A Survey of Artificial Neural Network in Wind Energy Systems

Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies

Dynamic Analysis of Cracked Rotor in Viscous Medium and its Crack Diagnosis Using Intelligent Techniques

Fatigue cracks have high potential to cause catastrophic failures in the rotor which can lead to catastrophic failure if undetected properly and in time. This fault may interrupt smooth, effective and efficient operation and performance of the machines. Thereby the importance of identification of crack in the rotor is not only for leading safe operation but also to prevent the loss of economy and lives.The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in dynamic response before and after damage. When the rotor with transverse crack immersed in the viscous fluid, analysis of cracked rotor is difficult and complex. The analysis of cracked rotor partially submerged in the viscous fluid is widely used in various engineering systems such as long spinning shaft used drilling the seabed for the extracting the oil, high-speed turbine rotors, and analysis of centrifuges in a fluid medium. Therefore, dynamic analysis of cracked rotor partially submerged in the viscous medium have been presented in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Amplitude) of multiple cracked mild steel rotor partially submerged in the viscous medium. The presence of the crack in rotor generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic characteristics of cracked rotor in a viscous fluid. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration characteristics of cracked rotor partially submerged in the viscous medium using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the viscosity of fluid, depth and position of the cracks affect the performance of the rotor and effectiveness of crack detection techniques. Various Artificial Intelligent (AI) techniques such as fuzzy logic, hybrid BPNN-RBFNN neural network, MANFIS and hybrid fuzzy-rule base controller based multiple faults diagnosis systems are developed using the dynamic response of rotating cracked rotor in a viscous medium to monitor the presence of crack. Experiments have been conducted to authenticate the performance and accuracy of proposed methods. Good agreement is observed between the results

Human face detection techniques: A comprehensive review and future research directions

Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection

Deep learning for gait prediction: an application to exoskeletons for children with neurological disorders

Cerebral Palsy, a non-progressive neurological disorder, is a lifelong condition. While it has no cure, clinical intervention aims to minimise the impact of the disability on individuals' lives. Wearable robotic devices, like exoskeletons, have been rapidly advancing and proving to be effective in rehabilitating individuals with gait pathologies. The utilization of artificial intelligence (AI) algorithms in controlling exoskeletons, particularly at the supervisory level, has emerged as a valuable approach. These algorithms rely on input from onboard sensors to predict gait phase, user intention, or joint kinematics. Using AI to improve the control of robotic devices not only enhances human-robot interaction but also has the potential to improve user comfort and functional outcomes of rehabilitation, and reduce accidents and injuries. In this research study, a comprehensive systematic literature review is conducted, exploring the various applications of AI in lower-limb robotic control. This review focuses on methodological parameters such as sensor usage, training demographics, sample size, and types of models while identifying gaps in the existing literature. Building on the findings of the review, subsequent research leveraged the power of deep learning to predict gait trajectories for the application of rehabilitative exoskeleton control. This study addresses a gap in the existing literature by focusing on predicting pathological gait trajectories, which exhibit higher inter- and intra-subject variability compared to the gait of healthy individuals. The research focused on the gait of children with neurological disorders, particularly Cerebral Palsy, as they stand to benefit greatly from rehabilitative exoskeletons. State-of-the-art deep learning algorithms, including transformers, fully connected neural networks, convolutional neural networks, and long short-term memory networks, were implemented for gait trajectory prediction. This research presents findings on the performance of these models for short-term and long-term recursive predictions, the impact of varying input and output window sizes on prediction errors, the effect of adding variable levels of Gaussian noise, and the robustness of the models in predicting gait at speeds within and outside the speed range of the training set. Moreover, the research outlines a methodology for optimising the stability of long-term forecasts and provides a comparative analysis of gait trajectory forecasting for typically developing children and children with Cerebral Palsy. A novel approach to generating adaptive trajectories for children with Cerebral Palsy, which can serve as reference trajectories for position-controlled exoskeletons, is also presented

Analysis of jointed plain concrete pavement systems with nondestructive test results using artificial neural networks

