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Design and comparison of PI and boundary-based predictive controller for control of aeration in activated sludge bioreactor – Simulation and laboratory research

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Journal of Process control 2025年 151卷

作者： Ujazdowski, Tomasz Piotrowski, Robert Nocoń, Witold Stebel, Krzysztof Pośpiech, Jakub Faculty of Electrical and Control Engineering Gdańsk University of Technology G. Narutowicza 11/12 Gdańsk80-233 Poland Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

In this paper, a classical PI and an on/off predictive boundary-based predictive controller (BBPC) algorithms are compared and verified using an ASM3-based model of an activated sludge system with a reactor and secondary settler. A laboratory-scale activated sludge setup is modelled in MATLAB/Simulink and verified using experimental data. BBPC and PI algorithms are compared in two scenarios of batch and continuous operation of the activated sludge process. To accommodate the on/off nature of the actuator, a pulse-width modulation (PWM) module is added to the PI controller, but a modification in the computation of control error is still needed for the PI to control the process properly. The BBPC, on the other hand, while its implementation is complex, proves to be superior in its ability to limit the control costs, the number of switching of the actuator and most importantly, in its ability to instantaneously compensate for the changes in process load. © 2025 Elsevier Ltd

关键词： Predictive control systems

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)

作者： Jilun Tian Jiusi Zhang Yuchen Jiang Hao Luo Congsheng Huang Mo-yuen Chow Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A.

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks.

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Palm Fruit Ripeness Detection and Classification Using Various YOLOv8 Models 9

Palm Fruit Ripeness Detection and Classification Using Vario...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah Yusoff, Nelidya Md. International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Kuala Lumpur Razak Faculty of Technology and Informatics Kuala Lumpur54100 Malaysia

ISBN: (纸本)9798350343380

The significance of palm oil, which contributes 30 % of the world's total vegetable oil production, cannot be overstated. Its numerous applications, ranging from soap to cosmetics, have increased demand, thereby increasing the importance of yield management. Human graders have traditionally been responsible for determining the ripeness of oil palm fresh fruit bunches (FFBs), a task upon which the oil extraction rate (OER) relies heavily. This rate has significant economic implications: a 0.13 % drop in OER due to unripe fruits can result in a staggering RM 340 million loss. Precision is stressed, prompting automated detection research. computer vision and Artificial Intelligence are becoming more effective at assessing oil palm fruit ripeness. However, many methods require complex operations, controlled settings, or manual calibrations. Although innovative, microwave sensors and inductive techniques have drawbacks like sample preparation and equipment dependence. This study investigates the potential of the YOLOv8 framework, particularly its YOLOv8m variant, for ripeness classification and detection. This model's mAP50-95 of 0.927 balances computational efficiency and accuracy, indicating its potential to revolutionize the palm oil industry's fruit assessment procedures. The findings here shed light on the model's efficacy and highlight its potential as an industry-standard solution, bridging gaps in ripeness detection methodologies. © 2023 IEEE.

关键词： Object detection

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Ultra-compact multifunctional Surface plasmon device with tailored optical responses

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Results in Physics 2024年 61卷

作者： Ebadi, Seyed Morteza Khani, Shiva Örtegren, Jonas Department of Engineering Mathematics and Science Education Mid Sweden University Sundsvall SE-85170 Sweden Faculty of Electrical and Computer Engineering Semnan University Semnan Iran

This paper presents the design and numerical investigation of a novel, tunable, and highly efficient metal–insulator-metal (MIM) plasmonic device specifically designed for near-infrared (NIR) applications. The device leverages strategically placed stub resonators within a MIM waveguide. We introduce two small perturbations, a triangle and a rectangle, to achieve remarkable functional versatility. Comprehensive numerical analysis, employing the finite element method (FEM) and validated by the transmission line method (TLM), demonstrates the working principle and excellent agreement between the approaches. Our simulation-driven approach, utilizing a Genetic Algorithm (GA) for accelerated optimization, was crucial in achieving performance levels difficult or costly to reach through purely experimental methods. The GA enabled efficient exploration of a vast parameter space, iterative refinement of device configurations, and fine-tuning of geometric characteristics. This meticulous optimization allows us to control complex interactions within the simulated structure. The proposed device offers diverse functionalities based on adjusted geometrical parameters, including: A. Flat-top band-pass filtering: Achieving a maximum transmission efficiency of 95.8 % within a compact footprint of 420 nm × 540 nm. B. Dual-band band-pass filtering: Maintaining a high transmission efficiency of 88.4 % within a slightly larger size of 450 nm × 540 nm. C. Triple-band notch filtering: Exhibiting minimum transmission (below 1 %) at specific resonance wavelengths for targeted signal suppression. D. Plasmon-induced transparency (PIT) effect: Offering potential applications in various optical functionalities. And, E. Perfect absorption: Achieving a maximum absorption efficiency of 99.62 %, paving the way for efficient light harvesting and manipulation. This multifunctional plasmonic device excels in its combination of compactness, tunability, and diverse NIR functionalities. It holds promise f

关键词： Metal-insulator–metal waveguide Optical filter Perfect absorber Plasmon-induced transparency Plasmonics Stub resonator

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Literature Review of Circulating Current of Parallel Voltage Source Converters

Literature Review of Circulating Current of Parallel Voltage...

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PELS Students and Young Professionals Symposium (SYPS), IEEE

作者： Haoyu Zhang Man-Chung Wong Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macao China

To increase the power density of voltage source converters (VSC) usually use parallel structures. However, parallel VSC will easily introduce circulating current, which can cause a power efficiency decline. This paper gives a review of the circulating current and the solutions of it. Through the analysis of the principle of circulating current generation and the detail of the solutions, this paper gives an overview of the current research stage of circulating current. The technology can be divided into hardware solutions and software solutions are then analyzed and evaluated.

