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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Real-Time Classification of Forest Fire Sound Using CNN on SONY Spresense Microcontroller 8

Real-Time Classification of Forest Fire Sound Using CNN on S...

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8th International Conference on information technology, InCIT 2024

作者： Chinda, Buncha Chantharangsee, Jongphon Prachamkit, Nutthawut Rattarom, Suwitchaya Teeravech, Kumpee Niyomwong, Tippawan Rambhai Barni Rajabhat University Faculty of Computer Science and Information Technology Chanthaburi Thailand

ISBN: (纸本)9798350366303

This paper introduces an autonomous system for fire detection using real-time audio processing and artificial intelligence on the Sony Spresense microcontroller. The system captures environmental sounds, applies low-pass filtering and downsampling, and generates spectrograms for classification by a Convolutional Neural Network. Trained on a dataset of 8,129 fire-related and 5,000 non-fire audio samples, the model achieved a 99.66% F1-score. Initial testing revealed variability in detection, but retraining on an expanded dataset improved accuracy, consistently identifying over 70% of fire sounds. The final 136 KB model balances performance and memory constraints, providing a solution for early wildfire detection with minimal human involvement. © 2024 IEEE.

关键词： Microcontrollers

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

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IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

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CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays

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Multimedia Tools and Applications 2024年第41期83卷 88681-88698页

作者： Mehmood, Muhammad Hamza Hassan, Farman Rahman, Auliya Ur Khan, Wasiat Mostafa, Samih M. Ghadi, Yazeed Yasin Algarni, Abdulmohsen Ali, Mudasser School of Computer Science and Technology University of Science and Technology of China Hefei China University of Bologna Bologna Italy UET Peshawar KPK Peshawar Pakistan UST Bannu KPK Bannu Pakistan Computer Science Department Faculty of Computers and Information South Valley University Qena83523 Egypt Department of Computer Science Al Ain University Al Ain United Arab Emirates Department of Computer Science King Khalid University Abha61421 Saudi Arabia Arid University Punjab Pakistan

In recent years, the COVID-19 outbreak has affected humanity across the globe. The frequent symptoms of COVID-19 are identical to the normal flu, such as fever and cough. COVID-19 disseminates rapidly, and it has become a prominent cause of mortality. Nowadays, the new wave of COVID-19 has created significant impacts in China. This virus can have detrimental effects on people of all ages, particularly the elderly, due to their weak immune systems. The real-time polymerase chain reaction (RT-PCR) examination is typically performed for the identification of coronavirus. RT-PCR is an expensive and time requiring method, accompanied by a significant rate of false negative detections. Therefore, it is mandatory to develop an inexpensive, fast, and reliable method to detect COVID-19. X-ray images are generally utilized to detect diverse respiratory conditions like pulmonary infections, breathlessness syndrome, lung cancer, air collection in spaces of the lungs, etc. This study has also utilized a chest X-ray dataset to identify COVID-19 and pneumonia. In this research work, we proposed a novel deep learning model CP_DeepNet, which is based on a pre-trained deep learning model such as SqueezeNet, and further added three blocks of convolutional layers to it for assessing the classification efficacy. Furthermore, we employed a data augmentation method for generating more images to overcome the problem of model overfitting. We utilized COVID-19 radiograph dataset for evaluating the performance of the proposed model. To elaborate further, we obtained significant results with accuracy of 99.32%, a precision of 100%, a recall of 99%, a specificity of 99.2%, an area under the curve of 99.78%, and an F1-score of 99.49% on CP_DeepNet for the binary classification of COVID-19 and normal class. We also employed CP_DeepNet for the multiclass classification of COVID-19, pneumonia, and normal person, in which CP_DeepNet achieved accuracy, precision, recall, specificity, area under curve

关键词： COVID-19

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Using Adversarial Training in Developing Robust Models for COVID-19 X-Ray Detection 14

Using Adversarial Training in Developing Robust Models for C...

