Miracle-dz/UniFAR-FTUs huggingface dataset:
ML-based Alert Correlation Algorithms For DER Cyber Situational Awareness

The wide integration of Distributed Energy Resources (DER) in the smart grid has exposed them to various cyber and physical threats. To support security and provide resilience, intrusion and anomaly detection systems (IADS) have been widely used. However, the dynamic real-time nature of DER networks poses several challenges to the detection process resulting mainly in low-level representation of intrusions, unmanageable number of IADS alerts and high false-positive rates, leading to an impractical and time-consuming response process. Alert correlation can help provide cyber situational awareness for DER networks by identifying the relationships between alerts. This paper proposes a hybrid 2-tier multi-stage alert correlation model that utilizes several similarity-based and spatial-temporal statistical-based machine learning (ML algorithms to provide cyber situational awareness and detection confidence tailored for DER characteristics while being protocol and environment neutral and IADS platform agnostic. The proposed model was trained with DER-specific datasets and features to reduce, verify, and track known/unknown attacks at a fine granularity. This results in higher-level correlated graphical and incident situational representations allowing for severity-based effective mitigations. The model was evaluated into a realistic 2-tier hardware-in-the-loop (HIL) smart grid DER testbed environment with multiple distributed correlation sensors and a centralized cloud-based correlation master engine. The proposed model achieved high correlation completeness (97.67%, high correlation accuracy (99.2%, real-time feasible latency ($\approx$0.12ms, high reduction ratio ($\approx$752:1, and very low false-positive and false-negative correlation rates (0.066% and (0.023% respectively, for real-time deployment.
A Blockchain based solution to manage vehicle documents using QR-Code

Driving became a life-skill with the increase in the number of vehicles on Indian Roads. You need all vehicle-related documents while driving a vehicle on public roads. Traffic Officers will ask you to produce them for verification. Carrying these documents physically is a cumbersome task with a high possibility of documents getting lost or in a poor readable form. Remember the authenticity of these vehicle documents are also debatable. With so many flaws in the current system, there is a need for a system competent of reducing the task of carrying these vehicle documents and also guarantees the authenticity and transparency. As per the Motor Vehicles Act in India, it is mandatory that when driving a vehicle, drivers must carry vehicle-related documents such as Registration Certificate Book, Vehicle Pollution under Control (PUC) Certificate, Vehicle Insurance Policy. As a solution, we are proposing a Decentralized Application where drivers can upload and access their vehicle documents details using OCR anywhere, anytime digitally. A Quick Response Code (QR-Code) is used to store URL generated by the vehicle owner by uploading the vehicle documents, which can be later scanned by the Traffic Officer for verification purpose. The application notifies the vehicle owner on expiration of any documents that must be renewed. The application helps in reducing the overhead efforts made by the Traffic officers for vehicle verification. The objective of the system is to digitize all documents, ensure authenticity of documents, eliminate the use of fake documents, manage vehicle documents, reduce the workload and time.
Answering Why-Not Questions on Range-Based Skyline Queries for Incomplete Data in Road Networks

Currently, knowledge and technology in data engineering are advancing rapidly. One area of development is data retrieval techniques with specific queries. Data that does not appear in certain query results raises the question of "why not? " Answering this question can be used to evaluate why the data does not appear in a particular search query.The why-not question can also be applied to range-based skyline queries. Skyline queries aim to find "interesting" data from any multidimensional data, e.g. set of spatial data with coordinates that can be mapped onto a Cartesian plane. In this case, the data being analyzed is road network data. However, not all acquired data has complete attributes. Sometimes, due to errors, certain attributes of the data are not recorded. Data that does not have a value for all attributes is referred to as incomplete data. This article aims to address the why-not problem in range-based skyline queries for incomplete road network data. Two strategies are used to answer this question: modifying attributes other than point coordinates and adjusting the maximum query distance. The computational algorithms compared are the local skyline approach and the naive one. The experimental results demonstrate that the proposed algorithm performs better than the naive approach in terms of execution time.
Effective Auto Data Processing Approach Based On Combined ML And CNN For Breast Cancer

