High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear...
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High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.
Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
The growing integration of renewable energy sources manifests as an effective strategy for reducing carbon emissions. This paper strives to efficiently approximate the set of optimal scheduling plans(OSPs) to enhance ...
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The growing integration of renewable energy sources manifests as an effective strategy for reducing carbon emissions. This paper strives to efficiently approximate the set of optimal scheduling plans(OSPs) to enhance the performance of the steady-state adaptive cruise method(SACM) of power grid, improving the ability of dealing with operational uncertainties. Initially, we provide a mathematical definition of the exact boxconstrained economic operating region(EBC-EOR) for the power grid and its dispatchable components. Following this, we introduce an EBC-EOR formulation algorithm and the corresponding bi-level optimization models designed to explore the economic operating boundaries. In addition, we propose an enhanced big-M method to expedite the computation of the EBCEOR. Finally, the effectiveness of the EBC-EOR formulation, its economic attributes, correlation with the scheduling plan underpinned by model predictive control, and the significant improvement in computational efficiency(over twelvefold) are verified through case studies conducted on two test systems..
Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptogr...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb-Se-Te(GSST)rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’*** a reorganization leads to different optical,electrical,and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission *** the great promise of conventional PCMs for realizing reconfigurable photonic memories,their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices *** addition,such materials do not offer a stable multi-level response over a long period of *** address these shortcomings,the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional(2D)birefringence PCMs such as SnSe,which offer anisotropic optical properties that can be switched *** demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material,the optical polarization state of the input optical signal can be *** enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing *** to the conventional PCMs,the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical *** a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding.
The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boo...
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The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.
Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing met...
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Coordinated investment and operations within renewable portfolio standards is one of the key technologies to meet the renewable energy target and realize the economic operations of the power *** paper proposes a unifi...
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Coordinated investment and operations within renewable portfolio standards is one of the key technologies to meet the renewable energy target and realize the economic operations of the power *** paper proposes a unified framework of coordinated planning and financial *** investment in renewable energy and energy storage and joint optimization of energy and ancillary services are integrated into a unified *** factors are taken into consideration by the social planner in the centralized electricity market,such as the sitting and sizing of renewable energy and energy storage,charge and discharge efficiency of the energy storage,transmission network constraints,reserve capacity,and financial *** framework provides a tool for the social planner to determine the optimal planning scheme of renewable energy and energy *** conclusion derived is that the sum of market revenue and financial subsidies of renewable energy and energy storage is exactly equal to their investment cost which is obtained by the Karush-Kuhn-Tucker(KKT)condition of maximizing social welfare problems.A numerical result based on the modified IEEE-39 bus test system demonstrates the effectiveness of the unified *** impact of financial incentives,reserve capacity,and production costs on capital investment are studied.
Aiming at the problem of irregular waveform of nonrectangular wave voltage sags, the least square method is proposed to fit the nonrectangular wave voltage sags. Based on the fitting curve, the severity of nonrectangu...
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