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Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems. [ABSTRACT FROM AUTHOR]

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips. In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles. While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency. This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems. [ABSTRACT FROM AUTHOR]

In Cyber-Social Systems (CSS), sensors play a key role in the intelligent perception and recognition of people, objects and the environments, in which target localizing is one of the important issues. It is often difficult to accurately and conveniently localize the sensors due to the presence of Non-Line Of Sight (NLOS) when there are obstacles. This paper proposes a sensor localization method based on WiFi (Wireless Fidelity) localization, which first collects the angle and intensity information of the WiFi signals of mobile base stations during a linear or curvilinear motion, then uses the Nonlinear Inequality Constrained-Unscented Kalman Filter (NIC-UKF) method presented in this paper to filter interference, so as to localize the positions of sensors. The method has stronger stability, high precision, and acceptable computing overhead, which is suitable for the localization of fixed or low-speed moving targets in Cyber-Social environments with obstacles. [ABSTRACT FROM AUTHOR]

• This paper proposes a smart contract-based architecture to improve the security of healthcare big data. • Here, healthcare big data is encoded and processed in IPFS decentralized storage, which decreases the processing overhead. • The access control and integrity checking features of the proposed work enhances healthcare data security. Big data refers to a very large and diverse set of data that grow at exponential rates. In the modern healthcare system, medical big data face many security issues due to the presence of hackers and malicious users. Nowadays, medical big data are tremendously benefitted by Blockchain technology due to its several features, namely decentralization, confidentiality, security, privacy, etc. Nevertheless, the conventional cloud and client-server-based information storage models in healthcare are suffering from single-point failure, centralized control of data resources and privacy leakage. This paper explores the usages of Blockchain technology to manage the healthcare system by providing a solution to these problems. Here, a distributed scheme is proposed for data management, which is used to implement Blockchain technology in the healthcare sector. The proposed scheme ensures security by specifying rules with a smart contract. Results and discussions show that the proposed scheme is more efficient than the existing schemes. [Display omitted] [ABSTRACT FROM AUTHOR]

The prime challenge of unsupervised symmetric heterogeneous cross-domain adaptation is to train the source domain and apply the trained knowledge to the target domain. Most of the existing algorithms for unsupervised transfer learning create the subspace of the source domain features and target domain features for training purposes. It is an extensive computational process as most of the techniques require labeled source data. Many techniques also suffer from the loss of originality of features in both domains. This paper aims to consider the feature vectors of both the source and target domain for training the data based on the similarity of exemplar (feature) vectors of different instances, known as Instance Similarity Feature (ISF). The use of vectorization method for the similarity of features is proposed in this paper. The exemplar vectors are chosen randomly for the target datasets. Hence, to acquire relevant factual data in the knowledge base for training in our research, we worked to increase the domain separation error between source and target instances. To avoid the instability caused due to poor exemplar vector selection, the K-means clustering approach is followed after feature similarity, known as K-means Instance Similarity Feature (KISF). Many existing transfer learning techniques are based on the original feature set, which can cause degeneracy, hence affecting Accuracy. In order to vanquish the limitations of existing approaches, we have introduced novel optimal models with KISF and Ant Lion Optimizer (KISFA), KISF with Particle Swarm Optimization (KISFP) and KISF with Biogeography Based Optimization (KISFB). High-dimensionality can impact efficacy of the model, hence, feature selection with nature-based optimizer namely: Ant Lion Optimizer, Particle Swarm Optimization and Biogeography-Based Optimization are applied. We measure the performance of the proposed models by using Support Vector Machine, Logistic Regression, Random Forest, Naive Baye's, K-Nearest Neighbor and Decision Tree as classifiers, and Accuracy and F1-score as fitness functions. Extensive experiments are performed on four datasets with 50 iterations. The proposed model is compared with eleven other techniques and our technique outperforms all other techniques in average Accuracy. The validation is performed on the dataset using 10-fold cross-validation. The statistical test was performed using ANOVA, proving that our technique is significantly better than other techniques. [ABSTRACT FROM AUTHOR]

In the era of advancing technology, energy networks have evolved into sophisticated smart networks, allowing seamless communication and exchange of information among network components. This transformation opens doors for innovative concepts such as the Multi-Carrier Energy System, or Energy Hub. While integrating energy hubs into traditional networks has posed challenges, the expansion of smart networks has facilitated their adoption within the power grid. Unlike conventional systems that optimize a single energy carrier, energy hubs focus on the optimal operation of a system encompassing multiple energy carriers like natural gas, electric grid, and thermal energy. Serving as intermediaries between energy supplier companies and diverse end-users, these hubs play a pivotal role in the intricate landscape of modern energy systems. This paper introduces a novel strategy for optimizing the utilization of residential and commercial energy hubs, specifically integrating Time-of-Use (ToU) tariffs and Load Response Programs (LRPs). To address inherent uncertainties, the proposed system employs the two-point estimation method within its model. For achieving optimal scheduling of energy resources aligned with ToU tariffs and load management plan signals, the well-established Cuckoo Optimization Algorithm (COA) is applied. The results presented in this study highlight the effectiveness of the proposed approach in significantly reducing energy costs for the energy hub. Notably, these cost reductions are achieved while adhering to the constraints of the ToU Load Management Program (LMP). Furthermore, the paper explores various scenarios to investigate the impact of LRPs on both residential and commercial energy hubs, providing valuable insights into the dynamics of these systems. The numerical outcomes of the study affirm the efficacy of the proposed energy hub system. Implementation of load management plans markedly reduces unsupplied electrical and thermal energy, with scenario 3 showcasing a significant drop from 0.52 to 0.2 in unsupplied electrical energy for the home hub. [Display omitted] [ABSTRACT FROM AUTHOR]

