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Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM Approach on the UNSW-NB15 Dataset

Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Zareen Tasnim Pear Hafsa Binte Kibria Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

In the era of rapid technological advancements, data and information have become significant assets that require continuous protection from unauthorized access and malicious attacks. Network Intrusion Detection Systems (NIDS) can effectively manage the increasing data demands assuring robust security. However, traditional or manual methods are often inadequate to cope with the sophisticated and evolving nature of cyber threats due to their inability to quickly process large volumes of data. Traditional machine learning models are widely used for this purpose. Recently deep learning approaches have become popular in classification tasks showing promising results. This paper introduces a hybrid deep-learning approach for detecting various network intrusions using binary and multi-class (ten classes) classification on the well-known UNSW-NB15 dataset. The proposed hybrid CNN-LSTM model demonstrates significant improvements in intrusion detection, achieving an accuracy of 97.19% for binary classification and 87.70% for multi-class classification, particularly in handling complex attack patterns and reducing false positives. Evaluated using metrics such as precision, recall and F1-score, the model consistently performs robustly across nine distinct attack categories. These results highlight the model's effectiveness in establishing enhanced network security.

关键词： Deep learning Measurement Accuracy Transfer learning Network intrusion detection Telecommunication traffic Network security Feature extraction Convolutional neural networks Protection

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Electric power system stability conditions with automatic voltage regulator 4

Electric power system stability conditions with automatic vo...

引用

4th international Youth conference on Radio Electronics, electrical and Power engineering, REEPE 2022

作者： Zubkova, Irina S. Kuznetsov, Olea N. National Research University 'MPEI' Electrical Power Systems Department Moscow Russia

ISBN: (纸本)9781665414340

Generators of modern power plants are equipped automatic voltage regulator with power system stabilizer (AVR with PSS). The AVR with PSS provide high damping capacities and a significant stability area of the generating equipment in the electric power system. In this case, it is required to choose the values of tuning parameters, such as gain and time constants in the transfer functions of regulator links. On the other hand, generators can operate under equipped automatic voltage regulator without power system stabilizer (AVR). The AVR does not require tuning to the same extent as the AVR with PSS. This paper presents analyzes the influence of the damping circuits of a synchronous generator on the damping of fluctuations of the operating parameters in the transient. Calculations of transients are carried out taking into account various excitation systems on the generator and the scope of application of ARV is proposed. © 2022 IEEE.

关键词： Voltage regulators

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Research on Test Technology of Substation Connected to Centralized Monitoring System under New Energy Background 4

Research on Test Technology of Substation Connected to Centr...

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4th IEEE China international Youth conference on electrical engineering, CIYCEE 2023

作者： Yu, Jingjing Zheng, Yongkang Wu, Hao Wang, Haidong Li, Mengli He, Wenbing State Grid Sichuan Electric Power Co. Ltd Research Institute Chengdu610041 China Sichuan University of Science and Engineering School of Automation and Information Engineering Yibin644000 China Chengdu Production and Operation Center of State Power Investment Group Chengdu610000 China State Grid Sichuan Electric Power Corporation Ganzi Power Supply Company Ganzi626000 China

ISBN: (纸本)9798350305562

In power system automation, substation access centralized monitoring system has a key position, especially in the context of new energy integration, with the rapid development of renewable energy, the power system is experiencing profound changes. To guarantee the reliability and safety of the system, comprehensive testing and verification of substation access to the centralized monitoring system is particularly important. This paper focuses on the research of testing technology of centralized monitoring systems for substation access and describes the current relevant research technology in this field. These include virtualization technologies, testing and commissioning technologies for master stations and substations, automatic point-to-point acceptance technologies, communication network quality assessment, and risk warning technologies. In addition, the paper also comprehensively analyzes the problems and challenges faced in practical applications, such as equipment aging and technical limitations in remote areas. Finally, it is concluded from four aspects that the test technology of the substation connected to the centralized control system needs further study to meet the complex requirements of the power system. © 2023 IEEE.

关键词： Deep learning

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Certain Investigations on Solar Energy Conversion System in Smart Grid 11th

Certain Investigations on Solar Energy Conversion System in ...

