AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

This work involves mathematical modeling and strain control of a two-link flexible manipulator carrying a payload; the system’s dynamics are derived using the Euler–Lagrange formalism joined with the assumed modes approach. The dominant assumed vibration modes are adopted for Euler–Bernoulli clamped-mass beam, coupled with nonlinear dynamics associated with the rigid rotations of joints to formulate Euler–Lagrange dynamic robot model. The control aim is to obtain accurate trajectory tracking with effective strain elimination. A passivity-based controller is developed relying on the concept of energy shaping of the system. This ensures that the closed-loop system remains passive. The global system stability is proven using the Lyapunov theory and making allowance for passivity property. The proposed controller has been simulated using Matlab/Simulink to demonstrate its effectiveness in suppressing unwanted elastic vibrations.

Application of process control techniques to biomedical problems has always been fascinating the medical community. Effective control strategies for automation of insulin infusion are seeking attention among researchers in lieu of the manual insulin infusion. This paper investigates the possibilities of designing an event triggered mechanism based constrained selective next generation RTD-A controller. The advantageous features of this technique are simplicity, flexibility in tuning, closed form solution similar to the PID controller and superior in performance like predictive controllers. Avoiding redundant data being transmitted to the controller and hence bandwidth utilization for the data transmission could be avoided by the incorporation of such event triggered mechanism. The performance of the developed controller is validated in the presence of constraints imposed on the insulin infusion input rate and amplitude to avoid hyperglycemia. The comparative simulation results of DMC, IMC and next generation RTD-A controllers in constrained and unconstrained environment are presented. Performance evaluation of the constrained blood glucose regulation with and without event triggered mechanism is also verified in simulation. The supreme quality of the event based constrained selective next generation RTD-A controller is its effective reduction in the redundancy in data transmission, effective bandwidth utilization and independent tuning capability.

In genetics and medical practice, structural variants (SV) in the genome are thought to be the root cause of numerous diseases, particularly genetic diseases. Accurate structural variant prediction is the foundation for identifying and screening pathogenic variants and performing medication genomics analysis, which is a challenging task. However, data in the field of genomics is typically massive, high-dimensional, and serialized, and existing variant prediction tools are affected by the range and type of variants, resulting in less accurate results. As a result, an effective method for predicting structural variation is critical. In this paper, a variation prediction model DEL-RESSP based on ResNet and attention mechanism is proposed for predicting deletion structural variants. To begin, the deletion variant feature information is derived from the three alignment data of read depth, split read pair, and discordant read pair, and the comparison data is transformed into artificial images by encoding to provide reliable input for the subsequent network models. Second, attention mechanisms are combined based on convolutional networks to improve image sensitivity to local information to improve prediction accuracy. Three SV prediction tools, CNVnator, BreakDancer, and Pindel, were used in this study to test the predictive effectiveness of DEL-RESSP in predicting large-scale deletion variants. The results show that DEL-RESSP can predict deletion variants with 96.93% accuracy, which is a 5–10% improvement over combining only a single strategy, as well as a comparison to existing deep learning methods. DEL-RESSP fully utilizes deep learning in image processing, providing some reference value in subsequent variant analysis and gene function annotation. Part of the classification model code used in this paper can be found on https://github.com/JQ1209/DEL-RESSP.

The widespread adoption of electric vehicles (EVs) has brought significant advancements in transportation technology, addressing the challenges of environmental sustainability and reducing dependence on fossil fuels. However, one of the critical aspects in the development of EVs is the efficient management of the battery system, particularly in terms of temperature control. The temperature of the battery cells plays a crucial role in determining their performance, lifespan, and overall safety. This paper presents a study on the application of fuzzy logic for electric vehicle battery temperature control. Fuzzy logic provides a flexible and robust framework for modeling and controlling complex systems with uncertain and imprecise information. By employing fuzzy logic-based algorithms, the temperature of the EV battery can be effectively regulated, ensuring optimal performance and longevity. To validate the effectiveness of the proposed approach, simulations and experiments are conducted using a representative EV battery system. The results demonstrate that the fuzzy logic-based temperature control system effectively maintains the battery temperature within the desired range, thereby improving battery performance, efficiency, longevity and reducing battery consumption by 10% compared to PID control.

The paper deals with a class of discrete nonlinear stochastic systems that are subject to the disturbing effect of noise with unknown distribution densities belonging to the class of distributions with bounded mean squares and observed under noise conditions with unknown distribution densities belonging to the same class. For these discrete stochastic systems, the problem of synthesis of a stable (robust) recurrent estimate of the state vector of the system is posed and solved. To solve this problem, a new robust estimation criterion is introduced, from the optimization condition of which a recurrent form of a robust estimate of the state vector of the studied class of discrete nonlinear stochastic systems is obtained. The advantages of this robust estimation algorithm are both its optimality in the sense of the proposed robust estimation criterion and its dimension, coinciding with the dimension of the state vector of the object being evaluated, in contrast to existing filtering algorithms, the dimension of which significantly exceeds the dimension of the object state vector due to estimates of the a posteriori covariance matrix, probabilistic characteristics of interference, etc. This circumstance makes it possible to significantly reduce computational costs in the implementation of this algorithm, which is especially important for on-board information-measuring and control systems of moving objects. The results of a numerical experiment are presented, illustrating the effectiveness of the practical use of the proposed approach.

