Automatic detection devices rely on accurate and reliable sensor readings, which is a prerequisite for their good running. Self-validating sensor technology that realizes sensor operating status estimation and data validation can provide an intelligent functional framework for sensor status monitoring to improve the reliability and maintainability of the system. After nearly thirty years of development, self-validating sensors have achieved numerous theoretical and application results. In particular, the stability and reliability of gas chemical sensors in the field of artificial olfaction have been a bottleneck that limits the industrialization of the electronic nose system. Obviously, self-validating sensor technology exactly provides a potential solution for artificial olfaction application. This paper aims to systematically review the current development and typical applications of self-validating sensor technology, summarize the implementation methods to its different functional modules as well and demonstrate its potential application value in artificial olfaction. © 2024 Elsevier Ltd

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Hyperspectral image (HSI) classification methods based on three-dimensional convolutional neural network (3DCNN) have problems of overfitting the in-sample training process and difficulty in highlighting the role of discriminant features, which reduce the classification accuracy. To solve the above problems, an HSI classification method based on M-3DCNN-Attention is proposed. First, the Mixup algorithm is used to construct HSI virtual samples to expand the original data set. The sample size of the expanded data set is twice that of the original data set, which greatly alleviates the overfitting phenomenon caused by the small sample of HSI. Second, the structure of 3DCNN is improved. A convolutional block attention module (CBAM) is added between each 3D convolutional layer and ReLU layer, and a total of three CBAMs are used so as to highlight the discriminant features in spectral and spatial dimensions of HSI and suppress the nondiscriminant features. Finally, the spectral-spatial features are transferred to the Softmax classifier to obtain the final classification results. The comparative experiments are conducted on three hyperspectral data sets (Indian Pines, University of PaviaU, and Salinas), and the overall accuracy of M-3DCNN-Attention is 99.90%, 99.93%, and 99.36%, respectively, which is better than the comparative methods. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

在“双碳”战略的背景下，针对国内对碳足迹采集终端及系统的迫切需求，提出了基于电力采集终端及通信系统的解决方案，并利用混合边界扫描技术提出了具体的“虚拟探针”可测性设计方案。还针对基于单一类型故障特征进行非线性“簇”电路故障诊断准确率低的难题，在研究小波包变换、PCA及Volterra核特征提取的基础上，提出了小波包变换与PCA特征层融合，并与基于Volterra核特征的初级诊断结果进行决策层融合的故障诊断方法。实验表明，该方法可以有效提高故障诊断的准确率。

为了解决模拟乘法器等多输入测量电路的智能故障诊断准确率低的问题,文中研究了多输入多输出(MIMO)电路的基于Volterra级数的建模方法,为电路的故障诊断提供模型,提出了整体退火遗传特征提取方法,利用整体退火遗传算法的全局寻优能力优

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Method based on partitioning Collaborative Representation (FRAPCR) is proposed in this paper. Firstly, the face image is divided into several sub-blocks. Secondly, the Collaborative Representation (CR) is used to calculate the minimum sparse representation coefficient of each sub-block and the residual between the sub-block and the corresponding samples of each class, taking the class corresponding to the minimum residual as the class to which the sub-block belongs. Thirdly, a voting mechanism is introduced to count the categories of all sub-blocks of each face image, and the category with the largest number of votes is the category to which the whole face image belongs. Through the experiments on face databases ORL, Extend Yale B, and AR by the proposed method (FRAPCR), the best partitioning way of the face image is obtained. When there is partial information missing (pixel information missing, corrosion block and occlusion) in the face image, the images in each face database is divided in its corresponding optimal partitioning way. And comparative experiments between the FRAPCR and traditional CR face recognition methods are performed. The results show that FRAPCR has high recognition rate and stable recognition effect when there is partial information missing in face images.

Facial expression recognition (FER) is an important task in the field of human-computer interaction. However, the traditional facial expression recognition task needs to be based on the hand-crafted features, and the feature extraction method is single; the facial expression recognition task based on deep learning cannot extract local texture features of the image and loss more information. Therefore, a facial expression recognition method based on image fusion and convolution neural network (FERFC) is proposed in this paper. Which fused the facial expression images after extracted by the local binary pattern (LBP) with the original images. It can effectively improved the utilization of images. Firstly, some image pre-processing approaches are used in this paper, such as data augmentation, face detection and data normalization. Secondly, the images of local texture features extracted by the LBP and the original images are fused in this step. Finally, the task of facial expression features learning and classification is completed by convolution neural network (CNN). The results show that the method proposed in this paper can accomplish the facial expression recognition task accurately. The recognition rate of reference database 'Jaffe', 'CK+' and 'FER2013' is 91.9%, 95.6% and 75.9%. The results show that the FERFC has significant advantages than traditional facial expression recognition. At the same time, the number of training samples is small, the FERFC still has obvious advantages and a higher robustness.