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The development of pineapple leaf diseases classification using convolutionary neural network for mobile apps

AIP Conference Proceedings 2024年第1期3135卷

作者： Muhammad Najmi Hasnol Mizam Sulaiman Mahzan Siti Fairuz Nurr Sadikan Mohd Ab Malek Md Shah Mohd Aliff Afira Sani College of Computing Informatics & Mathematic Universiti Teknologi MARA Melaka Malaysia. Faculty of Plantation & Agrotechnology Universiti Teknologi MARA Melaka Malaysia. Department of Law Universiti Teknologi MARA Melaka Malaysia. Department of Instrumentation and Control Engineering Universiti Kuala Lumpur Johore Malaysia.

Pineapple diseases are linked to worms, viruses, bacteria, and fungi. Insects including ants, scales, mealybugs, and souring beetles are some of the most prevalent pests that can harm the pineapple industry over time if early pineapple leaf disease detection is not made There are many smallholders especially in the rural area who lack of latest technology, knowledge, and experience about pineapple disease treatment and management. Thus, lack of a precise diagnosis method prevents them from understanding the disease and creates an effective control for its spread. This issue further impacts the effectiveness of pineapple production, subsequently affecting their income and the country’s, as well as jeopardizing national food security. This study presents an innovative approach aimed at enhancing early detection and management of pineapple leaf diseases by integrating a Convolutional Neural Network (CNN) algorithm and import it using TensorFlow Lite into an android mobile. The CNN model achieved an impressive total accuracy of 98% in precisely classifying three types of pineapple leaf diseases: Leaf Spot, Mealybug Wilt, and Pink Disease. When implemented in the mobile application, the system attained an overall confidence of 83.33% by leveraging both camera-captured and gallery images. Although the accuracy gained is impressive, additional research and adjustments can be conducted to improve the system’s performance and achieve even higher levels of accuracy.

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Qing Jiao, Tingyang Ma, Yinxing Zhang, Youmin Wu, Xiaohui Wu, Pengyue Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China The Faculty of Computing Science The University of Alberta EdmontonABT6G 2R3 Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi 'an University of Technology Xi'an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada Huaneng Weihai Power Generation Co. Ltd Weihai264200 China Xi 'an Thermal Power Research Institute Co. Ltd Xi'an710048 China

ISBN: (纸本)9781665426473

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system. © 2021 IEEE

关键词： Long short-term memory

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

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Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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Fractional-order biological system: chaos, multistability and coexisting attractors

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The European Physical Journal Special Topics 2021年第5期231卷 1061-1070页

作者： Debbouche, Nadjette Ouannas, Adel Momani, Shaher Cafagna, Donato Pham, Viet-Thanh Laboratory of Dynamical System and Control University of Larbi Ben M’hidi Oum El Bouaghi Algeria Department of Mathematics and Computer Sciences Larbi Ben M’hidi University Oum El Bouaghi Algeria Nonlinear Dynamics Research Center (NDRC) College of Humanities and Sciences Ajman University Ajman UAE Department of Mathematics Faculty of Science University of Jordan Amman Jordan Department of Innovation Engineering University of Salento Lecce Italy Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

In this paper, the nonlinear dynamics of the biological system modeled by the fractional incommensurate order Van der Pol equations are investigated. The stability of the proposed fractional non-autonomous system is analyzed by varying both the fractional order derivative and system parameters. Moreover, very interesting phenomena such as symmetry, multi-stability and coexistence of attractors are discovered in the considered biological system. Numerical simulations are performed by considering the Caputo fractional derivative and results are reported by means of bifurcation diagrams, computation of the largest Lyapunov exponent, phase portraits in 2D and 3D projections.