Most of the previous regression models for predicting the remaining useful life (RUL) of hard disks primarily focused on failure data obtained through SMART monitoring. However, models that only considered failure data were not accurate enough. In order to address the issue of imbalanced sample quantities in different categories within the dataset, this paper proposes a method for predicting the RUL of hard disks under imbalanced conditions. It takes into account the imbalance issues in both SMART failure data and healthy data, as well as the imbalance in monitoring data attributes between hard disk drives and solid-state drives. The paper improves upon the traditional Transformer time series model and constructs a comprehensive and unified model for the RUL of hard disks across multiple categories. The proposed model achieves an RMSE score of 3.325, and has 66 % in MAE improved to the conventional models. The proposed model has promising prospects for future applications in predicting the remaining life of hard disks. [ABSTRACT FROM AUTHOR]

With the rapid development of blockchain and digital currencies, cybercrimes targeting blockchain transactions are proliferating. Various abnormal transaction behaviors caused by malicious nodes and malicious transactions have emerged in blockchain systems. Therefore, blockchain transaction security research is a hot issue in blockchain research. Deep learning has made remarkable achievements in image and audio. However, deep learning is less effective when processing text-like data than tree-based models. Therefore, for the highly unbalanced textual financial dataset, this paper designs a deep learning anomaly detection method called 1D SA-Inception by combining CNN with Transformer structure on the basis of traditional one-dimensional CNN (1D-CNN), introduces the Inception structure to learn the information of different scales of tabular data, and introduces the Transformer (self-attention) self-attention mechanism for the research of blockchain anomaly transaction detection technology, effectively improving the prediction effect. Finally, by conducting experiments on the extreme class imbalance dataset, the results show that the 1D SA-Inception designed in this paper outperforms the traditional CNN with AUC, G-mean, and F1 indexes of 91.05 %, 84.95 %, and 83.52 %, respectively, on the textual dataset. This method can more efficiently achieve the detection of abnormal transaction behavior, safeguard consumer rights and interests, and ensure the safe development of the financial sector. [ABSTRACT FROM AUTHOR]

The present study introduces an unconventional and fast hybrid strategy for islanding detection in Distributed Generators (DGs) based on inverters. The active method utilized in this work is Sandia Frequency Shift (SFS), while the passive method is the Rate Of Change Of Frequency (ROCOF) Relay. The key contribution of this paper is the use of Maximum Likelihood to estimate the ROCOF as opposed to the conventional Phase-Locked-Loop (PLL) method. The objective of this paper is to improve the responsiveness of detection across various conditions by providing fast detection without compromising accuracy. Several simulation investigations, adhering to the anti-islanding test standards of IEEE 1547 and UL1741, have been carried out on a system integrated with the suggested hybrid method. These studies aim to showcase the high sensitivity and specificity of the proposed technique for detecting islanding. In addition, pertinent comparisons are made between the proposed technique and the PLL-based method. The simulation results illustrate that the proposed control system effectively complies with the DG islanding protection requirements. In Furthermore, compared to the PLL, the proposed technique mitigates the common drawbacks associated with other islanding detection techniques by reducing the Non-Detection Zone, enhancing the power quality of the system, and, most significantly, producing fast detection under different conditions. The proposed approach exhibits a response time nearly three times faster than the PLL method, while in less demanding conditions, it provides an instantaneous response. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

The evolution of power systems into Cyber Physical Power Systems (CPPS), characterized by the integration of information and communication technologies with traditional electrical infrastructure, has resulted in new opportunities and challenges. This paper presents a comprehensive and systematic review of advancements in CPPS. In response to the unique attributes of CPPS, such as device vulnerabilities and data falsification, this review critically examines the state-of-the-art literature. The primary contributions of this paper include the systematic organization of existing research and an in-depth analysis of significant milestones in the field of CPPS, leading to the development of a taxonomy for categorizing various drivers. Furthermore, this paper elucidates current strategies for defining clear terms, models, methodologies, and attributes essential to cybersecurity policies within CPPS. The incorporation of relevant standards employed in the CPPS domain is also a paramount aspect of this review. In addition to these contributions, the paper identifies areas warranting further research and highlights emerging trends within CPPS, aiming to offer valuable insights to industry practitioners and to catalyze future research endeavors. [ABSTRACT FROM AUTHOR]

As organizations grapple with an ever-evolving threat landscape, the need for effective security risk quantification methodologies becomes paramount. This research paper introduces and explores a novel approach to security risk quantification through the application of a hexagram model. Drawing inspiration from the I Ching, an ancient Chinese divination text, this hexagram model encompasses six key elements: Threat Landscape, Vulnerability Analysis, Asset Criticality, Control Effectiveness, Incident Response Capability, and Business Impact. By dividing these elements into two trigrams, a comprehensive view of external and internal factors influencing security risk emerges. The literature review examines existing security risk quantification methods, highlighting their strengths and limitations. The hexagram model's uniqueness lies in its holistic representation, integrating technical and organizational facets. The paper details the methodology, providing a clear framework for application. A practical case study demonstrates the model's implementation, showcasing its efficacy in real-world security assessments. Results and analysis reveal valuable insights into the security risk landscape, with comparisons to traditional quantification methods illustrating the hexagram model's added depth. The discussion interprets findings in the context of the security domain, addressing implications and areas for improvement. The paper concludes by summarizing key contributions, emphasizing the significance of the hexagram model in providing a nuanced understanding of security risk. Recommendations for future research underscore the potential for further refinement and broader adoption of this innovative approach. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) promises transformative benefits for patient care and remote monitoring but raises significant security and privacy concerns. Ensuring the trustworthiness and security of IoMT systems at the edge is a critical challenge. This research paper presents a novel framework to address the security and trust issues specific to IoMT environments. The primary objective of this framework is to establish a robust defense against intrusion attempts and to instill trust in edge networks, securing healthcare data and medical devices. To achieve this, the paper introduces an advanced Intrusion Detection System (IDS) optimized for edge networks in the context of IoMT. This paper emphasizes developing an evaluation methodology for appraising the trustworthiness of individual network components, developing an analytical technique for scrutinizing the participation of network entities in ongoing communications, establishing a means to quantify the trust level at the network's periphery or edge, and constructing an energy-efficient trust-based framework for the identification and mitigation of malicious entities operating within the IoMT. Through comprehensive testing, including real-world simulation scenarios, the paper evaluates the performance and effectiveness of the proposed framework, validating its ability to safeguard sensitive healthcare information and support the secure operation of IoMT devices. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of the Internet of Things (IoT), ensuring robust intrusion detection is paramount for device and data security. This paper proposes a novel method for intrusion detection in IoT networks that leverages a unique blend of subspace clustering and ensemble learning. Our framework integrates three innovative strategies: Clustering Results as Features (CRF), Two-Level Decision Making (TDM), and Iterative Feedback Loop (IFL). These strategies synergize to enhance detection performance and model robustness. We employ mutual information for feature selection and utilize four subspace clustering algorithms – CLIQUE, PROCLUS, SUBCLU, and LOF – to create additional feature sets. Three base learners – NB, LGBM, and XGB – are used in conjunction with a Logistic Regression (LR) meta-learner. To fine-tune our model, we apply Particle Swarm Optimization (PSO) for hyperparameter optimization. We evaluate our framework on the UNSW-NB15 dataset, which contains realistic and diverse IoT network traffic data. The results show that our framework outperforms the state-of-the-art methods in terms of accuracy (97.05%), precision (96.33%), recall (96.55%), F1-score (96.45%), and false positive rate (0.029). Our framework can effectively detect both known and unknown attacks in IoT networks and achieve high accuracy and low false positive rate. The paper contributes both practical implications for network security and theoretical advancements in intrusion detection research. [ABSTRACT FROM AUTHOR]

