Search results for: automatic flight control

Authors: Francesco La Torre, Angela Di Cesare, Donato Traversa

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The mosquito-transmitted nematode Dirofilaria repens is the causative agent of subcutaneous filariosis in dogs, other animals and humans. Adults and circulating microfilariae may cause different forms of skin conditions, and various allergic reactions. The infection is distributed in several countries and spreading in several areas of Europe. The control of D. repens is pivotal to reduce the transmission in dogs and to minimize the risk of infection in humans, but only few information is available for the chemoprevention of subcutaneous filariosis of dogs. A recent clinical field study showed the efficacy and safety of a monthly administration of an oral formulation containing milbemycin oxime (Milbemax®, Novartis Animal Health) in the chemoprevention of D. repens infection in dogs. Most recent and focused insights into epidemiology and control of zoonotic canine subcutaneous filariosis are here discussed.

Keywords: Dirofilaria repens, epidemiology, zoonosis, control

Procedia PDF Downloads 726

Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Trishen Naidoo, Anthony Walker, Shaniel Davrajh, Glen Bright

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An investigation into on-line inspection was performed where research is focused on the use of varied inspection (as opposed to 100% inspection) for mass customization (MC). Manufacturers need new methods for quality control in mass customization, and these methods need to address some of the old problems such as over-inspection and bottlenecking. Due to the risks of varied inspection, many manufacturers do not implement it and rather opt for sampling methods. However, there are many advantages of varied inspection and can have applications in mass customization. A control system incorporating fuzzy logic (FL) control is used to perform the variations in inspection usage in a simulated environment. The proposed system can have a key impact in appraisal costs reduction and possibly work-in-process reduction in high variety environments.

Keywords: appraisal costs, fuzzy logic, quality control, work-in-process

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Authors: Chivon Choeung, Heng Tang, Panha Soth, Vichet Huy

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This paper presents a robust control strategy for a single-phase DC-AC inverter with an output LC-filter. An all-pass filter is utilized to create an artificial β-signal so that the proposed controller can be simply used in dq-synchronous frame. The proposed robust controller utilizes a state feedback control with integral action in the dq-synchronous frame. A linear matrix inequality-based optimization scheme is used to determine stabilizing gains of the controllers to maximize the convergence rate to steady state in the presence of uncertainties. The uncertainties of the system are described as the potential variation range of the inductance and resistance in the LC-filter.

Keywords: single-phase inverter, linear matrix inequality, robust control, all-pass filter

Procedia PDF Downloads 123

Authors: Miguel Contreras, David Long, Will Bachman

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Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

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Authors: Wei Liu, Xionghui Zhou, Qiang Niu, Yunhao Ni

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3D part library is an ideal approach to reuse the existing design and thus facilitates the modeling process, which will enhance the efficiency. In this paper, we implemented the thought on the SolidWorks platform. The system supports the functions of type and parameter selection, 3D template driving and part assembly. Finally, BOM is exported in Excel format. Experiment shows that our method can satisfy the requirement of die and mold designers.

Keywords: part library, SolidWorks, automatic assembly, intelligent

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: Qiuling Xie, Shanliang Zhu, Zongdi Sun

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In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.

Keywords: grey relational degree, multiple linear regression, membership function, nonlinear programming

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Authors: M. J. Shofa, A. Hidayatno, O. M. Armand

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MRP as a production control system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic. Demand-Driven MRP (DDMRP) is a new approach for inventory control system, and it deals with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP work for a long lead time and uncertain demand in terms of on-hand inventory levels. The evaluation is conducted through a discrete event simulation using purchased part data from an automotive company. The result is MRP gives 50,759 pcs / day while DDMRP gives 34,835 pcs / day (reduce 32%), it means DDMRP is more effective inventory control than MRP in terms of on-hand inventory levels.

Keywords: Demand-Driven MRP, long lead time, MRP, uncertain demand

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Authors: A. Hassoune, M. Khafallah, A. Mesbahi, T. Bouragba

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This work presents an improved strategy of control for charging a lithium-ion battery in an electric vehicle charging station using two charger topologies i.e. single ended primary inductor converter (SEPIC) and forward converter. In terms of rapidity and accuracy, the power system consists of a topology/control diagram that would overcome the performance constraints, for instance the power instability, the battery overloading and how the energy conversion blocks would react efficiently to any kind of perturbations. Simulation results show the effectiveness of the proposed topologies operated with a power management algorithm based on voltage/peak current mode controls. In order to provide credible findings, a low power prototype is developed to test the control strategy via experimental evaluations of the converter topology and its controls.

