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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region 5

Analysis of Energy Potential of Micro Wind Turbines at Three...

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5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Valov, Nikolay Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Kadirova, Seher Evstatiev, Boris Mihailov, Nicolay University of Ruse "Angel Kanchev" Dept. of Automation and Electronics Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Science Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Electrical Power Engineering Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798350352863

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations - Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m2 in February, followed by Calarasi with 0.38 kWh/day/m2. The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m2. The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines. © 2024 IEEE.

关键词： Windmill

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An Architectural Approach for Enhanced Data Interoperability Across Building systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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Automatic Network Intrusion Detection System Using Machine Learning and Deep Learning

Automatic Network Intrusion Detection System Using Machine L...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Mynuddin, Mohammed Khan, Sultan Uddin Chowdhury, Zayed Uddin Islam, Foredul Islam, Md Jahidul Hossain, Mohammad Iqbal Ahad, Dewan Mohammed Abdul North Carolina A and T State University Dept. of Electrical Engineering United States Data Scientist Data Bid Machine Inc. Associate Power Systems Engineer-II Open Systems International Inc. Tuskegee University Dept. of Electrical Engineering United States University of North Carolina at Charlotte Dept. of Electrical and Computer Engineering United States

ISBN: (纸本)9798350350524

In recent years, the popularity of network intrusion detection systems (NIDS) has surged, driven by the widespread adoption of cloud technologies. Given the escalating network traffic and the continuous evolution of cyber threats, the need for a highly efficient NIDS has become paramount for ensuring robust network security. Typically, intrusion detection systems utilize either a pattern-matching system or leverage machine learning for anomaly detection. While pattern-matching approaches tend to suffer from a high false positive rate (FPR), machine learning-based systems, such as SVM and KNN, predict potential attacks by recognizing distinct features. However, these models often operate on a limited set of features, resulting in lower accuracy and higher FPR. In our research, we introduced a deep learning model that harnesses the strengths of a Convolutional Neural Network (CNN) combined with a Bi-directional LSTM (Bi-LSTM) to learn spatial and temporal data features. The model, evaluated using the NSL-KDD dataset, exhibited a high detection rate with a minimal false positive rate. To enhance accuracy, K-fold cross-validation was employed in training the model. This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm in achieving superior performance across metrics like accuracy, F1-score, precision, and recall. The binary classification model trained on the NSL-KDD dataset demonstrates outstanding performance, achieving a high accuracy of 99.5% after 10-fold cross-validation, with an average accuracy of 99.3%. The model exhibits remarkable detection rates (0.994) and a low false positive rate (0.13). In the multiclass setting, the model maintains exceptional precision (99.25%), reaching a peak accuracy of 99.59% for $\text{k-value} =10$. Notably, the Detection Rate for $\text{k-value}=10$ is 99.43%, and the mean False Positive Rate is calculated as 0.214925. © 2024 IEEE.

关键词： Classification (of information)

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Intrusion Detection System Based on Machine Learning Techniques: A Survey 2

Intrusion Detection System Based on Machine Learning Techniq...

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2nd International Conference on Advances in engineering Science and Technology, AEST 2022

作者： Sheet, Omar I. Ibrahim, Laheeb M. University of Mosul College of Computer Sciences & Math Software Engineering Dept. Iraq

ISBN: (纸本)9798350334906

The volume of network traffic data has become so big and complicated as a result of the development in Internet-based services that it is extremely difficult to process using typical data processing techniques. Due to the enormous and complicated nature of network traffic data, fast and effective cybersecurity intrusion detection is a highly difficult task. In order to detect hostile traffic as soon as feasible, a realistic cyber security intrusion detection system should be able to handle a huge volume of network traffic data as quickly as *** paper studies a classification-based intrusion detection algorithms based on Machine Learning for speedy and effective intrusion detection in huge network traffic, including Support Vector Machines (SVM), Random Forests(RF), Decision Trees(DT), Naïve Bayes (NB), Deep Neural Network (DNN), Extreme Gradient Boosting (XG Boost), Nondominated Sorting Genetic Algorithm (NSGA2), as well as Deep Belief Network (DBN) and artificial neural network(AI). It has been applied to ACTUAL TIME The KDD dataset, KDD Cup 99 dataset, NSL-KDD dataset, CICIDS-2017 dataset, CICIDS-2018 dataset, ISCX Dataset, CICAndMal2017 dataset and UNSW-NB15 to compare performance based on rating Intrusion detection systems which are assessed in terms of training time, prediction time, accuracy. © 2022 IEEE.

