Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties ...
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Microgrids(MGs)with high penetration of dis-tributed generators may cause congestion in the distribution net-work during *** address this issue,this paper proposes a two-time-scale congestion management scheme for mul...
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Microgrids(MGs)with high penetration of dis-tributed generators may cause congestion in the distribution net-work during *** address this issue,this paper proposes a two-time-scale congestion management scheme for multiple MGs integrated distribution ***-ahead hourly-scale dynamic congestion management(DCM)is formulated as a con-strained optimization problem,which can be solved by utilizing the proposed alternating iterative method,with the privacy of both the distribution network and MGs being *** sub-hourly-scale contract energy tracking aims at fully utilizing the controllable resources of the MGs to minimize the difference of the contract and actual exchanged energy between the MG and distribution *** coordination of the proposed two timescales of management schemes,the MGs integrated distribution networks can operate economically while avoiding the probable congestion predicament with high penetration of renewable *** studies with a i3-bus system MGs integrated distribution network demonstrated this proposed approach is effective to manage the congestion problem in the distribution network,while the energy tracking approach can improve the welfare of the MGs engaged in energy contracts ***-Alternating giterativemethod,congestion management,microgrids,renewable energy integration.
Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational trip...
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Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.
This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of...
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This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.
Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often requ...
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Epileptic seizure detection has been a complex task due to the chaos and non-stationariness observed in the electroencephalogram (EEG) signals. Most existing EEG-based seizure detection algorithms are patient-dependen...
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Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the desig...
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Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.
This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval(MHR).This algorithm resolves problems,removes limitations of sampling and provides a more robust beamformer....
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This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval(MHR).This algorithm resolves problems,removes limitations of sampling and provides a more robust beamformer.A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer(SHRB).Simulation results show that SHRB has a better performance,accuracy,and applicability and more powerful eigenvalues than conventional beamformers.A simple mathematical proof is *** changing the number of harmonics,as a degree of freedom that is missing in conventional beamformers,SHRB can achieve more optimal outputs without increasing the number of spatial or temporal *** will demonstrate that SHRB offers an improvement of 4 dB in signal to noise ratio(SNR) in bit error rate(BER) of 10~(-4) over conventional *** the case of direction of arrival(DOA) estimation,SHRB can estimate the DOA of the desired signal with an SNR of-25 dB,when conventional methods cannot have acceptable response.
Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from...
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Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b
The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Ar...
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