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The robot arm system with no or low-accuracy Lagrange dynamic identification is a typical unknown-structure MIMO coupled system. It is difficult to achieve fast-convergence and high-accuracy control for this practical system, especially with no empirical pre-adjustment of the initial input direction. To solve this practical problem, a novel prescribed finite-time nondirectional AT-S fuzzy control method is proposed.
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78 Views
Smart grids today are characterized by the integration of distributed energy resources, including renewable energy sources, traditional power sources, and storage systems. These components typically employ various control technologies that interface with generators through smart inverters, making them susceptible to numerous cyber threats. To address this vulnerability, there is a crucial need for datasets that document attacks on these systems, enabling risk evaluation and the development of effective monitoring algorithms. This dataset
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162 Views
Developing mind-controlled prosthetics that seamlessly integrate with the human nervous system is a significant challenge in the field of bioengineering. This project investigates the use of labelled brainwave patterns to control a bionic arm equipped with a sense of touch. The core objective is to establish a communication channel between the brain and the artificial limb, enabling intuitive and natural control while incorporating sensory feedback.
The project involves:
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411 Views
Energy-storage-equipped static synchronous compensators (E-STATCOM) are crucial devices in a modern
power grid to regulate both the active and reactive power, thereby improving the efficiency and power
quality. Current controllers commonly used with E-STATCOM devices include proportional-integral (PI),
repetitive, and deadbeat controllers. However, each has application-dependent restrictions and therefore lack
the ability to achieve optimal operation. This paper presents a novel hybrid current controller that combines
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214 Views
The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline. Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.
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843 Views
The dataset contains 5G positioning measurements simulated using a MATLAB raytracer tool in realistic environments (outdoors and indoors).
Outdoor scenarios include static and dynamic users in the urban area of Città Studi, Milan, Italy, near the Politecnico di Milano - Campus Leonardo.
The indoor context is reconstructed using a LiDAR acquisition in the MADE Competence Center I4.0 located in Politecnico di Milano - Campus Durandò, Bovisa, Milan, Italy.
The datasets include:
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53 Views
we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By applying inverse estimation on known Anchor Points (Aps) and improved unscented Kalman filter (IRUKF), the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%.
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270 Views
Electromyography (EMG) has limitations in human machine interface due to disturbances like electrode-shift, fatigue, and subject variability. A potential solution to prevent model degradation is to combine multi-modal data such as EMG and electroencephalography (EEG). This study presents an EMG-EEG dataset for enhancing the development of upper-limb assistive rehabilitation devices. The dataset, acquired from thirty-three volunteers without neuromuscular dysfunction or disease using commercial biosensors is easily replicable and deployable.
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1644 Views
The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods.
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627 Views
Surface EMG (sEMG) signals collected during activities of daily life (ADL) provide better insights toward understanding neuromuscular disorders, persons with limb disabilities, aging adults and neuromotor deficits. Hand movement and control mechanism analysis may improve the design of prosthetic devices, realistic biomechanical hands, and rehabilitation therapy. We present a sEMG signal database corresponding to the Indian population.
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1619 Views