Keywords: battery storage buffer, charging station, electric vehicle, experimental analysis, management algorithm, switches control

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Authors: C. A. Anser Pasha

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In this paper, a new Vehicular Speed Detection Camera System that is applicable as an alternative to traditional radars with the same accuracy or even better is presented. The real-time measurement and analysis of various traffic parameters such as speed and number of vehicles are increasingly required in traffic control and management. Image processing techniques are now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on image processing techniques have been applied to detect multiple vehicles and track them. The SDCS processes can be divided into three successive phases; the first phase is Objects detection phase, which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction. The second phase is Objects tracking, which consists of three successive operations - object segmentation, object labeling, and object center extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like simple tracking, the object has left the scene, the object has entered the scene, object crossed by another object, and object leaves and another one enters the scene. The third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass by the scene.

Keywords: radar, image processing, detection, tracking, segmentation

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Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

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In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, gaussian mixture models, learning motion patterns, tracked robots for urban search and rescue

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Authors: Pankaj Negi

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Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power. Custom power instruments are of the most well-known compensators of power quality in distributed network. This paper present a comprehensive review of compensating custom power devices mainly DSTATCOM (distribution static compensator),DVR (dynamic voltage restorer), and UPQC (unified power quality compensator) and also deals with sliding mode control and its applications to custom power devices. The sliding mode control strategy provides robustness to custom power device and enhances the dynamic response for compensating voltage sag, swell, voltage flicker, and voltage harmonics. The aim of this paper is to provide a broad perspective on the status of compensating devices in electric power distribution system and sliding mode control strategies to researchers and application engineers who are dealing with power quality and stability issues.

Keywords: active power filters(APF), custom power device(CPD), DSTATCOM, DVR, UPQC, sliding mode control (SMC), power quality

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Authors: Timothee Gidenne, Xia Pinqi

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In this paper, an investigation of the use of carbon nanotubes (CNTs) reinforced polymer as an actuator for an active flutter suppression to counter the flutter phenomena is conducted. The goal of this analysis is to establish a link between the behavior of the control surface and the actuators to demonstrate the veracity of using such a suppression system for the aeronautical field. A preliminary binary flutter model using simplified unsteady aerodynamics is developed to study the behavior of the wing while reaching the flutter speed and when the control system suppresses the flutter phenomena. The Timoshenko beam theory for bilayer materials is used to match the response of the control surface with the CNTs reinforced polymer (CNRP) actuators. According to Timoshenko theory, results show a good and realistic response for such a purpose. Even if the results are still preliminary, they show evidence of the potential use of CNRP for control surface actuation for the small-scale and lightweight system.

Keywords: actuators, aeroelastic, aeroservoelasticity, carbon nanotubes, flutter, flutter suppression

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Authors: Le Fu, Rui Wu, Gang Feng Liu, Jie Zhao

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Normally, impedance control methods are based on a model that connects a spring and damper in parallel. The series connection, namely the Maxwell model, has emerged as a counterpart and draw the attention of robotics researchers. In the theoretical analysis, it turns out that the two pattern are both equivalents to some extent, but notable differences of response characteristics exist, especially in the effect of damping viscosity. However, this novel impedance control design is lack of validation on realistic robot platforms. In this study, stability analysis and experimental evaluation are achieved using a 3-fingered Barrett® robotic hand BH8-282 endowed with tactile sensing, mounted on a torque-controlled lightweight and collaborative robot KUKA® LBR iiwa 14 R820. Object handover and incoming objects catching tasks are executed for validation and analysis. Experimental results show that the series connection pattern has much better performance in natural impact or shock absorption, which indicate promising applications in robots’ safe and physical interaction with humans and objects in various environments.

