Subspace learning is a popular machine learning method that has been frequently applied for gas sensor calibration; however, there are the following limitations in the latent subspace learning process: 1) the existence of data distribution differences is not considered and 2) ignoring the inherent information of the original space, such as discriminative information and structure information. To overcome these issues, we design a novel domain correction latent subspace learning (DCLSL) algorithm for gas sensor drift compensation by integrating subspace learning and domain adaptation into a unified framework in this study. First, domain correction is applied to alleviate the distribution difference before and after sensor drift by exploiting the mean distribution discrepancy criterion. Second, for better-local representation and classification results, we consider both discriminative information of the source data and structural information of the target data, revealing the intrinsic geometric structure in the latent subspace and forming a compact intraclass and interclass separated discriminative data layout. In addition, to improve the consistency of features and labels, we use a projection term, a reconstruction term, and a regularization term to simultaneously implement joint learning of the label space and the latent subspace and impose a row-sparsity constraint to enhance the model robustness to noise. Inspired by non-negative matrix factorization, we skillfully adopt multiplication update rules and thus solve the proposed optimization problem. Finally, we conduct experiments for different drift compensation tasks on two common gas sensor datasets, and the results are encouraging.

Task assignment is a critical part of human-robot collaboration. The traditional task assignment methods in the intuitionistic fuzzy (IF) environment only consider two options, i.e., the task is executed by humans or machines. However, some tasks may need to be completed by humans and machines working together, such as mecha. Therefore, we propose a human-robot task assignment method under the IF environment based on three-way decision (3WD) theory in this paper. By extending the traditional two-way assignment mode into the three-way one, our method can flexibly deal with the concerned situations. At first, the appropriateness of assigning each task to the machine is calculated by IF TOPSIS. When ranking the IF evaluation values, the decision maker's attitude toward uncertainty is considered. Then the appropriateness served as conditional probability and a cost-sensitive IF 3WD model is constructed to complete the assignment. Furthermore, we construct algorithms to deal with dynamic situations. Finally, a numerical example illustrates the feasibility of the proposed method.

This paper discusses the equivalence of the performance between the optimal distributed and centralized fusion based on best linear unbiased estimation (BLUE). A necessary and sufficient condition for the optimal distributed BLUE fusion to have identical performance as their centralized counterparts is obtained by setting the difference of two optimal estimates based on the two fusion rules be zero. Furthermore, under some very mild conditions on estimatee and observation error, we provide two theorems when the observations are linear in the estimatee. Specifically, the optimal distributed BLUE fusion is identical to the centralized BLUE fusion if observation errors are uncorrelated across sensors or the observation matrix of each sensor is full row rank. Numerical examples corroborate our analysis.