引用

11th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2023

作者： Ebbie Selva Kumar, C. Brindha, R. Eveline Pregitha, R. Department of Electrical and Electronics Engineering SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur India Department of Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu Kumarakoil629180 India

ISBN: (纸本)9789819967018

Today the world facing the huge gap between generation and demand of electrical energy, after the huge oil crises the Renewable Energy power generation helps to fulfil the energy gap. There are different Renewable Energy resources are available in nature, form that solar energy source plays a vital role in electrical power generation. In India huge MW of solar parks has been installed with better efficiency but those plants are facing some power quality issues when connected to the load due to some technical reasons like equipment failure, overheating of electrical distribution system, software corruption, circuit board failure, fluctuation on voltage and frequency, etc. The power quality is important characteristics of Renewable Energy system because now a day the connected loads are nonlinear like oscillation in frequency, voltage, and harmonics due to the met and also more sensitive. The smart grid connection also have same problem due to various source connected to the grid such as Solar, Wind, Thermal, Nuclear. This article provide solution to overcome this problem by introducing soft computing-based MPPT control system, filter control system and also for three phase system. Soft computing system will provide quick and cost-effective solution for various complex problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Inertial Navigation for Motion Tracking in Android 24

Inertial Navigation for Motion Tracking in Android

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24th international conference on control Systems and Computer science, CSCS 2023

作者： Dogaru, Iliuta-Daniel Carabas, Costin Tapus, Nicolae University Politehnica of Bucharest Romania

ISBN: (纸本)9798350313390

As the interest in IoT increases, the need for an accurate IPS is more and prominent. An accurate system, specifically designed for location retrieval system is the GNSS. Unfortunately, due to the the signal hindering properties of the building walls and roofs, GPS signals systems are rendered as almost useless. For this specific reason, the indoor positioning raises quite a challenge. Nevertheless, there are solutions which take advantage of existing infrastructures. For example, the current implementation of the Airdocs [1] middleware uses wireless technologies to aproximate location indoors. Even if the WiFi location retrieval technologies are highly useful, their disadvantage is the accuracy as most of the times they depend on the structure of the environment. To cater for this downside, extra systems may be included in the Airdocs IPS, such as inertial measuring units. This paper aims to improve the accuracy of current solutions such as Airdocs. Being already present on the android phones, the accelerometer, gyroscope and magnetometer may offer promising results for IMU construction and integration withing the existing solutions. © 2023 IEEE.

关键词： Ubiquitous computing

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Selective High-Latency Arithmetic Instruction Reuse in Multicore Processors 27

Selective High-Latency Arithmetic Instruction Reuse in Multi...

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27th international conference on System Theory, control and computing, ICSTCC 2023

作者： Buduleci, Claudiu Gellert, Arpad Florea, Adrian Computer Science and Electrical Engineering Department Lucian Blaga University of Sibiu Sibiu Romania

ISBN: (纸本)9798350337983

In this work, we present an original contribution which augments the Intel Nehalem multicore architecture with a selective high-latency arithmetic set-associative reuse buffer. The architecture is simulated using Sniper, which we adapted to estimate the power consumption, area of integration and chip temperature, including latency modifications for the newly added unit. The implementation of a set-associative reuse buffer is a new approach, along with the applicability in a multicore microprocessor, applied to long-latency arithmetical instructions targeting dataflow bottleneck and increasing CPU performance. Additionally, we have performed a manual design space exploration for the enhanced microarchitecture, by varying the associativity and the size of the proposed reuse buffer unit and evaluating the impact on the interested metrics. Our simulations on the Splash 2 benchmarks, revealed an average reuse rate up to 33.27% allowing a maximum speedup of 6.56%. While the energy consumption remains stable, we see an average chip temperature reduction of 2.8°C along with the increase in associativity. © 2023 IEEE.

关键词： Energy utilization

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A Novel LCC-S Compensated Inductive Power Transfer System with Inherent Constant Current and Constant Voltage Charging 2

A Novel LCC-S Compensated Inductive Power Transfer System wi...

引用

2nd IEEE international Power Electronics and Application Symposium, PEAS 2023

作者： Ren, Sheng Yang, Ping Wang, Xiaoqiang Xu, Jianping School of Electrical Engineering Southwest Jiaotong University Chengdu China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350313765

The inductive power transfer (IPT) system offers enhanced safety, convenience, and flexibility for battery charging compared to plug-in systems. The battery requires an initial charging mode of constant current (CC) followed by a subsequent charging mode of constant voltage (CV). To realize it, a novel LCC-S compensated IPT system is proposed in this paper. The proposed topology is equivalent to the conventional S-S topology in CC mode and to the novel LCC-S topology in CV mode as the equivalent resistance of the battery increases. The topology switching is automatic. Consequently, inherent CC and CV outputs can be achieved without the necessity for wireless communication, output sampling circuits, ac switches, additional components, or feedback control circuits, improving system reliability and reducing system cost. Besides, there is a simple two-coil structure to mitigate the issue of cross-coupling, and single capacitor compensation is achieved at the receiver side to facilitate the lightweight charging devices. The automatic switching of the proposed topology is analyzed, designed, and then validated through a laboratory prototype with an output of 48 V/2 A. © 2023 IEEE.

