Real Input Research Articles

Self-Consumption and Self-Sufficiency Improvement for Photovoltaic System Integrated with Ultra-Supercapacitor

This research study uses a computer simulation based on real input data to examine the impact of a supercapacitor module working as a fast response energy storage unit in renewable energy systems to increase energy self-consumption and self-sufficiency. The evaluated system includes a photovoltaic system with a capacity of 3.0 kWp and between 0 and 5 supercapacitor units with a capacity of 500 F per module. The study was carried out using experimental data for electrical load, solar irradiance, and ambient temperature for the year 2020, with a 1 min temporal resolution. The daily average ambient temperature was 10.7 °C, and the daily average solar irradiance was 3.1 kWh/m2/day. It is assumed that the supercapacitor could only be charged from a photovoltaic system using renewable energy and not from the grid. The simulation results showed that using the supercapacitors to feed the short and large peaks of the electrical load significantly increases energy self-consumption and self-sufficiency. With only five supercapacitor modules, yearly energy self-sufficiency increases from 28.09% to 40.77%.

Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.

Robust mixed performance of uncertain switched systems with interval time‐varying delay by synchronous switching on signal and sampled‐data input

AbstractThe real time switching signal design and control input are developed to guarantee the passivity and performance of switched systems with interval time‐varying delay and linear fractional perturbations. For the difficulty in real time switching, a synchronous switching on signal and sampled‐data control based on sampling instant is proposed to achieve the mixed performance (passivity/H2) of system under consideration. On the other hand, the sampled‐data controls under arbitrary switching are also considered for the capture anytime and at sampling instant of switching signal, respectively. The proposed LMI conditions can be implemented numerically by Matlab toolbox. Full matrix formulation approach is used to present the main results and improve the conservativeness of the published results. New inequality and Lyapunov–Krasovskii functional are developed to achieve better proposed results in this article. Finally, some numerical examples are illustrated to show the use of our main results.

Semi-analytical solutions for seismic responses of tunnel tube in pier-supported submerged floating tunnel under vertical excitation

ABSTRACT The concept of pier-supported submerged floating tunnel (PSFT) is feasible if the water depth is within 100 m. In this study, semi-analytical solutions are proposed for determining the seismic responses of a tunnel tube in a PSFT subjected to vertical excitation. The tunnel tube consisting of tube segments and flexible joints is simplified as beam components connected through bending springs. The supporting system comprising bearings and piers is regarded as a series of discrete Kelvin elements. Seismic action is considered as the inertia force and hydrodynamic pressure is calculated by the Morison equation. The tunnel tube displacement is derived by the modal superposition method. On this basis, the semi-analytical solutions of the displacement and bending moment of the tunnel tube are deduced using the Runge–Kutta method. The finite element method is employed to examine the validity of the proposed solutions. Furthermore, the effects of the fluid, flexible joints, and damping ratio are discussed. The results show that the Froude–Klylov force can significantly reduce the response amplitude, and the hydrodynamic mass force can alter the response frequency under real seismic input motion. Flexible joints can adjust the bending moment distribution in the tunnel tube. With the adoption of flexible joints, the hogging moments at the bearings considerably decrease. The change in damping ratio also have substantial effects on the structural seismic response.

Computational framework of Various Semi-active Control Strategies for Road Vehicles Thorough Bondgraphs

Vehicle suspension system plays a vital role in diminishing the vibration caused by the road roughness and prevent it from transmitting to the driver and the passenger. The semi-active suspensions contain spring and damping elements with variable properties, which can be changed by an external control. The work presented here is concerned with semi-active damper control for vibration isolation of base disturbances. Numerous control algorithms for semi active system had been suggested in the past, performed experimentally and validated with various computational models.In this work, the 2-DOF quarter car model with semi-active suspension, controlled by skyhook and balance logic with on-off and continuous control algorithms is being studied.The computational models are subjected to various road profiles like single half sine bump, random road disturbanceas typical Indian road scenario. So that the performance can be done as real time inputs. The simulation is being carried out on Matlab or Simulink.

Feature selection to classify lameness using a smartphone-based inertial measurement unit.

