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In the quest for human-robot interaction (HRI), leading to the development of emotion recognition, learning, and analysis capabilities, robotics plays a significant role in human perception, attention, decision-making, and social communication. However, the accurate recognition of emotions in HRI remains a challenge. This is due to the coexistence of multiple sources of information in utilizing multimodal facial expressions and head poses as multiple convolutional neural networks (CNN) and deep learning are combined. This research analyzes and improves the robustness of emotion recognition, and proposes a novel approach that optimizes traditional deep neural networks that fall into poor local optima when optimizing the weightings of the deep neural network using standard methods. The proposed approach adaptively finds the better weightings of the network, resulting in a hybrid genetic algorithm with stochastic gradient descent (HGASGD). This hybrid algorithm combines the inherent, implicit parallelism of the genetic algorithm with the better global optimization of stochastic gradient descent (SGD). An experiment shows the effectiveness of our proposed approach in providing complete emotion recognition through a combination of multimodal data, CNNs, and HGASGD, indicating that it represents a powerful tool in achieving interactions between humans and robotics. To validate and test the effectiveness of our proposed approach through experiments, the performance and reliability of our approach and two variants of HGASGD FER are compared using a large dataset of facial images. Our approach integrates multimodal information from facial expressions and head poses, enabling the system to recognize emotions better. The results show that CNN-HGASGD outperforms CNNs-SGD and other existing state-of-the-art methods in terms of FER. [ABSTRACT FROM AUTHOR]

During the drilling process, horizontal drill strings are often subject to stick-slip and back-pressure phenomena, which undermine drilling safety and affect drilling costs. However, most of the models proposed in the existing literature apply only to the analysis of the drill string motion state and are irrelevant to the rotary drilling process in coal mines. This paper presents a lumped-mass model of a drill string in a horizontal coal mine well. First, based on reasonable assumptions, the drill-string system was considered as four lumped-mass units. The lateral-torsional dynamics model was established based on the Lagrange dynamics theory. Further, combined with lateral movement, the intermittent contact between the drill string and borehole wall is described, and the friction torque due to this intermittent contact is calculated. Combined with the Karnopp bit-rock interaction, a drill-string dynamics model of horizontal wells that considers intermittent contact friction was developed. The proposed horizontal well drill-string model is more suitable for rotary drilling in a coal mine, and the nonlinear influencing factors are considered more comprehensive. [ABSTRACT FROM AUTHOR]

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process. [ABSTRACT FROM AUTHOR]

This study presents a repetitive control method based on a multi-stage particle swarm optimization (PSO) algorithm with variable intervals to enhance the tracking performance of Takagi–Sugeno (T–S) fuzzy systems. First, a T–S fuzzy model is used to describe a nonlinear system. A modified repetitive control structure with two repetitive loops guarantees the tracking accuracy of periodic signals. Taking advantage of the two-dimensional (2D) property with continuous control and discrete learning, a continuous-discrete 2D model is presented to describe the nonlinear repetitive control system. Next, a multi-stage PSO algorithm with variable intervals searches for the best parameter combination in the linear matrix inequality-based stability condition to regulate the control and learning actions, which avoids a suboptimal solution and guarantees high control accuracy. Finally, an application to control the speed of synchronous motor with a permanent magnet demonstrates the validity of the method, and comparisons with related methods show its superiority. [ABSTRACT FROM AUTHOR]

This paper concerns with trajectory azimuth control in directional drilling. The motion process of the drill bit and a series of stabilizers are described, and a state-space model of the trajectory azimuth is constructed. The scheme of the trajectory azimuth control system is designed based on the equivalent input disturbance approach. An internal model is inserted to track the drill bit to improve the quality of the drilling trajectory. A state observer is combined with a low-pass filter to estimate the trajectory azimuth by measuring the azimuth of the bottom hole assembly (BHA). The control parameters can be obtained by the condition of system stability, which is derived in terms of linear matrix inequalities. A typical case is used to illustrate the validity and robustness of our approach. [ABSTRACT FROM AUTHOR]

With the rapid development of control technology, increasing applications are using model predictive control (MPC) for deviation correction in vertical drilling. However, the accuracy of the predictive model is affected by the uncertainty of the stratum, which results in model mismatch and a reduction in control performance. In this paper, an intelligent compensating method is proposed for MPC-based deviation correction with stratum uncertainty in a vertical drilling process to increase control accuracy. First, a trajectory extension model is introduced as the predictive model for MPC, and the uncertainty of the stratum is discussed. Then, the compensation for the MPC is acquired based on a Gaussian fitting method and hybrid bat algorithm. Finally, based on the actual drilling data, a simulation is performed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]