The task of accurately classifying defect types and pinpointing their locations in the domain of industrial product defect detection remains a formidable challenge. This paper introduces an advanced industrial defect detection framework, named YOLOv7-SiamFF, which utilizes the YOLOv7 as a feature extraction and detection backbone with three feature reinforcement modules. Firstly, we employ a parallel Siamese network, facilitating differential feature extraction through dual-stream feature extraction channels, aimed at better highlighting defect features and suppressing background interference. Additionally, we introduce a depth information feature fusion module, which effectively integrates high and low-level features in the Siamese network, thus enhancing the model's detection accuracy for small target defects. Finally, an attention mechanism is integrated into the feature extraction network, further enhancing the model's precision in identifying defect-specific features. In the simulation experiment, a specialized visual dataset was created for object detection tasks focusing on industrial defects, dubbed the BC-DD dataset. Additionally, the effectiveness of the proposed model has been validated in this paper using the aforementioned dataset. [ABSTRACT FROM AUTHOR]

The increasing prevalence of artificial intelligence-based knowledge reasoning has contributed to more accurate and efficient auxiliary diagnoses. However, a majority of the disease prediction methods concentrate on the symptoms themselves while discarding the inherent properties of symptoms and the relationships underlying them. This paper proposes a feature aggregation-based intelligent diagnosis model employing a Heterogeneous Graph Convolutional Network (GCN), termed HeteroGCN. It focuses on symptoms' inherent properties and multiple hidden relationships among symptoms and properties. By aggregating features of nodes, it realizes effective and accurate symptom-based knowledge reasoning for disease-type prediction. The diagnosis-related information from the Electronic Medical Record (EMR) has been extracted and standardized by taking chronic obstructive pulmonary disease (COPD) as an instance. Then the presented model extracts the symptoms and their properties as nodes and the relationships underlying the nodes as edges to construct a heterogeneous graph. The adjacency matrix and feature matrix have been fused and taken as the input of this model, and then the node representations (embeddings) are generated by aggregating neighbor nodes' information. Finally, specific disease types (syndromes) will be predicted by the generated symptom node embeddings. The results of the model comparison and parameter sensitivity test demonstrate that the presented HeteroGCN model performs best on disease-type prediction. This paper provides a novel feature aggregation-based multi-relational knowledge reasoning approach for disease type (syndrome) prediction, which holds great significance in improving disease diagnosis. [ABSTRACT FROM AUTHOR]

The operation Economic efficiency of high-performance electric vehicles is relatively low. There are still certain limitations on the range of electric vehicles and the coverage of charging facilities, which affects the user experience and convenience. This paper aims to explore ways to improve the Economic efficiency of electric vehicle operation based on neural network algorithm. Based on the neural network algorithm, this paper establishes a prediction model for the operational Economic efficiency of high-performance electric vehicles. By analyzing and modeling the relevant data, we can predict the operational Economic efficiency of electric vehicles, and extract the influencing factors from it. In this paper, the operation Economic efficiency of high-performance electric vehicles is predicted and analyzed through neural network algorithm, and the methods to improve the operation Economic efficiency are proposed. These methods can provide certain reference and guidance for the development of the electric vehicle industry. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel 3D object detection model that leverages the advantages of the Voxel Transformer (VoTr) and the Confident IoU-Aware Single-Stage Object Detector (CIA-SSD) to address the challenges of detecting objects in 3D point clouds. Our model adopts the VoTr as its backbone, which enables long-range interactions between voxels via a self-attention mechanism. This overcomes the limitations of conventional voxel-based 3D detectors, which struggle to capture sufficient contextual information due to their restricted receptive fields. Our model also integrates the sparse voxel module and the submanifold voxel module, which efficiently process empty and non-empty voxel positions, effectively handling the natural sparsity and abundance of non-empty voxels. Moreover, inspired by the CIA-SSD design, our model incorporates the Spatial-Semantic Feature Aggregation (SSFA) module, which allows for the adaptive fusion of high-level abstract semantic features and low-level spatial features, ensuring accurate predictions of bounding boxes and classification confidence. Furthermore, based on the IoU-aware confidence rectification module, which refines the alignment between confidence scores and localization accuracy, we devise an Optimized RPN (Region Proposal Network) Detection Head module as a dense head to further predict the IoU loss and improve the accuracy. In this paper, we combine two state-of-the-art techniques to provide a precise and efficient solution for 3D object detection in point clouds. We evaluate our model on the KITTI dataset 1 and achieve 76.56 % accuracy in terms of AP3D (%) Hard. [ABSTRACT FROM AUTHOR]

