The role of granular computing in time series analysis is becoming increasingly important. Currently, there remains scope for enhancing the specificity of information description by information granules. To improve the specificity, a novel elliptical information granule is designed for time series similarity measurement in this paper. An elliptical information granule is defined by its centre and its long and short half-axes. Elliptical information granules can be constructed based on justifiability and specificity, guided by the principle of justifiable granularity. Multiple elliptical information granules are constructed using fuzzy C-means clustering and compactness principles. A time series similarity measurement method is then developed based on the geometric similarity of elliptical information granules. Experimental result shows that the constructed elliptical information granules provide a more specific information description compared to rectangular information granules, while offering significant advantages in time series similarity measurement. The proposed method has significant potential for time series analysis and modelling.

Measurement of ocean current velocity profiles is critical for marine operations, yet traditional methods such as Acoustic Doppler Current Profilers face limitations in cost, resolution, and penetration depth. Existing inversion techniques often rely on idealized assumptions limiting their practical applicability. To address these challenges, this paper proposes stratified hybrid convolutional network, a novel deep learning-based soft sensing method for reconstructing ocean current velocity profiles. The proposed method leverages a physically guided stratification of ocean currents into distinct layers based on temperature-salinity-density correlations and their driving mechanisms. An adaptive feature selection algorithm mitigates overfitting by filtering redundant inputs and aligning features with layer-specific dynamics. A hybrid neural network architecture combines two-dimensional convolutions along depth and feature dimensions to capture nonlinear relationships, followed by multi-layer perceptrons for high-dimensional nonlinear mapping. Experimental results demonstrate superior performance. This method provides a practical solution for the inversion of ocean current velocity profiles.

Density clustering is a pivotal algorithm for data clustering and analysis, finding extensive and significant industrial application. There are two key adjustable parameters in density clustering: cluster radius and minimum number of cluster points. At present, the selection of more suitable parameters predominantly depends on statistical methods and analysis, which lacks a precise and effective evaluation criterion. In this paper, a novel evaluation criterion for density clustering via circular information granules is proposed. It constructs circular information granules based on the density clustering results through the principle of justifiable granularity, and then finds the largest sum of volumes of circular information granules. Consequently, it determines the optimal clustering radius and the minimum number of clustering points. Experimental results show that the proposed method provides a more comprehensive evaluation of density clustering results compared to the existing evaluation criterion.

Ignition temperature in a sintering process is an important factor for the quality and yield of sinter ore. Due to the diversity of the calorific value and the flow rate in the sintering process, there are many different steady states in the combustion process. Under different steady states, the change interval and fluctuation range of calorific value and flow rate are completely different which shows the sintering data is one kind of typical imperfect data. In this case, if these data are used to model and control in conventional machine learning methods, it will lead to inaccurate results. This paper presents an intelligent control strategy for ignition temperature based on working-condition recognition to stabilize the ignition temperature. First, this paper describes the structure of the intelligent control system for ignition temperature, including the working-condition recognition module and the different fuzzy prediction controllers. Then, this paper explains a fuzzy C-means clustering algorithm to recognize the working-condition of an ignition process. Next, this paper presents the detailed design of the fuzzy prediction controller for each specific working-condition, including an Elman prediction model optimized by the particle swarm optimization and a fuzzy controller. Finally, some experimental results based on actual production data show that the method controls the ignition temperature accurately in the complex production environmentNote to Practitioners—The motivation of this paper is to control the ignition temperature in the sintering process to improve ignition efficiency. The existing methods for ignition temperature control mainly focus on the design of the model and controller, and rarely consider the influence of ignition conditions on the system. This paper presents an intelligent control method of ignition temperature by recognizing different working conditions to accurately control the ignition parameters. The working condition recognization effectively solves the problem of the large difference between different data and states. By applying the proposed control strategy, the control error of the sintering plant is kept in a small range to meet the actual control requirements of the sintering plant. Future work is to extend the proposed method to more industrial application scenarios that have imperfect datasets characterized.