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14th International Conference on information and Communication systems, ICICS 2023

作者： Odeh, AbdAlRahman Alomar, Ayah Aljawarneh, Shadi Jordan University of Science and Technology Faculty of Computer and Information Technology Irbid Jordan

ISBN: (纸本)9798350307870

COVID-19 is a respiratory system disease that has led to a global pandemic and researchers have studied this disease and come up with various solutions. As extra support for Reverse Transcription Polymerase Chain Reaction (RT-PCR) testing or rapid kit testing and to achieve the lowest possible diagnostic error, one of these computerized solutions is using deep learning models for COVID-19 diagnoses through lung X-Ray images. Nevertheless, using advanced technologies introduced advanced risks that require countermeasures such as adversarial attacks The criminal could add some perturbations to the images that cannot be noticed by the human eye but lead the model to misclassify the image. No research is done on developing more secure models for COVID-19 detection, thus in this paper, a new approach is introduced by using neural structured learning to apply adversarial training and train models on perturbed images to make them more robust and secure. After applying this approach to various transfer learning models and experimenting with different values for the hyperparameters, our results have shown that the originally trained models suffer from drawbacks in the performance of 55.25% accuracy on average when tested on perturbed data. On the other hand, the new models that are trained using transfer learning show more robust results with just a drawback of 0.91% in accuracy on average. Such robust models can benefit those who are associated with the medical field. © 2023 IEEE.

关键词： COVID-19

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The Optimum Switching Bilateral of Various Mix Noise Filters to reduce Medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

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Performance Analysis on Block Size Valuation of Hyperledger Fabric Blockchain

Performance Analysis on Block Size Valuation of Hyperledger ...

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Wai, Khin Su Su Thein, Nwe Nwe Myint University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9781665435994

Blockchain is an innovative technology and a decentralized ledger of immutable digital data. Hyperledger Fabric (HLF) blockchain is a permissioned ledger framework. The Fabric is made up of different parts, including endorsers, committers, validators, orderers and chaincodes. Although HLF is a scalable modular architecture, the configuration parameters of block size valuation in the orderer section impact the performance of a blockchain-based system. The ratio of batch timeout and block size should be balanced to increase the system performance. The block size valuation also affects the committed block height in the blockchain. In this paper, the effect of configuration factors such as batch timeout and block size (batch size) is analyzed to implement the optimizing blockchain-based storage system. Hyperledger Caliper tool is used to measure the transaction throughput and average latency for the system performance. The block height are studied based on the different batch timeout and block sizes for the numbers of concurrent users in an organization. © 2023 IEEE.

关键词： Blockchain

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Smart Energy Management system Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on information technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. system can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Enhancing Grid Reliability in the Baltic Region: Integrating Renewable Energy and ENTSO-E Synchronization 13

Enhancing Grid Reliability in the Baltic Region: Integrating...

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13th IEEE International Conference and Expositions on Electric and Power Engineering, EPEi 2024

作者： Sahnovskis, Aleksandrs Zicmane, Inga Kovalenko, Sergejs Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798350356199

The integration of renewable energy sources within the Baltic energy system, which includes Latvia, Lithuania, and Estonia, presents both challenges and opportunities. As the Baltic countries strive to increase the share of renewable energy to 42.5% by 2030, driven by the EU's 'Fit for 55' package, they must address the inherent variability of renewable sources like wind and solar, which can complicate grid *** to synchronize the Baltic energy system with the ENTSO-E (European Network of Transmission system Operators for Electricity) further emphasize the need for a resilient grid. A simplified model of the Baltic energy system is used to explore four scenarios-Optimistic, Realistic+, Realistic-, and Pessimistic-focusing on electrical connections in base load, export, and import modes. The analysis highlights the critical role of BESS systems (Battery Energy Storage systems) in stabilizing the grid and managing power deficits. Despite ongoing development of interregional transmission lines and BESS installations, enhancing energy storage and diversifying energy sources remain crucial for ensuring a stable and sustainable energy *** investments in alternative energy sources, such as small hydropower or nuclear energy, alongside advanced storage solutions, are essential to support the region's ambitious renewable energy goals. By boosting energy system resilience and reducing dependence on variable renewable sources, the Baltic region can secure a reliable and environmentally friendly energy supply, significantly contributing to global efforts to combat climate change. © 2024 IEEE.

关键词： Clean energy

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