In today's shopping platform, some enterprises can use all commercial means to obtain users' personal information. How to protect the privacy of consumers has become an important issue. This paper discusses the shortcomings of the traditional neural network algorithm, and puts forward some countermeasures to improve it. Based on the basic principle of standard neural network, a new improved algorithm is developed according to the needs of this paper. The basic principle of the algorithm is based on the added momentum method. This paper mainly uses data models to collect and organize user data through user behavior patterns, so as to prove that the record of target user data cannot predict the possibility of user transportation and consumption, so as to better promote the improvement of transactions and services through these data models. [ABSTRACT FROM AUTHOR]

Because of the openness and complexity of the mobile network environment, the problem of network security is becoming more and more serious, and the mobile network teaching system is also facing the risk of intrusion and attack. Therefore, this paper designs an intrusion detection system for mobile network teaching system. The system uses machine learning and data mining technology to analyze and process the data flow of the mobile network teaching system, extract the characteristics of the data flow, build the intrusion detection model, and realize the intrusion detection of the mobile network teaching system. The research results of this paper show that the intrusion detection system of mobile network teaching system based on intelligent algorithm can effectively detect the intrusion behavior of mobile network teaching system, with high detection accuracy and low false alarm rate, which provides a guarantee for the security and stability of mobile network teaching system. [ABSTRACT FROM AUTHOR]

Cyber Threat Intelligence (CTI) contains numerous Indicators of Compromise (IOCs) and contextual information, crucial for understanding threat actors' behavior and intentions. However, current information extraction predominantly relies on supervised learning algorithms, presenting challenges in the field of CTI for two reasons. Firstly, the scarcity of labeled data with IOCs hampers the effectiveness of supervised learning. Secondly, existing methods struggle to extract comprehensive contextual features, posing difficulties in IOC recognition within CTI. To address these limitations and better suit the unique characteristics of CTI text, this paper introduces STIOCS, a semi-supervised framework that combines active learning and self-training for IOC extraction. STIOCS enhances IOC extraction accuracy and efficiency by leveraging limited labeled data and a rich unannotated corpus. Firstly, the Active Learning (AL) approach uses the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to select reliable samples that can reduce noise pollution on pseudo-labeling in self-training. The extraction model integrates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms to extract local and sequential features from CTI text, respectively. Then, the semantic features are enhanced by using the different sizes of convolutional kernels to fuse the two types of features. Finally, the Conditional Random Fields (CRF) layer is employed to recognize IOC entities. Our experimental results demonstrate the effectiveness and robustness of our proposed method in IOC extraction, even with limited labeled data. Compared to supervised methods, our proposed method is only approximately 40% of the dataset is labeled, the F1 scores are achieved better than the existing methods and exhibit consistent performance improvements as the dataset size increases. STIOCS effectively suppresses weak label noise, reduces training costs, and enhances the recognition model's performance. It provides a cost-effective training framework for entity extraction in cyber threat intelligence. [Display omitted] • This paper proposes a semi-supervised active learning framework, STIOCS, aimed at improving the efficiency of IOC extraction in CTI and addressing the challenge of model degradation due to inadequate IOC annotation data. • Firstly, we extract valuable information from unlabeled data during self-training, but pseudo-labeling noise can potentially degrade model accuracy. • Secondly, we propose an active learning method aligned with the training objective to effectively select samples that enhance model performance. • Finally, we conduct extensive experiments on IOC extraction tasks using the fusion model, demonstrating superior performance compared to existing methods. [ABSTRACT FROM AUTHOR]

his paper begins with the assumption that a mobile phone is placed in the right trouser pocket. Its primary objective is to ascertain whether the mobile phone belongs to the driver or the passenger. To achieve this goal, the paper conducts an analysis of raw data collected from phone sensors, specifically the accelerometer, gyroscope, orientation, and GPS, in order to identify and detect movement in the right leg. Based on this analysis, the application makes the decision to disconnect the signal and Internet access on the driver's phone if the car's speed exceeds 60 miles per hour, while maintaining these services for passengers. Subsequently, relevant features are extracted from the data, and a variety of machine learning algorithms are evaluated to determine the most suitable model for the task. The results demonstrate promising performance, suggesting that the proposed method could serve as an effective tool for detecting distracted driving. [ABSTRACT FROM AUTHOR]

• Covered 60 primary studies on blockchain, consensus mechanisms & electric vehicles. • Classified the primary studies in 10 categories for in depth analysis. • Evaluated various consensus protocols of blockchain using multiple factors. • Assessed performance scalability in blockchain with improved consensus mechanisms. • Examined the role of blockchain in electric vehicle charging and associated challenges Blockchain is a secure distributed system having a shared resource that enables participants to read and update a common shared ledger in a decentralized fashion. It provides an immutable and tamper-resistant distributed ledger with no central authority and no single point of failure. Consensus protocols are the backbone of any blockchain system as these protocols enable participant nodes to reach an agreement in a trustless environment. The performance of a typical blockchain system largely depends upon selecting the best-fit consensus protocol. Blockchain has influenced many application areas apart from crypto currencies such as document verification, copyrighting of art and craft, verification of counterfeit products and electric vehicle (EV) charging. This review paper discusses various consensus mechanisms in public, private, and consortium-based blockchains. The review study also covers the impact of consensus protocols on the performance scalability of the blockchain solution. The blockchain application area focused in this study is related to electric vehicle management and charging. The paper elaborates on how blockchain is enabling energy-efficient and secure EV charging. It also covers energy trading in a typical EV environment. [Display omitted] [ABSTRACT FROM AUTHOR]

