In a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, a large number of antennas can be placed into a very limited space. It is not practical to equip each antenna with one ...independent radio frequency (RF) chain. Fortunately, the hybrid analog/digital beamforming (HBF) can be utilized to greatly reduce the number of RF chains, while providing an acceptable performance. However, the conventional multiuser MIMO beamforming algorithms cannot be directly applied to the mmWave system with HBF structure. In this letter, based on the idea that interuser interference reduction should be done for both analog beamforming and digital beamforming, we propose a novel HBF algorithm for the uplink multiuser scenario, which has low computational complexity. Simulations indicate that the proposed algorithm performs very close to the full digital algorithm, and its robustness is verified by comparisons with other existing algorithms under two different channel assumptions.
Chiral metasurfaces can achieve giant chiral optical responses and have been expanded from the optical band to other electromagnetic bands. Here, we propose a new method for dynamic terahertz ...circular dichroism (CD) manipulation in metasurfaces. By introducing a patterned and electrically doped graphene into the metamirror consists of double layer C-shaped split ring resonators (SRRs), efficient terahertz CD modulation is observed. Since the electrical doping of graphene changes the absorption loss of the metasuface cavity, the structure shows switching between a chiral metasurface and an ordinary metal mirror, which can be seen as "on" and "off" states. The calculation results show an efficient modulation of the terahertz CD in a large dynamic range. In addition, we also use the new method to design two metasurfaces for dynamic terahertz wavefront modulation and near-field digital imaging, both of which show a high-performance electrical switching. This method provides a new way for the design of active terahertz devices based on metasurfaces, and also promotes the applications of terahertz chiral metasurfaces in high-speed wireless communication and dynamic imaging.
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Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful ...life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.
Abstract
We study the semiclassical limit of the anisotropic two-photon Dicke model with a dissipative bosonic field and describe its rich nonlinear dynamics. Besides normal and ‘superradiant’-like ...phases, the presence of localized fixed points reflects the spectral collapse of the closed-system Hamiltonian. Through Hopf bifurcations of superradiant and normal fixed points, limit cycles are formed in certain regions of parameters. We also identify a pole-flip transition induced by anisotropy and a region of chaotic dynamics, which appears from a cascade of period-doubling bifurcations. In the chaotic region, collision and fragmentation of symmetric attractors take place. Throughout the phase diagram we find several examples of phase coexistence, leading to the segmentation of phase space into distinct basins of attraction.
In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately ...reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings' performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing's degradation. The stability and accuracy of the model were effectively improved.
In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical ...challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Carbon-free hydrogen as a promising clean energy source can be produced with electrocatalysts via water electrolysis. Oxygen evolution reaction (OER) as anodic reaction determines the overall ...efficiency of water electrolysis due to sluggish OER kinetics. Thus, it’s much desirable to explore the efficient and earth-abundant transition-metal-based OER electrocatalysts with high current density and superior stability for industrial alkaline electrolyzers. Herein, we demonstrate a significant enhancement of OER kinetics with the hybrid electrocatalyst arrays in alkaline via judiciously combining earth-abundant and ultrathin NiCo-based layered double hydroxide (NiCo LDH) nanosheets with nickel cobalt sulfides (NiCoS) with a facile metal-organic framework (MOF)-template-involved surface sulfidation process. The obtained NiCo LDH/NiCoS hybrid arrays exhibits an extremely low OER overpotential of 308 mV at 100 mA·cm
−2
, 378 mV at 200 mA·cm
−2
and 472 mV at 400 mA·cm
−2
in 1 M KOH solution, respectively. A much low Tafel slope of 48 mV·dec
−1
can be achieved. Meanwhile, with the current density from 50 to 250 mA·cm
−2
, the NiCo-LDH/NiCoS hybrid arrays can run for 25 h without any degradation. Our results demonstrate that the construction of hybrid arrays with abundant interfaces of NiCo LDH/NiCoS can facilitate OER kinetics via possible modulation of binding energy of O-containing intermediates in alkaline media. The present work would pave the way for the development of low-cost and efficient OER catalysts and industrial application of water alkaline electrolyzers.
As a new type of capacitor–battery hybrid energy storage device, metal‐ion capacitors have attracted widespread attention because of their high‐power density while ensuring energy density and long ...lifespan. Potassium‐ion capacitors (KICs) featuring the merits of abundant potassium resources, lower standard electrode potential, and low cost have been considered as potential alternatives to lithium‐/sodium‐ion capacitors. However, KICs still face issues including unsatisfactory reaction kinetics, low energy density, and poor lifetime owing to the large radius of the potassium ion. In this Review, the importance of emerging potassium‐ion capacitor is addressed. The Review offers a brief discussion of the fundamental working principle of KICs, along with an overview of recent advances and achievements of a variety of electrode materials for dual carbon and non‐dual carbon KICs. Furthermore, electrolyte chemistry, binders as well as electrode/electrolyte interface, are summarized. Finally, existing challenges and perspectives on further development of KICs are also presented.
A strong building block: Potassium‐ion capacitors holding the merits of the high energy density of potassium‐ion batteries and high output power of supercapacitors are receiving intensive attention owing to the abundant resources and low standard redox potential of potassium. In this Review, the fundamental working principle, recent progress on electrode materials, and future perspective of potassium‐ion capacitors are presented.