In current era, there are various auto intelligent approaches to perform enormous activities related to healthcare as it needs. As learning concept plays a vital role to perform certain activities like learning the data’ s obtained and monitoring that patient behaviour in healthcare. As patient is concern, the heterogeneity problem occurs during the process of data extraction and prediction and also it overcome the problem of improper data accessibility as it needs to be mitigated for the patient’ s data related to healthcare. Here In order to better evaluate patient monitoring using patient care indicators like cost and staying length, we present an automated approach that integrates AI and IoT while also taking into consideration the challenge of heterogeneity. As the problem of heterogeneity is considered in the data processing to extract the data through certain set of rules to form another representation of data. Based on the pre-treatment information, post-treatment can be monitoring based on the prediction phase through modified Combined Random Forest and Convolutional Neural Network Algorithm to discuss how the effective prediction helps the doctor helps to processes in process treatment by deploying the automated framework with the integration of AI and IoT for real time health monitoring system. Here we have integrated the three phases process namely, pre-treatment, in treatment and post treatment. Here the breast cancer patient is considered, as it takes numerous numbers of small information’ s as it helps the patient to stay for longer time with longer treatment, which needs proper diagnosis for the treatment and improve the cost and staying possibilities parameters. In the performance analysis, history of the preference of the specific data and retrieve those data effectively based on cost and resource parameters to monitor health of the patient. Here the validation of the framework evaluation based on scalability and reliability improvement.
Research on Defect Detection Method of Transmission Line Insulator

Insulators are the key components of transmission lines, and their defect fault detection plays an important role in the patrol inspection of transmission lines. Traditional methods mainly use image processing technology to detect by manually labeling features, and cannot realize automatic recognition. Deep learning method uses convolutional neural network to learn defect features, which can automatically detect defects. Compared with traditional methods, it is more efficient and robust, and is more conducive to insulator defect detection. This paper reviews the research progress of traditional methods, deep learning methods R-CNN and YOLO series algorithms in insulator defect detection, analyzes the problems of sample imbalance and small target detection difficulties, and analyzes the future research trend based on the current difficulties.
Temporal Convolutional Network-based Locational Detection of False Data Injection Attacks in Power System State Estimation

In power system state estimation, it is important to detect and remove false data from measurements to ensure accurate estimation of the system state. The locational detection of false data injection attacks (FDIAs) is crucial for the timely and effective mitigation of the attack. In this paper, the FDIA detection is carried out by employing the temporal convolution network (TCN) model in the classification mode and its performance is compared with other state-of-the-art machine learning (ML) and deep learning (DL) models. A TCN has the ability to capture the spatial as well as temporal dynamics of the data in the transmission system and this feature can be used to identify the location of FDIAs. The optimal performance of the TCN model is achieved by testing the model with a different number of layers and dilation rates. The FDIAs include structured and unstructured attacks, the former can be detected with the TCN model and the latter with the traditional bad data detection (BDD) algorithm. The simulation studies are carried out on the IEEE 118-bus system and the corresponding results show that the TCN model has outperformed the other ML/DL approaches in the identification of FDIAs.
PM2.5-O3 Compound Pollution Pattern Recognition Based on Hybrid Model of tensor Decomposition and Spectral Clustering

Clustering intra-urban areas with the same pollution characteristics can help to accurately manage intra-urban air pollution. In this study, a new classification model for PM2.5-O3 composite pollution features is established based on tensor decomposition and spectral clustering methods. The tensor decomposition was utilized to extract pollution features from massive air pollutant concentration data, and the pollution concentration data were decomposed into day-by-day, intra-day, and spatial modes, which provided a low-dimensional space for the subsequent spectral clustering and fine clustering classification process of the urban spatial-scale PM2.5-O3 composite pollution conditions. The model is explored by taking the annual PM2.5 and O3 pollution conditions in Beijing in 2017 and 2020 as an example, discussing the differences in geographic locations and pollution characteristics between the different models, and explaining the changes in the city’s pollution characteristics from 2017 to 2020. Through this new hybrid method, scientific support can be provided for urban refinement and precise management.
Unveiling Alzheimer's Disease Diagnosis with Convolutional Neural Networks: Visualizing Features and Class Activation Mapping