Online news and social networking sites have been significantly used in recent years. There has been a lot of efforts to provide appropriate contents for the end users; however, they proved not to be effective. We believe the semantic web (as the third generation of the web) is mature enough to undertake the responsibility of generating more user-centric content. One way to exploit the semantic web's capabilities for such purpose is to construct an ontology that establishes the relationships between hashtags. In this paper, we present the construction process of a news feed system based on the hierarchical relationships of geographic hashtag. Our experiments demonstrated that our proposed semantic-based hierarchical location tagging news feed system increases the quality of the user experience as well as the publication rate of the news while boosting the content match rate to the target audiences. The proposed system of this paper can be considered as a real step towards the realization of semantic web. [ABSTRACT FROM AUTHOR]

• The objective of this paper is to modify the previous results on the robust stability analysis of the work proposed by [1]. • We focus on the convergence of main signals and boundedness of other signal in MIMO form subjected to actuator voltage input constraint. • The overall closed-loop system composed by full actuated robotic manipulator for n degrees of freedom and the proposed controller is proved to be stable, while the joint position and velocity tracking errors are Uniformly-Ultimately Bounded (UUB) stable in agreement with Lyapunov's direct method in any finite region of the state space. This paper points out an improvement on the robust stability analysis for electrically driven robots given in the paper "robust control of electrically driven robots using adaptive uncertainty estimation". In the aforementioned paper, a robust adaptive control scheme for electrically driven robots has been presented. The uncertainties have been estimated and compensated using Taylor series expansion. The nonlinearities associated with actuator input constraints have been considered in that paper. It discussed the saturated and unsaturated region of the control input separately and neglected the transition state between these regions. Moreover, it did not guarantee the stability of the closed-loop system in the saturated area. In this paper, an alternative stability proof is presented that do not require this separation and guarantee the stability in a more general framework. Moreover, in the performance evaluation, the upper bound for the error vector of Taylor series coefficients estimations is calculated. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) nanotechnology is one of the proposed technologies for replacing CMOS technology. Shift registers are one of the important areas in the design of digital circuits and memory structures. Given the importance of shift registers, in this paper, new structures have been proposed for shift registers in QCA nanotechnology. At first, proposed D-latch is presented to be used in the main block of shift registers, which has only 18 cells, a delay of 0.5 cycles and less area than previous designs. Subsequently, a 4-bit series register will be presented using the proposed D-latch, which is at least 30% lower than the previous ones in terms of cell numbers and area. In addition, a reset pin is needed in shift registers which are used in microprocessors. Most of the previous designs did not have the reset pins and therefore a new version of 4-bit shift register which has the reset pin will be presented in this paper. New and former designs have been expanded from 2 bits to 8 bits, and parameters variations are presented in the table and diagrams. The proposed new designs have interesting features in terms of stability, number of cells and occupied area, compared to previous designs that do not have the reset capability. In addition, the proposed shift register is used to design an optimized LFSR circuit in QCA nanotechnology with only 157 cells and 0.2 µm2 area. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch is one of the major issues in operation of power systems which aims to minimize losses, voltage stability, voltage deviation, etc. through an optimal adjustment of control variables. Recently, some alterations have been involved in control procedures, management and scheduling of these systems due to the power systems restructure, electricity markets and smart grids. These changes are notable in methods used for optimal dispatch of reactive power as well. According to the hierarchical structure of the power grids, this paper presents a hierarchical distributed framework based on a System of Systems (SoS) concept. In this structure, every section of the grid is controlled by a smart agent. Agents are interconnected and exchange the required data via a telecommunication network. In this paper, the amounts of active and reactive power exchanged between agents are considered as boundary and common parameters. Our proposed method is implemented on the IEEE 118-bus network connected to 7 active power distribution networks. The results are compared to the ones obtained from a distributed method based on an Incident Command System (ICS) and a centralized control method. It turns out that the proposed method outperforms the other two competing methods. [ABSTRACT FROM AUTHOR]

Underactuated AUV (automatic underwater vehicle) possess a lower number of actuators than its degrees of freedom, which helps to conserve energy and improve maneuverability in complex underwater environments. However, the complex and variable disturbance environment under the sea makes the control of underactuated AUVs extremely difficult, so it is urgent to study controllers with strong disturbance capability. This paper focuses on 3D trajectory tracking under strong disturbance environment. The 5-DOF underactuated AUV kinematics and dynamics equations are established and simplified by adopting appropriate assumptions. The NDOB (nonlinear disturbance observer) combined with sliding mode control is utilized to complete the 3D trajectory tracking of underwater unmanned cluster under strong disturbance environment. The effectiveness of the control algorithm is verified through multiple sets of simulation experiments. Through the comparison of experimental data, the anti-disturbance ability of the algorithm proposed in this paper is more than 80% higher than that of the adaptive sliding mode controller under strong disturbance environment. This research provides an effective solution for 3D Trajectory tracking of underactuated AUV, which is of great practical significance for improving the efficiency of underwater exploration, ocean observation, and underwater operations. [ABSTRACT FROM AUTHOR]

• A method of exchange rate market trend prediction based on sentiment analysis. • Forecast the exchange rate market trend by combining CNN with LSTM. • The "emotional" factors in the text data of Weibo are excavated. • It can still perform stably on small data sets. Exchange rate market is an important part of the global economy to reflect national economic development.With the rise of the Internet, more and more people choose to express their opinions on the Internet. Therefore, we believe that the comments in the online text will also reflect the trend of the exchange rate market. In this paper, a hybrid model of exchange rate market trend prediction based on sentiment analysis is proposed. The sentiment analysis module is constructed by combining Word2vec with Long Short-Term Memoryto extract "emotion" factors in text data and improve the performance of the model.After data cleaning, the model generates word vector and conducts sentiment analysis to get three emotional classifications of "positive", "ordinary" and "negative" by crawling the contents of "exchange rate" entries on "Weibo", and obtains the emotional weight of each day.Then, the emotional weights of about 3 months were randomly inserted into the historical exchange rate data of 3-4 years to conduct the mixed training combining Convolutional Neural Network and LSTM.In this paper, the historical exchange rate data of RMB against US dollar, Japanese yen, Euro and Australian dollar in 3-4 years are used as experimental samples. Experimental results show that compared with some traditional models, the method proposed in this paper is used to conduct comparative analysis by Mean absolute error, Mean squared error, Mean absolute percentage error and R2_Score after inserting small emotional data. The results show that our proposed method outperforms all comparison models in all indicators. Taking the trend prediction of the Australian dollar/RMB exchange rate as an example, our method outperforms the comparison models by an average of about 25.32% in the MAE results, 38.51% in the MSE results, and 2.76% in the MAPE results, R2_Score The average result of Score is 6.16% better than the comparison models, indicating a certain degree of robustness. In order to verify the effectiveness of the emotional weights we extracted in predicting exchange rate trends, we extracted the proposed method from the ''emotional analysis'' module and conducted a self comparison. The results showed that the results of the module with the addition of ''emotional analysis'' were significantly better than those without the addition of ''emotional analysis" module. [Display omitted] [ABSTRACT FROM AUTHOR]

