Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

Purpose Quality is considered one of the essential competitive priorities for managing the Small, Medium Electronics Enterprises (SMEE) market in China; here, the most critical competing priorities and precondition for sector survival and growth are analysed. Instead, small and medium-sized enterprises are significantly disadvantaged and limited to validation capital. Design/methodology/approach Quality results are controlled by continuous measurement, irrespective of the product form, as a fundamental issue among firms. Hence, it enables the electronics markets to deal with various aspects of quality programs with effective deployment of IoT-assisted identify management (IIDM) model. Findings It enables the electronics markets to deal with various aspects of quality programs with effective deployment of IIDM model available to them without wasting in unimportant and non-productive areas. IIDM makes it essential for SMEEs to analyze total quality management (TQM) towards better understanding the relationship between internal structures and external ones to achieve better quality results. Originality/value Analyzes TQM towards better understanding shows the relationship between internal and external structures to achieve better quality results.

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The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices. INDEX TERMS Deep learning, fall detection, motion characteristics, the two-stream model, the MobileVGG.

The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB) classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI) showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB), tree augmented naive Bayes (TAN), Averaged one-dependence estimators (AODE), and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.