Nonlinear Time Spectrum Features Algorithm for Large Vocabulary Mandarin Automatic Speech Recognition

This work describes an improved feature extractor algorithm to extract the peripheral features of point x(ti,fj) using a nonlinear algorithm to compute the nonlinear time spectrum (NL-TS) pattern. The algo- rithm observes n×n neighborhoods of the point in all directions, and then incorporates the peripheral fea- tures using the Mel frequency cepstrum components (MFCCs)-based feature extractor of the Tsinghua elec- tronic engineering speech processing (THEESP) for Mandarin automatic speech recognition (MASR) sys- tem as replacements of the dynamic features with different feature combinations. In this algorithm, the or- thogonal bases are extracted directly from the speech data using discrite cosime transformation (DCT) with 3×3 blocks on an NL-TS pattern as the peripheral features. The new primal bases are then selected and simplified in the form of the ?dp- operator in the time direction and the ?dp- operator in the frequency di- t f rection. The algorithm has 23.29% improvements of the relative error rate in comparison with the standard MFCC feature-set and the dynamic features in tests using THEESP with the duration distribution-based hid- den Markov model (DDBHMM) based on MASR system.

基于特征模式识别的农田监控系统研究

针对农田监控系统中存在的识别准确度不高、数据处理效率低等问题,提出了一种基于特征模式识别的农田监控系统。对农田监控系统的功能进行了设计,包括系统整体架构设计、数据采集与传输模块设计、特征提取与模式识别算法设计,结合用户界面设计实验与结果分析,验证了所提出系统的有效性和性能优势。对农田监控系统在实际农业生产中的应用前景进行了展望,并提出了未来改进方向和研究趋势。

基于关键区域特征匹配的生产现场隐患识别方法

为精准识别生产现场的各类隐患问题,避免不安全生产事件的出现,提出一种基于关键区域特征匹配的生产现场隐患识别方法。获取隐患信息关键区域特征,并定义隐患信息分类标准,完成基于关键区域特征匹配的生产现场隐患信息处理。排查隐患问题,并对其等级进行针对性评估,再联合相关参数指标确定隐患识别阈值的取值范围,完成基于关键区域特征匹配的生产现场隐患识别方法的设计。实验结果表明,利用所提方法可以准确识别出设备老化、操作不规范、物料储存不当这三类隐患问题,能够有效避免不安全生产事件的出现,符合实际应用需求。

基于特征融合的空压机故障诊断算法研究

空气压缩机作为工业生产的重要设备,其运行状态直接影响到生产的成败。然而,传统的故障诊断方法不易获得准确的故障特征,不同工作条件之间的特征分布差异的度量不是充分的域自适应,难以达到较好的识别精度,并且空气压缩机运行时产生一定的背景噪声,形成一定干扰,影响故障识别准确性。为了克服上述限制,提出了一种基于特征融合的空气压缩机故障诊断方法。首先,分别提取空气压缩机的梅尔倒谱系数特征和小波变换特征。然后,在决策层对置信度分数和预测边界框进行晚期融合,并根据评估指标选择最佳网络模型完成分类。对比实验结果表明,该特征融合方法显著提高了故障识别的准确性。

基于木材微观特征的BP神经网络算法红木识别研究

我国实木家具产业链发展较为成熟。作为一种珍贵木材,红木在实木家具产业中占有重要地位,我国对红木资源的进口量也在逐年增加。传统识别红木的方法主要依靠人工经验,而准确科学地识别红木种类对于红木家具产业和红木工艺品都具有重要的意义。本文提出了一种基于木材微观特征的红木识别方法,并利用BP神经网络算法,建立了识别模型,表现出较好的识别效果,可为红木树种检测提供新方法。

基于深度学习的牛脸识别系统设计

为了提高畜牧保险理赔行业中牛只身份识别准确率,试验建立了基于计算机视觉提取牛脸特征的牛只身份识别系统,即使用SOLOv2实例分割模型提取牛脸前景图像,结合FaceNet提取牛脸特征;基于Tornado Web和TF Serving框架进行实例分割模型和牛脸特征提取模型的部署,完成牛脸身份识别系统的搭建,并制作了手机APP,最后对添加SOLOv2牛脸身份识别模型的准确率和验证率进行了验证。结果表明:添加了SOLOv2模型的牛脸身份识别准确率达到了98.063%,验证率达到了92.451%,与未添加SOLOv2模型相比,准确率提高了0.270百分点,验证率提高了6.275百分点。说明添加SOLOv2实例分割模型能提升牛脸身份识别的准确率和验证率。