Time series anomaly in the sintering process is a direct manifestation of equipment failure and abnormal operating mode, and effective detection of time series anomaly is important to improve the stability of the sintering process. This paper presents a time series anomaly detection via rectangular information granulation, whose originality is to apply the similarity of information granules as a reference for anomaly detection. It converts time series into rectangular granules, and the similarity of time series is measured with rectangular granules. The one-way analysis of variance method is used to detect the difference for the similarity between the time series to be detected and the historical time series and the similarity between any two historical time series, thus achieving the anomaly detection of the time series. The experiment is conducted on real-world data from an enterprise. The result shows that the proposed method outperforms the probability density analysis method and can effectively detect abnormal time series.

The wide applications of 3-D sonar measurements are severely limited by factors such as water column interference, acoustic shadows, complex structures, and scattering noise. Outliers in 3-D sonar data are difficult to remove using traditional methods because the inference factors are different from other types of point cloud data. Therefore, this article presents a novel outlier filtering method by analyzing the sequential characteristics of the 3-D sonar data. First, the underwater point cloud is processed by super-voxel clustering method to decompose complex point cloud structures into several super-voxels with simple structures. Then we convert the point cloud data into subsequence data according to the surveying principle of 3-D sonar scanning and super-voxel results. After that, an anomaly score calculation and anomaly region determination method based on the rectangular information granulation of subsequence data is proposed. This method can capture the intrinsic changing characteristics of each subsequence and has a good recognition effect on the abnormal subsequence. Finally, an outlier detection method combining the Grubbs principle and the abnormal score is proposed and applied to the abnormal subsequences, which considers the distortion not only in the vertical direction but also in the horizontal direction. The experimental results show that the proposed comprehensive filtering method has good accuracy for both horizontal and vertical point cloud data. The average overall accuracy of the test results is 99.1%, and the average kappa coefficient is 0.88, which can be effectively applied to the 3-D sonar point cloud data filtering processing in complex underwater areas.

The temperature of the cooling stave (TCS) is an important state parameter to indicate the states of the slag crust during the blast furnace ironmaking process. The state of the slag crust affects the quality and production of pig iron, and the gas flow distribution in the blast furnace. Thus, it is necessary to recognize the states of the slag crust. This article proposes a condition recognition strategy based on fuzzy clustering endowed with a novel distance with information granulation for recognizing the states of the slag crust. First, the raw TCS time-series data are split into segments according to the appropriate segmentation length, and the segments are represented in a granular form by the information granulation method. Then, information granules are clustered using fuzzy clustering endowed with a novel distance. After completing the data representation, each information granule is compounded of a lower bound and an upper bound that indicate the dynamic characteristics of the corresponding segments. In the fuzzy clustering, information granulation distance, a new distance, is established to measure the similarity between two information granules. Finally, the data experiments using the datasets from the UCR time-series database and actual industrial data from the blast furnace demonstrate the effectiveness and superiority of the proposed condition recognition strategy.

Complicated geological environments lead to a high risk of drilling incidents. Early warning of loss and kick for the drilling process is essential to ensure process safety. On account of the nonlinear and temporal correlation of drilling parameters, an early warning method for loss and kick based on sparse autoencoder with multivariate time series is proposed. The sparse autoencoder is utilized for multivariate time series abnormality detection of the drilling process. Abnormal drilling parameter isolation is performed through contribution analysis. Reconstruction analysis and time series segmentation approaches are integrated for abnormal time series trend evaluation. The characteristic of drilling parameters under normal operation learned by the sparse autoencoder and the property of the original time series are taken into account. The final early warning result can be obtained through expert rules based on the trend evaluation result. Case studies are presented based on the data from an actual drilling project. The experiment result shows the effectiveness of the proposed method.

Abstract With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.

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.

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.