Keywords: impedance control, Maxwell model, force control, dexterous manipulation

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Authors: Edwin Javier Cortes, Surupa Shaw

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In recent years, there has been a growing emphasis on enhancing the efficiency and performance of wind and water turbines to meet the increasing demand for sustainable energy sources. One promising approach is the utilization of advanced flow control techniques to optimize aerodynamic performance. This paper explores the application of advanced flow control techniques in both wind and water turbines, aiming to maximize their efficiency and output. By manipulating the flow of air or water around the turbine blades, these techniques offer the potential to improve energy capture, reduce drag, and minimize turbulence-induced losses. The paper will review various flow control strategies, including passive and active techniques such as vortex generators, boundary layer suction, and plasma actuators. It will examine their effectiveness in optimizing turbine performance under different operating conditions and environmental factors. Furthermore, the paper will discuss the challenges and opportunities associated with implementing these techniques in practical turbine designs. It will consider factors such as cost-effectiveness, reliability, and scalability, as well as the potential impact on overall turbine efficiency and lifecycle. Through a comprehensive analysis of existing research and case studies, this paper aims to provide insights into the potential benefits and limitations of advanced flow control techniques for wind and water turbines. It will also highlight areas for future research and development, with the ultimate goal of advancing the state-of-the-art in turbine technology and accelerating the transition towards a more sustainable energy future.

Keywords: flow control, efficiency, passive control, active control

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Authors: Shang-Yu Chiang, Yu-Shan Tsai, Shih-Hsien Sung, Chung-Ming Lo

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Mitral regurgitation (MR) is a heart disorder which the mitral valve does not close properly when the heart pumps out blood. MR is the most common form of valvular heart disease in the adult population. The diagnostic echocardiographic finding of MR is straightforward due to the well-known clinical evidence. In the determination of MR severity, quantification of sonographic findings would be useful for clinical decision making. Clinically, the vena contracta is a standard for MR evaluation. Vena contracta is the point in a blood stream where the diameter of the stream is the least, and the velocity is the maximum. The quantification of vena contracta, i.e. the vena contracta width (VCW) at mitral valve, can be a numeric measurement for severity assessment. However, manually delineating the VCW may not accurate enough. The result highly depends on the operator experience. Therefore, this study proposed an automatic method to quantify VCW to evaluate MR severity. Based on color Doppler ultrasound, VCW can be observed from the blood flows to the probe as the appearance of red or yellow area. The corresponding brightness represents the value of the flow rate. In the experiment, colors were firstly transformed into HSV (hue, saturation and value) to be closely align with the way human vision perceives red and yellow. Using ellipse to fit the high flow rate area in left atrium, the angle between the mitral valve and the ultrasound probe was calculated to get the vertical shortest diameter as the VCW. Taking the manual measurement as the standard, the method achieved only 0.02 (0.38 vs. 0.36) to 0.03 (0.42 vs. 0.45) cm differences. The result showed that the proposed automatic VCW extraction can be efficient and accurate for clinical use. The process also has the potential to reduce intra- or inter-observer variability at measuring subtle distances.

Keywords: mitral regurgitation, vena contracta, color doppler, image processing

Procedia PDF Downloads 356

Authors: Najib K. Dankadai, Ahmad 'Athif Mohd Faudzi, Khairuddin Osman, Muhammad Rusydi Muhammad Razif, IIi Najaa Aimi Mohd Nordin

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Researches in soft actuators are now growing rapidly because of their adequacy to be applied in sectors like medical, agriculture, biological and welfare. This paper presents system identification (SI) and control of the force generated by a two chambers pneumatic soft actuator (PSA). A force mathematical model for the actuator was identified experimentally using data acquisition card and MATLAB SI toolbox. Two control techniques; a predictive functional control (PFC) and conventional proportional integral and derivative (PID) schemes are proposed and compared based on the identified model for the soft actuator flexible mechanism. Results of this study showed that both of the proposed controllers ensure accurate tracking when the closed loop system was tested with the step, sinusoidal and multi step reference input through MATLAB simulation although the PFC provides a better response than the PID.

Keywords: predictive functional control (PFC), proportional integral and derivative (PID), soft actuator, system identification

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Authors: Getachew Tilahun, Oluwole Makinde, David Malonza

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We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically.