This research paper explores the utilization of CNNs for diagnosing Alzheimer's disease, delving into techniques that enhance their interpretability and decision-making process. Feature visualization and class activation mapping provide insights into how CNNs detect the disease, emphasizing the importance of interpretability in medical imaging. The methodology involved skull stripping to isolate brain structures by removing non-brain tissues, followed by skull cropping to focus on the region of interest, improving the CNN model's accuracy and efficiency. Through training on the preprocessed dataset, the research achieved an impressive 99.92% accuracy in Alzheimer's disease detection, highlighting CNNs' potential for diagnosis. The study underscores the significance of understanding neural network decision-making, offering valuable insights into deep-learning models for medical imaging tasks. These findings contribute to improved Alzheimer's detection methods and enhance our understanding of applying deep-learning models in medical imaging, benefiting other diagnostic imaging tasks and advancing disease detection.
Innovative Communication Solutions for Specially-Abled Individuals: A Machine Learning-Approach to EEG Interpretation

In this study, the use of electroencephalography (EEG) as a brain-computer interface (BCI) for people with communication difficulties is being investigated. The study aims to decode EEG data into words, providing an alternative means of communication for patients with ailments like ALS, stroke, or any physical brain injuries. Raw data were preprocessed, and features were extracted using 24 signal features to convert continuous time-series data to discrete features for word classification. Out of the total features, feature selection was performed using two techniques: K -best features using ANOVA f-score and recursive feature elimination using an SVM kernel. Then various machine learning and deep learning techniques were used to analyse the data to classify the intended communication activities from the EEG signals. The results concluded by providing the best interclass accuracy of 46.10% using a multi-layered perceptron model with selected 200 features using recursive feature elimination. The findings of this research will aid in creating more effective and convenient BCI systems that can enhance the quality of life for individuals with communication disabilities.
Performance Evaluation of 5G Sidelink Positioning

With the advent of Release 18 in the 5G standard, the direct link between the users, termed sidelink (SL), has been specified to support positioning of a target user, in addition to the uplink/downlink-based positioning. In this paper, we describe the SL positioning specifications at a high level, present relevant challenges, and evaluate the impact of various system-level parameters on the achievable positioning accuracy. In particular, we analyze the impact of the reference signal bandwidth, availability of anchor users, as well as the synchronization error among the users on the horizontal positioning accuracy. Obviously, a reduced bandwidth or number of anchors as well as synchronization errors will have a detrimental effect on the accuracy. However, our analysis reveals the necessary bandwidth, the tolerable number of anchors and synchronization error for a given positioning accuracy requirement.
Estimation of 5G End-to-End Delay through Deep Learning based on Gaussian Mixture Models

Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an End-to-End (E2E) delay estimation method for 5G networks through Deep Learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are then used as labeled data in a supervised deep-learning stage. A multilayer neural network model is trained using the labeled data and assuming different lengths for each training sample. The accuracy and computation time of the proposed Deep Learning Estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for various inputs in different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on EM in terms of the accuracy of the E2E delay estimates. The estimation method is characterized for different 5G scenarios, showing that when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.8 to 2.7 times.
Electric Vehicle Battery Swapping Demand Prediction Based on Feature Selection and Bidirectional Temporal Convolutional Network

Electric vehicle swapping station is an innovative facility gradually emerging with the rapid development of renewable energy vehicle market in recent years. The proposal of swapping mode effectively makes up for the problems of long charging time and inconvenient charging of electric vehicles in charging mode, so it is of great significance to reasonably predict the swapping demand power of electric vehicle battery swapping station (BSS). This paper proposes an electric vehicle swapping demand prediction method for swapping stations based on feature selection and bidirectional temporal convolutional network (BiTCN). Firstly, Pearson Correlation Coefficient (PCC) was used to identify the related features of electricity swapping demand as the input series. Secondly, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was implemented to smooth the electricity swapping demand series. Then, the relationship between the high-frequency series and the filtered influence features was extracted through the BiTCN, and the prediction results of the high-frequency series were obtained, and the low-frequency series were predicted by XGBoost. Finally, the prediction results of the sub-series were reconstructed to obtain the final load prediction results.
Physics-Informed Neural Network-Based Predictive Model for Virtual Synchronous Machines in Inverter-Based Microgrids

