| 1. |
- Qian, Zhen, et al.
(författare)
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Vectorized dataset of roadside noise barriers in China using street view imagery
- 2022
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Ingår i: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 14:9, s. 4057-4076
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Tidskriftsartikel (refereegranskat)abstract
- Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).
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| 2. |
- Chen, Zhuo, et al.
(författare)
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Reliability-Oriented Multi-Objective Optimization of Electrical Machines Considering Insulation Thermal Lifetime Prediction
- 2023
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Ingår i: IEEE Transactions on Transportation Electrification. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7782. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- With the trend toward transportation electrification, the power density of electrical machines faces ever-increasing requirement owing to the stringent limit of weight, especially for aerospace applications. Conventionally, the reliability of electrical machines in such safety-critical application is guaranteed by considerable safety margins, i.e., the over-engineering approach, which prevents electrical machines from reaching higher power densities and leads to a design conflict. This paper proposes a reliability-oriented design approach for low-voltage electrical machines by integrating model-based lifetime prediction into a multi-objective optimization process. Accelerated thermal degradation tests are carried out on mainwall insulation and turn insulation, then the thermal degradation model is built to predict the lifetimes, accordingly. Thermal lifetime models are developed at several lifetime percentiles for both continuous duty and variable duty applications. Finally, a feasible reliability-oriented multi-objective optimization platform is established, based on which a study-case electrical machine for aerospace application is designed and optimized. The prototype is manufactured to verify the optimized performances.
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| 3. |
- Cheng, Yirui, et al.
(författare)
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NDFIP1 limits cellular TAZ accumulation via exosomal sorting to inhibit NSCLC proliferation
- 2023
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Ingår i: Protein & Cell. - : Springer Nature. - 1674-800X .- 1674-8018. ; 14:2, s. 123-136
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Tidskriftsartikel (refereegranskat)abstract
- NDFIP1 has been previously reported as a tumor suppressor in multiple solid tumors, but the function of NDFIP1 in NSCLC and the underlying mechanism are still unknown. Besides, the WW domain containing proteins can be recognized by NDFIP1, resulted in the loading of the target proteins into exosomes. However, whether WW domain-containing transcription regulator 1 (WWTR1, also known as TAZ) can be packaged into exosomes by NDFIP1 and if so, whether the release of this oncogenic protein via exosomes has an effect on tumor development has not been investigated to any extent. Here, we first found that NDFIP1 was low expressed in NSCLC samples and cell lines, which is associated with shorter OS. Then, we confirmed the interaction between TAZ and NDFIP1, and the existence of TAZ in exosomes, which requires NDFIP1. Critically, knockout of NDFIP1 led to TAZ accumulation with no change in its mRNA level and degradation rate. And the cellular TAZ level could be altered by exosome secretion. Furthermore, NDFIP1 inhibited proliferation in vitro and in vivo, and silencing TAZ eliminated the increase of proliferation caused by NDFIP1 knockout. Moreover, TAZ was negatively correlated with NDFIP1 in subcutaneous xenograft model and clinical samples, and the serum exosomal TAZ level was lower in NSCLC patients. In summary, our data uncover a new tumor suppressor, NDFIP1 in NSCLC, and a new exosome-related regulatory mechanism of TAZ.
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| 4. |
- Li, F., et al.
(författare)
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A Cobalt@Cucurbit[5]uril Complex as a Highly Efficient Supramolecular Catalyst for Electrochemical and Photoelectrochemical Water Splitting
- 2021
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Ingår i: Angewandte Chemie International Edition. - : Wiley-VCH Verlag. - 1433-7851 .- 1521-3773. ; 60:4, s. 1976-1985
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Tidskriftsartikel (refereegranskat)abstract
- A host–guest complex self-assembled through Co2+ and cucurbit[5]uril (Co@CB[5]) is used as a supramolecular catalyst on the surface of metal oxides including porous indium tin oxide (ITO) and porous BiVO4 for efficient electrochemical and photoelectrochemical water oxidation. When immobilized on ITO, Co@CB[5] exhibited a turnover frequency (TOF) of 9.9 s−1 at overpotential η=550 mV in a pH 9.2 borate buffer. Meanwhile, when Co@CB[5] complex was immobilized onto the surface of BiVO4 semiconductor, the assembled Co@CB[5]/BiVO4 photoanode exhibited a low onset potential of 0.15 V (vs. RHE) and a high photocurrent of 4.8 mA cm−2 at 1.23 V (vs. RHE) under 100 mW cm−2 (AM 1.5) light illumination. Kinetic studies confirmed that Co@CB[5] acts as a supramolecular water oxidation catalyst, and can effectively accelerate interfacial charge transfer between BiVO4 and electrolyte. Surface charge recombination of BiVO4 can be also significantly suppressed by Co@CB[5].
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| 5. |
- Li, Tianhao, et al.
(författare)
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Controllability of networked multiagent systems based on linearized Turing's model
- 2024
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on controllability of Turing's model by linearization and spatial discretization. This linearized model is a networked system whose agents are second order linear systems and these agents interact with each other by Laplacian dynamics on a graph. A control signal can be added to agents of choice. Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics. When the graph is a grid graph or a cylinder grid graph, we then give precisely the minimal number of control nodes and a corresponding control node set such that the Laplace dynamic systems on these graphs with agents of first order dynamics are controllable.
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| 6. |
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| 7. |
- Sun, Hui, et al.
