Existing Learning from Demonstration (LfD) methodsthat can learn the peg-in-hole assembly skills mainly adopts the impedance control strategy, and generally do not pay attention to the assembly skill segmentation pro...
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the wide prospect of bilateral teleoperation system in various practical applications has attracted many researchers to study its control methods. this scholarly work addresses the control challenges inherent to bilat...
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ISBN:
(数字)9798331541699
ISBN:
(纸本)9798331541705
the wide prospect of bilateral teleoperation system in various practical applications has attracted many researchers to study its control methods. this scholarly work addresses the control challenges inherent to bilateral teleoperation systems subjected to hybrid cyber-attacks. A model is developed to describe the working state of a bilateral remote operating system, taking into account the impact of denial of service (DoS) attacks and false data injection (FDI) attacks that exist with probability. An adaptive controller is designed to mitigate issues arising from data loss due to DoS attacks and packet tampering resulting from FDI attacks. When an adversary launches an attack, there will be DoS or FDI attacks in the channel of a communication network therefore, this paper uses Bernoulli component to model the attack situation of the system. By utilizing the Lyapunov functional methodology, a criterion for global asymptotic stability is established, thereby substantiating that the master-slave system is capable of attaining global asymptotic synchronization. Finally, by selecting a manipulator system for numerical simulation, the effectiveness of the control strategy is verified.
Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D...
ISBN:
(纸本)9798331314385
Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. the linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency. the source code is available at https://***/gwenzhang/Voxel-Mamba.
Compliant mechanisms have drawn a lot of attention in recent years. Discrete elastic rod theory has paved the way in designing and analyzing linkage mechanisms with flexure hinges (compliant linkage mechanism). In ord...
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ISBN:
(数字)9798331509231
ISBN:
(纸本)9798331509248
Compliant mechanisms have drawn a lot of attention in recent years. Discrete elastic rod theory has paved the way in designing and analyzing linkage mechanisms with flexure hinges (compliant linkage mechanism). In order to achieve a same kinematic as the rigid linkage mechanism, this paper gives a general model for the kinematic of a compliant linkage mechanism: crank and rocker mechanism: Firstly, modeling of a compliant linkage mechanism based on discrete elastic rod theory, taking a typical example of a cranker and rod mechanism; Secondly, kinematic modeling of the rigid cranker and rod mechansim; thirdly, optimization flow is introduced, including BFGS optimization method, cubic hermix spline method and other relevant settings; Lastly, the simulation results demonstrate that this model could be used in the analysis and design of the compliant linkage mechanism.
Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions. To handle the various length of videos and repetitive actions, also optimization challenges i...
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In the fault diagnosis of bearings in hydropower station units, the information provided by a single sensor is limited, and the dynamic characteristics of the operating conditions vary under different load conditions....
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ISBN:
(数字)9798350355642
ISBN:
(纸本)9798350355659
In the fault diagnosis of bearings in hydropower station units, the information provided by a single sensor is limited, and the dynamic characteristics of the operating conditions vary under different load conditions. To address these issues, this paper proposes a bearing fault diagnosis method that integrates multi-source information with deep transfer learning. Firstly, multi-scale features of the input signals are extracted through a fusion convolution module to ensure the completeness and effectiveness of fault information. Subsequently, channel attention and spatial attention mechanisms are used to retain effective information with minimal data, enhancing the efficiency and accuracy of information fusion. Finally, the fault diagnosis method based on transfer learning effectively copes with detection under different loads. To more comprehensively mine and utilize the hidden features in fault signals, this paper combines vibration and temperature signals to build a bearing fault diagnosis experimental platform, collecting vibration and temperature signals at different speeds to generate a dual-channel dataset. Compared to common domain adaptation methods such as deep correlation alignment and domain adversarial neural networks, the proposed model's fault diagnosis performance under different working conditions is validated. Experimental results show that the proposed multi-source information fusion method performs with higher diagnostic accuracy both under the same and varying working conditions, significantly outperforming traditional methods.
Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, t...
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ISBN:
(数字)9798350380323
ISBN:
(纸本)9798350380330
Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. the proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.
this paper proposes a discrete control scheme based on performance-prescribed dynamic surface technology for adaptive neural network with implicit inverse compensation to address the control problem of dielectric elas...
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ISBN:
(数字)9798350352399
ISBN:
(纸本)9798350352405
this paper proposes a discrete control scheme based on performance-prescribed dynamic surface technology for adaptive neural network with implicit inverse compensation to address the control problem of dielectric elastomer actuators (DEAs). Compared to continuous control, discrete control is more in line withthe working rules of computers. Innovatively introducing performance-prescribed functions in discrete control systems, enabling tracking errors to converge within a preset range in advance, utilizing implicit inverse compensation for hysteresis nonlinearity, and integrating it into adaptive dynamic surface control methods to achieve precise control of DEAs. Finally, the effectiveness of the proposed scheme was verified based on the constructed dielectric elastomer driven motion experimental platform.
this paper describes an identifier for a class of nonlinear systems based on continuous recurrent neural networks (CRNN). the identifier is proposed considering the approximation properties of artificial neural networ...
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ISBN:
(数字)9798331517519
ISBN:
(纸本)9798331517526
this paper describes an identifier for a class of nonlinear systems based on continuous recurrent neural networks (CRNN). the identifier is proposed considering the approximation properties of artificial neural networks. the learning or adaptive laws are obtained using Barrier Lyapunov functions with an exponentially decreasing barrier. the application of such a function results in a bounded identification error with an exponential convergence and predefined decay. Additionally, it ensures the convergence of the weights for the activation functions to the fitting values. the proposed identifier was used to identify a virtual Cartesian robot with two degrees of freedom. the results showed the performance of the identification error, which does not violate the imposed exponential barrier. Moreover, the effect of the predefined convergence parameter was observed in the identification error evolution without the need for the change of any other parameter in the CRNN.
As the artificial intelligence market develops, demand for semiconductors also increases. In order to increase the efficiency of the semiconductor production process, anomaly detection technology is needed based on se...
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ISBN:
(数字)9788993215380
ISBN:
(纸本)9798331517939
As the artificial intelligence market develops, demand for semiconductors also increases. In order to increase the efficiency of the semiconductor production process, anomaly detection technology is needed based on sensor values attached to process machines. In this paper, we propose an anomaly detection system that determines whether there are abnormalities in the values obtained by attaching a sensor to the actual deposition process. the attached sensors include gas, temperature, and pressure sensors. A communication module is designed and made into a database, and the presence or absence of anomalies is determined using the characteristics of multivariate time series data through transformer-based anomaly detection network. An attention module-based transformer network is used in the AI-based anomaly detection network. through evaluation, we confirmed that the transformer-based anomaly detection network achieves the best performance in terms of accuracy relative to other anomaly detection methods.
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