Dynamic Shadow Research Articles

A case report of critical aortic stenosis diagnosed utilizing non-imaging continuous wave Doppler probe

Abstract Background Aortic stenosis (AS) is the most commonly acquired valvular pathology in the western world. Aortic stenosis severity is typically evaluated with Doppler echocardiography. Evaluation of aortic gradients using standard Doppler echocardiography from the apical window may underestimate the true gradient due to misalignment of blood flow to the ultrasound beam and is often better evaluated from other imaging windows using a non-imaging continuous wave Doppler (NI-CWD) probe. Herein, we describe a unique case of AS being underestimated by dynamic acoustic shadowing from the apical window rather than beam misalignment. Case summary The patient is a Hispanic 31-year-old male with a congenital bicuspid aortic valve who underwent a balloon aortic valvuloplasty at age 13. At age 31, the patient underwent a repeat transthoracic echocardiogram (TTE) that was largely unchanged from his prior TTE from 15 years prior. Notably on this TTE, there was acoustic shadowing of colour Doppler in the distal left ventricular outflow tract and aortic valve during systole. While gradients only suggested moderate AS, the degree of left ventricular hypertrophy was suspicious for more severe AS. Only by using the NI-CWD probe at the right sternal border were we able to identify very severe AS with a peak velocity of 6.5 m/s and a mean gradient of 100 mmHg. Discussion In our specific case, dynamic acoustic shadowing of the aortic valve from the apical window obscured both imaging and Doppler signals. This acoustic shadowing was not present from the right sternal border with the NI-CWD probe, leading to an over 100% increase in aortic valve peak velocity and proper correction of AS severity. This allowed for expedited care and underscores the importance of such techniques.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

A General Implicit Framework for Fast NeRF Composition and Rendering

A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF relies on sampling along rays, it is possible to provide general guidance for acceleration. To that end, we propose a general implicit pipeline for composing NeRF objects quickly. Our method enables the casting of dynamic shadows within or between objects using analytical light sources while allowing multiple NeRF objects to be seamlessly placed and rendered together with any arbitrary rigid transformations. Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces. It leverages an intersection neural network to query NeRF for acceleration instead of depending on an explicit spatial structure.Our proposed method is the first to enable both the progressive and interactive composition of NeRF objects. Additionally, it also serves as a previewing plugin for a range of existing NeRF works.

Microscopic and Biopharmaceutical Evaluation of Emulsion and Self-Emulsifying Oil with Cyclosporine.

Among the currently available commercial eye drops with cyclosporine A (Cs) there is a lack of long-acting dosage forms and products with a concentration of the drug substance higher than 0.1%, although Cs is widely used in ophthalmology. The aim of the research was to conduct the microscopic and biopharmaceutical evaluation of two formulations, an emulsion (EM) and a self-emulsifying oil (SEO), both with 0.5% of Cs, proposed for use in eye drops, and the comparison of both. SEO eye drops with Cs or any other drug substance are currently not available as marketed products, and the highest concentration of Cs in the ocular emulsion is only 0.1%. The microscopic evaluation of the emulsion and the SEO after emulsification with water was carried out using a high-resolution digital microscopy. The properties of both preparations were compared using the high dynamic range function or optical shadow effect mode. Images in the 3D composition mode were also recorded. The in vivo study of the Cs formulations was performed on male albino rabbits. The eye tolerance of the preparations was assessed using the ocular irritation test, which is a modified Draize test. Placebo carriers (without the drug substance) were also subjected to irritation testing. The concentration of Cs in the tissues (cornea and conjunctiva) and fluids (tear fluid and aqueous humor) of the rabbit eye was determined after multiple instillations of Cs-EM or Cs-SEO. The tested preparations were compared using the digital microscopy technique, which highlights the features of the formulations and eliminates the risk of unnoticeable properties that are difficult to observe in classical optical microscopy. Both tested Cs-loaded formulations are classified as practically non-irritating. There were also no significant differences when testing the placebo carriers. After a topical administration, Cs was widely distributed in all tissues (e.g., in cornea 1.3 ng/mg and 1.0 ng/mg) and fluids of the eye (e.g., in tear fluid 11.6 µg/mL and 4.3 µg/mL), after the administration of Cs-SEO and Cs-EM, respectively. The obtained results allow us to recognize both tested formulations, the emulsion and the self-emulsifying oil with 0.5% Cs content, as carriers safe for ophthalmic use and effective in delivering the drug substance to the structures of the eye.