(författare)
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Luminosity Function and Event Rate Density of XMM-Newton-selected Supernova Shock Breakout Candidates
- 2022
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Ingår i: Astrophysical Journal. - : IOP Publishing Ltd. - 0004-637X .- 1538-4357. ; 927:2
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Tidskriftsartikel (refereegranskat)abstract
- A dozen X-ray supernova shock breakout (SN SBO) candidates were reported recently based on XMM-Newton archival data, which increased the X-ray-selected SN SBO sample by an order of magnitude. Assuming that they are genuine SN SBOs, we study the luminosity function (LF) by improving on the method used in our previous work. The light curves and the spectra of the candidates were used to derive the maximum volume within which these objects could be detected with XMM-Newton by simulation. The results show that the SN SBO LF can be described by either a broken power law (BPL) with indices (at the 68% confidence level) of 0.48 +/- 0.28 and 2.11 +/- 1.27 before and after the break luminosity at log(L-b/erg s(-1)) = 45.32 +/- 0.55 or a single power law (SPL) with an index of 0.80 +/- 0.16. The local event rate densities of SN SBOs above 5 x 10(42) erg s (-1) are consistent for two models, i.e., 4.6(-1.3)(+1.7) x 10(4) Gpc(-3) yr(-1) and 4.9(-1.4)(+1.9 )x 10(4) Gpc(-3) yr(-1) for BPL and SPL models, respectively. The number of fast X-ray transients of SN SBO origin can be significantly increased by wide-field X-ray telescopes such as the Einstein Probe.
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| 8. |
- Wang, Deyu, et al.
(författare)
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Mapping the BCPNN Learning Rule to a Memristor Model
- 2021
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Ingår i: Frontiers in Neuroscience. - : Frontiers Media SA. - 1662-4548 .- 1662-453X. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in a way that allows mapping to neural and synaptic processes in the human cortexandhas been used extensively in detailed spiking models of cortical associative memory function and recently also for machine learning applications. In conventional digital implementations of BCPNN, the von Neumann bottleneck is a major challenge with synaptic storage and access to it as the dominant cost. The memristor is a non-volatile device ideal for artificial synapses that fuses computation and storage and thus fundamentally overcomes the von Neumann bottleneck. While the implementation of other neural networks like Spiking Neural Network (SNN) and even Convolutional Neural Network (CNN) on memristor has been studied, the implementation of BCPNN has not. In this paper, the BCPNN learning rule is mapped to a memristor model and implemented with a memristor-based architecture. The implementation of the BCPNN learning rule is a mixed-signal design with the main computation and storage happening in the analog domain. In particular, the nonlinear dopant drift phenomenon of the memristor is exploited to simulate the exponential decay of the synaptic state variables in the BCPNN learning rule. The consistency between the memristor-based solution and the BCPNN learning rule is simulated and verified in Matlab, with a correlation coefficient as high as 0.99. The analog circuit is designed and implemented in the SPICE simulation environment, demonstrating a good emulation effect for the BCPNN learning rule with a correlation coefficient as high as 0.98. This work focuses on demonstrating the feasibility of mapping the BCPNN learning rule to in-circuit computation in memristor. The feasibility of the memristor-based implementation is evaluated and validated in the paper, to pave the way for a more efficient BCPNN implementation, toward a real-time brain emulation engine.
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| 9. |
- Wang, Deyu, et al.
(författare)
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Memristor-Based In-Circuit Computation for Trace-Based STDP
- 2022
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Ingår i: 2022 Ieee International Conference On Artificial Intelligence Circuits And Systems (Aicas 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the online learning process. It is challenging for memristor-based implementations to support the trace-based STDP learning rules that have been widely used in neuromorphic applications. This paper proposed a versatile memristor-based architecture to implement the synaptic-level trace-based STDP learning rules. Especially, the similarity between synaptic trace dynamics and the memristor nonlinearity is explored and exploited to emulate the trace variables of trace-based STDP. As two typical trace-based STDP learning rules, the pairwise STDP and the triplet STDP, are simulated on two typical nonlinear bipolar memristor devices. The simulation results show that the behavior of physical memristor devices can be well estimated (below 6% in terms of the relative root-mean-square error), and the memristor-based in-circuit computation for trace-based STDP learning rules can achieve a high correlation coefficient over 98%.
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| 10. |
- Xu, Jiawei, et al.
(författare)
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A Memristor Model with Concise Window Function for Spiking Brain-Inspired Computation
- 2021
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Ingår i: 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a concise window function to build a memristor model, simulating the widely-observed non-linear dopant drift phenomenon of the memristor. Exploiting the non-linearity, the memristor model is applied to the in-situ neuromorphic solution for a cortex-inspired spiking neural network (SNN), spike-based Bayesian Confidence Propagation Neural Network (BCPNN). The improved memristor model utilizing the proposed window function is able to retain the boundary effect and resolve the boundary lock and inflexibility problem, while it is simple in form that can facilitate large-scale neuromorphic model simulation. Compared with the state-of-the-art general memristor model, the proposed memristor model can achieve a 5.8x reduction of simulation time at a competitive fitting level in cortex-comparable large-scale software simulation. The evaluation results show an explicit similarity between the non-linear dopant drift phenomenon of the memristor and the BCPNN learning rule, and the memristor model is able to emulate the key traces of BCPNN with a correlation coefficient over 0.99.
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