Virtual synchronous machines (VSMs) have recently emerged as a practical solution for incorporating virtual mechanical inertia into power systems using power electronic converters to add frequency support to the grid to handle the increase in the rate of change of frequency (RoCoF) that may lead to stability problems. Analyzing this concept compared to frequency-droop-based control schemes provides valuable physics-based insights into the tuning and operation of VSM controllers. However, the design and control of VSMs necessitate accurate predictive models that can adapt to microgrids' dynamic and complex nature. Physics-informed neural network (PINN) is an emerging discipline that combines principles from physics and neural networks to enhance the accuracy and efficiency of predictive models. By incorporating the fundamental laws and equations that govern a system, PINNs guide the learning process of neural networks and improve the interpretability of the resulting models. This study proposes PINNs trained to predict the VSMs model while ensuring compliance with the fundamental principles of physics described by the conventional swing equation governing synchronous machines (SMs). The effectiveness of the predicted model has been verified via simulations carried out using MATLAB software, demonstrating evidence of its potential to improve microgrid stability and power quality.
Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control operations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. In this paper, a cybersecurity assessment framework is introduced, alongside several pertinent operational scenarios that researchers can leverage for DER-rich distribution grids. The feeder model is accurately represented within ePHASORSIM, a real-time simulation tool, allowing users to conduct simulations of cyber-attacks within a real-world environment and to analyze power distribution operations under vulnerable conditions. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.
An Automatic Generation of Test Cases in JUnit Testing Framework Using Genetic Algorithm

The testing phase is one of the most important phases of the software life cycle, which ensures the efficiency and quality of the software product. Many tests are conducted on the software, not only to detect errors, but also to provide a complete picture of the quality of the outputs provided by the program and to increase confidence in all of the functions. Due to the difficulty in estimating most of the test data and the time spent in this process, many techniques are used in the automatic generation of test cases. The genetic algorithm is one of the most important used methods in this process, and it has achieved great success in generating limited and appropriate test cases to give a complete conceptualization of the function under test. The Junit testing framework is used in automated testing processes within test-based development methodologies, but it lacks of a mechanism to specify and generate a specific test data that put the program under test in the required scenario. In order for the programmer to be able to perform the test automatically in an integrated way within JUnit testing framework, the genetic algorithm has been improved in this research and emerged into the JUnit testing framework to fully automatic testing and generate various numeric test cases at the unit-test stage. Based on testing of the proposed algorithm within the Junit testing framework, it has proven its effectiveness and facilitation of generating perfect specific numeric test cases.
Advanced GaN Wideband/Multiband Power Amplifier for Sub-6 GHz 5G and Beyond Wireless Communication: Toward Future Flexible Base Station by AI-Based Digital Assisted PA

The ever-increasing data rate and number of connections in mobile communication offer exciting user experiences in everyday life. Technological developments for beyond-5G and 6G wireless communications have occurred in recent years. Compared with current 5G networks, the next-generation network aims to deliver 10–1,000 times higher data rates with improved enhanced mobile broadband, a 1/10th lower latency based on ultrareliable and low-latency communications, and our society is expected to undergo a revolutionary change within the coming of a new era <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>.
Color-Based Time Synchronization for Future Networks: Advantages, System Architecture, and Potential Use Cases

Given its relevance to industrial applications, over-the-air time synchronization (TS) is anticipated to play a crucial role in future communication networks. Thus, we investigate the potential of optical wireless signals for TS in various applications, including high-precision manufacturing and power distribution. More specifically, in this article, we propose the use of color-based encoded timestamps for TS. In the proposed technique, the master node transmits timestamps to the existing slave nodes encoded in optical signals. To do this, we introduce the implementation of the color-based TS technique and highlight its merits. It is concluded that color-based optical signals can be efficiently used for TS in future networks.
Feature Collusion Attack on PMU Data-driven Event Classification

Event classification is a critical task to ensure the reliability of the power system. Recent developments in event classification methods leverage data-driven techniques with fine-grained Phasor Measurement Units (PMU) data. However, the existing event classification methods are vulnerable to different types of adversarial attacks that can significantly degrade the event classification performance. In this paper, we evaluate the vulnerability of the classification models against feature collision attacks on PMU data. Feature collusion attack leverages a surrogate model to learn the victim’s classification model which in turn makes it a plausible attack strategy for both black-box and white-box settings. Specifically, this attack strategy undermines the accuracy of the classification models by crafting poisonous samples that share common features with benign samples which in turn changes the decision boundaries of the classification models. The experimental results on real-world PMU data in a black-box setting show that generating and adding poisonous samples into the model training dataset can significantly degrade the accuracy of current event classification methods.