Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 124

World Academy of Science, Engineering and Technology

[Electrical and Information Engineering]

Online ISSN : 1307-6892

124 Blockchain Solutions for IoT Challenges: Overview

Authors: Amir Ali Fatoorchi

Abstract:

Regardless of the advantage of LoT devices, they have limitations like storage, compute, and security problems. In recent years, a lot of Blockchain-based research in IoT published and presented. In this paper, we present the Security issues of LoT. IoT has three levels of security issues: Low-level, Intermediate-level, and High-level. We survey and compare blockchain-based solutions for high-level security issues and show how the underlying technology of bitcoin and Ethereum could solve IoT problems.

Keywords: Blockchain, security, data security, IoT

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123 Efficient Neural and Fuzzy Models for the Identification of Dynamical Systems

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

Abstract:

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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122 Small Fixed-Wing UAV Physical Based Modeling, Simulation, and Validation

Authors: Ebrahim H. Kapeel, Ehab Safwat, Hossam Hendy, Ahmed M. Kamel, Yehia Z. Elhalwagy

Abstract:

Motivated by the problem of the availability of high-fidelity flight simulation models for small unmanned aerial vehicles (UAVs). This paper focuses on the geometric-mass inertia modeling and the actuation system modeling for the small fixed-wing UAVs. The UAV geometric parameters for the body, wing, horizontal and vertical tail are physically measured. Pendulum experiment with high-grade sensors and data analysis using MATLAB is used to estimate the airplane moment of inertia (MOI) model. Finally, UAV’s actuation system is modeled by estimating each servo transfer function by using the system identification, which uses experimental measurement for input and output angles through using field-programmable gate array (FPGA). Experimental results for the designed models are given to illustrate the effectiveness of the methodology. It also gives a very promising result to finalize the open-loop flight simulation model through modeling the propulsion system and the aerodynamic system.

Keywords: unmanned aerial vehicle, geometric-mass inertia model, system identification, Simulink

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121 AI-Enabled Smart Contracts for Reliable Traceability in the Industry 4.0

Authors: Harris Niavis, Dimitra Politaki

Abstract:

The manufacturing industry was collecting vast amounts of data for monitoring product quality thanks to the advances in the ICT sector and dedicated IoT infrastructure is deployed to track and trace the production line. However, industries have not yet managed to unleash the full potential of these data due to defective data collection methods and untrusted data storage and sharing. Blockchain is gaining increasing ground as a key technology enabler for Industry 4.0 and the smart manufacturing domain, as it enables the secure storage and exchange of data between stakeholders. On the other hand, AI techniques are more and more used to detect anomalies in batch and time-series data that enable the identification of unusual behaviors. The proposed scheme is based on smart contracts to enable automation and transparency in the data exchange, coupled with anomaly detection algorithms to enable reliable data ingestion in the system. Before sensor measurements are fed to the blockchain component and the smart contracts, the anomaly detection mechanism uniquely combines artificial intelligence models to effectively detect unusual values such as outliers and extreme deviations in data coming from them. Specifically, Autoregressive integrated moving average, Long short-term memory (LSTM) and Dense-based autoencoders, as well as Generative adversarial networks (GAN) models, are used to detect both point and collective anomalies. Towards the goal of preserving the privacy of industries' information, the smart contracts employ techniques to ensure that only anonymized pointers to the actual data are stored on the ledger while sensitive information remains off-chain. In the same spirit, blockchain technology guarantees the security of the data storage through strong cryptography as well as the integrity of the data through the decentralization of the network and the execution of the smart contracts by the majority of the blockchain network actors. The blockchain component of the Data Traceability Software is based on the Hyperledger Fabric framework, which lays the ground for the deployment of smart contracts and APIs to expose the functionality to the end-users. The results of this work demonstrate that such a system can increase the quality of the end-products and the trustworthiness of the monitoring process in the smart manufacturing domain. The proposed AI-enabled data traceability software can be employed by industries to accurately trace and verify records about quality through the entire production chain and take advantage of the multitude of monitoring records in their databases.

Keywords: blockchain, data quality, industry4.0, product quality

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120 Penguins Search Optimization Algorithm for Chaotic Synchronization System

Authors: Sofiane Bououden, Ilyes Boulkaibet

Abstract:

In terms of security of the information signal, the meta-heuristic Penguins Search Optimization Algorithm (PeSOA) is applied to synchronize chaotic encryption communications in the case of sensitive dependence on initial conditions in chaotic generator oscillator. The objective of this paper is the use of the PeSOA algorithm to exploring search space with random and iterative processes for synchronization of symmetric keys in both transmission and reception. Simulation results show the effectiveness of the PeSOA algorithm in generating symmetric keys of the encryption process and synchronizing.