Background and objectivesGait can be severely affected by pain, muscle weakness, and aging resulting in lameness. Despite the high incidence of lameness, there are no studies on the features that are useful for classifying lameness patterns. Therefore, we aimed to identify features of high importance for classifying population differences in lameness patterns using an inertial measurement unit mounted above the sacral region.MethodsFeatures computed exhaustively for multidimensional time series consisting of three-axis angular velocities and three-axis acceleration were carefully selected using the Benjamini–Yekutieli procedure, and multiclass classification was performed using LightGBM (Microsoft Corp., Redmond, WA, USA). We calculated the relative importance of the features that contributed to the classification task in machine learning.ResultsThe most important feature was found to be the absolute value of the Fourier coefficients of the second frequency calculated by the one-dimensional discrete Fourier transform for real input. This was determined by the fast Fourier transformation algorithm using data of a single gait cycle of the yaw angular velocity of the pelvic region.ConclusionsUsing an inertial measurement unit worn over the sacral region, we determined a set of features of high importance for classifying differences in lameness patterns based on different factors. This completely new set of indicators can be used to advance the understanding of lameness.

Rational loads of turbine inlet air absorption-ejector cooling systems

An increase in gas turbine efficiency is possible by inlet air cooling in chillers converting a heat of exhaust gas into refrigeration. In traditional absorption lithium-bromide chillers of a simple cycle an inlet air can be cooled to 15°С. More decrease of turbine inlet air temperature and greater fuel saving accordingly is possible in refrigerant ejector chiller as a simple in design and cheap. The innovative turbine inlet air cooling (TIC) system with absorption chiller as a high-temperature and ejector chiller as a low-temperature stages for cooling air to 7 or 10 °C is proposed. Its application in temperate climate provides annual fuel saving by 1.5 to 2 times higher compared with traditional air cooling in absorption chiller to 15 °C. A novel universal method of analysing the efficiency of TIC system operation and rational designing has been developed. The method involves the simple numerical simulation based on real input data of site actual climatic conditions. The annual fuel saving is chosen as a primary criterion. The novelty of the methodological approach consists in replacing the current yearly changeable fuel reduction due to TIC by its hour-by-hour summation as an annual fuel saving. The increment of annual fuel saving referred to needed refrigeration capacity of TIC system is used as an indicator to select a design refrigeration capacity. A rational design refrigeration capacity determined by applying the novel methodology allows to decrease the TIC system sizes by 10 to 20% compared with traditional designing issuing from the peaked thermal load during a year. So far as it was developed analytically by introducing quite reasonable criterion indicator and based on the simple summation procedure the method is quite applicable for designing in power and energy.

Evaluation of efficiency and reliability of airport processes using simulation tools

The purpose of this paper is to evaluate the efficiency of airport processes using simulation tools. A critical review of selected scientific studies relating to the performance of airport processes with respect to reliability, particularly within the apron, has been undertaken. The developed decision-making model evaluates the efficiency of airport processes in terms of minimizing penalties associated with aircraft landing before or after the scheduled landing time. The model takes into account, among other things, aircraft take-offs and landings and separation times between successive aircraft. In order to be able to verify the correctness of the decision-making model, a simulation tool was developed to support decision making in the implementation of airport operations based on a genetic algorithm. A novel development of the structure of a genetic algorithm as well as crossover and mutation operators adapted to the determination of aircraft movement routes on the apron is presented. The developed simulation tool was verified on real input data.

Production Line Virtualization Process Using Plant Simulation Tool

The constant changes in the world generate demands for improvements in processes, either by reducing costs or increasing capacity. One of the most used methods today for process optimization is Discrete Simulation. This research presents a discrete simulation application, using the Tecnomatix Plant Simulation software to simulate a production line in the Manaus Industrial Pole. Mathematical modeling made it possible to understand the parameters involved in the production process and worked as a guide for the production line's composition in the Plant Simulation environment. The production line modeled in Plant Simulation used real input data obtained in two months of production in 2020. The results obtained showed that the modeling reached the objective of virtualizing the production process, once that the differences between the simulation and the real process were at most 1.07%.

Public satisfaction with health care system in 30 countries: The effects of individual characteristics and social contexts

In this article, the associations among individual socio-economic characteristics, the institutional set-up of health care systems, and satisfaction with the health care system are investigated. Data from the 2011 International Social Survey Program (30 countries, 34,212 respondents) is used. Multilevel analyses across countries have shown how the state financing context affects satisfaction at the individual level. Consistent with previous research, at individual level, personal experiences with medical providers, age, gender as well as income are significant predictors of satisfaction with the health care system. At the country level, real input indicators such as density of physicians and density of hospital beds are negative predictors of satisfaction with the health care system whereas the percentage of total health expenditures comprised by public sources is a positive predictor of satisfaction with the health care system. However, findings from the cross-level interactions indicate that the negative effect of lower income is more prominent in predominantly publicly-funded health care systems. Specifically, in primarily publicly-funded health care systems, the model-predicted probability of satisfaction with the health care system is higher, but the gap in the probability of satisfaction with the health care system between individuals with lower income and those with higher income is greater than that in mostly privately-financed health care systems. The findings in this study suggest that the future direction of health care system reform should be focused on balancing the distribution of resources between private and public sectors.

