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Author

Nita Yodo

Other affiliations: Wichita State University
Bio: Nita Yodo is an academic researcher from North Dakota State University. The author has contributed to research in topics: Resilience (network) & Computer science. The author has an hindex of 10, co-authored 33 publications receiving 526 citations. Previous affiliations of Nita Yodo include Wichita State University.

Papers
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Journal ArticleDOI
29 Jul 2019
TL;DR: This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization and directions for future research in this field are suggested.
Abstract: Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

252 citations

Journal ArticleDOI
18 Feb 2019-Energies
TL;DR: The preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the State of Health and the Remaining Useful Life of the lithium-ion battery is presented.
Abstract: Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the NASA Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

160 citations

Journal ArticleDOI
TL;DR: This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.
Abstract: Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.

160 citations

Journal ArticleDOI
01 Nov 2016
TL;DR: A literature survey of engineering resilience from the design perspective, with a focus on engineering resilience metrics and their design implications is provided in this paper. But, despite an increase in the usage of the engineering resilience concept, the diversity of its applications in various engineering sectors complicates a universal agreement on its quantification and associated measurement techniques.
Abstract: A resilient system is a system that possesses the ability to survive and recover from the likelihood of damage due to disruptive events or mishaps. The concept that incorporates resiliency into engineering practices is known as engineering resilience. To date, engineering resilience is still predominantly application-oriented. Despite an increase in the usage of engineering resilience concept, the diversity of its applications in various engineering sectors complicates a universal agreement on its quantification and associated measurement techniques. There is a pressing need to develop a generally applicable engineering resilience analysis framework, which standardizes the modeling, assessment, and improvement of engineering resilience for a broader engineering discipline. This paper provides a literature survey of engineering resilience from the design perspective, with a focus on engineering resilience metrics and their design implications. The currently available engineering resilience quantification metrics are reviewed and summarized, the design implications toward the development of resilient-engineered systems are discussed, and further, the challenges of incorporating resilience into engineering design processes are evaluated. The presented study expects to serve as a building block toward developing a generally applicable engineering resilience analysis framework that can be readily used for system design. [DOI: 10.1115/1.4034223]

138 citations


Cited by
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Journal ArticleDOI
TL;DR: Current gaps and challenges on use of BNs in fault diagnosis in the last decades with focus on engineering systems are explored and several directions for future research are explored.
Abstract: Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

314 citations

Journal ArticleDOI
TL;DR: This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches and delivers potential recommendations for the development of SOC estimation method of lithium-ion battery in EV applications.
Abstract: Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery's behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.

289 citations

Journal ArticleDOI
TL;DR: How machine learning methods and high-throughput experimentation provide a data-driven approach to this problem are discussed, and challenges in building models which provide fast and accurate battery state predictions are highlighted.
Abstract: Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future. Predicting the properties of batteries, such as their state of charge and remaining lifetime, is crucial for improving battery manufacturing, usage and optimisation for energy storage. The authors discuss how machine learning methods and high-throughput experimentation provide a data-driven approach to this problem, and highlight challenges in building models which provide fast and accurate battery state predictions.

285 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview of manufacturing methods for FGMs, thus describing the fundamental difficulties and strengths of these methods based on the available literature over 30 years and give a comprehensive summary of the various applications and the future trends for research required for the design and production of these materials properly with a smooth graded.
Abstract: Functionally graded materials (FGMs) are a broad research area and attract considerable tremendous attention today in the materials science and engineering society. In recent years, FGMs have experienced remarkable developments in manufacturing methods. FGMs can be produced using several well-known processing techniques from conventional to advanced. This research provides an overview of manufacturing methods for FGMs, thus describing the fundamental difficulties and strengths of these methods based on the available literature over 30 years. Besides, this critical review gives a comprehensive summary of the various applications and the future trends for research required for the design and production of these materials properly with a smooth graded. The anticipated findings of the current paper can be considered as a milestone for future production and analysis investigations for FGMs and are beneficial for researchers, designers, and manufacturers in this scope.

259 citations

Journal ArticleDOI
TL;DR: This paper offers a means to quantify resilience as a function of absorptive, adaptive, and restorative capacities with Bayesian networks, a popular tool to structure relationships among several variables.

248 citations