Keywords: meta-heuristic, PeSOA, chaotic systems, encryption, synchronization optimization

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119 A Robust Model Predictive Control for a Photovoltaic Pumping System Subject to Actuator Saturation Nonlinearity and Parameter Uncertainties: A Linear Matrix Inequality Approach

Authors: Sofiane Bououden, Ilyes Boulkaibet

Abstract:

In this paper, a robust model predictive controller (RMPC) for uncertain nonlinear system under actuator saturation is designed to control a DC-DC buck converter in PV pumping application, where this system is subject to actuator saturation and parameter uncertainties. The considered nonlinear system contains a linear constant part perturbed by an additive state-dependent nonlinear term. Based on the saturating actuator property, an appropriate linear feedback control law is constructed and used to minimize an infinite horizon cost function within the framework of linear matrix inequalities. The proposed approach has successfully provided a solution to the optimization problem that can stabilize the nonlinear plants. Furthermore, sufficient conditions for the existence of the proposed controller guarantee the robust stability of the system in the presence of polytypic uncertainties. In addition, the simulation results have demonstrated the efficiency of the proposed control scheme.

Keywords: PV pumping system, DC-DC buck converter, robust model predictive controller, nonlinear system, actuator saturation, linear matrix inequality

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118 Reinforcement Learning for Robust Missile Autopilot Design: TRPO Enhanced by Schedule Experience Replay

Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

Abstract:

Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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117 Comparison of Conventional Control and Robust Control on Double-Pipe Heat Exchanger

Authors: Hanan Rizk

Abstract:

A heat exchanger is a device used to mix liquids having different temperatures. In this case, the temperature control becomes a critical objective. This research work presents the temperature control of the double-pipe heat exchanger (multi-input multi-output (MIMO) system), which is modeled as first-order coupled hyperbolic partial differential equations (PDEs), using conventional and advanced control techniques and develops appropriate robust control strategy to meet stability requirements and performance objectives. We designed a PID controller and H-infinity controller for a heat exchanger (HE) system. Frequency characteristics of sensitivity functions and open-loop and closed-loop time responses are simulated using MATLAB software, and the stability of the system is analyzed using Kalman's test. The simulation results have demonstrated that the H-infinity controller is more efficient than PID in terms of robustness and performance.

Keywords: heat exchanger, multi-input multi-output system, MATLAB simulation, partial differential equations, PID controller, robust control

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116 Model-Based Code Generation Module for High Performance Acquisition and Control Systems Implemented on Heterogeneous Hardware

Authors: Rens Baeyens, Joachim Denil, Jan Steckel, Walter Daems

Abstract:

High performance sensing and control systems have an important role in Industry 4.0. However, with the current solutions, the development effort is high and requires specialized skills in electronic engineering. Therefore, a model-based approach on the control and signal processing systems using affordable heterogeneous hardware is proposed. A model-based module generator is being developed to abstract the user from the actual soft-and hardware implementation. Starting from a modeled timing diagram, autogenerated code is combined with the necessary peripherals and with the System on Chip by generating the device tree scripts and PCB layouts for the necessary glue electronics. This module generator enables system engineers without deep software and hardware knowledge to set up complex control systems. Furthermore, it equips software and hardware engineers with a solid framework for faster development. In addition, a high-level simulation model will be integrated into the toolchain to simulate the effects of low-level implementation decisions. This allows the user to be well aware of the trade-offs during design, as well as enabling system validation and verification. In this paper, the model-based code generation module is elaborated. The module transforms a combined model of an embedded platform and a timing diagram into platform-specific firmware using a model-to-text transformation and a minimalistic platform-specific library.