Performance simulation and optimization of building façade photovoltaic systems under different urban building layouts

Urban building layout has an important influence on the formation of shadows on building façades, and photovoltaic (PV) power generation performance is greatly affected by shadows. However, there is limited research on detailed modeling of the power generation of photovoltaic systems on facades with different building layouts. In this paper, according to different urban building layouts, an annual dynamic shadow model of the building and a mathematical model of PV array power generation were established and validated. Then the model was used to calculate the annual power generation hour by hour, optimize the PV array topology, and analyze the shadow shading ratio and PV power generation performance of different building layouts. The results show that 95 % of the best topologies of all partially shaded façades in the four layouts are Total-Cross-Tied (Vertical and Horizontal); the highest annual shading ratio is layout 4 with a value of 60.161 %, the largest annual power generation is layout 3, but layout 4 has the highest broad generation efficiency, with layouts 1, 2, and 3 lower than it by 4.29 %, 0.20 %, and 0.11 %, respectively. This paper provides new ideas for the early design of building layouts and the performance research of PV systems.

Wind turbine dynamic shading: The effects on combined solar and wind farms

The Dutch climate agreement anticipates the large-scale implementation of solar and wind energy systems on land and water. Combining solar and wind farms has the benefit of multiple surface area use, and it also has the advantage of energy generation from both solar and wind energy systems, which is rather complementary in time; thus, a better balance can be found between electricity generation and demand and the load on the electricity grid. In combined solar and wind farms (CSWFs), the turbines will cast shadows on the solar panels. This concerns the static shadow from the construction tower of the turbine as well as the dynamic shadow caused by the rotating blades. This paper reports on the results of millisecond data monitoring of the PV farm of a CSWF in the Netherlands on land. Static and dynamic shadow effects are discussed, as well as their dependency on farm design. It is observed that the dynamic shade of the wind turbine blade causes serious disturbances of the DC inputs of the inverter, resulting in deviation of the maximum power point tracking monitored. The shadow of the wind turbine results in a total energy loss of about 6% for the given period, park configuration, PV modules, inverter type, and setting.

Pavement PV array reconfiguration strategy based on traveling salesman problem

The dynamic random vehicle shadow caused by high-speed vehicles on the pavement PV arrays not only easily induces the series and parallel mismatch of the PV array on the pavement, resulting in power loss, but also leads to the complex fast-changing multi-peak phenomenon of its output characteristics, which increases the difficulty of the maximum power point tracking control and intensifies the power loss. To address this issue, this paper proposes a novel dynamic reconfiguration strategy for pavement PV arrays based on the traveling salesman problem (TSP). In the proposed method, the optimal pavement PV array reconfiguration is transformed into a traveling salesman problem. Through comparative experiments, the simulated annealing (SA) algorithm is verified to be able to effectively solve the reconfiguration problem based on TSP. Finally, the simulation and experiment results show that the proposed reconfiguration method can reduce the number of local maximum power points, improve the global maximum power of pavement PV array, and effectively improve the power generation efficiency of the PV system.

NeLT: Object-Oriented Neural Light Transfer

This article presents object-oriented neural light transfer (NeLT), a novel neural representation of the dynamic light transportation between an object and the environment. Our method disentangles the global illumination of a scene into individual objects’ light transportation represented via neural networks, then composes them explicitly. It therefore enables flexible rendering with dynamic lighting, cameras, materials, and objects. Our rendering features various important global illumination effects, such as diffuse illumination, glossy illumination, dynamic shadowing, and indirect illumination, which completes the capability of existing neural object representation. Experiments show that NeLT does not require path tracing or shading results as input but achieves rendering quality comparable to state-of-the-art rendering frameworks, including the recent deep learning based denoisers.

MPPT control technology based on the GWO-VINC algorithm

It is a challenging task to accurately track the global maximum power point (GMPP) in a changing environment in widely used photovoltaic (PV) systems. So far, a variety of maximum power point (MPP) tracking algorithms have been used in solar PV power systems. The classical algorithm is simple and fast to track the speed effectively in a constant environment, but it can get stuck at an extreme point in a variable environment. In this paper, the variable-step incremental conductance (VINC) method is combined with the gray wolf optimization (GWO) algorithm. Firstly, GWO conducts a global search. When the search reaches the area near GMPP, the next step of the search process is carried out based on the iteration number conditions of GWO. Enter the search process of VINC and determine whether the current search process is on the left or right side of the vertex based on the current search value. And adjust the duty cycle during the VINC search process using different variable step size methods based on the left and right sides, and finally accurately locate the GMPP value. To verify the robustness of the proposed algorithm, simulation, and experimental comparisons were conducted between the proposed method in the article and GWO and VINC. The tracking efficiency of static shadows, simulated dynamic shadows, and experimental static and dynamic shadows is 99.80%, 98.82%, 99.43%, and 98.51%, respectively. The tracking time of simulation and experiment is 46.49% and 89.34% faster than GWO and VINC technologies, respectively. The results show that compared with the GWO and VINC methods, the proposed method has improved tracking speed and efficiency. Moreover, compared with the method that combines the two intelligent algorithms, this method has fewer algorithm parameters, a simple calculation process, lower complexity, lower hardware requirements, and better actual implementation performance.

Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

Under the same environmental conditions, photovoltaic arrays at different operating voltages will significantly affect the size of solar power. To obtain as much power as possible, the maximum power point tracking (MPPT) control method has been invented. Previous studies focused on the tracking problem under local shadow conditions, but the dynamic shadow problem with rapid changes in local shadow was rarely discussed. To solve the tracking problem that the maximum power point has multi-peak and fast-changing characteristics under this condition, this paper proposes a simulated annealing MPPT control method based on particle swarm optimization, which compensates the disadvantages of the memoryless Markov chain in the optimization algorithm by adaptive asymmetric memory factor, thus improving the local search ability and convergence speed in the later stage. The control effect is verified by modeling and simulation in Simulink of MATLAB, and the adaptability of the heuristic control method is reflected by simulating the tracking mode under the moving shadow condition through the actual hardware deployment on the moving car.

Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems.

Modeling and Impact Analysis for Solar Road Integration in Distribution Networks

Solar road (SR), as an emerging generation technic with increasing potential, could save valuable land resources and promote the low-carbon development of both the transport and energy sectors. However, the strong uncertainties of SR may make it challenging for the secure operation of power systems. Thus, this paper aims to investigate the influences of uncertainties of SR on the safe operation of distribution networks. To this end, a novel analytical SR generation model considering the dynamic shadow of vehicles (DSoV) and mixed traffic flow (MTF) is proposed for the first time based on solar geometry and shoelace theorem. The proposed SR generation model can directly reflect the influence of shading effects composed of different types of vehicles on SR generation. The uncertainty of MTF is modeled by improved kernel density estimation (IKDE) and Gaussian mixture model (GMM). Finally, a learning-based probabilistic power flow (PPF) is introduced for the impact assessment of SR integration, while reducing the computational complexity of PPF. Case studies performed on practical power-traffic networks with real historical data corroborate the effectiveness and scalability of the proposed method.

MPPT Control Research of Improved Gray Wolf Algorithm According to Levy Flight and Greedy Strategy

MPPT control study of improved Gray Wolf algorithm (GWO) according to Levy flight and greedy strategy: Solving the problems of maximum power point tracking (MPPT) with traditional Gray Wolf algorithm (GWO) under local shadows of photovoltaic arrays and sudden environmental changes, which is easily trapped in local optimum and has a slow convergence rate, and poor solution accuracy, a improved gray wolf algorithm (LGWO) according to Levy flight and greedy strategy is presented. It is used for the first time for maximum power point tracking under partial shadow and dynamic shadow changes of photovoltaic array. It is based on the traditional Grey Wolf algorithm (GWO), it introduces Levy’s flight search strategy, improve the algorithm of global search ability, expands search range, and filters optimal range through greedy strategy to further accelerate the convergence speed. MPPT simulation model according to Boost circuit is built using MATLAB/Simulink to verify. Experiments in the presence of partial shadows and shadow mutations. The results of simulation experiments show that the ameliorated Gray Wolf algorithm (LGWO) improves the tracking accuracy of MPPT by 0.03%, improves the convergence speed by 1.1 times and is more stable after reaching the maximum number of iterations. This verifies the feasibility and superiority of the ameliorated Grey Wolf algorithm in maximum power point tracking control.

A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array

The shadows formed by fast-moving vehicles on a pavement PV array exhibit complex dynamic random distribution characteristics, which can cause a dynamic multipeak PV curve. Dynamic vehicle shadow will cause a reduction in pavement PV power, so the question is how to maximize the power in such conditions by operating at different maximum power point (MPP) quickly and continually. To address this issue, this article proposes an MPP voltage forecasting method based on convolutional neural network (CNN). This method inputs the environmental information of pavement PV array into the proposed CNN model for learning and then uses this model to forecast the MPP voltage. Finally, simulation and experimental test with ResNet, MLP, and CNN methods are carried out and the comparison results show that this model can accurately predict the MPP voltage of pavement PV array under different vehicle shading conditions.