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Cell Viability And Electrical Response of Breast Cancer Cell Treated In Aquoes Graphene Oxide Solution Deposition On Interdigitated Electrode

Research Square

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Research Square 2021年

作者： Ramli, Muhammad M. Rosman, Anis S. Mazlan, Nur S. Ahmad, Mohd F. Halin, Dewi Suriyani Che Mohamed, Rafeezul Osman, Nurul H. Reshak, Ali H. Perlis Malaysia Perlis Malaysia Advance Medical and Dental Institute Universiti Sains Malaysia Bertam Jln Tun Hamdan Sheikh Tahir Kepala Batas Pulau Pinang13200 Malaysia Applied Electromagnetic Lab 1 Department of Physics Faculty of Science Universiti Putra Malaysia Serdang Selangor43400 Malaysia Physics Department College of Science University of Basrah Basrah Iraq Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering CTU in Prague Technicka 4 Prague166 07 Czech Republic

Breast cancer is one of the most reported cancers that can lead to death. Despite the advances in diagnosis and treatment procedures, the possibility of cancer recurrences is still high in many cases. With that in consideration, researchers from all over the world are showing interest in the unique features of Graphene oxide (GO), such as its excellent and versatile physicochemical properties, to explore further its potential and benefits towards breast cancer cell treatment. In this study, the cell viability and electrical response of GO, in terms of resistivity and impedance towards the breast cancer cells (MCF7) and normal breast cells (MCF10a), were investigated by varying the pH and concentration of GO. Firstly, the numbers of MCF7 and MCF10a were measured after being treated with GO for 24 and 48 hours. Next, the electrical responses of these cells were evaluated by using interdigitated electrodes (IDEs) gold electrodes that are connected to an E4980 20 Hz – 2 MHz Precision LCR meter. Based on the results obtained, as the pH of GO increased from pH 5 to pH 7, the number of viable MCF7 cells decreased while the number of viable MCF10a slightly increased after the incubation period of 48 hours. Similarly, the MCF7 also experienced higher cytotoxicity effects when treated with a GO concentration of more than 25 µg/mL. The findings from the electrical characterization of the cells observed that the number of viable cells has corresponded to the impedance of the cells. The electrical impedance of MCF7 decreased as the number of highly insulating viable cells membrane decreased, but in contrast, the electrical impedance of MCF10a increased as the number of highly insulating viable cells membrane increased. Hence, it can be deduced that the GO with higher pH and concentration impacts the MCF7 cancer cell line and MCF10a normal breast cell. © 2021, CC BY.

关键词： Graphene

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What is the cost and potential of low carbon electricity transition in the MENA region?

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Energy Strategy Reviews 2025年 59卷

作者： Bamisile, Olusola Xing, Yankai Acen, Caroline Cai, Dongsheng Zhang, Guangdou Adun, Humphrey Staffell, Iain Electrical Engineering Department College of Nuclear Science and Automation Engineering Chengdu University of Technology Sichuan China Centre for Environmental Policy Faculty of Natural Science Imperial College London United Kingdom Energy and Environmental Science Division CEPMLP University of Dundee United Kingdom Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University China Operational Research Centre in Healthcare Near East University TRNC Mersin 10 Nicosia99138 Turkey

Replacing fossil fuels with renewable energy sources, including wind and solar power, is a validated approach for mitigating carbon emissions. To maximize renewable energy potential and account for costs and impacts of low carbon energy transitions, research models or modelling tools must provide an accurate representation of the technological and economic capabilities of renewable energy technologies. Hence, this study assesses the electricity generation potential, and costs associated with onshore and offshore wind power, and solar photovoltaic (PV) system, in the Middle East and North Africa (MENA) region based on wind and solar atlas datasets. Unlike existing works in literature, the assessment of offshore wind energy potential and the calculation of the levelized cost of electricity (LCOE) in this study incorporates factors like water depth, distance from shore, and wind speed. Also, the potential of onshore wind energy and solar photovoltaic systems is evaluated by considering variables such as population density, availability of suitable land area, and geographical location, for every grid cell in the MENA region. We found that the average LCOE can be as low as US$40/MWh for solar PV and US$45/MWh for wind power, achieved in Saudi Arabia and Kuwait respectively. This work contributes to existing literature by providing reliable LCOE, capacity factor, and productivity data for solar and wind energy potential estimation in the MENA region. © 2025 The Authors

关键词： Solar fuels

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Distributed Coordinated Emergency Frequency control Strategy for Renewables-Rich Power systems Based on Multi-Agent Reinforcement Learning