Keywords: control systems, heterogeneous, model based design, code generation, validation, verification

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115 Validation of Mapping Historical Linked Data to International Committee for Documentation (CIDOC) Conceptual Reference Model Using Shapes Constraint Language

Authors: Ghazal Faraj, András Micsik

Abstract:

Shapes Constraint Language (SHACL), a World Wide Web Consortium (W3C) language, provides well-defined shapes and RDF graphs, named "shape graphs". These shape graphs validate other resource description framework (RDF) graphs which are called "data graphs". The structural features of SHACL permit generating a variety of conditions to evaluate string matching patterns, value type, and other constraints. Moreover, the framework of SHACL supports high-level validation by expressing more complex conditions in languages such as SPARQL protocol and RDF Query Language (SPARQL). SHACL includes two parts: SHACL Core and SHACL-SPARQL. SHACL Core includes all shapes that cover the most frequent constraint components. While SHACL-SPARQL is an extension that allows SHACL to express more complex customized constraints. Validating the efficacy of dataset mapping is an essential component of reconciled data mechanisms, as the enhancement of different datasets linking is a sustainable process. The conventional validation methods are the semantic reasoner and SPARQL queries. The former checks formalization errors and data type inconsistency, while the latter validates the data contradiction. After executing SPARQL queries, the retrieved information needs to be checked manually by an expert. However, this methodology is time-consuming and inaccurate as it does not test the mapping model comprehensively. Therefore, there is a serious need to expose a new methodology that covers the entire validation aspects for linking and mapping diverse datasets. Our goal is to conduct a new approach to achieve optimal validation outcomes. The first step towards this goal is implementing SHACL to validate the mapping between the International Committee for Documentation (CIDOC) conceptual reference model (CRM) and one of its ontologies. To initiate this project successfully, a thorough understanding of both source and target ontologies was required. Subsequently, the proper environment to run SHACL and its shape graphs were determined. As a case study, we performed SHACL over a CIDOC-CRM dataset after running a Pellet reasoner via the Protégé program. The applied validation falls under multiple categories: a) data type validation which constrains whether the source data is mapped to the correct data type. For instance, checking whether a birthdate is assigned to xsd:datetime and linked to Person entity via crm:P82a_begin_of_the_begin property. b) Data integrity validation which detects inconsistent data. For instance, inspecting whether a person's birthdate occurred before any of the linked event creation dates. The expected results of our work are: 1) highlighting validation techniques and categories, 2) selecting the most suitable techniques for those various categories of validation tasks. The next plan is to establish a comprehensive validation model and generate SHACL shapes automatically.

Keywords: SHACL, CIDOC-CRM, SPARQL, validation of ontology mapping

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114 Identification of Switched Reluctance Motor Parameters Using Exponential Swept-Sine Signal

Authors: Abdelmalek Ouannou, Adil Brouri, Laila Kadi, Tarik

Abstract:

Switched reluctance motor (SRM) has a major interest in a large domain as in electric vehicle driving because of its wide range of speed operation, high performances, low cost, and robustness to run under degraded conditions. The purpose of the paper is to develop a new analytical approach for modeling SRM parameters. Then, an identification scheme is proposed to obtain the SRM parameters. Since the SRM is featured by a highly nonlinear behavior, modeling these devices is difficult. Then, it is convenient to develop an accurate model describing the SRM. Furthermore, it is always operated in the magnetically saturated mode to maximize the energy transfer. Accordingly, it is shown that the SRM can be accurately described by a generalized polynomial Hammerstein model, i.e., the parallel connection of several Hammerstein models having polynomial nonlinearity. Presently an analytical identification method is developed using a chirp excitation signal. Afterward, the parameters of the obtained model have been determined using Finite Element Method analysis. Finally, in order to show the effectiveness of the proposed method, a comparison between the true and estimate models has been performed. The obtained results show that the output responses are very close.

Keywords: switched reluctance motor, swept-sine signal, generalized Hammerstein model, nonlinear system

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113 Frequent Pattern Mining for Digenic Human Traits

Authors: Atsuko Okazaki, Jurg Ott

Abstract:

Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

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112 Cryptocurrency Crime: Behaviors of Malicious Smart Contracts in Blockchain

Authors: Malaw Ndiaye, Karim Konate

Abstract:

Blockchain and smart contracts can be used to facilitate almost any financial transaction. Thanks to these smart contracts, the settlement of dividends and coupons could be automated. The blockchain would allow all these transactions to be saved in a single ledger rather than in many databases through many organizations as is currently the case. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. This paper takes stock of cryptocurrency crime by assessing attacks due to smart contracts and the cost of losses. These losses are often the result of two types of malicious contracts: vulnerable contracts and criminal smart contracts. Studying the behavior of malicious contracts allows us to understand the root causes and consequences of attacks and the defense capabilities that exist although they do not definitively solve the crime problem. It makes it possible to approach new defense perspectives which will be concretized in future work.

