Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a ...popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.
Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a ...distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.
Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved substantial success in landslide risk management applications. However, the complexity of classically trained ML is ...often beyond nonexperts. With the rapid growth of practical applications, an “off-the-shelf” ML technique that can be easily used by nonexperts is highly relevant. In the present study, a new paradigm for an end-to-end ML modeling was adopted for LSM in the Three Gorges Reservoir area (TGRA) using automated machine learning (AutoML) as the backend model support for the paradigm. A well-defined database consisting of data from 290 landslides and 11 conditioning factors was collected for implementing AutoML and compared with classically trained ML approaches. The stacked ensemble model from AutoML achieved the best performance (0.954), surpassing the support vector machine with artificial bee colony optimization (ABC-SVM, 0.931), gray wolf optimization (GWO-SVM, 0.925), particle swarm optimization (PSO-SVM, 0.925), water cycle algorithm (WCA-SVM, 0.925), grid search (GS-SVM, 0.920), multilayer perceptron (MLP, 0.908), classification and regression tree (CART, 0.891), K-nearest neighbor (KNN, 0.898), and random forest (RF, 0.909) in terms of the area under the receiver operating characteristic curve (AUC). Notable improvements of up to 11% in AUC demonstrate that the AutoML approach succeeded in LSM and could be used to select the best model with minimal effort or intervention from the user. Moreover, a simple model that has been customarily ignored by practitioners and researchers has been identified with performance satisfying practical requirements. The experimental results indicate that AutoML provides an attractive alternative to manual ML practice, especially for practitioners with little expert knowledge in ML, by delivering a high-performance off-the-shelf solution for ML model development for LSM.
The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance ...compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.
Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the ...electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.
The eruption and collapse of the Anak Krakatau volcano generated tsunamis in the Sunda Strait on December 22, 2018, leading to damage and casualties. In this paper, we use the two-layer model and ...nonlinear shallow equation model to study the triggering mechanism of the tsunami event. We first simulate the tsunami generated by volcano eruption and landslide, respectively. The tsunami source is analyzed by comparing with gauge measurements. It indicates that the volume of partial collapse for the landslide is 0.2–0.3 km
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. The comparison between the numerical results of landslide and tide gauge measurements presents well-fitted results, especially for the leading tsunami waves and arrival time. Computed maximum tsunami amplitude distribution points out that the most hazardous area is located at the south of the Sunda Strait (Pandeglang), which suffered the most casualties.
Due to the limitations of textual datasets, there are currently few studies on character inpainting. Second, unlike image inpainting, character inpainting cannot replicate the results of image ...inpainting, and some methods are even inaccessible. Finally, the extraction of character features in the existing model is insufficient, resulting in the model being unable to reconstruct characters based on complete and accurate character features. In this paper, we first threshold the obtained inscription dataset to obtain a visually better-binarized inscription dataset. Second, we improve the Context encoders to design BCEs(Binary Context Encoders) and add dilated convolution to learn the structural features of the character. It has been experimentally proven that BCEs is not only slightly better than methods in the same field, but can also restore real-life inscription characters with missing strokes.
ABSTRACT We present a multiwavelength observational study of the NGC 2024 filament using infrared to submillimeter continuum and the and inversion transitions centered on FIR-3, the most massive core ...therein. FIR-3 is found to have no significant infrared point sources in the Spitzer/IRAC bands. But the kinetic temperature map shows a peak value at the core center with K, which is significantly higher than the surrounding level ( 15-19 K). Such internal heating signature without an infrared source suggests an ongoing core collapse possibly at a transition stage from first hydrostatic core (FHSC) to protostar. The eight dense cores in the filament have dust temperatures between 17.5 and 22 K. They are much cooler than the hot ridge ( K) around the central heating star IRS-2b. Comparison with a dust heating model suggests that the filament should have a distance of 3-5 pc from IRS-2b. This value is much larger than the spatial extent of the hot ridge, suggesting that the filament is spatially separated from the hot region along the line of sight.
Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering ...potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.
Heterohelicenes are of increasing importance in the fields of materials science, molecular recognition, and asymmetric catalysis. However, enantioselective construction of these molecules, especially ...by organocatalytic methods, is challenging, and few methods are available. In this study, we synthesize enantioenriched 1-(3-indol)-quinonhelicenes through chiral phosphoric acid-catalyzed Povarov reaction followed by oxidative aromatization. The method has a broad substrate scope and offers rapid access to an array of chiral quinohelicenes with enantioselectivities up to 99%. Additionally, the photochemical and electrochemical properties of selected quinohelicenes are explored.
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