Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dim...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process.
With the aim of 2-AMT electric vehicles,a comprehensive shift schedule that considers both power and economy is ***,the objective function of the comprehensive shift schedule is constructed,which is the weighted sum o...
With the aim of 2-AMT electric vehicles,a comprehensive shift schedule that considers both power and economy is ***,the objective function of the comprehensive shift schedule is constructed,which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit ***,Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift ***,AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an *** results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance,and the economy is optimized by 3%.The feasibility of the comprehensive shift schedule is verified.
This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this,the study proposes the Domain Kernel-Alignment Adaptation Network(DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy(KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology.
In sequential recommender systems,the main problems are the long-tailed distribution of data and noise interference.A Contrastive Framework for Sequential Recommendation(CFSeRec) is proposed to solve these two problem...
In sequential recommender systems,the main problems are the long-tailed distribution of data and noise interference.A Contrastive Framework for Sequential Recommendation(CFSeRec) is proposed to solve these two problems *** shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data,so that the long-tailed problem is *** the application of projection head method,the sequence representation becomes more general and robust,rather than just adapted to the task of contrastive ***,the impact of noise on sequence recommender systems is effectively *** on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain,when comparing to the seven previous frameworks.
This paper is concerned with the controller design and the theoretical analysis for time-delay systems,a two degree of freedom(feedforward and feedback) control method is proposed,which combines advantages of the Smit...
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This paper is concerned with the controller design and the theoretical analysis for time-delay systems,a two degree of freedom(feedforward and feedback) control method is proposed,which combines advantages of the Smith predictor and the active disturbance rejection control(ADRC).The feedforward part of controller is used to track the set point,the feedback part of controller(ADRC) is used to suppress interferences and the Smith predictor is used to correct time *** proposed control design is easy to tune parameters and has been proved to effectively controlsystems with large time *** bounded input bounded output(BIBO) stability of closed-loop system is ***,numerical simulations show the effectiveness and practicality of the proposed design.
Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel auto...
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Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-age...
Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this work. To ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function (CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards.
The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast ***,due to the inherent nonlinearity of multiplication,it brings certain difficulties to t...
The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast ***,due to the inherent nonlinearity of multiplication,it brings certain difficulties to the *** electric double-variable pump[1] is a dual-input single-output system,and it is a nonlinear system[2].It is necessary to linearize the system or use a nonlinear control method to control and solve the control problem of the *** this paper,an intelligentcontrol rule is proposed for the nonlinear problem of double input and single *** backstepping design[3],the nonlinear system is transformed into multiple linear *** the original system is turned into two independent subsystems with single input and single output,which are controlled *** co-simulation platform based on AMESIM and Simulink[4] has been verified and compared with a single PID control algorithm to simulate the step response and sinusoidal tracking performance of the *** results show that the response speed of the system has been greatly improved.
Optical phased array (OPA) on silicon platform is developed as a hot topic in the past decade. In order to achieve both large field of view (FOV) and high side mode suppression ratio (SMSR), large-scale antenna with s...
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Optical phased array (OPA) on silicon platform is developed as a hot topic in the past decade. In order to achieve both large field of view (FOV) and high side mode suppression ratio (SMSR), large-scale antenna with spacing of half wavelength is usually required, resulting in large footprint and complex scanning design. Recently, OPAs with nonuniform antenna are proposed as efficient solutions to achieve large FOV with simplified layout. Here, we analyze the performance of various OPAs with different nonuniform antenna designs. In addition, a genetic algorithm optimization method is further proposed for nonuniform antenna design. OPA with the proposed nonuniform antenna is simulated with a steering range of ±50° and SMSR of 11.3dB.
The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. H...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications.
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