Keywords: blockchain, malicious smart contracts, crypto-currency, crimes, attacks

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111 Obtaining High-Dimensional Configuration Space for Robotic Systems Operating in a Common Environment

Authors: U. Yerlikaya, R. T. Balkan

Abstract:

In this research, a method is developed to obtain high-dimensional configuration space for path planning problems. In typical cases, the path planning problems are solved directly in the 3-dimensional (D) workspace. However, this method is inefficient in handling the robots with various geometrical and mechanical restrictions. To overcome these difficulties, path planning may be formalized and solved in a new space which is called configuration space. The number of dimensions of the configuration space comes from the degree of freedoms of the system of interest. The method can be applied in two ways. In the first way, the point clouds of all the bodies of the system and interaction of them are used. The second way is performed via using the clearance function of simulation software where the minimum distances between surfaces of bodies are simultaneously measured. A double-turret system is held in the scope of this study. The 4-D configuration space of a double-turret system is obtained in these two ways. As a result, the difference between these two methods is around 1%, depending on the density of the point cloud. The disparity between the two forms steadily decreases as the point cloud density increases. At the end of the study, in order to verify 4-D configuration space obtained, 4-D path planning problem was realized as 2-D + 2-D and a sample path planning is carried out with using A* algorithm. Then, the accuracy of the configuration space is proved using the obtained paths on the simulation model of the double-turret system.

Keywords: A* algorithm, autonomous turrets, high-dimensional C-space, manifold C-space, point clouds

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110 Method for Tuning Level Control Loops Based on Internal Model Control and Closed Loop Step Test Data

Authors: Arnaud Nougues

Abstract:

This paper describes a two-stage methodology derived from internal model control (IMC) for tuning a proportional-integral-derivative (PID) controller for levels or other integrating processes in an industrial environment. Focus is the ease of use and implementation speed which are critical for an industrial application. Tuning can be done with minimum effort and without the need for time-consuming open-loop step tests on the plant. The first stage of the method applies to levels only: the vessel residence time is calculated from equipment dimensions and used to derive a set of preliminary proportional-integral (PI) settings with IMC. The second stage, re-tuning in closed-loop, applies to levels as well as other integrating processes: a tuning correction mechanism has been developed based on a series of closed-loop simulations with model errors. The tuning correction is done from a simple closed-loop step test and the application of a generic correlation between observed overshoot and integral time correction. A spin-off of the method is that an estimate of the vessel residence time (levels) or open-loop process gain (other integrating process) is obtained from the closed-loop data.

Keywords: closed-loop model identification, IMC-PID tuning method, integrating process control, on-line PID tuning adaptation

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109 PLO-AIM: Potential-Based Lane Organization in Autonomous Intersection Management

Authors: Berk Ecer, Ebru Akcapinar Sezer

Abstract:

Traditional management models of intersections, such as no-light intersections or signalized intersection, are not the most effective way of passing the intersections if the vehicles are intelligent. To this end, Dresner and Stone proposed a new intersection control model called Autonomous Intersection Management (AIM). In the AIM simulation, they were examining the problem from a multi-agent perspective, demonstrating that intelligent intersection control can be made more efficient than existing control mechanisms. In this study, autonomous intersection management has been investigated. We extended their works and added a potential-based lane organization layer. In order to distribute vehicles evenly to each lane, this layer triggers vehicles to analyze near lanes, and they change their lane if other lanes have an advantage. We can observe this behavior in real life, such as drivers, change their lane by considering their intuitions. Basic intuition on selecting the correct lane for traffic is selecting a less crowded lane in order to reduce delay. We model that behavior without any change in the AIM workflow. Experiment results show us that intersection performance is directly connected with the vehicle distribution in lanes of roads of intersections. We see the advantage of handling lane management with a potential approach in performance metrics such as average delay of intersection and average travel time. Therefore, lane management and intersection management are problems that need to be handled together. This study shows us that the lane through which vehicles enter the intersection is an effective parameter for intersection management. Our study draws attention to this parameter and suggested a solution for it. We observed that the regulation of AIM inputs, which are vehicles in lanes, was as effective as contributing to aim intersection management. PLO-AIM model outperforms AIM in evaluation metrics such as average delay of intersection and average travel time for reasonable traffic rates, which is in between 600 vehicle/hour per lane to 1300 vehicle/hour per lane. The proposed model reduced the average travel time reduced in between %0.2 - %17.3 and reduced the average delay of intersection in between %1.6 - %17.1 for 4-lane and 6-lane scenarios.