The safe and efficient operation of geological drilling systems is critically dependent on proper process monitoring. A monitoring model based on probabilistic principal component analysis is presented to deeply exploit performance feature information hidden among the process data in this paper. First, the offline monitoring model is established with historical process data collected from different sources to reveal different characteristics, and the monitoring statistics as a benchmark are obtained. Then, actual operating data are introduced to the established model to realize online monitoring. Finally, the monitoring effect is discussed, and the causes of inefficient drilling are tracked. The experimental results indicate that the proposed method can effectively monitor the operating performance of the drilling process.

Complicated geological environments lead to a high risk of drilling incidents. Incident early warning for drilling process is in demand for industry field. An incident early warning method for loss and kick based on sparse autoencoder and decision fusion is proposed in this paper. Sparse autoencoder is employed to detect the abnormality of the drilling parameter time series. Mann-Kendall trend test approach is performed to extract the trend of the time series that is detected as abnormal. The abnormality detection and trend extraction results of each drilling parameter are fused to get the final incident early warning result. Experiments are executed with the actual data collected from a practical drilling process. The experiment results indicate the effectiveness of the proposed method.

To reduce energy consumption and harmful emission, it is of great significance to improve carbon efficiency in sintering process, which is able to be achieved if the carbon efficiency can be accurately predicted. In this article, the ratio of CO and CO2(CO/CO2) is taken as a measurement of the carbon efficiency. As CO/CO2is hard to measure, and there exist multiple working conditions, multiple variables, and nonlinearity, a hybrid CO/CO2prediction model is devised based on the aforementioned characteristics. First, the sintering process is analyzed, and the key characteristics to predict the CO/CO2are extracted. Next, the configuration of the prediction model is given based on the analysis. The model consists by two submodels, one is to predict the state variables by an improved just-in-time learning model, combining three neural network (NN) models. The other is to predict CO/CO2with semisupervised algorithm, based on deep belief network with a combination of the three NN regression methods. Then, the configurations of the two submodels are introduced in detail. The test results based on actual running data exhibit the good performance of the model.

This article presents the mechanical design and locomotion control of a novel tuna-inspired robot system for both fast swimming and high maneuverability. Mechanically, the developed robotic fish named CasiTuna comprises three important parts, i.e., an innovative two-motor-actuated propulsive mechanism, a buoyancy adjustment structure, and a pair of pectoral fins. Unlike most robotic fishes' multiple concatenated links-based propulsive mechanism, CasiTuna's two-motor-actuated one places both motors in the anterior body and utilizes a transmission system to achieve tuna-like lateral undulations. Meanwhile, the buoyancy adjustment mechanism in conjunction with pectoral fins endows the robot with the capability of three-dimensional maneuverability. Kinematic and dynamic analyses are further conducted to reveal the interactive hydrodynamic forces. Regarding the locomotion control method, a bio-inspired central pattern generator-based controller is adopted to achieve multimodal swimming. In particular, two kinds of turning maneuvers are implemented and discussed. Aquatic experiments, including straight swimming, circular turning, and nearly static pitching validate the effectiveness of proposed mechatronic design and locomotion control methods. Remarkably, CasiTuna achieved a peak forward speed of 0.8 m/s (corresponding to 1.52 body lengths per second) and a minimum turning radius of less than 0.3 body lengths.

Sinter ore is the main raw material for ironmaking, and burn-through point (BTP) is one of the significant factors to measure the stability of the sintering process. In this article, through the feature extraction of time-series trend, a fuzzy control strategy is presented for the BTP. First, the Hurst exponent of the time series for the BTP is calculated by resorting to the rescaled range analysis method, by which the trend feature is analyzed. Then, by using the Mann-Kendall test, both global and local trend feature variable of the time series for the BTP are extracted and regarded as the inputs of the fuzzy controller. Next, a fuzzy controller for the BTP is designed to produce the control quantity of the strand velocity. Finally, based on a semiphysical simulation system and the raw data collected from an iron and steel plant, an experiment is carried out to demonstrate the effectiveness of the proposed control strategy.