The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to e...
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The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.
作者：
Liang, SicongHuang, XunPeking Univ
Coll Engn Dept Aeronaut & Astronaut Beijing 100871 Peoples R China Peking Univ
Coll Engn Dept Aeronaut & Astronaut State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China
This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy-Riemann equations into mac...
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This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy-Riemann equations into machine learning architectures. By incorporating Cauchy integrals and boundary conditions, the neural network successfully learns to numerically produce matrix kernel factorization for Wiener-Hopf analytical models. To validate and demonstrate the approach, a benchmark case of wave scattering from parallel hard-soft plates is studied by comparing the machine learning results with the available analytical solutions. The proposed MINN approach could provide a new route to extensively enhance the theoretical modeling capability for several wave scattering and fluid mechanics problems. The code can be found at https://***/lscapku/MINN.
An analytical solution for spacecraft rendezvous within closed Keplerian orbits is presented using continuous radial thrust. The two spacecraft are assumed to be initially placed within the same closed orbit with arbi...
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An analytical solution for spacecraft rendezvous within closed Keplerian orbits is presented using continuous radial thrust. The two spacecraft are assumed to be initially placed within the same closed orbit with arbitrary phase angle separation. The target spacecraft is assumed to always remain in the same initial orbit. The chaser vehicle, however, uses a judiciously designed maneuver sequence comprising constant-acceleration radial thrust phases interspersed with coast phases. Importantly, the proposed maneuver design is analytically characterized by one or two (depending on whether the parking orbit is circular or elliptical, respectively) nonlinear algebraic equations that determine the time intervals for the different phases. The set of initial conditions from which rendezvous is feasible is also characterized. Additionally, orbit rotation (that is, changing the orbit argument of periapsis in plane) is also demonstrated for the elliptical parking orbit case. Finally, the remarkable emergence of the golden and silver ratios, as well as their connections to Kepler's triangle, is demonstrated for a specific sequence of thrust and coast arcs.
We present a new inversion formula for a weighted conical Radon transform modelling Compton camera data. The formula exploits a large proportion of the acquired events and is easy to implement into fast algorithms. We...
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We present a new inversion formula for a weighted conical Radon transform modelling Compton camera data. The formula exploits a large proportion of the acquired events and is easy to implement into fast algorithms. We give for it two equivalent formulations relying on known properties of the two-dimensional Radon transform and we test a semi-iterative algorithm for one of them. From a practical point of view, methods robust to measurement noise and to low number of events are required. We show that adding a constraint on the total variation of the final image strongly improves the results. We illustrate our arguments with Monte-Carlo simulated data in both low and realistic noise configurations.
In conventional helical computed tomography (CT), the field-of-view is a cylinder centered on the axis of the helix. Here, we consider the situation where all measurement lines are blocked except those intersecting a ...
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In conventional helical computed tomography (CT), the field-of-view is a cylinder centered on the axis of the helix. Here, we consider the situation where all measurement lines are blocked except those intersecting a small cylindrical region-ofinterest (ROI) not necessarily centered on the axis of the system. We address the question of image reconstruction inside the ROI. The patient boundary is assumed known, and we avoid the "interior problem" by assuming that the ROI includes part of the patient boundary. By applying analytic image reconstruction theory, we show that the entire cylindrical ROI can be reconstructed provided the pitch of the helix does not violate the well-known Tam-Danielsson detector condition. Using an iterative algorithm, we performed ROI reconstruction from simulated phantom data and from real patient data, and compared the results with full-field reconstructions. Visually, the ROI reconstructed images perfectly matched the full-field reconstructions. However, there were small quantitative discrepancies near the interior boundaries of the ROIs, which we attribute to the known reduced stability at one side of the inverse truncated Hilbert transform. In conclusion, we have demonstrated mathematically that accurate transverse ROI reconstruction is possible for helical CT, although care must be taken near the interior boundary to achieve quantitative accuracy.
Computing first-order sensitivity information is crucial for many gradient-based optimization strategies, where the algorithms employed play a key role in determining the computational efficiency of the optimization p...
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Computing first-order sensitivity information is crucial for many gradient-based optimization strategies, where the algorithms employed play a key role in determining the computational efficiency of the optimization process. For complex multibody system optimization problems, the numerical accuracy, stability, convergence characteristics, and computational order of the underlying formulations all contribute to the overall cost of the optimization process. The computational efficiency of the underlying forward problem and the associated sensitivity analysis must each be considered if one is to properly manage these design problems under time and computational resource constraints. An algorithm is presented that determines the key state derivatives, central to first-order sensitivity analysis, in a fully recursive manner. The algorithm significantly reduces the cost of determining analytic first-order sensitivity information for large-scale, tree-type multi-rigid-body dynamic systems. Qualitative and quantitative validation on the operational requirement of the present method are made through analytical means and empirical studies.
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