SSRN

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SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Spectral Super-resolution and Band-limited Extrapolation Using Slepian Series

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IFAC-PapersOnLine 2019年第27期52卷 388-393页

作者： O. Mihálik D. Michalík P. Jura P. Fiedler Department of Control and Instrumentation Faculty of Electrical Engineering Brno University of Technology Brno Czech republic

Band-limited signal extrapolation and spectral super-resolutions are closely related. They both can be achieved using Slepian series. This method is often believed to be dependent on computing definite integrals. Instead of using numerical integration, a simple and reliable method, which can be implemented in MATLAB, is presented. This way, it is much easier to investigated the extent to which a signal can be extrapolated from its samples taken in a finite interval. The proposed method is tested with data from a real microphone array.

关键词： Prolate spheroidal wave functions Slepian series Signal extrapolation Sampled signals Frequency spectrum Fourier analysis Bandwidth

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Estimation of International Standards for Unconventional Wind Turbine Testing

Estimation of International Standards for Unconventional Win...

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International Universities Power engineering Conference (UPEC)

作者： Aleksandr Lukin Anton Rassõlkin Galina L. Demidova Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9781728110783

ISBN: (纸本)9781728110790

There is a quantum leap in the design of new types of wind turbines in connection with the transformation of energy systems around the world which goes to be distributed. It's need not only be certified but also be analyzed by using international standards to verify the designed prototype quality. Recent study compares technical standards maintained by the Euro-Asian Council for Standardization, Metrology and Certification (EASC) known as GOST with International Electrotechnical Commission (IEC) regulations for wind generators. The motivation for such standardization study is the development of Magnus effect-based wind turbine, which requires special attention as any unconventional wind turbine. This paper presents an analytical review of industry standards applicable to the experimental Magnus effect-based wind turbine. The main groups of the standard that regulates the field of wind energy generation are identified.

关键词： Wind turbines IEC Standards Wind speed Rotors Wind energy generation Testing

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Machine learning-integrated computational analysis on a blood-based ternary nanofluid flow in a stenosed artery with the artificial neural networks and modified finite difference approach

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Chaos, Solitons & Fractals 2025年 199卷

作者： Mohib Hussain Du Lin Hassan Waqas Bagh Ali Nehad Ali Shah School of Mathematics and Statistics Northwestern Polytechnical University Xian 710072 China MIIT Key Laboratory of Dynamics and Control Complex System Northwestern Polytechnical University Xi’an 710072 China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan Hubei 430063 China Department of Software Engineering Faculty of Science & Technology ILMA University 75190 Karachi Pakistan Department of Mechanical Engineering Sejong University Seoul 05006 Republic of Korea

Heart failure and stroke continue to be the most common cause of global death, with atherosclerosis artery stenosis becoming a significant contributor. Although prior research has progressed in comprehending blood circulation behaviour, however, the incorporation of artificial intelligence (AI) and machine learning (ML) in the examination of ternary nanofluids for stenosed arterial diseases signifies a notable breakthrough in this domain. We proposes an integration of ML technique with computational fluid dynamics (CFD) to analyse the non-linear dynamics of thermal characterization in blood-based tri-hybrid nano-fluid flow, influenced by thermal radiation, variable heat sources and sinks, and aligned magnetic field effects within a blood artery exhibiting cosine stenosis. The investigation is based on AI approach, the Levenberg–Marquardt algorithm (LMA), with back propagation Artificial Neural Network (ANN-BP). The mathematical model is developed in the form of partial differentia equations and transformed into ordinary differential equation by similarity scaling, and then numerically evaluated by a modified finite difference approach, the Keller-Box method. The suggested ANN-LMA accuracy is compared to the ML solution for boundary layer flow. Regression values indicate an excellent fit between the predictions and the real data. It is observed that the inclined magnetic angle affects the drag force and heat transfer rate. There is a 27.9% increase in the heat transfer rate for ternary nano-fluid. Conclusively, the non-linear interaction between the magnetic field and nanofluid flow may significantly enhance heat transfer rates, which could have potential applications in biomedical sciences.

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