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The compound directional drilling process, including slip and rotation, is the key to realizing long-hole drilling in coal mines. First, a compound directional drilling process control scheme is proposed to realize intelligent control of the directional drilling process. An intelligent drilling process optimization method was designed to improve drilling efficiency. A robust controller for the drilling rate based on gain scheduling was designed to achieve stable control of the drilling rate during sliding deflection drilling. A robust controller based on hybrid sensitivity was designed to achieve stable control of the drilling rate during rotary inclined stabilization drilling. The results of this study can provide a theoretical basis for realizing the intelligent optimization and control of compound directional drilling in coal mines. [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]

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 disturbance rejection problem of T–S fuzzy systems is concerned. Since the T–S fuzzy system is characterised by its membership function, less conservative stabilisation conditions can be derived from membership function-dependent Lyapunov function which contributes to the improvement of disturbance rejection performance. Specifically, we utilise a configuration composed of a membership function-dependent state observer for the state estimation, a membership function-dependent equivalent-input-disturbance estimator for the estimation and compensation of disturbance and an internal model for the reference tracking. It is revealed that this membership function-dependent Lyapunov function naturally leads to control gains switching in accordance with the derivative signs of the normalised premise variables. The switching rules and the design conditions for all control gains are obtained explicitly. In particular, the free-weighting-matrix approach is used to lessen the conservatism in the stability condition. Moreover, a concrete procedure for the controller design including the switching rule is given. Finally, the developed method is tested via simulations. The advantage of the membership function-dependent equivalent-input-disturbance method is validated by comparing with conventional methods. [ABSTRACT FROM AUTHOR]

In this article, K-meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human–robot interaction (HRI). The multimodal features (gray pixels; time and frequency domain) extracted from facial expression and speech are fused based on Kernel canonical correlation analysis. K-means clustering is used to select features from multiple modalities and reduce dimensionality. The proposed approach can improve the heterogenicity among different modalities and make multiple modalities complementary to promote multimodal emotion recognition. Experiments on two datasets, namely SAVEE and eNTERFACE‘05, are conducted to evaluate the accuracy of the proposed method. The results show that the proposed method produces good recognition rates that are higher than the ones produced by the methods without K-means clustering; more specifically, they are 2.77% higher in SAVEE and 4.7% higher in eNTERFACE‘05. [ABSTRACT FROM AUTHOR]

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance. [ABSTRACT FROM AUTHOR]

Information granules can discover interpretable and meaningful relationships offering a full description for time series. This article presents an information granulation method with rectangular information granules and applies it to time-series similarity measurement. First, the fuzzy $c$ -means clustering algorithm transforms the time series and its first-order difference time series to data clusters. With the maximum volume of rectangular information granules viewed as the criterion, the optimal rectangular information granules are formed using the data cluster by the principle of justifiable granularity and the gravitational search algorithm. The time-series similarity is measured by calculating the similarity between the upper and lower bounds of the optimal rectangular information granules built from the time series. Finally, an experiment is performed on a public dataset to verify the feasibility of the proposed method. The result shows that the rectangular information granulation method can capture the change characteristics of time series. The similarity measurement method can effectively evaluate the similarity of the time series belonging to different classes. [ABSTRACT FROM AUTHOR]

The build-up rate of the bottom hole assembly (BHA) fluctuates violently during the deviation correction in the vertical drilling process due to the mutation of stratum, low operation accuracy of the BHA, etc. As a result, the uncertainty of the build-up rate of the BHA has great influence on the performance of correction control and the quality of the drilling trajectory. To deal with this control problem, a tube-based adaptive model predictive control (MPC) method is developed in this article. By dividing the trajectory extension model into two lower order models, the calculability of the disturbance invariant set of the tube-based MPC controller is ensured. Meanwhile, the bootstrap sampling method with a sliding window is used to estimate the range of the uncertainty online to reduce the conservativeness of the controller based on practical drilling data. Moreover, rules and a small interim deviation are introduced to adjust the parameters adaptively to decrease the curvature of the drilling trajectory and the calculation time. Simulations and hardware-in-the-loop experiment tests are carried out to illustrate the effectiveness of the control method. The results show that the proposed method completes the drilling task with a higher quality of trajectory and needs less time for calculation. [ABSTRACT FROM AUTHOR]

A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy saving and its prerequisite is a time-series prediction of carbon efficiency. The existing carbon efficiency models usually have a complex structure, leading to a time-consuming training process. In addition, a complete retraining process will be encountered if the models are inaccurate or data change. Analyzing the complex characteristics of the sintering process, we develop an original prediction framework, that is, a weighted kernel-based fuzzy C-means (WKFCM)-based broad learning model (BLM), to achieve fast and effective carbon efficiency modeling. First, sintering parameters affecting carbon efficiency are determined, following the sintering process mechanism. Next, WKFCM clustering is first presented for the identification of multiple operating conditions to better reflect the system dynamics of this process. Then, the BLM is built under each operating condition. Finally, a nearest neighbor criterion is used to determine which BLM is invoked for the time-series prediction of carbon efficiency. Experimental results using actual run data exhibit that, compared with other prediction models, the developed model can more accurately and efficiently achieve the time-series prediction of carbon efficiency. Furthermore, the developed model can also be used for the efficient and effective modeling of other industrial processes due to its flexible structure. [ABSTRACT FROM AUTHOR]