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Identity Division Multiplexing Based Location Preserve in 5G

Identity Division Multiplexing Based Location Preserve in 5G

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2021 International Conference of Technology, Science and Administration, ICTSA 2021

作者： Saeed, Mamoon M. Saeed, Rashid A. Saeid, Elsadig Alzaiem Alazahri University Faculty of Engineering Electrical Engineering Khartoum Sudan Taif University Department of Computer Engineering Taif Saudi Arabia Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences Sana'a Yemen

ISBN: (纸本)9781665428620

Protection of the user is the one of primary points within the coming of 5G portable communications. Area protection has recently been broadly tended to within the writing as crucial tricky issues for the client security. The area protection has moreover been tended to in realeases in versatile eras guidelines. In LTE release 13, the proposal is to utilize pseudonyms such as Cell Radio Network Temporary Identifiers (C-RNTI) rather than permanent identity for protecting area security. The point of this paper is to assess the 3GPP security design and propose a protection capability for protecting area security of the 5G design. The proposed strategy is based on presenting modern variable nom de plumes that can supplant the settled Cell- Radio Network Temporary Identifier (C-RNTI) and in cruel times keep up the compatibility with the standard. The proposed arrangement employments an Identity-Division Multiplexing (IDM) strategy to guarantee the unlinkability and untraceability of the client and to avoid aggressors from following or distinguish clients through C-RNTI personalities. The plot gives secure and viable area administration regarding the security of client security in 5G. The conspire is formally confirmed of satisfactory ensure of user's area security assurance by utilizing ProVerif. © 2021 IEEE.

关键词： 5G mobile communication systems

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Special issue on: Optical wireless communications for emerging connectivity requirements

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 82-86页

作者： Khalighi, Mohammad Ali Ghassemlooy, Zabih Alouini, Mohamed-Slim Hranilovic, Steve Zvanovec, Stanislav Aix-Marseille University Cnrs Centrale Marseille Institut Fresnel Marseille France Faculty of Engineering and Environment Optical Communications Research Group Northumbria University Newcastle upon Tyne United Kingdom Thuwal Saudi Arabia Department of Electrical and Computer Engineering McMaster University Hamilton Canada Department of Electromagnetic Field Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic

The Increasing number of smart devices in different forms and capabilities combined with the worldwide adoption of advanced multimedia applications are contributing to the significant growth of the mobile data traffic. The fifth generation (5G) and beyond-5G wireless networks aim at providing wireless connectivity with very high data rates, low latency, high reliability, and scalability, and will support the emerging Internet-of-Things (IoT) applications and services with massively interconnected devices [items 1), 2) in the Appendix].

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Analysis and modelling of a ML-based NIDS for IoT networks

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Procedia computer Science 2022年 204卷 187-195页

作者： Martina Karanfilovska Teodora Kochovska Zdravko Todorov Ana Cholakoska Goran Jakimovski Danijela Efnusheva Computer Science and Engineering Department Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University Skopje N. Macedonia

As cyber-attacks become increasingly sophisticated, cybersecurity threats continue to increase. Therefore, it becomes a great challenge to detect, identify and prevent adversary attacks. The inability to detect and avert such attacks can break down the credibility of security services, e.g. data confidentiality, availability, and integrity. An intrusion detection system aims to detect, identify and respond to malicious activities involving computing and network resources. This is even more difficult when the network contains Internet of Things (IoT) devices. The process of developing an intrusion detection system is a tremendous challenge due to false alarm rates, low detection rates, unbalanced datasets and response time. This paper gives a comprehensive model of a network intrusion detection system (NIDS) by applying supervised and unsupervised machine learning (ML) over the NF-ToN-IoT-v2 dataset. Using supervised learning, implemented by Azure automated ML (AML), it is shown that the best results are achieved by algorithm XGBoostClassifier, obtaining an F-Score of 98.8%. When a custom made automated ML (AE2EML) is implemented, the best results are achieved by Random Forest Classifier, obtaining an F-Score of 98.6%. Using clustering with PCA (Principal Component Analysis), implemented by PyCaret automated ML, the proposed ML-based NIDS achieves a Silhouette score of 0.553, Calinski-Harabasz index of 1533106 and Davies-Bouldin index of 0.631.

关键词： Automated Machine Learning Classification Clustering Internet of Things Intrusion Detection System NF-ToN-IoT-v2 Dataset

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Modeling of the Network Reconfiguration Considering Electric Vehicle Charging Load 8

Modeling of the Network Reconfiguration Considering Electric...

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8th International Conference on computer and Communication engineering, ICCCE 2021

作者： Manan, Wan Izzat Aiman Bin Wan Abdul Bin Saedi, Azrin Peeie, Mohamad Heerwan Bin Abu Hanifah, Mohd Shahrin Bin International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Universiti Malaysia Pahang Faculty of Mechanical Engineering Pekan Malaysia

ISBN: (纸本)9781728110646

The new electrical motor used as the automotive engines seen to be very efficient which use only clean energy generated as one of the efforts to minimize the carbon emissions. The high usage of electric vehicle around the world for responding to the demands of taking care the ecological system has establish many Electric Vehicles (EV) charging station to accommodate the user. However, high charging loads of fast charging stations result in increased peak load demand, reduced reserve margins and voltage instability. Thus, the optimal distribution network reconfiguration with interconnection of charging station is this solution for this issue. The finding of the optimal reconfiguration in this study are used to improve the voltage profile and minimize the power loss of the distribution system. The best reconfiguration is searched by using Cuckoo Search Algorithm (CSA). The result of the finding then will be verified by comparing to other research papers for its efficiency of the method used. © 2021 IEEE.

关键词： Electric vehicles

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