关键词： automatic and smooth switching constant current (CC) constant voltage (CV) novel LCC-S Wireless charging

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An Enhanced Hybrid Deep Learning Model for Efficient automatic Image Captioning 7

An Enhanced Hybrid Deep Learning Model for Efficient Automat...

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2023 7th international conference On computing, Communication, control And Automation, ICCUBEA 2023

作者： Eliyah Immanuel Thavaraj, A. Juliet, Sujitha Anila Sharon, J. Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350304268

Image captioning is a field that overlaps natural language processing and computer vision from deep-learning algorithms. In order to provide a coherent description for high-level picture semantics, it is also essential to be able to analyze the characteristics and the relation between the objects. This paper focuses on developing an approach to creating a semantic image caption that makes maximal use of context and image cognition. To handle image captioning, Linking the detected objects with attention-enhanced deep-learning models is something that interests us significantly. The YOLOv3 is utilized to extract the image's features, and an extended Recurrent Neural Network, RNN (Long Short Term Memory, LSTM) with attention enrichment is then employed in order to construct the caption. We have utilized the attention mechanism to generate captions for images after considering the recognized items in the image scene. The COCO datasets are used for all four proposed variant models and the subset used for testing. To assess how well the various models performed, the semantic similarity analysis between produced descriptions and the actual image description is carried out. Finally, our results are compared to other traditional models. © 2023 IEEE.

关键词： attention-enrichment deep learning Image captioning object detection semantic similarity

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Dual-Population-Iterating and Preys-Gathering MPA for XGBoost Optimization 16th

Dual-Population-Iterating and Preys-Gathering MPA for XGBoos...

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16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Li, Jiacheng Lv, Li Kang, Ping Tian, Xiumei School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819615308

The marine predator algorithm (MPA) often struggles with issues such as low population diversity, susceptibility to local optima, and population aggregation when optimizing XGBoost, which is sensitive to numerous parameters. To address these challenges, we propose the dual-population-iterating and prey-gathering marine predator algorithm (DPGMPA) and apply it to XGBoost parameter optimization. Initially, we utilize a dual-population-iterating strategy to create the original population and an opposition-based learning population. Brownian motion and Laplace distribution respectively control the step size to guide population updates toward the global optimum, enhancing population diversity. Subsequently, the preys-gathering strategy is employed, selecting prey with the highest accuracy in each iteration and having neighboring preys converge towards them to mitigate local optima. Furthermore, we optimize the Fish Aggregating Devices (FADs) by introducing variable weight control, addressing the population aggregation issue. To validate the parameter optimization capability of this algorithm for XGBoost, we conducted experiments on six datasets, using accuracy and F1 score as evaluation metrics, and compared the results with five other algorithms. The experimental results demonstrate that the DPGMPA algorithm more effectively escapes local optima when optimizing the XGBoost classifier, significantly improving XGBoost's classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Laplace distribution Marine Predator Algorithm normal distribution parameter optimization population diversity XGBoost

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Implementation of an automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition 1

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4th international conference on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (数字)9789811971693

ISBN: (纸本)9789811971686

Emotion is a complicated state that influences one's thoughts and behaviour. Recognizing the emotions of a human being is a major research interest in the affective computing after this pandemic situation and which can be applied in medical related fields to cure physical and mental illness. Early detection of stress helps humans to avoid or prevent many diseases related to it. The development of an emotion recognition system using machine learning algorithms has taken a lot of time and effort for researchers and is less focused with Electroencephalography (EEG) signals because EEG signals are noisy, non-linear, and non-stationary. Deep learning algorithms are the most popular solution due to its ability to see images as data. In this paper, we propose a deep learning framework, Gated Recurrent Unit Emotion Recognizer (GRUER) that can detect human emotion with help of EEG signals. This is achieved by implementing four feature extraction algorithms such as Short-Time Fourier Transform (STFT), Wavelet Entropy, Hjorth and Statistical features on dataset and the feature selection method Principal Component Analysis (PCA) is applied to the extracted features to select most significant features to obtain high-accuracy emotion recognizing model. Keras libraries are used to train the model in an appropriate way so that it is neither overfit nor underfit with the data using the Early-stopping function. The performance of the GRUER model is measured using performance metrics such as accuracy, precision, recall and F1-Score are illustrated in the results. The accuracy of the GRUER is 98% and it is a 3-dimensional model which has valence, arousal and dominance for emotion detection. The model loss obtained by GRUER is 1.12 which is low when compared to other models. Finally, the suggested method and its results show that this proposed method outperforms numerous existing emotion recognition systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Classification (of information)

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