Modelling pavement photovoltaic arrays with cellular automata

This paper proposes a dynamic modelling of two-lane pavement photovoltaic (PV) arrays based on cellular automata theory, and the influence of random vehicle shadows on the output characteristics is explored and analysed. In the proposed model, a mathematical model of two-lane pavement PV array is established with considering the influence of bypass diodes and blocking diodes. In order to characterize the change of irradiation intensity caused by vehicle shadow, an asymmetric two-lane Nagel-Scheckenberg (ATNS) model is introduced in the driving process of vehicles. Moreover, the vehicle position at each time is obtained to further establish the randomly changing vehicle shadow matrix. The actual irradiance matrix can be obtained by combining vehicle shadow matrix with the irradiance matrix related to the external environment, and then a dynamic model of two-lane pavement PV arrays is established. To evaluate the effectiveness of the proposed model, the influence of the slowing probability and shading degree on the proposed model is analysed, where three representative scenarios are set to study the randomness of vehicle shading and its effects. Finally, simulations and experiments are conducted and the results show that the slowing probability and shading degree are the key two factors on the output characteristics of two-lane pavement PV arrays, and the dynamic random vehicle shadows significantly affect the output characteristics, resulting in a changing multi-peak state of the power-voltage curve and the global maximum power point being in a floating fast-changing state.

Daylight variation and contrast effect on human perception of spatial quality: A case study of selected contemporary residential spaces in Sri Lanka

Visual perception of space is influenced by the composition and behaviour of light. Daylight as a natural element creates different atmospheres in the same space through its variation of intensity, colour, and direction. It creates dynamic shadow patterns, fluctuating brightness, and contrast levels. Although the capacity to create variation - therefore contrast, is an important characteristic of daylight - it is often neglected in research, where comfort and visual performance take prominence. This is a research initiative to explore the effect of daylight variation on spatial quality and human perception. The research adopts a two-step process, where, digital images are first mapped to generate contrast diagrams, and secondly, the generated digital images form the basis for a survey utilising a seven-point semantic differential scale, to explore the relationship between daylight variability and human perception. The temporal variation of daylight is explored by diagramming spaces at selected times of the day and year. The scope of the study is limited to selected residential spaces as case studies and focuses on the manner of integrating daylight into spaces. Results show that the level of contrast correlates to the perception of ‘low contrast – high contrast’, ‘unvaried – varied’, ‘calming – exciting’, for the observer.

Research on the Concept of Time in Kinetic Shadow Media Art

McLuhan declared: "In the electronic age, everywhere is the center, everywhere is the edge",[1] which shows the huge impact of media on human social life. In the world constructed by media, people perceive time through media and give time meaning. Time is "alienated"; it is no longer the original external natural time, but a media time, a manifestation of the inner form of human beings. This paper takes the concept of time in moving image media art as the research object, and adopts the method of literature analysis and case analysis as the research method. Taking the influence of various art genres on the formation and development of dynamic shadow media art from the beginning of the 20th century to the present, it is found that the formation of dynamic shadow media art is influenced by Bergson's philosophy and dynamic media technology. Therefore, the author analyzes the works of individual artists and concludes that the time concept of dynamic shadow media art is a diversified, virtual and perceptual time concept.

Shadow-Price DRL: A Framework for Online Scheduling of Shared Autonomous EVs Fleets

This paper studies the online scheduling of shared autonomous electric vehicle (SAEV) fleets. The study includes charging management, routing and rebalancing strategies for SAEVs to serve the trip demands in the coupled power and transportation network (PTN). It aims to minimize the total social cost of PTN. The difficulty lies in how to deal with time-varying trip demands with the time-coupled SAEV scheduling and PTN operation considered. To address this challenge, for the first time, we propose a novel framework named the shadow-price deep reinforcement learning (shadow-price DRL), which combines the rigorous PTN operation model and the data-driven model-free DRL-based algorithm. Within the shadow-price DRL, the policy neural network adaptively learns the system dynamics and imposes its actions on the online SAEV scheduling problem as the dynamic shadow prices. By doing so, the SAEV schedule will be determined in the online manner. In addition, the connection between the proposed framework and Lagrangian Relaxation method is discussed, which illustrates the principles and effectiveness of the proposed method. The case studies include the practical Xi’an city which verifies the effectiveness of the shadow-price DRL and illustrates its significant superiority over the safe DRL and model predictive control (MPC) based methods.

Real-time Modeling of Dynamic Terrain Shadows based on Multilevel Ray Casting

Real-time Modeling of Dynamic Terrain Shadows based on Multilevel Ray Casting

Joint Tracking of Moving Target in Single-Channel Video SAR

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.