The primary goal of this research was to show that artificial neural network (ANN) models could be developed to perform rapid and accurate predictions of jointed plain concrete pavement system (JPCP) parameters which will enable pavement engineers to incorporate the state-of-the-art finite element (FE) solutions into routine practical design. The ISLAB2000 finite element program has been used as an advanced structural model for solving the responses of the concrete pavement systems and generating a large knowledge database.;Totally, fifty-six ANN-based backcalculation and forward calculation models were developed as part of this research for the analysis of JPCP systems under traffic and temperature loading combinations to predict the concrete pavement parameters and critical pavement responses. In this research, BCM stands for the ANN-based backcalculation model and FCM stands for the ANN-based forward calculation model. BCM-EPCC, BCM-kS, BCMTELTD, FCM-RRS, and FCM-sigma MAX models were developed for the prediction of elastic modulus of Portland cement concrete (PCC) layer (EPCC), coefficient of subgrade reaction (kS) of the pavement foundation, total effective linear temperature difference (TELTD) between top and bottom of the PCC layer, radius of relative stiffness (RRS) of the pavement system, and maximum tensile stresses at the bottom of the Portland cement concrete layer (sigmaMAX), respectively. These ANN-based models gave average errors less than 1% for synthetic database. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns collected from the Falling Weight Deflectometer (FWD) field tests, several network architectures were also trained with varying levels of noise in them.;One of the most important advantages of the presented ANN approach is that the use of the ANN-based models resulted in a drastic reduction in computation time. Rapid prediction ability of the ANN-based models (capable of analyzing 100,000 FWD deflection profiles in one second) provides a tremendous advantage to the pavement engineers by allowing them to nondestructively assess the condition of the transportation infrastructure in real time while the FWD testing takes place in the field. In the developed approach, there is also no need a seed moduli or iteration process of the solution in order to predict the JPCP system parameters. The prediction of temperature difference (TELTD) in PCC layer which causes the slab curling and warping in concrete pavements is another tremendous advantage of the developed approach over the other methods since no other method does not take into account this parameter in the analyses. Finally, it can be concluded that ANN-based analysis models can provide pavement engineers and designers with state-of-the-art solutions, without the need for a high degree of expertise in the input and output of the problem, to rapidly analyze a large number of concrete pavement deflection basins needed for project specific and network level pavement testing and evaluation

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Design and optimization of self-deployable damage tolerant composite structures: A review

Composite deployable structures are becoming increasingly important for the space industry, emerging as an alternative to conventional metallic mechanical systems in space applications. In most cases, the life-cycle of these structures includes a single deployment sequence, once the spacecraft is in orbit. So long as reliability is ensured, this fact opens the possibility of using the materials past their elastic regime and, possibly, beyond the initiation of damage, increasing the efficiency and applicability of the developed designs. This review explores this possibility, surveying the design of deployable structures, as well as the state of the art on the design and damage tolerance in composites. An overview of the developments performed on the topology optimization of composite structures is included for its novelty and potential application in the design of deployable structures. Finally, the possibility of combining these topics into a single efficient design approach is discussed

Data-driven methodologies for supporting decision-making in roadway safety and pavement management

There has been a significant rise in the utilization of data-driven methods within the contemporary realm of transportation engineering. This trend is primarily attributed to the limitations associated with experience-based methods, such as subjectivity and non-reproducibility. In contrast, data-driven methods have proven to offer a more objective and effective approach to problem analysis, thereby providing decision-makers with a reliable basis for informed decision-making. This present research focuses on two types of data-driven methodologies: geostatistical analyses utilizing geographic information systems (GIS) and cutting-edge algorithms associated with artificial intelligence (AI). In numerical analysis, data provides a means to gain valuable insights into a problem of interest. While AI-oriented methods have been shown in many studies to be more effective than traditional approaches, the accuracy of the analysis still heavily depends on the quality of the data. This dissertation endeavors to shed light on the pivotal role that data plays in both roadway safety analysis and pavement management. To accomplish this, four distinct studies are proposed that examine different aspects of data-driven methods. The studies encompass an evaluation of data consistency in motor vehicle crash databases, the identification of crash hot spots within a road network, a synthesis of advancements in the application of AI algorithms to various activities of pavement management, and an exploration of the relationship between pavement conditions and roadway safety using AI-oriented methods. The knowledge acquired from these studies serves as a foundation for future research, advancements, and the adoption of innovative approaches to enhance the efficiency of safety analysis and pavement management. This research ultimately facilitates informed decision-making, effective resource allocation, and the implementation of cost-effective interventions to enhance roadway safety and optimize pavement management practices.Civil, Architectural, and Environmental Engineerin

Nondestructive Evaluation of Iowa Pavements-Phase I

Evaluating structural conditions of existing, in-service pavements is a part of the routine maintenance and rehabilitation activities undertaken by the most departments of transportation (DOTs). In the field, the pavement deflection profiles (or basins) gathered from the nondestructive falling weight deflectometer (FWD) test data are typically used to evaluate pavement structural conditions. Over the past decade, interest has increased in a new class of computational intelligence system, known as artificial neural networks (ANNs), for use in geomechanical and pavement systems applications. This report describes the development and use of ANN models as pavement structural analysis tools for the rapid and accurate prediction of layer parameters of Iowa pavements subjected to typical highway loadings. ANN models trained with the results from the structural analysis program solutions have been found to be practical alternatives. The ILLI-PAVE, ISLAB2000, and DIPLOMAT programs were used as the structural response models for solving the deflection parameters of flexible, rigid, and composite pavements, respectively. The trained ANN models in this study were capable of predicting pavement layer moduli and critical pavement responses from FWD deflection basins with low errors. The developed methodology was successfully verified using results from long-term pavement performance (LTPP) FWD tests, as well as Iowa DOT FWD field data. All successfully developed ANN models were incorporated into a Microsoft Excel spreadsheet-based backcalculation software toolbox with a user-friendly interface. The final outcome of this study was a field-validated, nondestructive pavement evaluation toolbox that will be used to assess pavement condition, estimate remaining pavement life, and eventually help assess pavement rehabilitation strategies by the Iowa DOT pavement management team

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things