• The investigation of the modular smart transformer structure based on a five-level cascaded H-bridge rectifier at its medium voltage stage, six dual active bridge converters at its high-frequency stage, and a three-phase four-leg inverter at its low voltage stage, as well as six MVDC and LVDC buses. • The vector-oriented control, sliding mode control, and adaptive backstepping control have been developed and implemented for the studied smart transformer model. • A voltage balance control loop is introduced to these control strategies in order to prevent the voltage unbalance problems of cascaded H-bridge rectifier cells and MVDC bus voltages. • The performance and robustness evaluation of these controllers under various operating conditions, namely source voltage variations, parametric variations, and fault occurrence. This paper proposes a Smart transformer (ST) model comprising a five-level Cascaded H-Bridge (CHB) rectifier at its medium voltage stage, dual active bridge converters at its high-frequency stage, and a four-leg inverter at its low voltage stage. Indeed, the CHB rectifier enables the improvement of ST modularity and the integration of many Renewable Energy Sources (RESs). Nevertheless, the high integration of RESs can lead to voltage fluctuations due to their intermittent energy production which can affect the ST behavior. Therefore, a robust controller is required to improve the ST's dynamic performance. In this context, vector-oriented control, Sliding Mode Control (SMC), and adaptive backstepping control are studied in this paper. To select the most appropriate controllers, a series of simulations under anomalous conditions, including voltage variations, parametric variations, and fault occurrence, are performed using Matlab software. The obtained simulation results demonstrate that SMC provides the best performance and the greatest robustness. [Display omitted] [ABSTRACT FROM AUTHOR]

• LCL filters are applied to reduce the total harmonic distortion of grid-injected current by inverters. • The stability margins of the LCL-filtered grid-connected inverter will be affected by the resonance frequency of LCL filters. • This paper design optimal active damping of capacitor current feedback and optimal proportional resonant controller. • Metaheuristic algorithms are applied to design optimal parameters of the LCL filter components. The inductor-capacitor-inductor (LCL) filter is used to lower the high-frequency switching noise of a grid-connected inverter (GCI). However, a robust design of the LCL filter is a challenge due to its complex model, variations in the operating conditions of the grid, and its stability gain margin. Therefore, this paper designs an optimal LCL filter for GCI and considers the changing grid conditions. This paper utilized four metaheuristic optimization algorithms, including the Whale Optimization Algorithm, the Circle Search Algorithm, the Particle Swarm Optimization, and the gray Wolf Optimization. These algorithms are applied to minimize the total harmonic distortion (THD) and the error between the reference and real grid current. The robustness of the optimal LCL filter is investigated under transient and steady states of the grid by using MATLAB/SIMULINK and real-time simulations. The obtained results revealed that the optimal LCL filter is capable of reducing the THD under different grid variations. (a) Schematic diagram of LCL filtered grid-connected inverter b) basic block diagram of LCL filtered grid-connected inverter (c) control methodology with Proportional Capacitor current feedback damping loop [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel VSI incorporating the phasor data available through PMU is suggested. • Objective Function consists of novel VSI, loss minimization and voltage deviation. • The optimization techniques AOA and PSO are used. • Multiple DGs supplying only active power, only reactive power is considered. With the extensive application of renewable energy operated distributed generation plants in the power system, certain issues need to be addressed. One such problem is the optimal location of these DG units, as their random placement may result in congestion in certain paths of the transmission network. This paper proposed a novel voltage stability indicator (VSI) that is incorporated in the objective function along with real power loss reduction and minimum voltage deviation. The prominent feature of this VSI is the utilization of phasor data available through Phasor Measurement Units (PMUs) in the recent power systems. This VSI performs satisfactorily for both mesh and radial networks. The developed method under the paper is tested on IEEE-30 bus network. To optimize the objective function, Arithmetic Optimization Algorithm (AOA) is used and the obtained results have been compared with Particle swarm optimization. Simulation results justified the applicability of this technique for reducing losses in the network. [Display omitted] [ABSTRACT FROM AUTHOR]

• Electronic system-level HW/SW co-design of heterogeneous parallel embedded systems. • SystemC-based electronic system-level functional and timing HW/SW co-simulation. • System-level multi model of computation co-analysis (communication and concurrency). • System-level multi model of computation co-estimation (load and bandwidth). • Support to automatic electronic system-level design space exploration. Heterogeneous parallel devices are becoming increasingly common in the embedded systems field. This is primarily due to their ability to improve timing performance, while simultaneously reducing costs and energy. In this context, this study addresses the role of a hardware/software (HW/SW) co-simulation and analysis tool for embedded systems designed on heterogeneous parallel architectures. In particular, it presents an extended System C-based tool for functional and timing HW/SW co-simulation/analysis within a reference Electronic System-Level HW/SW co-design flow. The description of the main features of the tool, and the main design and integration issues represent the core of the paper. Furthermore, the paper presents two case studies that demonstrate the enhanced effectiveness and efficiency of the extended tool. This is achieved through reduced simulation. Thanks to all this, the paper contributes to fully motivate the industrial and research communities to adopt and further investigate system-level approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