Keywords: cost effectiveness analysis, optimal control, pneumonia dynamics, stability analysis, numerical simulation

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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Authors: Habtemariam Alemu

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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

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Authors: A. Yanik, U. Aldemir

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This study investigates the benefits of implementing the semi-active devices in relation to passive viscous damping in the context of seismically isolated bridge structures. Since the intrinsically nonlinear nature of semi-active devices prevents the direct evaluation of Laplace transforms, frequency response functions are compiled from the computed time history response to sinusoidal and pulse-like seismic excitation. A simple semi-active control policy is used in regard to passive linear viscous damping and an optimal non-causal semi-active control strategy. The control strategy requires optimization. Euler-Lagrange equations are solved numerically during this procedure. The optimal closed-loop performance is evaluated for an idealized controllable dash-pot. A simplified single-degree-of-freedom model of an isolated bridge is used as numerical example. Two bridge cases are investigated. These cases are; bridge deck without the isolation bearing and bridge deck with the isolation bearing. To compare the performances of the passive and semi-active control cases, frequency dependent acceleration, velocity and displacement response transmissibility ratios T_a(w), T_v(w), and T_d(w) are defined. To fully investigate the behavior of the structure subjected to the sinusoidal and pulse type excitations, different damping levels are considered. Numerical results showed that, under the effect of external excitation, bridge deck with semi-active control showed better structural performance than the passive bridge deck case.

Keywords: bridge structures, passive control, seismic, semi-active control, viscous damping

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Authors: Azam Salimi, Majid Delshad

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This paper presents a modified design of a sliding mode controller based on fuzzy logic for a New ZVThigh step up DC-DC Converter . Here a proportional - integral (PI)-type current mode control is employed and a sliding mode controller is designed utilizing fuzzy algorithm. Sliding mode controller guarantees robustness against all variations and fuzzy logic helps to reduce chattering phenomenon due to sliding controller, in that way efficiency increases and error, voltage and current ripples decreases. The proposed system is simulated using MATLAB / SIMULINK. This model is tested under variations of input and reference voltages and it was found that in comparison with conventional sliding mode controllers they perform better.

Keywords: switching mode power supplies, DC-DC converters, sliding mode control, robustness, fuzzy control, current mode control, non-linear behavior

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Lochan Basyal

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Global Automation is an emerging technology of today’s era and is based on Internet of Things (IoT). Global automation deals with the controlling of electrical appliances throughout the world. The fabrication of this system has been carried out with interfacing an electrical control system module to Raspberry Pi. An electrical control system module includes a relay driver mechanism through which appliances are controlled automatically in respective condition. In this research project, one email ID has been assigned to Raspberry Pi, and the users from different location having different email ID can mail to Raspberry Pi on assigned email address “[email protected]” with subject heading “Device Control” with predefined command on compose email line. Also, a notification regarding current working condition of this system has been updated on respective user email ID. This approach is an innovative way of implementing smart automation system through which a user can control their electrical appliances like light, fan, television, refrigerator, etc. in their home with the use of email facility. The development of this project helps to enhance the concept of smart home application as well as industrial automation.

Keywords: control circuit, e-mail, global automation, internet of things, IOT, Raspberry Pi

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Authors: Izabella Szollosi, Klara Marton

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Aphasia can be defined as a weakness in accessing linguistic information. Accessing linguistic information is strongly related to information processing, which in turn is associated with the cognitive control system. According to the literature, a deficit in the cognitive control system interferes with language processing and contributes to non-fluent speech performance. The aim of our study was to explore this hypothesis by investigating how cognitive control interacts with language performance in participants with non-fluent aphasia. Cognitive control is a complex construct that includes working memory (WM) and the ability to resist proactive interference (PI). Based on previous research, we hypothesized that impairments in domain-general (DG) cognitive control abilities have negative effects on language processing. In contrast, better DG cognitive control functioning supports goal-directed behavior in language-related processes as well. Since stroke itself might slow down information processing, it is important to examine its negative effects on both cognitive control and language processing. Participants (N=52) in our study were individuals with non-fluent Broca’s aphasia (N = 13), with transcortical motor aphasia (N=13), individuals with stroke damage without aphasia (N=13), and unimpaired speakers (N = 13). All participants performed various computer-based tasks targeting cognitive control functions such as WM and resistance to PI in both linguistic and non-linguistic domains. Non-linguistic tasks targeted primarily DG functions, while linguistic tasks targeted more domain specific (DS) processes. The results showed that participants with Broca’s aphasia differed from the other three groups in the non-linguistic tasks. They performed significantly worse even in the baseline conditions. In contrast, we found a different performance profile in the linguistic domain, where the control group differed from all three stroke-related groups. The three groups with impairment performed more poorly than the controls but similar to each other in the verbal baseline condition. In the more complex verbal PI condition, however, participants with Broca’s aphasia performed significantly worse than all the other groups. Participants with Broca’s aphasia demonstrated the most severe language impairment and the highest vulnerability in tasks measuring DG cognitive control functions. Results support the notion that the more severe the cognitive control impairment, the more severe the aphasia. Thus, our findings suggest a strong interaction between cognitive control and language. Individuals with the most severe and most general cognitive control deficit - participants with Broca’s aphasia - showed the most severe language impairment. Individuals with better DG cognitive control functions demonstrated better language performance. While all participants with stroke damage showed impaired cognitive control functions in the linguistic domain, participants with better language skills performed also better in tasks that measured non-linguistic cognitive control functions. The overall results indicate that the level of cognitive control deficit interacts with the language functions in individuals along with the language spectrum (from severe to no impairment). However, future research is needed to determine any directionality.