Keywords: AIM project, autonomous intersection management, lane organization, potential-based approach

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108 The Application of Internet of Things in Healthcare: Building an Interconnected Health Environment

Authors: Quinn Au, Amedeo Carmine, Tauheed Khan Mohd

Abstract:

The Internet of Things (IoT) is emerging as a new development in information technology in recent years, with the potential to improve convenience and efficiency in life. Following the rise of IoT, the Social Internet of Things (SIoT) is another new development in which the benefits of connectivity and user-friendliness from social network services (SNS) are its main features. With the introduction of IoT, the world will be much more modernized, convenient, and industrialized. This paper will discuss the applications of IoT in different sectors such as healthcare services, education, and lifestyle. The privacy challenges that IoT still poses to user data will also be discussed. Finally, an empirical study to evaluate the number of active installed IoT connections in recent years demonstrates the increase in usage of IoT regardless of the privacy challenges. The study also examines some types of IoT devices that are being preferred in the market and predictions from researchers about IoT in the upcoming years.

Keywords: IoT, health care, robotics, social Internet of Things

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107 Adaptive Few-Shot Deep Metric Learning

Authors: Wentian Shi, Daming Shi, Maysam Orouskhani, Feng Tian

Abstract:

Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance.

Keywords: few-shot learning, triplet network, adaptive margin, deep learning

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106 Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles

Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

Abstract:

With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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105 Application of Artificial Neural Network in Initiating Cleaning Of Photovoltaic Solar Panels

Authors: Mohamed Mokhtar, Mostafa F. Shaaban

Abstract:

Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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104 Flow Visualization around a Rotationally Oscillating Cylinder

Authors: Cemre Polat, Mustafa Soyler, Bulent Yaniktepe, Coskun Ozalp

Abstract:

In this study, it was aimed to control the flow actively by giving an oscillating rotational motion to a vertically placed cylinder, and flow characteristics were determined. In the study, firstly, the flow structure around the flat cylinder was investigated with dye experiments, and then the cylinders with different oscillation angles (θ = 60°, θ = 120°, and θ = 180°) and different rotation speeds (15 rpm and 30 rpm) the flow structure around it was examined. Thus, the effectiveness of oscillation and rotation speed in flow control has been investigated. In the dye experiments, the dye/water mixture obtained by mixing Rhodamine 6G in powder form with water, which shines under laser light and allows detailed observation of the flow structure, was used. During the experiments, the dye was injected into the flow with the help of a thin needle at a distance that would not affect the flow from the front of the cylinder. In dye experiments, 100 frames per second were taken with a Canon brand EOS M50 (24MP) digital mirrorless camera at a resolution of 1280 * 720 pixels. Then, the images taken were analyzed, and the pictures representing the flow structure for each experiment were obtained. As a result of the study, it was observed that no separation points were formed at 180° swing angle at 15 rpm speed, 120° and 180° swing angle at 30 rpm, and the flow was controlled according to the fixed cylinder.

Keywords: active flow control, cylinder, flow visualization rotationally oscillating

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103 Numerical Analysis of Passive Controlled Turbulent Flow around a Circular Cylinder

Authors: Mustafa Soyler, Mustafa M. Yavuz, Bulent Yaniktepe, Coskun Ozalp

Abstract:

In this study, unsteady two-dimensional turbulent flow around a circular cylinder and passive control of the flow with groove on the cylinder was examined. In the CFD analysis, solutions were made using turbulent flow conditions. Steady and unsteady solutions were used in turbulent flow analysis. Numerical analysis of the flow around the circular cylinder is difficult since flow is not in a stable regime when Reynold number is between 1000 and 10000. The analyses in this study were performed at a subcritical Re number of 5000 and the results were compared with available experimental results of the drag coefficient (Cd) and Strouhal (St) number values in the literature. The effect of different groove types and depths on the Cd coefficient has been analyzed and grooves increase the Cd coefficient compared to the smooth cylinder.

Keywords: CFD, drag coefficient, flow over cylinder, passive flow control

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102 'Systems' and Its Impact on Virtual Teams and Electronic Learning

Authors: Shavindrie Cooray

Abstract:

It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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101 Air Cargo Network Structure Characteristics and Robustness Analysis under the Belt and Road Area

Authors: Feng-jie Xie, Jian-hong Yan

Abstract:

Based on the complex network theory, we construct the air cargo network of the Belt and Road area, analyze its regional distribution and structural characteristics, measure the robustness of the network. The regional distribution results show that Southeast Asia and China have the most prominent development in the air cargo network of the Belt and Road area, Central Asia is the least developed. The structure characteristics found that the air cargo network has obvious small-world characteristics; the degree distribution has single-scale property; it shows a significant rich-club phenomenon simultaneously. The network robustness is measured by two attack strategies of degree and betweenness, but the betweenness of network nodes has a greater impact on network connectivity. And identified 24 key cities that have a large impact on the robustness of the network under the two attack strategies. Based on these results, recommendations are given to maintain the air cargo network connectivity in the Belt and Road area.

