A hydrogen energy storage system(HESS)is one of the many risingmodern green innovations,using excess energy to generate hydrogen and storing it for various *** that,there have been many discussions about commercializi...
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A hydrogen energy storage system(HESS)is one of the many risingmodern green innovations,using excess energy to generate hydrogen and storing it for various *** that,there have been many discussions about commercializing HESS and improving it ***,the design and sizing process can be overwhelming to comprehend with various sources to examine,and understanding optimal design methodologies is crucial to optimize a HESS *** that,this review aims to collect and analyse a wide range of HESS studies to summarise recent *** different collections of studies are studied,one was sourced by the main author for preliminary readings,and another was obtained via *** findings from the Web of Science platform were also examined for amore comprehensive *** findings include the People’sRepublic of China has been active in HESS research,as most works and active organizations originate from this *** has been mainly researched to support power generation and balance load demands,with financial analysis being the common scope of *** is a common tool used for HESS design,modelling,and optimization as it can handle complex *** neural network(ANN)has the potential to be used to model the HESS,but additional review is required as a formof future *** a commercialization perspective,pressurized hydrogen tanks are ideal for hydrogen storage in a HESS,but other methods can be considered after additional research and *** this review,it can be implied that modelling works will be the way forward for HESS research,but extensive collaborations and additional review are ***,this review summarized various takeaways that future research works on HESS can use.
Hybrid power systems need frequent voltage regulation to handle fluctuations in load profile and renewable energy output, ensuring effective voltage management. This increased need for regulation extends the operating...
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As the world advances, the global population is projected to reach an estimated 9 billion by 2050, leading to a higher demand for food. To boost agricultural production without harming the environment, new and sustain...
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Current trend projected the global population to increase to 9 billion by the year 2050 which will increase the demand of food production by around 70%. The agricultural sector also supplies produce to bio-based produ...
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Service composability, introduced by service-oriented architecture (SOA), is a design principle that encourages the design of reusable services that themselves also consist of reusable services. In domain driven desig...
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ISBN:
(纸本)9789897586927
Service composability, introduced by service-oriented architecture (SOA), is a design principle that encourages the design of reusable services that themselves also consist of reusable services. In domain driven design (DDD), which inspired microservice architectures, the scope of composable service design is interpreted as a software solution domain, while the problem domain lies in the detached business world. This results in IT solutions that are often redundant at the enterprise level or tend to be composable only within a specific enterprise IT ecosystem as a result of the design without understanding the business domain or how the new solution fits into the overall delivery and enterprise architecture. On the other hand, it is not uncommon for company's "business", motivated by revenue increase, to push frequent deliveries of business changes, putting pressure on company's IT to implement quick fix solutions that only solve immediate business problems. All this leads to inconsistent and redundant software systems that increase the complexity of the organization and result in higher maintenance costs and less flexibility in implementing future changes. As a solution, this paper proposes Composable Enterprise, a business-IT approach for architecting the enterprise that introduces Business Composability and a holistic understanding of the enterprise. Business Composability is a business-IT-aligned service abstraction that starts with the notion of first applying service composability to business assets (business capabilities) to achieve the scale and pace required to realize business changes. The purpose of this paper is to provide a methodology for implementing Composable Enterprise in large, complex organisations, not as a massive, enterprise-wide rationalization and consolidation initiative, but in an evolutionary way through the joint and holistic business-IT delivery of business initiatives. The application of the proposed methodology is illustrated using a
An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Techniqu...
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An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t
In this paper, we present a survey of deep learning-based methods for the regression of gaze direction vector from head and eye images. We describe in detail numerous published methods with a focus on the input data, ...
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The COVID-19 pandemic sparked a new age of conspiracy theories in society. This has become an issue, especially since these theories are mixed in with reasonable arguments that criticize the measures taken by governme...
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This paper describes the development of an Open-Source Generative AI Chatbot, utilizing free Large Language Models (LLM) to enrich the student learning experience for a university course in 'Introduction to Progra...
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3D vision recognition offers a significantly more robust tool for achieving machine cognition compared to traditional 2D vision techniques. However, similar to the vulnerabilities present in 2D vision, many 3D vision ...
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