• Considering the diversity of original trajectories to improve the utility of dataset. • Adding different sizes of noise to the count value can improve the utility of the data. • Considering spatiotemporal characteristics of trajectories can improve privacy protection degree. Promoted by the Internet of Thing era, the widespread use of mobile sensing devices equipped with positioning functions has led to the generation of substantial trajectory data. Mining and analyzing trajectory data has high research value, but poses a risk of user privacy leakage, resulting in fewer publicly available trajectory datasets for research and analysis. Therefore, a trajectory data publishing method that ensures high data utility while protecting user privacy has become a hot topic. In this paper, we propose a spatiotemporal trajectory data protection and publishing method based on kernel density estimation (STP-KDE), which protects the trajectory data and trajectory count values while improving data utility. In the protection process of trajectory data, we designed a kernel density clustering framework that is combined with the differential privacy exponential mechanism. In the protection process of trajectory counts value, the adaptive Laplace noise perturbation mechanism is proposed to differentially protect these counts. Experimental results show that STP-KDE can provide more useful data and stronger privacy protection than existing studies. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of Lithium-ion batteries (Li-ion batteries) degradation plays a critical role in improving the maintenance of energy storage technology. This paper presents a method based on a novel deep network model combined with a data transformation technique to diagnose Li-ion battery degradation modes. Different from conventional studies based on specific experimental and numerical methods to estimate and predict the degradation, the proposed method is based on data-driven approach, by leveraging datasets consisting of voltage/capacity curves, these were converted into incremental capacity (IC) curves and then transformed into images using the gramian angular summation field (GASF) technique. The study adopted two models: Inception-v3 and the proposed model, both underwent fine-tuning and a subsequent transfer learning process. Degradation modes, namely loss of lithium inventory (LLI) and the loss of active materials in both the positive (LAMPE) and negative electrodes (LAMNE), were diagnosed in relation to IC curves. Finally, the model was tested using two different datasets, and the results showed that the proposed method achieved high performance, especially across three Li-ion batteries, three degradation modes, three cells, and various cycles (totaling 378 cases) the proposed method outperformed in 233 cases, thereby outperforming other methods in comparison. Our method provides a flexible data-driven approach that accurately predicts various degradation modes across different cell chemistries throughout their lifespan. [ABSTRACT FROM AUTHOR]

Inverters are essential in converting solar panel-generated direct current (DC) into alternating current (AC) for seamless integration with electrical grids. This paper presents the modeling and analysis of a single-phase grid-connected H-bridge neutral point clamped inverter using the Sliding Mode Control (SMC) method. Addressing challenges in meeting control objectives – like fast dynamic response, precise reference tracking, robustness against parameter variations, cost-effectiveness, and compliance with grid standards such as low harmonic distortion and minimal DC transfer to the grid – the study employs the boundary layer method to minimize the SMC method's chattering phenomenon. The use of proportional-resonant (PR) control maintains the robustness of the SMC method against parameter changes, while the boundary layer effectively reduces chattering. A transfer function between the measured grid current and the reference grid current was also derived to investigate the effect of the filter parameters on the system. Furthermore, the proposed control method naturally mitigates resonance in the LCL filter and minimizes leakage current via a symmetrical LCL filter and specialized modulation scheme. Simulations conducted using MATLAB/Simulink® validate achievement of control objectives, steady-state performance, dynamic response, robustness to parameter variations, and adherence to grid standards. [ABSTRACT FROM AUTHOR]

Grid-connected micro-inverters, known for their straightforward wiring and operational stability, have garnered increasing interest from both industry and academia. However, the operational inefficiency of conventional inverters, the issue of distorted output waveforms, and the safety concerns with alternating current (AC) and direct current (DC) safety isolation limit the wide availability and utilization of micro-inverters. In order to overcome this problem, this research proposes a circuit topology consisting of two flyback circuits connected in parallel on the primary side of the transformer for safe isolation. Additionally, soft switches are incorporated into the flyback circuit at the front stage, with a voltage total harmonic distortion (THD) of less than 3.0 %. This study also develops a small-signal model to help with obtaining the current loop parameters. Parametric design and simulation studies with PLECS software confirmed the circuit architecture and control technique's accuracy. A 600 W prototype's design, analysis, and experimental validation are described. The proposed inverter has an efficiency of up to 97.2 %. The design and control methods employed in this paper offer a conceptual framework and engineering reference for relevant research and practical applications. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies. • VMD reduces the training data volume, easing the burden on decision-making systems. • DCNN removes the need for pilots, tackling nonlinear challenges. • The VDN network boosts the intelligence of acoustic signal recognition for UUVs. [ABSTRACT FROM AUTHOR]

In addressing the complexities of time series analysis, two primary challenges emerge: high dimensionality and inherent non-linearity, which often obstruct effective data processing and analysis. Representation learning emerges as a pivotal solution, enabling the efficient transformation of complex, high-dimensional time series into formats conducive to deeper analysis and understanding. This paper proposes a dynamic weight-based granular representation method for time series and validates it in collective anomaly detection tasks. The proposed method innovatively incorporates a weight allocation mechanism within the framework of justifiable granularity, a principle that accentuates the central tendency and extreme features of the data. By representing the original time series through this enhanced principle, the proposed method generates three distinct sets of interval granules: central, maximum value, and baseline granules, each reflecting crucial characteristics of the original data. This granular combination provides a holistic representation of time series, facilitating a more comprehensive analysis and interpretation. Subsequently, the proposed method evaluates the similarity across all subsequences, pinpointing those with notably low similarity as potential anomalies. Through extensive validation, the proposed method has shown high effectiveness in capturing essential time series features such as central tendency and amplitude variations, outperforming existing methods in key metrics including Accuracy, Sensitivity, Specificity, and F1 Score. Furthermore, the Friedman test confirms the proposed method's significant advantages in three of these indicators, showcasing its capability to address the complexities of time series analysis effectively. [ABSTRACT FROM AUTHOR]