Modelling the spatial and sectoral benefits of productivity enhancing innovations using a transport oriented multiregional IO framework: the ‘megatruck’ in Spain

We render operational the model outlined by Carter (1990) via the introduction of the research methods necessary for studying the spatial and sectoral (upstream and downstream) benefits of productivity-enhancing innovations within a real interregional input–output framework. As case study we examine the reduction in production costs derived from the adoption of longer and heavier vehicles in freight road transportation. We exploit a new Spanish regional table including a detailed disaggregation of the transportation sector. The productivity gains at the national level, resulting from a 30% reduction in transport costs, amount to 2.95% of the GVA at market prices. Results show that firms operating in this niche market appropriate most of the gross operation surplus (which increases by 10%), consistent with the existence of market power. The remaining transportation sectors see profits slightly worsened, suggesting limited substitution effects. A high regional heterogeneity exists because of the different input–output structures.

AgileGAN

Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images. Such StyleGAN-based methods have focused on finding the best latent inversion mapping for reconstructing input images; however, our key insight is that this does not lead to good generalization to different portrait styles. Hence we propose AgileGAN, a framework that can generate high quality stylistic portraits via inversion-consistent transfer learning. We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. To better capture attribute-dependent stylization of facial features, we also present an attribute-aware generator and adopt an early stopping strategy to avoid overfitting small training datasets. Our approach provides greater agility in creating high quality and high resolution (1024×1024) portrait stylization models, requiring only a limited number of style exemplars (~100) and short training time (~1 hour). We collected several style datasets for evaluation including 3D cartoons, comics, oil paintings and celebrities. We show that we can achieve superior portrait stylization quality to previous state-of-the-art methods, with comparisons done qualitatively, quantitatively and through a perceptual user study. We also demonstrate two applications of our method, image editing and motion retargeting.

Computational Framework of Various Semi-Active Control Strategies for Road Vehicles Thorough Bondgraphs

Vehicle suspension system plays a vital role in diminishing the vibration caused by the road roughness and prevent it from transmitting to the driver and the passenger. The semi-active suspensions contain spring and damping elements with variable properties, which can be changed by an external control. The work presented here is concerned with semi-active damper control for vibration isolation of base disturbances. Numerous control algorithms for semi active system had been suggested in the past, performed experimentally and validated with various computational models.In this work, the 2-DOF quarter car model with semi-active suspension, controlled by skyhook and balance logic with on-off and continuous control algorithms is being studied.The computational models are subjected to various road profiles like single half sine bump, random road disturbanceas typical Indian road scenario. So that the performance can be done as real time inputs. The simulation is being carried out on Matlab or Simulink.

An internet of things belief rule base smart system to predict earthquake

<p><span>An intelligent belief rule base (BRB) based system with internet of things (IoT) integration can evaluate earthquake prediction (EP). This ingenious and rational system can predict earthquake by aggregating changed animal behavior combined with environmental and chemical changes which are taken as real time inputs from sensors. The BRB expert system blends knowledge demonstration criterion like attribute weight, rule weight, belief degree. The intelligent BRB system with IoT predicts the probable occurrence of the earthquake in a region based on the sign and symptoms culled by the persistent sensors. The final result taken from Intelligent BRB system with IoT integration is compared with expert and fuzzy-based system. The projected method gives a better prediction than the up-to-date expert system and fuzzy system</span></p>

Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering

AbstractReconstructing the shape and appearance of real‐world objects using measured 2D images has been a long‐standing inverse rendering problem. In this paper, we introduce a new analysis‐by‐synthesis technique capable of producing high‐quality reconstructions through robust coarse‐to‐fine optimization and physics‐based differentiable rendering.Unlike most previous methods that handle geometry and reflectance largely separately, our method unifies the optimization of both by leveraging image gradients with respect to both object reflectance and geometry. To obtain physically accurate gradient estimates, we develop a new GPU‐based Monte Carlo differentiable renderer leveraging recent advances in differentiable rendering theory to offer unbiased gradients while enjoying better performance than existing tools like PyTorch3D [RRN*20] and redner [LADL18]. To further improve robustness, we utilize several shape and material priors as well as a coarse‐to‐fine optimization strategy to reconstruct geometry. Using both synthetic and real input images, we demonstrate that our technique can produce reconstructions with higher quality than previous methods.

