Modelling, Simulation, and Implementation of Effective Controller for KY Stepping Up Converter

This paper studies on a new Hybrid Posicast Control (HPC) for Fundamental KY Boost Converter (FKYBC) worked in Continuous Current Mode (CCM). Posicast is a feed-forward compensator. It reduces the overshoot in the step result of the flippantly damped plant. But the conventional controller approach is sensitive owing to the changes in the natural frequency. So, as to reduce this undesirable sensitivity and load potential control of FKYBC, a HPC is designed in this article. Structure of HPC is posicast with feedback loop. The independent computational time delay is the main design function of the posicast. The enactment of the FKYBC with HPC is confirmed at various operating regions by making the MATLAB/Simulink and experimental model. The posicast function values are implemented in Arduino Uno-ATmega328P microcontroller. The results of new HPC have produced minimal noise in control signal in comparison with traditional PID control.

Features of Adjusting the Frozen Soil Properties Using Borehole Temperature Measurements

The paper examines the theoretical issues of using borehole temperature survey data to control a frozen wall formed around the sinking mine shafts of the Nezhinsk mining and processing plant potash mine. We consider adjusting the parameters of the mathematical model of the frozen soil based on temperature measurements in boreholes. Adjustment of the parameters of the mathematical model (thermophysical properties of the soil) is usually carried out by minimizing the discrepancy functional between the experimentally measured and model temperatures in the temperature control boreholes. An important question about the form of this functional and the existence of minima remained after the previous studies. The study aimed at this question included analysis of heat transfer in two horizontal layers (sand and chalk) for two shafts under construction using artificial ground freezing. It was shown that the discrepancy functional minimum under certain conditions moves over time or is nonunique. This phenomenon results in ambiguity in adjusting the mathematical model parameters in the frozen soil to fit the borehole temperature survey data. At the stage of the frozen wall growth, the effective thermal conductivity in the frozen zone can be determined ambiguously from the temperature measurements in the boreholes—its value can change over time. At the stage of maintaining the frozen wall, the solution turns out to be dependent on the ratio of effective thermal conductivities in the frozen and unfrozen zones.

Optimization of Subsurface Smart Irrigation System for Sandy Soils of Arid Climate

The Arabian Peninsula is an arid zone with a hot desert climate and severe water scarcity. The low humidity, elevated ambient temperatures, and high evaporation rates in the region deemed conventional surface irrigation unsustainable. The IoT-based subsurface smart irrigation systems can be essentially developed for these regions to avoid surface evaporation losses. In this research, the sandy soil conditions of western Saudi Arabia have been considered in numerical simulations to evaluate the performance of a subsurface smart irrigation system. The influence zone of saturation generated by subsurface diffusers in the target root region has been analysed for two different types of sandy soils. The simulation results generated by the COMSOL Multiphysics program reveal that the subsurface smart irrigation system can be effectively applied to simultaneously manage the target root zone at the ideal saturated conditions and prevent surface evaporation losses.

Parallel Algorithms of Well-Balanced and Weighted Average Flux for Shallow Water Model Using CUDA

Compute Unified Device Architecture (CUDA) implementations are presented of a well-balanced finite volume method for solving a shallow water model. The CUDA platform allows programs to run parallel on GPU. Four versions of the CUDA algorithm are presented in addition to a CPU implementation. Each version is improved from the previous one. We present the following techniques for optimizing a CUDA program: limiting register usage, changing the global memory access pattern, and using loop unroll. The accuracy of all programs is investigated in 3 test cases: a circular dam break on a dry bed, a circular dam break on a wet bed, and a dam break flow over three humps. The last parallel version shows 3.84x speedup over the first CUDA implementation. We use our program to simulate a real-world problem based on an assumed partial breakage of the Srinakarin Dam located in Kanchanaburi province, Thailand. The simulation shows that the strong interaction between massive water flows and bottom elevations under wet and dry conditions is well captured by the well-balanced scheme, while the optimized parallel program produces a 57.32x speedup over the serial version.

Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training

The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.

A Classical LTE Cellular System Simulator for Computer Network Education

The proposal of LTE in the standardization of cellular network systems has received considerable attention in the research domain, and most subscribers widely use it. Despite the enormous acceptance of the system, academia as an industry is usually disadvantaged in training students due to the cost implication in setting up a prototype. In bridging this gap, simulators are traditionally developed as a testbed to aid students appreciate how these systems work. Although there are several simulators available on the market, these simulators are quite expensive to acquire while others come with license restrictions. In this study, a classical LTE cellular system simulator is proposed as a testbed to aid the education of computer networks at college. The proposed simulator is an extension of the functionality of LTE-Sim frameworks. Usability testing of the proposed study reveals that the system is much easier to simulate the various scenarios in wireless communication.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.

Digital Modeling of Heat Transfer during the Baking Process

Numerical modeling and analysis of the baking process are challenging biochemical processes occurring in bread. These changes result from mass engineering tasks, usually characterized by the complex chain of chemical, physical, and heat transfer processes impacting the baking at the same time primarily caused by a variation of two dominating factors: (i) the heat and (ii) the internal moisture content at different temperatures and during the time’s process. This study presents an analysis of the 1-D computational fluid dynamics model for simultaneous heat transfer within a cylindrical bread sample. The numerical simulations were performed using the finite difference model (FDM) and the finite element model (FEM). In the first case, the proposed numerical model considered radiation and convection during sample heating and described the sample’s simultaneous heat, water, and vapor diffusion mechanisms. The calculations indicated that the FDM was susceptible to the time step; consequently, the range of 10 s and 100 s yielded the only relevant results. In the second case, the FEM was used to describe the phenomena of transportation during baking. Results obtained by the FEM showed a large temperature gradient near the surface. The study showed the presence of some critical cases that are considered the most influential on the stages of bread production. The first critical value is the time when the baking temperature reaches 100° C. The second critical value is the time when the liquid water content in the baking medium reaches its peak. The boundary conditions were examined and illustrated by figures in the center and the surface of the bread.

Novel Nonlinear Control and Optimization Strategies for Hybrid Renewable Energy Conversion System

This article deals with a hybrid renewable energy conversion system (HRECS) interconnected to the three-phase grid in association with their power conversion components, i.e., AC/DC rectifier and DC/AC inverter. The HRECS is built around a permanent magnet synchronous wind turbine generator and a photovoltaic energy conversion system. Comparing to traditional control methods, a new multiobjective control strategy is developed to enhance system performances. This makes it possible to account in addition to optimal turbine speed regulation and PV-MPPT and three other important control objectives such as DC-link voltage regulation and the injected reactive power in the grid. To achieve these objectives, a novel control strategy is developed, based on a nonlinear model of the whole “converters-generators” association. The robustness and the stability analysis of the system have been proved using the Lyapunov theory and precisely the backstepping control and the sliding mode control. The performances of the proposed controllers are formally analyzed with respect to standard control solutions illustrated through simulation.