This article investigates fixed-time stabilization of fuzzy neural networks with distributed delay by designing an adaptive sliding mode controller. First, according to stability theory and related inequalities, a new...
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This article investigates fixed-time stabilization of fuzzy neural networks with distributed delay by designing an adaptive sliding mode controller. First, according to stability theory and related inequalities, a new fixed-time stability theorem is put forward, and the settling time is given. In order to stabilize the system, a new integral sliding mode surface is designed, and the corresponding sliding mode control strategy and adaptive sliding mode control strategy are established. Some criteria that can be obtained, and it is shown that the neuronal states of neural networks will arrive at the sliding surface in a fixed time, and then approach zero along the sliding surface. Compared with existing sliding mode control techniques, this work extends the previous related results by choosing different parameters of the controller and the sliding mode manifold to gain various protocols. Finally, two examples are provided to verify the validity of the theorems in this work.
Tellurium (Te), a chalcogenide with higher electrical conductivity than sulfur (S) and selenium (Se), can be made into liquid metal batteries with high energy density. The Te-Bi alloy positive electrode inhibits Te di...
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Tellurium (Te), a chalcogenide with higher electrical conductivity than sulfur (S) and selenium (Se), can be made into liquid metal batteries with high energy density. The Te-Bi alloy positive electrode inhibits Te dissolution and enhances lithium diffusion, which further improves the coulombic efficiency and cycling stability of lithium batteries. Due to the dual-activity mechanism, Li & Vert;Te5Bi5 achieves similar to 100% capacity utilization. Our results highlight a viable strategy for advancing high-voltage LMBs, providing an option for achieving long-life stationary energy storage solutions.
Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. Source-free domain adaptation (SFDA) uses a pre-trained source model, instead of the source data, for privacy-...
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Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. Source-free domain adaptation (SFDA) uses a pre-trained source model, instead of the source data, for privacy-preserving transfer learning. SFDA is useful in seizure subtype classification, which can protect the privacy of the source patients, while reducing the amount of labeled calibration data for a new patient. This paper introduces semi-supervised transfer boosting (SS-TrBoosting), a boosting-based SFDA approach for seizure subtype classification. We further extend it to unsupervised transfer boosting (U-TrBoosting) for unsupervised SFDA, i.e., the new patient does not need any labeled EEG data. Experiments on three public seizure datasets demonstrated that SS-TrBoosting and U-TrBoosting outperformed multiple classical and state-of-the-art machine learning approaches in cross-dataset/cross-patient seizure subtype classification.
Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise rati...
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Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e. there are multiple source patients with labeled EEG data, which are used to label the EEG trials of a new patient. Approach. We propose an source domain selection (SDS)-global domain adaptation (GDA)-target agent subdomain adaptation (TASA) approach, which includes SDS to filter out dissimilar source domains, GDA to align the overall distributions of the selected source domains and the target domain, and TASA to identify the most similar source domain to the target domain so that its labels can be utilized. Main results. Experiments on two public seizure datasets demonstrated that SDS-GDA-TASA outperformed 13 existing approaches in unsupervised cross-patient seizure classification. Significance. Our approach could save clinicians plenty of time in labeling EEG data for epilepsy patients, greatly increasing the efficiency of seizure diagnostics.
A multi-level feature weighted fusion based image classification method for silicon single crystal growth process has been proposed to address the issue of the long silicon single crystal pulling times at industrial s...
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This paper studies the problem of location and capacity for direct charging (DC) fast charging stations (CS). A new model with the random spatial-temporal distribution of charging demands is proposed to plan both the ...
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In the real-world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven t...
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In the real-world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability-based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high-stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability-based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg-Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability-based path planning framework can effectively plan the high-quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.
Robust optimization (RO) has been widely used in the hydrogen-data-center microgrid (H-2- DCMG) optimal operations. However, the operation results based on RO are too conservative. Statistical feasibility can be intro...
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Robust optimization (RO) has been widely used in the hydrogen-data-center microgrid (H-2- DCMG) optimal operations. However, the operation results based on RO are too conservative. Statistical feasibility can be introduced into RO to reduce conservatism. Therefore, statistical feasibility-based RO is adopted in the H-2- DCMG operations optimization. In this study, a data-driven statistical-feasibility-based robust rolling optimization framework is constructed for the optimal operations of the c-DCMG. In this framework, DC temperature is influenced by the uncertain outside temperature, and an uncertainty set is needed to express the uncertain outside temperature. Unfortunately, the existing studies that construct the uncertainty set satisfying statistical feasibility only constructs the ellipsoid uncertainty sets. The ellipsoid uncertainty sets will be converted into the second-order cone constraints, which will increase the complexity when solving. In this study, the Statistical-Guarantee-based Vertex Link (SGVL) algorithm is proposed to construct the polyhedron uncertainty sets, which are used to describe the uncertainty of the outside temperature. Moreover, statistical feasibility-based DC temperature bound is guaranteed by the optimal operations obtained based on these uncertainty sets. Compared with the traditional ellipsoid uncertainty set, the polyhedron uncertainty set can reduce the complexity of the optimization problem and improve the efficiency of the solution process. Case studies based on the real-world temperature dataset are processed. The results show that the introduction of statistical feasibility can reduce the total operation cost by 0.29%similar to 0.64%. The average solving time of the optimization problems based on the polyhedron uncertainty sets constructed using the SGVL algorithm also reduces by 7%similar to 13%. The cases also verify that other uncertainty parameters and different kinds of forecasters do not influence the performance and e
The Large-scale Group Success Likelihood Index Method (LG-SLIM) can eliminate bias caused by a single expert in human error assessment. The traditional LG-SLIM uses trust degrees to cluster and reach a consensus. Howe...
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The Large-scale Group Success Likelihood Index Method (LG-SLIM) can eliminate bias caused by a single expert in human error assessment. The traditional LG-SLIM uses trust degrees to cluster and reach a consensus. However, the existing clustering algorithms do not consider the trust degrees between a given pair of experts to be multiple and vary according to different evaluated tasks. Besides, the existing consensus models do not consider various combinations of the evaluated tasks and trusted experts' professions when managing trust degrees and self-confidence. Therefore, the similarity-trust-based clustering algorithm is improved using the comprehensive trust degree integrated from diverse trust degrees concerning all evaluated tasks. Moreover, expert credibility is proposed to reflect the quality of the expert's evaluation results, determined by self-confidence and trust degree simultaneously according to various combinations of the expert profession and target task. Accordingly, under the social network derived from expert credibility, the incompatible outliers change their opinions by referring to the views of those with the highest expert credibility. Finally, the sensitivity experiment and comparative analysis verify the effectiveness of the proposed model. The proposed LG-SLIM model is useful for human error assessment when critical operations need many experts to obtain reliable and accurate results.
Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability ma...
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