Image to Speech Conversion using CV to Help Visually Challenged People

This work aims to assist the visually impaired people for reading a text material and detect objects in their surroundings. The input is taken in the form of an image captured from the web camera. This image is then processed either for the purpose of text reading or for object detection based on user choice. The main aim of this project is to build a system that detects objects from the image or a stream of images given to the system in the form of previously recorded video or the real time input from the camera. Bounding boxes will be drawn around the objects that are being detected by the System. The system will also classify the object to the classes the object belongs. Python programming and a machine Learning technique named yolo (you only look once) algorithm using convolutional neural network is used for the object detection. The smart blind navigation is fill gap, providing accurate and contextually rich information about the environment around the user current location, and simplifying the navigation and increasing the overall accuracy of the System. Preventing the user from dangerous locations. They have very little information on self-velocity objects, direction which is essential for travel. The navigation systems is costly which is not affordable by the common blind people. The navigation system are heavy complicated to operate.

Everyday music in infancy.

Infants enculturate to their soundscape over the first year of life, yet theories of how they do so rarely make contact with details about the sounds available in everyday life. Here, we report on properties of a ubiquitous early ecology in which foundational skills get built: music. We captured daylong recordings from 35 infants ages 6–12 months at home and fully double‐coded 467 h of everyday sounds for music and its features, tunes, and voices. Analyses of this first‐of‐its‐kind corpus revealed two distributional properties of infants’ everyday musical ecology. First, infants encountered vocal music in over half, and instrumental in over three‐quarters, of everyday music. Live sources generated one‐third, and recorded sources three‐quarters, of everyday music. Second, infants did not encounter each individual tune and voice in their day equally often. Instead, the most available identity cumulated to many more seconds of the day than would be expected under a uniform distribution. These properties of everyday music in human infancy are different from what is discoverable in environments highly constrained by context (e.g., laboratories) and time (e.g., minutes rather than hours). Together with recent insights about the everyday motor, language, and visual ecologies of infancy, these findings reinforce an emerging priority to build theories of development that address the opportunities and challenges of real input encountered by real learners.

Robust structured controller synthesis for interleaved boost converters using an H∞ control method

The regulation of output voltage and equivalent distribution of phase currents of multi-phase converters which have non-minimum phase characteristic are still challenges, especially in the presence of uncertainties in real parameters, duty cycle, input voltage, and load disturbances. However, in classical third-order integral-lead (Type-III) controller design methodologies, the controller is synthesized considering only the nominal performance conditions. This paper proposes a structured [Formula: see text] synthesis framework based on an optimization methodology to the design of a robust Type-III controller for interleaved boost converters. The structured [Formula: see text] control approach is adapted for optimization of Type-III feedback and feedforward controllers in two-degree-of-freedom (2-DOF) control system configuration. The robust stability of the closed-loop interleaved boost converter system against model uncertainties is ensured via the classical [Formula: see text]-analysis technique. Numerical comparisons are made among the classical, i.e. unstructured or full order, [Formula: see text]-based controller design method, a dual-loop PI controller, and proposed 1-DOF and 2-DOF structured controller synthesis approaches on an interleaved boost converter model. Simulation results verify the effectiveness and advantages of the proposed approach from the viewpoint of the output voltage regulation under different disturbance points.

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

Objective : To develop crop recommendation system depending on location specific soil and climatic conditions. Method: The study introduces a novel recommendation system which uses Artificial Neural Networks (ANN) for recommending the suitable crop. The crops are recommended based on (a) Soil properties (b) Crop characteristics (c) Climate parameters. The crops namely maize, Finger millet, Rice and sugarcane is considered for the study. Depending on degree of relationship and limitations of the factors considered, following suitability classes are established: (a) Highly suitable: S1 (b) Moderately suitable: S2 (c) Marginally suitable: S3 (d) not suitable. The system uses the climate data from Meteorological survey of India and the soil data of Hadonahalli and Durgenahalli of Doddaballapur (dist.), Karnataka, India. The user interface developed takes the location specific soil properties as real time input and recommends the suitable crop considering the input and climate parameters. Findings: For the measurement of accuracy the model was tested on with ANN and decision tree. Overall accuracy value of ANN is 96% where the accuracy value of Decision tree is 91.5%. Hence the results obtained from ANN can be considered more efficient. Novelty: The number of models developed for crop recommendation is limited and the proposed model serves as the promising aspect in the planning of crops. Keywords: Crop recommendation; ANN; Soil characters; Climate; MongoDB

Bit complexity of computing solutions for symmetric hyperbolic systems of PDEs with guaranteed precision

We establish upper bounds on bit complexity of computing solution operators for symmetric hyperbolic systems of PDEs, combining symbolic and approximate algorithms to obtain the solutions with guaranteed prescribed precision. Restricting to algebraic real inputs allows us to use the classical (“discrete”) bit complexity concept.