关键词： Intrusion detection

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PREDICTING SHORT-TERM TRAFFIC WITH RANDOM FOREST: A GRANULAR DATA APPROACH

PREDICTING SHORT-TERM TRAFFIC WITH RANDOM FOREST: A GRANULAR...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Mustafa, Ahmad Khattak, Khurram Shehzad Khan, Zawar Hussain Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar Pakistan College of Computer Science and Engineering University of Ha’il Ha’il Saudi Arabia

ISBN: (纸本)9781837243235

Accurate short-term traffic flow prediction is critical for improving the efficiency of Intelligent Transportation systems (ITS), particularly in heterogeneous traffic environments. This paper presents a data-driven model based on the Random Forest (RF) algorithm to predict traffic flow using high-resolution data collected over a week. The dataset encompasses individual vehicle speed, vehicle classification (car, bicycle, motorbike, bus), timestamp, flow rate, peak hour factor, traffic density, time and distance headway, and density range, recorded from 9 AM to 5 PM over seven consecutive days. Six days of data are used for training the RF model, with Thursday’s traffic flow serving as the test case. The dataset features high-dimensional, heterogeneous traffic attributes, enabling the model to capture non-linear and complex interactions effectively. Our results demonstrate that the RF model effectively captures complex, non-linear relationships in the data, delivering highly accurate predictions with an R-squared value of 0.997. This study contributes to advancing machine learning applications in traffic management, emphasizing the importance of feature selection in enhancing model performance. Future research will investigate the inclusion of additional factors such as weather conditions and external events to further refine prediction accuracy. © The Institution of engineering & Technology 2025.

关键词： Ensemble intelligent transportation system Machine learning Random Forest traffic flow prediction

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Improvement of Protection Factor for a Controlled Laboratory computer Network Using Artificial Intelligence

Improvement of Protection Factor for a Controlled Laboratory...

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2023 International Conference on engineering, Science and Advanced Technology, ICESAT 2023

作者： Daoud, Raid W. Aziz, Enas F. Abdullah, Zaki M. Saleh, Ziyaad H. Northern Technical University Electrical Tech. Dept. Kirkuk Iraq Northern Technical University Computer Systems Dept. Kirkuk Iraq College of Petroleum Processes Engineering Petroleum Systems and Control Dept. Tikrit Iraq

ISBN: (纸本)9798350344400

computer networks provide exceptional privacy, primarily for testing and other educational activities. However, the security of computer networks is an exceptionally touchy subject, mainly when applied in labs and tests for educational purposes. Any software system created on a server typically requires a username and password to access a collection of computers on a particular network. The proposed work devised and implemented a neural network feedback network with four inputs (I.P. address, MAC address, user number, and time) and an output that represents the user's acceptance or rejection. A few instances of the data and numbers needed to train the neural network include the lab's address, the student's name, the academic stage's address, etc. Good results were obtained through the company's training and education, which was tested on more than 200 models, and the response aligned with the data. No user can access the network without first being recognized by the proposed network by comparing the pre-trained data to ensure they are authorized to take the exam or enter for any other reason. © 2023 IEEE.

关键词： Network security

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An In-dept. Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Semi-Analytical Methodology for Advanced Filter Design in Chirped-Managed Lasers

Semi-Analytical Methodology for Advanced Filter Design in Ch...

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2024 Optical Fiber Communications Conference and Exhibition, OFC 2024

作者： Maram, Reza Alam, Md Samiul Arif Shahriar, Kh Ricciardi, Pasquale Plant, David V. Fonex Data Systems Inc. MontréalQC Canada McGill University Dept. of Electrical and Computer Engineering MontrealQC Canada

We introduce a novel semi-analytical method for the deterministic design of advanced optical filters in chirped-managed lasers (CMLs), enhancing transmission reach for access networks. This approach can be applied to ... 详细信息

ISBN: (纸本)9781957171326

关键词： Optical fibers

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications 24

Resource Efficient Federated Learning for Deep Anomaly Detec...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Lalos, Aris Industrial Systems Institute Athena Research Center Greece University of Patras Dept. of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350339598

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%. © 2023 IEEE.

关键词： Anomaly detection

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