The importance of using variable-speed permanent magnet synchronous motors in various fields requires the use of speed control techniques. This document presents the vector control of permanent magnet synchronous motors based on proportional integrated controllers using the Xinlinx System Generator tool. The implementation based on the use of Xilinx System Generator blocks aims to achieve speed variation of the machine. The obtained implementation and synthesis results have demonstrated the effectiveness and proper functioning of the control, enabling the machine’s speed to remain stable according to the chosen reference. The synthesis allows real-time hardware implementation on the FPGA board using the FPGA in the LOOP technique.

With the development of digital media, its security has also been emphasized, and reversible data hiding (RDH) algorithms has been widely studied. This paper studied RDH algorithms for JPEG images, designed a method to embed secret information by frequency band selection based on discrete cosine transform (DCT) coefficients, called an improved RDH (IRDH) algorithm, and conducted experiments on the USC-SIPI image set. It was found that the designed RDH algorithm had a higher embedding capacity, with an average embedding capacity of 37 031 bits at quantization factor = 50, as well as a higher peak signal-to-noise ratio (PSNR) value and a lower file size increase (FSI) value, compared with the other methods; the dispersion degree of the data obtained by this algorithm was also low. The results demonstrate the reliability of the IRDH algorithm on JPEG images, which can be extended and applied in practice.

This paper puts forward a new strategy current sensor unanticipated faults detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) in a telescope drive system. This approach uses axes transformation, PMSM model and Luenberger observer to generate residuals, and the influence of unanticipated faults (UFs) in different phases on the current components is analyzed. The sensor UFs detection is performed by processing residuals obtained from the observer. In addition, based on the information provided by fault detection, an innovative logic judgment algorithm is devised to realize fault isolation. The proposed method can discriminate between single UF of different types and multiple simultaneous UFs of various categories as well as faulty current sensors. Extensive simulation experiments prove that the designed logic judgment algorithm is effective, and the FDI can be implemented successfully.

Because the focus information is obtained under different optical depth, it is impossible to collect all relevant information of objects from the only one image. The multifocus image fusion technique enables it to gather all of the focus data from the partially focused images, enhancing contrast and sharpness. To overcome the troubling weakness of the already-existing fusion methods, such as the incomplete boundary information and partial loss of focus, a new network called “BCNN”, combining the layered Bayesian and the convolutional neural network (CNN for short), is constructed. The hierarchical Bayesian can well maintain the texture features and edge information, and change the traditional way of learning a fixed value of the weight by learning the obvious features that are represented by the mean and variance. Meanwhile, the activity levels and the fusion rules can be jointly and deeply learned by the CNN model, avoiding the sophisticated plan and special design for the fusion rules. According to the aforementioned concepts, a novel BCNN-based fusion model for multifocus images is proposed. After detailed experimental implementation, the accuracy and efficacy of the proposed method are extensively illustrated and proved, not only in the way of the numeric evaluation, but also the highlighted visual comparison.

An on-time and accurate analysis of the problem is essential to prevent and treat any illness. The utilization of machine learning (ML) for diagnosing a wide range of diseases is increasingly prevalent in the field of medical science based on symptoms experienced during diseases. The main objective of the research is to make a comparative analysis of different ML models that accurately predicts diseases based on symptoms. To do so, the dataset obtained from Kaggle comprises information related to 41 diseases including their symptoms which are in 17 columns with their weights. In other words, we have a group of 17 symptoms, independent variables (symptoms differ for each patient except some), and 1 target variable (disease). Furthermore, preprocessing is applied to data to make it suitable for the various machine learning approaches. After that, three scaling techniques are used: standard scaling, min-max, and PCA (principal component analysis) for normalization. The present study utilized a variety of ML models, which includes LGB classifier, KNN, random forest (RF), CatBoost, support vector machine (SVM), XGBoost, and a hybrid model that combined two existing approaches (SVM and XGBoost). Each scaling technique was assessed using various evaluative parameters such as root mean squared error (RMSE), cross-validation score, R2 score, mean squared error and accuracy. Random forest, LGB classifier, and XGBoost demonstrated superior performance when compared and evaluated to one another with regards to accuracy, R2 score, and RMSE, achieving scores of 98, 96, and 2.08% respectively. Also, the RF algorithm required less computation time in contrast to other scaling techniques, particularly in standard scaling, with a time of only 0.129 s.