Keywords: air cargo, complex network, robustness, structure properties, The Belt and Road

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100 Performance of BLDC Motor under Kalman Filter Sensorless Drive

Authors: Yuri Boiko, Ci Lin, Iluju Kiringa, Tet Yeap

Abstract:

The performance of a BLDC motor controlled by the Kalman filter-based position-sensorless drive is studied in terms of its dependence on the system’s parameters' variations. The effects of system’s parameters changes on the dynamic behavior of state variables are verified. Simulated is a closed-loop control scheme with a Kalman filter in the feedback line. Distinguished are two separate data sampling modes in analyzing feedback output from the BLDC motor: (1) equal angular separation and (2) equal time intervals. In case (1), the data are collected via equal intervals Δθ of rotor’s angular position θᵢ, i.e., keeping Δθ=const. In case (2), the data collection time points tᵢ are separated by equal sampling time intervals Δt=const. Demonstrated are the effects of the parameters changes on the sensorless control flow, in particular, reduction of the torque ripples, switching spikes, torque load balancing. It is specifically shown that an efficient suppression of commutation induced torque ripples is achievable selection of the sampling rate in the Kalman filter settings above certain critical value. The computational cost of such suppression is shown to be higher for the motors with lower induction values of the windings.

Keywords: BLDC motor, Kalman filter, sensorless drive, state variables, torque ripples reduction, sampling rate

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99 Unified Coordinate System Approach for Swarm Search Algorithms in Global Information Deficit Environments

Authors: Rohit Dey, Sailendra Karra

Abstract:

This paper aims at solving the problem of multi-target searching in a Global Positioning System (GPS) denied environment using swarm robots with limited sensing and communication abilities. Typically, existing swarm-based search algorithms rely on the presence of a global coordinate system (vis-à-vis, GPS) that is shared by the entire swarm which, in turn, limits its application in a real-world scenario. This can be attributed to the fact that robots in a swarm need to share information among themselves regarding their location and signal from targets to decide their future course of action but this information is only meaningful when they all share the same coordinate frame. The paper addresses this very issue by eliminating any dependency of a search algorithm on the need of a predetermined global coordinate frame by the unification of the relative coordinate of individual robots when within the communication range, therefore, making the system more robust in real scenarios. Our algorithm assumes that all the robots in the swarm are equipped with range and bearing sensors and have limited sensing range and communication abilities. Initially, every robot maintains their relative coordinate frame and follow Levy walk random exploration until they come in range with other robots. When two or more robots are within communication range, they share sensor information and their location w.r.t. their coordinate frames based on which we unify their coordinate frames. Now they can share information about the areas that were already explored, information about the surroundings, and target signal from their location to make decisions about their future movement based on the search algorithm. During the process of exploration, there can be several small groups of robots having their own coordinate systems but eventually, it is expected for all the robots to be under one global coordinate frame where they can communicate information on the exploration area following swarm search techniques. Using the proposed method, swarm-based search algorithms can work in a real-world scenario without GPS and any initial information about the size and shape of the environment. Initial simulation results show that running our modified-Particle Swarm Optimization (PSO) without global information we can still achieve the desired results that are comparable to basic PSO working with GPS. In the full paper, we plan on doing the comparison study between different strategies to unify the coordinate system and to implement them on other bio-inspired algorithms, to work in GPS denied environment.

Keywords: bio-inspired search algorithms, decentralized control, GPS denied environment, swarm robotics, target searching, unifying coordinate systems

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98 Developing an Advanced Algorithm Capable of Classifying News, Articles and Other Textual Documents Using Text Mining Techniques

Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

Abstract:

The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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97 Predicting the Product Life Cycle of Songs on Radio - How Record Labels Can Manage Product Portfolio and Prioritise Artists by Using Machine Learning Techniques

Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

Abstract:

This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

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96 A Comparative Study of Deep Learning Methods for COVID-19 Detection

Authors: Aishrith Rao

Abstract:

COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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95 Real Time Multi Person Action Recognition Using Pose Estimates

Authors: Aishrith Rao

Abstract:

Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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