IoT-Based Hybrid Renewable Energy System for Smart Campus

Eltamaly, Ali M.; Alotaibi, Majed A.; Alolah, A.I.; Ahmed, Mohamed A.

doi:10.3390/su13158555

Cited by 48 publications

(31 citation statements)

References 31 publications

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“…For example, Lai et al [98] proposed a new framework for long-term electrical power system modeling, because different energy storage technologies need to be accounted for; Petkov and Gabrielli [99] analyzed P2H as a seasonal energy storage option in low-carbon multienergy systems, where the interactions of energy carriers, such as electricity, natural gas (methane), hydrogen or heat can enable new value propositions; while Sánchez et al [100] considered methane, methanol, dimethyl ether (DME) and ammonia to determine an optimal infrastructure to provide energy storage or use these outputs in other energy applications. In line with these approaches Figure 10 showed that exploration cannot only mean technological exploration (e.g., P2L in grid-scale), but innovative approaches to system integration; for example, P2G and fossil fuel power plants [101] or developing hybrid renewable energy systems using already known technologies (e.g., wind turbines, battery storage, internet of things and diesel generators) [102]. The same will be true with the integration of novel P2X technologies, fulfilling an energy storage role, regarding which future energy systems will certainly require (1) collaborative and (2) explorative learning in practice:…”

Section: Exploration Can Be Interpreted Asmentioning

confidence: 94%

Hydrogen Economy Development Opportunities by Inter-Organizational Digital Knowledge Networks

et al. 2021

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Innovative power-to-X (P2X) technologies, as a set of emerging new solutions, could play a crucial role in creating sustainable, carbon-neutral economies, such as the hydrogen economy. These technologies, however, are generally not yet implemented on a commercial scale. This research focuses on how innovative, digital inter-organizational knowledge networks of industry representatives and universities could contribute to the commercial implementation of P2X technologies and increase the pace of sustainable hydrogen-based development. The findings of an extended case study with a hybrid (qualitative–quantitative) methodology and a five-year time horizon, suggest the need for a digital knowledge platform, where universities and industry representatives add and combine their knowledge. In contrast with expectations, however, the empirical results show that academia would, not only be capable of supporting the exploration of new solutions, but foster the exploitation of more mature technologies as well. Similarly, large energy companies could also drive exploratory activities, not only exploitative ones. The findings highlight the possible central role of the “system builder” actor, who integrates exploitative-explorative learning and facilitates the formation of a (digital) innovation ecosystem. By exceeding the dominant techno-economic and environmental aspects, this research contributes to the literature by highlighting the applicability of network-based innovation management theory for hydrogen economy research.

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Section: Exploration Can Be Interpreted Asmentioning

confidence: 94%

Hydrogen Economy Development Opportunities by Inter-Organizational Digital Knowledge Networks

et al. 2021

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“…e convolution result is further de-linearized, and the link requires the introduction of an activation function, as shown in formula ( 4). e processed structure is then stitched to obtain the feature matrix F, as shown in formula (5).…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

“…With the aid of various digital and intelligent technologies, universities are gradually transitioning to the age of the smart campus. Relying on these technologies, a smart campus is an effort to create an intelligent teaching environment, intelligent management mode, and digital teaching resources to form a new ecology of learner-centered education and teaching, which is an inevitable trend for digital school reform [ 4 , 5 ]. In the intelligent campus environment, students' life and learning data will be recorded into various management systems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Feng

2022

Computational Intelligence and Neuroscience

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Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students’ physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students’ psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students’ work on their affective cognition.

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“…To overcome the above limitations, the Internet of Things (IoT) paradigm presents advanced key solutions to establish new and accurate diagnosis systems for microscopic blood images, as is described in this study. The IoT has been deployed in diverse areas, such as smart cities [ 10 , 11 ], vehicular communications [ 12 ], smart ecosystems [ 13 ], smart farming and precision agriculture [ 14 , 15 , 16 ], and smart campuses [ 17 , 18 ]. Consequently, the Internet of Medical Things (IoMT) or smart healthcare [ 19 , 20 ] has been proposed for the improvement of quality of life for patients.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

Karar

Alotaibi

2022

Sensors

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Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.

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IoT-Based Hybrid Renewable Energy System for Smart Campus

Cited by 48 publications

References 31 publications

Hydrogen Economy Development Opportunities by Inter-Organizational Digital Knowledge Networks

Hydrogen Economy Development Opportunities by Inter-Organizational Digital Knowledge Networks

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

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