Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely inter...
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ISBN:
(数字)9798331520205
ISBN:
(纸本)9798331520212
Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model’s decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance.
With the growth of the used car market and the development of e-commerce platforms, the need for accurate valuation of used car prices is becoming more urgent. Accuracy of price evaluation is the key to the success of...
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Recommendation systems should adopt different recommendation strategies for different users' usage abilities. For the question of what testing items are required for a food, we have designed two food-testing item ...
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To enhance the accuracy of load forecasting, this paper considers both the inherent patterns within historical load data and external influencing factors. A short-term load forecasting approach is proposed that combin...
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This paper investigates the estimation of lateral tire forces and predictive control for vehicles equipped with intelligent tires. Motivated by the capability of intelligent tires to estimate lateral tire forces, we p...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
This paper investigates the estimation of lateral tire forces and predictive control for vehicles equipped with intelligent tires. Motivated by the capability of intelligent tires to estimate lateral tire forces, we propose a control scheme that includes a predictor for lateral tire forces, utilizing the Gaussian Process Regression technique. In addition, a metric for online data management is proposed, which has the characteristic of retaining more data in regions where the change of the function value is relatively large. The proposed metric can be interpreted as an extension of the existing method, allowing for the control of dataset quality within its limited size. We apply the proposed control scheme to the model predictive contouring control problem. Numerical simulations demonstrate the robustness of the proposed control scheme to tire parameter uncertainty, in comparison to a baseline controller.
Compact models are useful to avoid spending a numerous amount of time in numerical simulations which can accurately evaluate the ultra-short channel device properties. One numerical compact model proposed here is desc...
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In this research, the problem of predictive maintenance using the Industrial Internet of Things (IIoT) systems is investigated with the use of hybrid Convolutional Neural Networks (CNN) and Long Short Term Memory (LST...
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Recently, Deep Reinforcement Learning (DRL) has been used to solve complex robot control tasks with outstanding success. However, previous DRL methods still exist some shortcomings, such as poor generalization perform...
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The paper presents a practical implementation of the developed method for extracting physical and technical structured information from natural language documents. The novelty of the method lies in the cooperation of ...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
The paper presents a practical implementation of the developed method for extracting physical and technical structured information from natural language documents. The novelty of the method lies in the cooperation of deep learning technologies based on a tagged corpus of texts containing descriptions of technical and physical effects and methods of semantic-ontological text analysis. •An unlabeled corpus of natural language texts extracted from a unified knowledge base on physical topics was prepared. A tagged corpus of texts containing physical and technical structured information in the form of descriptions of physical effects and their technical implementations was formed for deep learning. Neural network models keyT5, T5 and Bert were trained to extract physical and technical information.
Micro-surgical robotic systems are gaining prominence in minimally invasive surgery within the medical field. However, accurately tracking the position of the moving agents at the micro-scale remains a significant cha...
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ISBN:
(数字)9798350376807
ISBN:
(纸本)9798350376814
Micro-surgical robotic systems are gaining prominence in minimally invasive surgery within the medical field. However, accurately tracking the position of the moving agents at the micro-scale remains a significant challenge, particularly for multi-agent systems operating in cluttered and unknown environments. Traditional image analysis methods can falter when confronted with issues such as mutual and obstacle occlusion, especially in dynamic and unstructured scenarios. In order to address this issue, this study introduces a graph-based multi-agent 3D tracking algorithm for a micro-agent control system. This algorithm integrates image information with the control inputs used to navigate the micro agents. We combine the power of Convolutional Neural Networks and Graph Neural Networks to effectively extract features from image sources, and combine them with historical data and control inputs. The primary novelty of this algorithm is its ability to make predictions when the target is occluded in the 2D detection results. The proposed system achieved a tracking error of 0.15 mm, outperforming standard model-based tracking techniques.
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