The armature coil of the superconducting synchronous motor for electric aircraft requires a current of 1,000 A or more. However, a single superconducting tape at liquid nitrogen temperatures is insufficient for this o...
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Modern machine learning problems, such as hyperparameter optimization, meta learning, and adversarial training, adopt a bilevel learning formulation. Such problems involve a nested relation between inner- and outer-le...
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In an era of rapid technological innovation, this article provides a comprehensive examination of Gallium Nitride (GaN) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). The background section highlights t...
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A silicon micromachined gravimetric chemical sensor which is modified with a high surface area 3D-printed polymer coating is presented. The 3D-printed polymer improves both sensor sensitivity and response time compare...
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Common Information Model (CIM) is an essential technique for ensuring interoperability in Energy Management systems (EMS) by defining data profiles for information exchange. Given the changing system environment, CIM ...
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This study presented a surface-functionalized sensor probe using 3-aminopropyltriethoxysilane(APTES)self-assembled monolayers on a Kretschmann-configured plasmonic *** probe featured stacked nanocomposites of gold(via...
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This study presented a surface-functionalized sensor probe using 3-aminopropyltriethoxysilane(APTES)self-assembled monolayers on a Kretschmann-configured plasmonic *** probe featured stacked nanocomposites of gold(via sputtering)and graphene quantum dots(GQD,via spin-coating)for highly sensitive and accurate uric acid(UA)detection within the physiological *** encompassed the field emission scanning electron microscopy for detailed imaging,energy-dispersive X-ray spectroscopy for elemental analysis,and Fourier transform infrared spectroscopy for molecular *** functionalization increased sensor sensitivity by 60.64%,achieving 0.0221°/(mg/dL)for the gold-GQD probe and 0.0355°/(mg/dL)for the gold-APTES-GQD probe,with linear correlation coefficients of 0.8249 and 0.8509,*** highest sensitivity was 0.0706°/(mg/dL),with a linear correlation coefficient of 0.993 and a low limit of detection of 0.2 mg/***,binding affinity increased dramatically,with the Langmuir constants of 14.29μM^(-1)for the gold-GQD probe and 0.0001μM^(-1)for the gold-APTES-GQD probe,representing a 142900-fold *** probe demonstrated notable reproducibility and repeatability with relative standard deviations of 0.166%and 0.013%,respectively,and exceptional temporal stability of 99.66%.These findings represented a transformative leap in plasmonic UA sensors,characterized by enhanced precision,reliability,sensitivity,and increased surface binding capacity,synergistically fostering unprecedented practicality.
Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense...
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Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.
This study addresses the challenge of manual soldering skill evaluation and transfer in the context of small and medium-sized enterprises where automation is less prevalent. Recognizing the limitations of current sing...
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The direct modulation and fiber transmission performances of a bi-layer-oxide-confined 850-nm vertical cavity surface emitting laser (VCSEL) containing an emission aperture size of 3.5 μm with versatile novel complex...
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The direct modulation and fiber transmission performances of a bi-layer-oxide-confined 850-nm vertical cavity surface emitting laser (VCSEL) containing an emission aperture size of 3.5 μm with versatile novel complex data formats are compared. With delivering quadrature amplitude modulation-orthogonal frequency-division multiplexing (QAM-OFDM), generalized frequency-division multiplexing (GFDM), and discrete multi-tone (DMT) data streams, the highest data rate allowable for encoding the 3.5-μm-aperture VCSEL is demonstrated under back-to-back (BtB) and 100-m OM5-multi-mode fiber (OM5-MMF) transmissions. The newly designed 3.5-μm-aperture VCSEL contains InGaAs/AlGaAs multiple quantum wells and bi-layer-oxide-confined aperture to enhance its differential gain, reduce its thermal resistance, and improve its parasitic resistance for providing broader encoding bandwidth. Optimizing the VCSEL bias at 9 mA (11Ith) makes the lasing with three to four transverse modes suffer from a low modal dispersion effect, which also provides its 3-dB bandwidth as wide as 24.9 GHz and suppresses its RIN level to −138.0 dBc∕Hz for broadband OFDM/GFDM/DMT data encoding. By using the pre-distortion technique on the OFDM to compensate the SNR degradation, the OFDM modified with a pre-leveling slope of 0.5 dB/GHz can provide 160 Gbit/s for BtB and 140 Gbit/s for 100-m OM5-MMF transmissions. By dividing the N OFDM subcarriers into K equally-sized GFDM subcarriers and M equally-sized GFDM subsymbols, the QAM-GFDM with K = 2 can effectively enhance the out-of-band suppression to 35.9 dB and reduce the peak-to-average-power ratio to 9.7 dB, which can support 168 Gbit/s under BtB and 144 Gbit/s in 100-m OM5-MMF transmissions. To achieve the maximal spectral-usage efficiency, the DMT with the adaptive bit-loading algorithm is employed to improve the data rate up to 172 Gbit/s for BtB and 146 Gbit/s for 100-m OM5-MMF conditions. These advanced pre-leveled 16-QAM OFDM, GFDM, and DMT formats carried b
Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanob...
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Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.
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