3D face recognition algorithm based on detecting reliable components

Fisherfaces algorithm is a popular method for face recognition.However,there exist some unstable com- ponents that degrade recognition performance.In this paper,we propose a method based on detecting reliable com- ponents to overcome the problem and introduce it to 3D face recognition.The reliable components are detected within the binary feature vector,which is generated from the Fisherfaces feature vector based on statistical properties,and is used for 3D face recognition as the final feature vector.Experimental results show that the reliable components fea- ture vector is much more effective than the Fisherfaces feature vector for face recognition.

Design of Drilling and Riveting Multi-functional End Effector for CFRP and Aluminum Components in Robotic Aircraft Assembly

To fulfill the demands for higher quality,efficiency and flexibility in aviation industry,a multi-functional end effector is designed to automate the drilling and riveting processes in assembling carbon fiber reinforced polymer(CFRP)and aluminum components for a robotic aircraft assembly system.To meet the specific functional requirements for blind rivet installation on CFRP and aluminum materials,additional modules are incorporated on the end effector aside of the basic processing modules for drilling.And all of these processing modules allow for a onestep-drilling-countersinking process,hole inspection,automatic rivet feed,rivet geometry check,sealant application,rivet insertion and installation.Besides,to guarantee the better quality of the hole drilled and joints riveted,several online detection and adjustment measures are applied to this end effector,including the reference detection and perpendicular calibration,which could effectively ensure the positioning precision and perpendicular accuracy as demanded.Finally,the test result shows that this end effector is capable of producing each hole to a positioning precision within ±0.5 mm,aperpendicular accuracy within 0.3°,a diameter tolerance of H8,and a countersink depth tolerance of±0.01 mm.Moreover,it could drill and rivet up to three joints per minute,with acceptable shearing and tensile strength.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Fault detection of excavator’s hydraulic system based on dynamic principal component analysis

In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.

Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

Position detection of component aperture based on integral imaging technology

Aiming at the problem that it is difficult to locate all the aperture positions of the large size component using Houghcircle detection method,this article presents a non-contact measurement method combining the integral imaging technology withHough circle detection algorithm.Firstly,a set of integral imaging information acquisition algorithms were proposed accordingto the classical imaging theory.Secondly,the camera array experiment device was built by using two-dimensional translationstage and charge coupled device(CCD)camera.When the system is operating,element image array captured with the camera isused to achieve the positioning of the component aperture using Hough circle detection and coordinate acquisition algorithm.Based on the above theory,a verification experiment was carried out.The results show that the detection error of the componentaperture position is within0.3mm,which provides effective theoretical support for the application of integral imagingtechnology in high precision detection

Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Effect of Two Kinds of Similarity Factors on Principal Component Analysis Fault Detection in Air Conditioning Systems

Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.

Improved performance of process monitoring based on selection of key principal components

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

Progress and Developmental Direction of Post-harvest Component Detection and Preservation Techniques of Nanfeng Citrus in China

This paper was summarized the research status and the development tendency of post-harvest component detection and preservation techniques of Nanfeng citrus in China, also analyzed the characteristics of various techniques, and proposed the developmental direction of novel techniques for post-harvest component detection and preservation of Nanfeng citrus.

Foreground Detection Based on Nonlinear Independent Component Analysis

Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

Android Malware Detection Using Local Binary Pattern and Principal Component Analysis

Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

基于局部离群因子与隔离森林的激光超声缺陷检测

针对激光超声(LU)缺陷检测中最大振幅图存在伪像的问题,结合主成分分析(PCA)和两种无监督的机器学习算法局部离群因子(LOF)与隔离森林(IF),以实现对LU数据的无监督异常检测。首先,利用PCA算法对LU数据进行降维处理,减轻了LU数据的复杂度;其次,利用LOF算法和IF算法进行了数据异常值的识别分析,并利用累积分布函数和核密度估计确定异常值的阈值大小;最后,对比了LOF算法、IF算法以及最大振幅图的检测结果。结果表明:LOF算法有更优的缺陷识别精度和更低的误判率。

基于多尺度融合金字塔焦点网络的接触网零部件检测

作为高铁牵引供电系统的重要组成部分,接触网系统承担着向动车组传输电能的重要功能.实际工程运营表明,受弓网交互产生的持续冲击以及外部环境的影响,接触网支撑部件可能会出现“松、脱、断、裂”等缺陷,导致接触网结构可靠性下降,严重影响接触网系统稳定运行.因此,及时精确定位接触网支撑部件(CSCs),对保障高铁安全运行和完善接触网检修维护策略具有重大意义.然而,CSCs的检测通常面临着零部件种类多、尺度差异大、部分零部件微小的问题.针对以上问题,本文提出一种基于多尺度融合金字塔焦点网络的接触网零部件检测算法,将平衡模块和特征金字塔模块相结合,提高对小目标的检测性能.首先,设计了可分离残差金字塔聚合模块(SRPAM),用于优化模型多尺度特征提取能力、扩大感受野,缓解CSCs检测的多尺度问题;其次,设计了一种基于平衡特征金字塔的路径聚合网络(PA-BFPN),用于提升跨层特征融合效率和小目标检测性能.最后,通过对比试验、可视化实验和消融实验证明了所提方法的有效性和优越性.其中,所提的MFPFCOS在CSCs数据集上的检测精度(mAP)能够在达到48.6%的同时,实现30的FLOPs(Floating point operations per second),表明所提方法能够在检测精度和检测速度之间保持良好的平衡.

多次冻融循环后成品异体肌腱性能的变化

背景:用于临床移植的异体肌腱在应用前需进行相应处理,在处理成品至正式使用期间一般会面临多周期冻融循环的情况,目前针对成品异体肌腱经历多周期冻融循环后的性能变化还未见相关报道。目的:探究异体肌腱在经历多周期冻融循环后的力学性能及成分变化。方法:取经法规规定工艺处理后的成品异体肌腱,在-40℃(或-80℃)条件下分别冻融循环3,6,10次,对相应条件冻融后的异体肌腱进行力学实验及DNA、硫酸糖胺聚糖、羟脯氨酸含量和总蛋白含量检测。结果与结论:①在-40℃条件下冷冻保存,冻融循环3,6,10次组之间的最大载荷、最大载荷伸长率、抗拉强度和弹性模量比较差异均无显著性意义(P>0.05);在-80℃条件下冷冻储存,冻融循环3,6,10次组之间的最大载荷、最大载荷伸长率比较差异无显著性意义(P>0.05),冻融循环3次组抗拉强度高于冻融循环6,10次组(P<0.05),冻融循环3次组弹性模量高于冻融循环10次组(P<0.05);②在-40℃(或-80℃)条件下冷冻保存,冻融次数不影响异体肌腱中DNA、羟脯氨酸、总蛋白和硫酸糖胺聚糖含量;③结果表明,在-40℃储存时,10次以内冻融循环不影响异体肌腱的性能;而在-80℃储存时,异体肌腱冻融循环次数应控制在6次以内。