Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.

Predicting Flying Robot Dynamics with Deep Learning

With the rapid increase in the power of computing and technological advances in robotics, research in the field of robotics has rapidly become very expansive. Being able to accurately predict movements of a robot is vital to many applications within this field, allowing for more precise simulation and prototyping as well as more accurate control of robotic systems. In this paper, we present an adaptable neural network that accurately predicts the movement of quadcopter robotic agents which can be expanded to encompass many more robots and applications given the requisite data, producing accurate results within a small margin for error.

Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

The precise simulation of urban space evolution and grasping of the leading factors are the most important basis for urban space planning. However, the simulation ability of current models is lacking when it comes to complicated/unpredictable urban space changes, resulting in flawed government decision-making and wasting of urban resources. In this study, a macro–micro joint decision model was proposed to improve the ability of urban space evolution simulation. The simulation objects were unified into production, living and ecological space to realize “multiple planning in one”. For validation of the proposed model and method, remote sensing images, geographic information and socio-economic data of Xuzhou, China from 2000 to 2020 were collected and tested. The results showed that the simulation precision of the cellular automata (CA) model was about 87% (Kappa coefficient), which improved to 89% if using a CA and multi-agent system (MAS) joint model. The simulation precision could be better than 92% using the prosed model. The result of factor weight determination indicated that the micro factors affected the evolution of production and living space more than the macro factors, while the macro factors had more influence on the evolution of ecological space than the micro factors. Therefore, active policies should be formulated to strengthen the ideological guidance towards micro individuals (e.g., a resident, farmer, or entrepreneur), and avoid disordered development of living and production space. In addition, ecological space planning should closely link with the local environment and natural conditions, to improve urban ecological carrying capacity and realize urban sustainable development.

Precise Simulation of Heat-Flow Coupling of Pipe Cooling in Mass Concrete

For a long time, temperature control and crack prevention of mass concrete is a difficult job in engineering. For temperature control and crack prevention, the most effective and common-used method is to embed cooling pipe in mass concrete. At present, there still exists some challenges in the precise simulation of pipe cooling in mass concrete, which is a complex heat-flow coupling problem. Numerical simulation is faced with the problem of over-simplification and inaccuracy. In this study, precise simulation of heat-flow coupling of pipe cooling in mass concrete is carried out based on finite element software COMSOL Multiphysics 5.4. Simulation results are comprehensively verified with results from theoretical solutions and equivalent algorithms, which prove the correctness and feasibility of precise simulation. Compared with an equivalent algorithm, precise simulation of pipe cooling in mass concrete can characterize the sharp temperature gradient around cooling pipe and the temperature rise of cooling water along pipeline more realistically. In addition, the cooling effects and local temperature gradient under different water flow (0.60 m3/h, 1.20 m3/h, and 1.80 m3/h) and water temperature (5 °C, 10 °C, and 15 °C) are comprehensively studied and related engineering suggestions are given.

FINITE ELEMENT MODELING OF STEEL PLATES UNDER MONOTONIC LOADING

Steel plate fuses can be used as energy dissipating devices in earthquake-resistant structures. After an earthquake, the structure remains essentially elastic and only the deformed fuse require replacement. This report simulates the monotonic response of steel plate specimens. The effects of different inputs such as imperfection, shape and size of the fuse openings, and different meshing types on yield strength, deformation, stress distribution, and displacement are studied by using ANSYS Mechanical APDL. The study found that increasing imperfection increases displacement and decreases yield strength. It was also concluded that as the hole size in the steel plate is increased, the fuse yield strength is slightly increased to a point then is decreased. Double diamond shape showed better response in terms of displacement and stress distribution, this is because of the link shape formed by the two holes. Finer quadrilateral meshing method provide precise simulation results over longer time.

FINITE ELEMENT MODELING OF STEEL PLATES UNDER MONOTONIC LOADING

Steel plate fuses can be used as energy dissipating devices in earthquake-resistant structures. After an earthquake, the structure remains essentially elastic and only the deformed fuse require replacement. This report simulates the monotonic response of steel plate specimens. The effects of different inputs such as imperfection, shape and size of the fuse openings, and different meshing types on yield strength, deformation, stress distribution, and displacement are studied by using ANSYS Mechanical APDL. The study found that increasing imperfection increases displacement and decreases yield strength. It was also concluded that as the hole size in the steel plate is increased, the fuse yield strength is slightly increased to a point then is decreased. Double diamond shape showed better response in terms of displacement and stress distribution, this is because of the link shape formed by the two holes. Finer quadrilateral meshing method provide precise simulation results over longer time.

Advantages of metaheuristics for multi-dataset calibration of hydrological models

<p>Hydrological models are crucial components in water and environmental resource management to provide simulations on streamflow, snow cover, and glacier mass balances. Effective model calibration is however challenging, especially if a multi-objective or multi-dataset calibration is necessary to generate realistic simulations of all flow components under consideration.</p><p>In this study, we explore the value of metaheuristics for multi-dataset calibration to simulate streamflow, snow cover and glacier mass balances using the HBV model in the glaciated catchment of the Rhonegletscher in Switzerland. We evaluate the performance of three metaheuristic calibration methods, i.e. Monte Carlo (MC), Simulated Annealing (SA) and Genetic Algorithms (GA), in regard to these three datasets. For all three methods, we compare the model performance using 100 best and 10 best optimized parameter sets.</p><p>Our results demonstrate that all three metaheuristic methods can generate realistic simulations of the snow cover, the glacier mass balance and the streamflow. The comparison of these three methods reveals that GA provides the most accurate simulations (with lowest confidence intervals) for all three datasets, for both 100 and 10 best simulations. However, when using all 100 simulations, GA yields also some worst solutions which are eliminated if only 10 best solutions are considered.</p><p>Based on our results we conclude that GA-based multi-dataset calibration provides more accurate and more precise simulation than MC or SA. This conclusion is fortified by a reduction of the parameter equifinality and an improvement of the Pareto frontier for GA in comparison to both other metaheuristic methods. This method should therefore lead to more reproducible and consistent hydrological simulations.</p>

Modular Supercomputing for Neuroscience

The precise simulation of the human brain requires coupling different models in order to cover the different physiological and functional aspects of this extremely complex organ. Each of this brain models is implemented following specific mathematical and programming approaches, potentially leading to diverging computational behaviour and requirements. Such situation is the typical use case that can benefit from the Modular Supercomputing Architecture (MSA), which organizes heterogeneous computing resources at system level. This architecture and its corresponding software environment enable to run each part of an application or a workflow on the best suited hardware.This paper presents the MSA concept covering current hardware and software implementations, and describes how the neuroscientific workflow resulting of coupling the codes NEST and Arbor is being prepared to exploit the MSA.