作者：
Zhang, ChenRen, HongruMa, HuiZhou, QiSchool of Automation
Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics
Guangdong University of Technology Guangzhou510006 China
This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computingsystems. Firstly, a novel cloud computing system architecture is designed, in which ...
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The rising demand for deploying low-latency data analysis and protecting privacy in a cloud-based setting has led to the emergence of federated learning (FL) as an important learning paradigm over wireless sensor netw...
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ISBN:
(纸本)9798350304060;9798350304053
The rising demand for deploying low-latency data analysis and protecting privacy in a cloud-based setting has led to the emergence of federated learning (FL) as an important learning paradigm over wireless sensor networks. Due to the success of FL, generative models such as generative adversarial networks (GANs) are now utilized in FL to provide higher privacy and utility. However, existing GAN-based FL approaches are power-hungry which poses unbearable demands on resource-limited distributed users. Considering practical learning systems involving limited computational power and unlabeled data over wireless networks, this work investigates FL in a resource-constrained and label-free data environment. Specifically, we propose a novel framework known as UFed-GAN that captures sensor-side data distribution without local classification training. We analyze the convergence and privacy of the proposed UFed-GAN. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed...
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As human-robot interaction (HRI) advances, the nuanced interpretation of implicit commands embedded in human gestures becomes paramount for fostering seamless collaboration. In this context, we present a novel machine...
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ISBN:
(纸本)9798350355376;9798350355369
As human-robot interaction (HRI) advances, the nuanced interpretation of implicit commands embedded in human gestures becomes paramount for fostering seamless collaboration. In this context, we present a novel machine learning algorithm designed to endow robots with the ability to decipher implicit commands from Inertial Measurement Unit (IMU) sensor data worn at specific locations on the human body. Our approach integrates memory and attention mechanisms inspired by ideomotor cues, allowing the robot to comprehend both temporal and spatial relationships within the sensor data. The attention mechanism operates bidirectionally, enhancing the system's awareness of the temporal sequence of human movements and the spatial interdependencies between sensor data across different body locations. This unique spatial attention enables the robot to understand the kinematic chain between joints during human motion, accommodating variations in sensor data arising from factors such as height differences and motion range capacity. Drawing on prior research in attention mechanisms, ideomotor cues, and memory augmentation, our algorithm represents a significant advancement in addressing the challenges of implicit command understanding in HRI. The proposed system's adaptability and nuanced comprehension of human gestures make it well-suited for diverse anatomies and movement patterns. Through comprehensive experiments, we demonstrate the effectiveness of our algorithm, paving the way for more intuitive and adaptable robotic systems in real-world applications.
UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and traceability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market...
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ISBN:
(纸本)9781665491907
UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and traceability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market-based methods are widely favored but they rely on the environmentally influenced communication network to complete negotiation, while the onboard computing of UAV is robust and redundant. This paper proposes a distributed scheduling method for networked UAV swarm based on computing for communication, which trades a modest increase in computing for a significant decrease in communication. First, by analyzing the task removal strategies of two representative methods, the consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, a new removal strategy is proposed, which expands the exploration of the bundle and can potentially reduce communication rounds. Second, the proposed task-related optimization method can extract task conflict nodes from the native communication protocol, and use the sampling and estimation strategies to resolve task conflicts in advance. Third, historical bids are cleverly used to infer others' locations, which is necessary for task-related optimization. Fourth, to verify the algorithm in real communication, a hardware-in-the-loop (HIL) ad-hoc network simulation system is constructed, which uses real network protocols and simulated channel transmissions. Finally, the HIL Monte Carlo simulation results show that, compared with CBBA and PI, the proposed method can significantly reduce the number of communication rounds and the total scheduling time, without increasing the communication protocol overhead and loss of optimization.
Forming a collaborative computing network among the deployed satellites in space can process data rapidly and reduce data transmission delay by leveraging the communication, storage, and computing capacities of the sa...
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ISBN:
(纸本)9798350328554
Forming a collaborative computing network among the deployed satellites in space can process data rapidly and reduce data transmission delay by leveraging the communication, storage, and computing capacities of the satellites. Due to the dynamics of satellite orbits, the distance between satellites varies over time, bringing new challenges to the designing of dedicated distributed file systems for collaborative satellites. Traditional distributed file systems did not consider the network topology and dynamics of satellite clouds and the limited computing resources of satellite node cores. In view of the torus network topology of satellite clouds, we design a highly available distributed metadata management mechanism for satellite clouds to ensure efficient metadata reading and reliability. On this basis, a topology-aware replica placement strategy is proposed to minimize communication costs and energy consumption for replica placement. Based on the metadata strategy and replica placement strategy mentioned above, we design and implement a distributed file system for satellite clouds. Experimental results demonstrate that our proposed placement strategy can improve the data transmission performance of the replica placement by more than double compared to the random placement strategy.
Skill transfer from humans to robots is challenging. Presently, many researchers focus on capturing only position or joint angle data from humans to teach the robots. Even though this approach has yielded impressive r...
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ISBN:
(纸本)9781665491907
Skill transfer from humans to robots is challenging. Presently, many researchers focus on capturing only position or joint angle data from humans to teach the robots. Even though this approach has yielded impressive results for grasping applications, reconstructing motion for object handling or fine manipulation from a human hand to a robot hand has been sparsely explored. Humans use tactile feedback to adjust their motion to various objects, but capturing and reproducing the applied forces is an open research question. In this paper we introduce a wearable fingertip tactile sensor, which captures the distributed 3-axis force vectors on the fingertip. The fingertip tactile sensor is interchangeable between the human hand and the robot hand, meaning that it can also be assembled to fit on a robot hand such as the Allegro hand. This paper presents the structural aspects of the sensor as well as the methodology and approach used to design, manufacture, and calibrate the sensor. The sensor is able to measure forces accurately with a mean absolute error of 0.21, 0.16, and 0.44 Newtons in X, Y, and Z directions, respectively.
R esource allocation is a key topic in distributed cloud systems, especially as more and more cloud products and service offerings introduce decision complexity. It's a common challenge for cloud users to predict ...
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ISBN:
(纸本)9798350377330;9798350377323
R esource allocation is a key topic in distributed cloud systems, especially as more and more cloud products and service offerings introduce decision complexity. It's a common challenge for cloud users to predict usage and budget accordingly. This paper studies the industry practices on smart resource allocation in the cloud and proposes a novel approach using data-driven combination of LSTM and K-means for cloud resource allocation.
The distributedcomputing system is a hot research field. Deep learning, the Internet of Things, and other technologies are rapidly advancing, necessitating better levels of computation, storage, and communication eff...
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Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different...
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ISBN:
(纸本)9798350377712;9798350377705
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the target location while also employing multiple modalities for enhanced robustness and accuracy. Recently, such systems have employed end-to-end deep neural models trained on large datasets due to their superior performance and ability to handle data from diverse sensor modalities. However, such neural models are often trained on data collected from a particular set of sensor poses (i.e., locations and orientations). During real-world deployments, slight deviations from these sensor poses can result in extreme inaccuracies. To address this challenge, we introduce FlexLoc, which employs conditional neural networks to inject node perspective information to adapt the localization pipeline. Specifically, a small subset of model weights are derived from node poses at run time, enabling accurate generalization to unseen perspectives with minimal additional overhead. Our evaluations on a multimodal, multiview indoor tracking dataset showcase that FlexLoc improves the localization accuracy by almost 50% in the zero-shot case (no calibration data available) compared to the baselines. The source code of FlexLoc is available in https://***/nesl/FlexLoc.
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