Keywords: cognitive control, information processing, language performance, non-fluent aphasia

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Authors: Melkamu Tadesse Getachew

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Intelligent control systems have revolutionized biomedical engineering, advancing research and enhancing medical practice. This review paper examines the impact of intelligent control on various aspects of biomedical engineering. It analyzes how these systems enhance precision and accuracy in biomedical instrumentation, improving diagnostics, monitoring, and treatment. Integration challenges are addressed, and potential solutions are proposed. The paper also investigates the optimization of drug delivery systems through intelligent control. It explores how intelligent systems contribute to precise dosing, targeted drug release, and personalized medicine. Challenges related to controlled drug release and patient variability are discussed, along with potential avenues for overcoming them. The comparison of algorithms used in intelligent control systems in biomedical control is also reviewed. The implications of intelligent control in computational and systems biology are explored, showcasing how these systems enable enhanced analysis and prediction of complex biological processes. Challenges such as interpretability, human-machine interaction, and machine reliability are examined, along with potential solutions. Intelligent control in biomedical engineering also plays a crucial role in risk management during surgical operations. This section demonstrates how intelligent systems improve patient safety and surgical outcomes when integrated into surgical robots, augmented reality, and preoperative planning. The challenges associated with these implementations and potential solutions are discussed in detail. In summary, this review paper comprehensively explores the widespread impact of intelligent control on biomedical engineering, showing the future of human health issues promising. It discusses application areas, challenges, and potential solutions, highlighting the transformative potential of these systems in advancing research and improving medical practice.

Keywords: Intelligent control systems, biomedical instrumentation, drug delivery systems, robotic surgical instruments, Computational monitoring and modeling

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Authors: M. R. Vaezi, R. Ghasemi, A. Akramizadeh

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Frequency stability of microgrids under islanded operation attracts particular attention recently. A new cooperative frequency control strategy based on centralized multi-agent system (CMAS) is proposed in this study. On this strategy, agents sent data and furthermore each component has its own to center operating decisions (MGCC). After deciding on the information, they are returned. Frequency control strategies include primary and secondary frequency control and disposal of multi-stage load in which this study will also provide a method and algorithm for load shedding. This could also be a big problem for the performance of micro-grid in times of disaster. The simulation results show the promising performance of the proposed structure of the controller based on multi agent systems.

Keywords: frequency control, islanded microgrid, multi-agent system, load shedding

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Authors: Yang Li, Xiaorong Guan, Cheng Xu

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In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload

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Authors: Shiuh-Jer Huang, Yun-Han Yeh

Abstract:

A four-wheel drive electric vehicle was built with hub DC motors and FPGA embedded control structure. A 40 steps manual adjusting motorcycle shock absorber was refitted with DC motor driving mechanism to construct as a semi-active suspension system. Accelerometer and potentiometer sensors are installed to measure the sprung mass acceleration and suspension system compression or rebound states for control purpose. An intelligent fuzzy logic controller was proposed to real-time search appropriate damping ratio based on vehicle running condition. Then, a robust fuzzy sliding mode controller (FSMC) is employed to regulate the target damping ratio of each wheel axis semi-active suspension system. Finally, different road surface conditions are chosen to evaluate the control performance of this semi-active suspension and compare with that of passive system based on wheel axis acceleration signal.

Keywords: acceleration, FPGA, Fuzzy sliding mode control, semi-active suspension

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