Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NO_X) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R²) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R² = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NO_X concentration (RMSE = 83.6716, R² = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

In this article, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion toward human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments.

A finite-time control method is presented for n-link robots with actuator saturation under time-varying constraints of work space. Barrier Lyapunov functions (BLFs) are designed for ensuring that the robot remains under time-varying constraints of the work space. In order to deal with asymmetric saturation nonlinearity, we transform asymmetric saturation into a symmetric one by using a hyperbolic tangent function, which is introduced to avoid the discontinuous problem existing in the auxiliary system-based saturation method. Combining fuzzy-logic systems (FLSs) with the backstepping technique, a finite-time control policy is designed for ensuring the stability of the closed-loop system. With the use of the Lyapunov stability theory, all the error signals are proved to be semiglobal finite-time stable (SGFS). Finally, the experiment is carried out to verify the effectiveness of the finite-time method.

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

In recent years, WiFi-based device-free localization (DFL) has attracted attentions due to the rapid development of location-based applications. The localization performance of data-driven DFL models highly relies on the quality of the fingerprint. However, it is difficult to obtain high-quality fingerprints in cluttered environments due to various uncertainties, such as environmental dynamics. To mitigate effects of uncertainties, this article proposes a variance-constrained local–global modeling method to enhance the localization performance. To be specific, the collected channel state information data from a specific environment are first divided into several groups depending on their statistical characteristics using the clustering method, and the local–global modeling mechanism is then implemented based on the extreme learning machine, a kind of noniterative single hidden layer feedforward neural network, to achieve the good representation of the whole environment. During the local–global modeling process, the proposed method is to simultaneously minimize the output weights of the neural network, training errors of the DFL model, and intragroup variances of the grouped data, which makes the created DFL model robust in cluttered environments and not sensitive to uncertainties. Comprehensive experiments in several scenarios, including different indoor environments, device positions and heights, target's orientations, and body shapes, are performed. Experimental results indicate that the proposed method could achieve better localization performance than selected baseline methods, demonstrating the effectiveness of the proposed variance-constrained local–global modeling mechanism in DFL.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems’ uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.