The two-stage fuzzy fusion based-convolution neural network is proposed for dynamic emotion recognition by using both facial expression and speech modalities, which not only can extract discriminative emotion features which contain spatio-temporal information, but also can effectively fuse facial expression and speech modalities. Moreover, the proposal is able to handle situations where the contributions of each modality data to emotion recognition are very imbalanced. The local binary patterns coming from three orthogonal planes and spectrogram are considered first to extract low-level dynamic emotion, so that the spatio-temporal information of these modalities can be obtained. To reveal more discriminative features, two deep convolution neural networks are constructed to extract high-level emotion semantic features. Moreover, the two stage fuzzy fusion strategy is developed by integrating canonical correlation analysis and fuzzy broad learning system, so as to take into account the correlation and difference between different modal features, as well as handle the ambiguity of emotional state information. The experimental results obtained on benchmark databases show that the accuracies of the proposed method are higher than those of existing methods (such as the hybrid deep model, and the rule-based and machine learning method) on SAVEE, eNTERFACE’05, and AFEW databases. [ABSTRACT FROM AUTHOR]

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman’s rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy. [ABSTRACT FROM AUTHOR]

The heating furnace is a sizeable energy-consuming device in iron and steel industry. In compact strip production (CSP), the size of the heating furnace is large, and the working conditions are complicated. Furthermore, the large fluctuation of furnace temperature leads to large loss of billet combustion and energy consumption. This article deals with the design of hybrid intelligent control based on condition identification for the combustion process of the CSP heating furnace. By analyzing the process of the heating furnace and existing problems, the structure of a hybrid intelligent control system is proposed, and the working conditions are divided into two categories: stable and fluctuating. A fuzzy controller is designed to improve the control accuracy of furnace temperature under the stable working condition, and an expert controller is used to adjust the temperature rapidly under the fluctuating working condition. Besides, calorific value compensation is introduced to reduce the influence of calorific value of gas fluctuation on temperature. After verifying the effectiveness, the proposed method has been used in a steel plant and achieved a good control result. The intelligent control system improves the control precision of the furnace temperature and reduces energy consumption compared with the traditional control system. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory optimization problem of a drilling process, which is of significance to drilling efficiency and safety. Due to the difference between an actual trajectory and a planned trajectory, this problem is a multiobjective optimization problem (MOP) with parameter uncertainties. To solve the problem, this study devises a new approach named nondominated sorting genetic Algorithm II using outlier removal (OR-NSGA-II). First, the optimization problem with parameter uncertainties is formulated, including two objective functions: 1) a trajectory length and 2) an expected value of drill-string torque. Then, an outlier-removal mechanism is devised in the sorting process of NSGA-II to reduce the negative effects of parameter uncertainties. Next, the crowding distance calculation in NSGA-II is improved to ensure population diversity. Comparison of simulation results show that our method is effective on the MOP with parameter uncertainties. [ABSTRACT FROM AUTHOR]

This paper is concerned with observer‐based trajectory control of directional drilling system with rotary steering devices. Since the trajectory orientation cannot be measured directly, most of the existing results implicitly assume that the trajectory orientation is equal to the measurement of orientation of the bottom hole assembly (BHA) or the drill bit, which would bring in certain control errors in practice. To avoid such control errors, in this paper, a trajectory control approach is established, which includes a state observer to estimate the trajectory orientation based on the measurement of the BHA orientation. Firstly, the relationship between the trajectory orientation and the BHA orientation is analyzed, and a trajectory evolution model is introduced according to the analysis. Secondly, based on this trajectory evolution model, a variable transformation technique is applied such that a trajectory tracking control problem is formulated. Thirdly, a feedback control strategy is devised based on the estimation of the trajectory orientation for trajectory tracking. Fourthly, stability analysis of the closed‐loop system is discussed, and the design of the controller and state observer is done. Finally, a typical case is used to illustrate the correctness and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The design of a trajectory is significant to the efficiency and safety of a drilling process. In this paper, wellbore stability, indicated by the safe mud weight, is introduced as constraints to formulate a drilling trajectory optimisation problem. The upper and lower bounds of the safe mud weight are analysed according to the real data of a well. To solve the optimisation problem with strict constraints, we devise a new multi-objective optimisation algorithm. The devised algorithm takes advantage of inverted generational distance and knee points, and improves the convergence and diversity of the obtained solutions by modifying the selection process. Simulation results show that the devised algorithm is effective on the drilling trajectory optimisation problem constrained to wellbore stability. [ABSTRACT FROM AUTHOR]

Proper burden distribution is an important way to improve conditions of a blast furnace, which indicate the stability and energy consumption. However, it is difficult for operators to recognize the stability and energy consumption at the same time and adjust them through burden distribution, due to complex iron-making mechanism. Therefore, this article presents a two-stage decision-making method for the burden distribution parameters based on recognizing the conditions. First, the stability and energy consumption are recognized, respectively, according to the classification of stability of slag crust (SSC) and the gas utilization rate (GUR). And then, two prediction models are presented for the SSC and GUR to predict the conditions. Finally, the burden distribution parameters are determined in two stages, where edge parameters of the burden distribution are determined at first according to the SSC, and then, center parameters are determined according to the GUR. Experimental results based on industrial data show that the proposed method provides an effective decision-making way for improving the conditions. [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]