Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient ...distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.
Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and ...comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX 1 Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.
To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent ...image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.
Prestin (SLC26A5) is responsible for acute sensitivity and frequency selectivity in the vertebrate auditory system. Limited knowledge of prestin is from experiments using site-directed mutagenesis or ...domain-swapping techniques after the amino acid residues were identified by comparing the sequence of prestin to those of its paralogs and orthologs. Frog prestin is the only representative in amphibian lineage and the studies of it were quite rare with only one species identified. Here we report a new coding sequence of SLC26A5 for a frog species, Rana catesbeiana (the American bullfrog). In our study, the SLC26A5 gene of Rana has been mapped, sequenced and cloned successively using RNA-Seq. We measured the nonlinear capacitance (NLC) of prestin both in the hair cells of Rana's inner ear and HEK293T cells transfected with this new coding gene. HEK293T cells expressing Rana prestin showed electrophysiological features similar to that of hair cells from its inner ear. Comparative studies of zebrafish, chick, Rana and an ancient frog species showed that chick and zebrafish prestin lacked NLC. Ancient frog's prestin was functionally different from Rana. We mapped and sequenced the SLC26A5 of the Rana catesbeiana from its inner ear cDNA using RNA-Seq. The Rana SLC26A5 cDNA was 2292 bp long, encoding a polypeptide of 763 amino acid residues, with 40% identity to mammals. This new coding gene could encode a functionally active protein conferring NLC to both frog HCs and the mammalian cell line. While comparing to its orthologs, the amphibian prestin has been evolutionarily changing its function and becomes more advanced than avian and teleost prestin.
Abstract
Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the ...inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%.
Catenary-free trams with an onboard supercapacitor have been an emerging and energy-efficient way for urban rail transportation. However, the high-power charging load from catenary-free trams brings ...a new challenge to the design of charging systems. The traditional single-module charging system typically suffers from a high power and thermal pressure, which reduces the reliability of the charging system. In this paper, we propose a multimodule charging system to improve the system reliability by decomposing power pressure to multiple charging modules. Specifically, a cooperative charging protocol is proposed to improve the dynamic performance of the charging system by suppressing the current imbalance and avoiding the current overshoot during the startup process. First, we mathematically model the multimodule charging system using the state-space averaging method and graph theory. Second, the cooperative charging protocol is designed using the leader-follower consensus algorithm, where the virtual leader represents the desired charging current for each module with a warm-start mechanism. Third, multiple-input multiple-output root locus technique is exploited to characterize the dynamic performance of the charging system. Both simulation and experiment results show that the proposed protocol effectively suppresses the imbalance and avoids the overshoot during the startup of the charging system.
Pneumatic brake systems are crucial for the operation of trains. However, due to switching characteristics of on/off solenoid valves, the precise pressure control and low switching activities of ...valves are difficult to guarantee simultaneously during braking. To address the issue, a hybrid model predictive control (MPC) method is proposed for implementing the multi‐objective optimisation in this paper, i.e. the precise pressure tracking and the valve switching reduction. In order to model the hybrid behaviour caused by continuous dynamics of the pressure and discrete features of solenoid valves, the mixed logic dynamical system theory is applied. Then, the braking control problem is recast as a hybrid MPC problem, which jointly optimises the pressure tracking precision and the valve switching. A mixed integer linear program is formulated to solve the MPC problem in an efficient way. Both simulation and experiment results show that the accurate pressure control and low switching activities of valves can be achieved by the proposed method when compared with existing methods.
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•The antioxidant, antimicrobial, stability, and safety properties of the essential oil were significantly enhanced following encapsulation in the nano-emulsion.•The antibacterial ...mechanism of nano-emulsion was elucidated.•Nano-emulsion displayed outstanding thermal stability and storage durability.•Ultrasound-assisted preparation of the nano-emulsion consumed very low energy.•Nano-emulsion was easily digested.
Litsea cubeba essential oil (LCEO) has garnered widespread attention due to its robust biological activity. However, challenges such as high volatility, limited water solubility, and low bioavailability impede its application. Nano-emulsion encapsulation technology offers an effective solution to these issues. In this study, we prepared litsea cubeba essential oil nano-emulsion (LCEO-NE) for the first time using whey protein (WP) as the emulsifier through an ultrasonic-assisted method, achieving high efficiency with minimal energy consumption. Transmission electron microscopy and dynamic light scattering analyses revealed that the nanoparticles were uniformly spherical, with a particle size of 183.5 ± 1.19 nm and a zeta potential of −35.5 ± 0.95 mV. Stability studies revealed that LCEO-NE exhibited excellent thermal and salt stability, maintaining its integrity for up to four weeks when stored at 4 °C and 25 °C. In vitro digestion assays confirmed the digestibility of LCEO-NE. Furthermore, evaluation of the DPPH, ABTS, and antimicrobial activities revealed that LCEO-NE displayed superior bacteriostatic and antioxidant properties compared to LCEO. Scanning electron microscopy elucidated that its bacteriostatic effect involved the disruption of bacterial microstructure. Hemocompatibility and cytotoxicity assays demonstrated the safety of LCEO-NE within the effective concentration range. This research supports the utilization of nanoparticles for encapsulating LCEO, thereby enhancing its stability and bioactivity, and consequently expanding its applications in the food and pharmaceutical industries.
Significant sex differences exist in hearing physiology, while few human studies have investigated sex differences in noise-induced hearing loss (NIHL), and the sex bias in previous studies resulted ...in inadequate female data. The study aims to investigate sex differences in the characteristics of NIHL to provide insight into sex-specific risk factors, prevention strategies and treatment for NIHL.
This cross-sectional study included 2280 industrial noise-exposed shipyard workers (1140 males and 1140 females matched for age, job and employment length) in China. Individual noise exposure levels were measured to calculate the cumulative noise exposure (CNE), and an audiometric test was performed by an experienced technician in a soundproof booth. Sex differences in and influencing factors of low-frequency (LFHL) and high-frequency hearing loss (HFHL) were analyzed using logistic regression models stratified by age and CNE.
At comparable noise exposure levels and ages, the prevalence of HFHL was significantly higher in males (34.4%) than in females (13.8%), and males had a higher prevalence of HFHL (OR = 4.19, 95% CI 3.18 to 5.52) after adjusting for age, CNE, and other covariates. Sex differences were constant and highly remarkable among subjects aged 30 to 40 years and those with a CNE of 80 to 95 dB(A). Alcohol consumption might be a risk factor for HFHL in females (OR = 3.12, 95% CI 1.10 to 8.89).
This study indicates significant sex differences in NIHL. Males are at higher risk of HFHL than females despite equivalent noise exposure and age. The risk factors for NIHL might be different in males and females.
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