面向无人机通信网络的信道全域特性空间聚类和识别

为提高无人机通信网络的稳定性和可靠性,提出了一种基于信道全域特性的信道子空间聚类与识别方法。首先,利用距离域、时延域、空间域和多普勒域特性对信道进行完备表征,并提出了一种信道子空间聚类方法,将全域特性相似的信道组成信道子空间,作为无人机通信场景分类的依据。然后,提出了一种基于反向传播神经网络的信道子空间识别方法,判断新的信道数据是否属于原有信道子空间的结构,并利用信道全域特性作为特征张量以提高识别精度。同时,通过计算信道与信道子空间中心的距离,消除训练数据异常值的影响,从而提高识别的鲁棒性。最后,通过入射及反弹射线法/镜像法对176个典型数字城市场景进行仿真,获得176000个信道的全域特性和对应信道状态信息,用于验证本文提出的聚类和识别方法的准确性。仿真结果表明,本文提出的场景识别方法可以将传统场景分类方法的176个识别目标减少至20个,且信道子空间中信道状态特性的吻合度达到99%,识别方法的准确度也达到98.7%。因此,本文提出的方法可以精确识别无人机通信工作中所处的信道子空间,为无人机通信性能优化提供依据。

基于跨模态共享特征学习的夜间牛脸识别方法

【目的】解决夜间环境下牛只身份信息难以有效识别的问题,以期为牛只全天候监测提供技术基础。【方法】提出了一种基于跨模态共享特征学习的夜间牛脸识别方法。首先,模型框架采用浅层双流结构,有效提取不同模态的牛脸图像中的共享特征信息;其次,引入Triplet注意力机制,跨维度地捕捉交互信息,以增强牛只身份信息的提取;最后,通过嵌入扩展模块进一步挖掘跨模态身份信息的表征。【结果】本文提出的夜间牛脸识别模型在测试集上的平均精度均值、一阶累积匹配特征值(CMC-1)和五阶累积匹配特征值(CMC-5)分别为90.68%、94.73%和97.82%,相较于未进行跨模态训练的模型,提高了19.67、18.91和12.00个百分点。【结论】本研究所提出的模型为夜间牛只身份识别问题提供了可靠的解决方案,为实现牛只全天候持续监测奠定了坚实的技术基础。

基于特征识别与云边协同的安全智能检测技术研究

由于电力施工环境的复杂性和多样性,监管人员难以在电力作业安全巡检过程中对违规行为及安全措施进行及时、准确地检查。针对此,文中提出了一种基于特征识别与云协同的安全智能检测算法。该算法利用卷积神经网络(CNN)与样本数据训练出作业中常见的安全帽、安全绳等5种识别模型,使用训练好的模型对经过预处理操作的电力施工现场图像数据进行分析与识别,从而实现了对安全措施的智能监测。实验结果表明,所提算法对典型电力施工安全措施的识别准确率可达到87%以上,证明了算法的有效性。

实体类别增强的汽车领域嵌套命名实体识别

针对中文汽车领域实体抽取任务中对嵌套实体、长实体识别效果差的问题,提出一种实体类别增强的嵌套实体抽取(ECE-NER)模型。首先,基于特征融合编码,提高模型对领域实体边界的感知能力;然后,尾词识别模块利用多层感知机得到实体尾词集合;最后,前向边界识别模块基于义原构造的实体类别特征和自注意力机制得到实体类别增强的候选尾词表征,融合领域实体类别特征,利用双仿射编码器计算特定尾词和实体类型的实体跨度概率,从而确定命名实体。在某汽车企业生产线故障数据集、汽车工业故障抽取评测数据集CCL2022和中文医学文本数据集CHIP2020上进行模型验证。实验结果表明,所提模型在前两个数据集上的实体识别F1值比序列标注模型(BERT+BiLSTM+CRF)、基于跨度的实体抽取模型(PURE(Princeton University Relation Extraction)、SpERT(Span-based Entity and Relation Transformer))分别提高了4.1、1.8、1.6个百分点和9.0、5.4、7.3个百分点;在第一个数据集和第三个数据集中嵌套实体识别F1值与PURE、SpERT模型相比提高了13.3、8.3个百分点和21.7、9.3个百分点,验证了所提模型在嵌套实体识别上的有效性。

基于二维特征和CNN分析的无人机操控员情绪状态检测

为了实时检测无人机操控员的情绪状态,提出了一种基于二维特征和卷积神经网络(CNN)分析的无人机操控员情绪状态检测算法;针对脑电信号(EEG)中眼电伪迹干扰的问题,设计实现了一种基于二阶盲辨识(SOBI)的去除伪迹算法;针对其它模型检测率低的问题,通过微分熵特征(DE)提取、2-DMapping映射及稀疏运算将一维脑电信号转化为包含情感信息的二维特征图,并对脑电信号进行扩增处理,提出二维特征图与CNN相结合的方式,使得各通道的情感特征相互关联;利用CNN自动学习深层次特征的优势,深度挖掘二维特征图里的脑电情感信息,较好地实现了无人机操控员积极、中性以及消极三种情绪状态检测。

基于多路卷积聚合的动作识别

为解决视频数据中不同的动作行为时序长短不一,输入的视频帧序列长度固定而导致不同时序特征被忽略的问题,提出了基于多路卷积网络聚合深度学习模型的动作识别方法。网络以不同序列长度和模态的图像作为输入源,构成三条支路,采用多路分支逐层捕捉不同尺度的特征信息,在网路的最后对特征进行聚合并利用softmax分类器对识别结果进行分类。实验结果表明,该模型在UCF101数据集上准确率达到了88.36%,均优于对比实验模型,有效地提高了识别精度,具有一定的竞争力。