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for Home Energy Management System based on the combination of integer programming and Reinforcement Learning (RL) for achieving efficient home-based Demand Response (DR). In particular, RL is exploited to manage the charge and discharge of Battery Energy Storage System (BESS), and Mixed Integer Linear Programming is exploited for load scheduling. The idea is to focus the RL specifically on BESS management, as its behavior is stochastic and is mainly affected by Photovoltaic (PV) production and user behavior changes. The scheduling decisions of household appliances, Electric Vehicles (EVs), and charging/discharging batteries can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR-induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV, PV panels, and electric storage. The test results demonstrate the effectiveness of the proposed home energy management framework under the application of different demand-side flexibility strategies. • Reinforcement Learning is exploited for Battery Energy Storage System management. • The problem decoupling allowed to ease of Reinforcement Learning training. • 11.4% increase in self-consumption and 4.51% decrease in energy drawn from the grid. • The home energy management system software has been deployed in the cloud. [ABSTRACT FROM AUTHOR]

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In this paper, we propose an optimisation-based robust watermarking algorithm for the copyright protection of colour medical images. Initially, we decompose a host medical image by lifting wavelet transform (LWT), followed by discrete cosine transform (DCT) into transformed coefficients, and then embed multiple watermarks into the transformed host image by using hybrid optimisation algorithms. To further improve security and reduce the channel noise distortion, we then apply hashing and Hamming code to image and text mark, respectively, before the embedding process. Finally, we utilise 2D chaotic map to encrypt the marked media, aiming to achieve enhanced security and confidentiality. The experimental outcome shows satisfactory invisibility, watermark payload and robustness against image-processing attacks. Furthermore, through numerous comparative experiments with nine state-of-the-art algorithms on two standard datasets, the effectiveness of the proposed algorithm is demonstrated. [Display omitted] • An optimisation-based robust watermarking algorithm for colour medical images. • Combination of LWT-DCT is used to embed dual watermarks for better robustness and security. • Hashing and hamming code are employed for additional security and robustness of the image and text mark, respectively. • Various experiments are performed to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure. [ABSTRACT FROM AUTHOR]

This paper introduces the manta ray foraging optimization algorithm (MRFO) and its enhanced version, the oppositional-based manta ray foraging optimization algorithm (OMRFO), as effective meta-heuristic approaches for solving challenging economic dispatch (ED) problems in power systems. Specifically tailored for economic power dispatch (EPD), combined economic emission dispatch (CEED), and combined heat and power economic dispatch (CHPED) problems, considering factors like valve-point loading effects (VPL), transmission power losses, and prohibited operating zones (POZs) inherent in real-world power systems. To address MRFO's limitations, including slow convergence, susceptibility to local optima, and limited exploration capacity due to foraging behavior, this study integrates oppositional-based learning (OBL) with MRFO to enhance solution quality, speed up convergence, and improve exploration of the search space. Extensive testing is conducted on benchmark functions and ED problems of four standard test systems with non-convex solution spaces. The comprehensive comparative assessment shows that both MRFO and OMRFO outperform other algorithms in terms of solution quality and system constraint satisfaction. Additionally, OMRFO exhibits significant improvements over MRFO, especially for more complex problems. For instance, for small test systems like the 6-unit test system, both algorithms achieve a cost of 15,441.84 $/h. However, for larger systems with higher complexity, such as the 40-unit test system, OMRFO significantly outperforms MRFO with a cost of 119,733.27 $/h compared to 120,221.34 $/h. Similarly, for a 24-unit test system with VPL only and with VPL and POZs, OMRFO achieves costs of 58,054.78 $/h and 58,191.69 $/h, respectively, surpassing MRFO's costs of 58,125.803 $/h and 58,223.578 $/h. [ABSTRACT FROM AUTHOR]

Reduced distinguishability significantly challenges the detection of Model Stealing (MS). Existing methods for identifying MS attacks exhibit key limitations: (1) Sample-level detection methods that use fixed feature thresholds often inadvertently include benign samples or overlook malicious ones; (2) Distribution-level detection methods with static divergence benchmarks may misclassify benign query samples that deviate from these benchmarks This paper introduces GuardNet, an innovative model stealing detection method. By combining boundary features with inter-sample distance features, GuardNet more precisely identifies malicious sample pairs and employs distribution divergences to adjust decision thresholds, thus enhancing its detection capabilities. The method incorporates a variational autoencoder to reconstruct query samples and uses the Wasserstein distance between pre- and post-reconstruction samples as a measure of distribution divergences, effectively minimizing the influence of distribution shifts on benign query samples. Experimental results indicate that this approach significantly reduces the number of adversarial queries and markedly decreases false positives. [ABSTRACT FROM AUTHOR]

In today's digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification. [Display omitted] • An innovative hybrid feature extraction method for training of DDoS attack detection model. • We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks. • Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models. • The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score). • The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature. [ABSTRACT FROM AUTHOR]

Policies aim to increase the penetration of renewable energy resources (RERs) to minimize the dependence on fossil fuels, have compelled different countries to devise their diverse blueprints by transforming smart grids infrastructure into futuristic grids or super smart grids (SSGs). Europe is taking the lead by developing SSGs by 2050, based on two exclusive alternatives, i.e., totally based on RERs and dispersed large decentralized networks. Critically assessing tensions in balancing load flow associated with RERs is a complicated task in SSGs. To solve this concern, the advanced probabilistic model is adopted in this research to observe the variations in demand and response profiles and mitigate it through transmission network planning using a super smart node transmission network topology (SSN). The proposed SSN transmission network topology outperforms the power synergy hub (PSHub) topology used in the existing literature through achieving an optimal load flow balancing in SSGs power infrastructure. This is also verified through simulation results, which provides an effective utilization of continuous spinning reserves (CSRs) and vehicle to grid (V2G) topologies integrated in an SSN transmission network topology for managing an optimized load flow balancing in SSGs. Additionally, this research work also provides a comprehensive overview of technical challenges in SSGs in terms of power flow control, their possible solutions, and future prospects. Finally, this paper concluded with the 2050 framework of futuristic SSGs, which provides a road map path for a sustainable and green energy environment for European infrastructure. [ABSTRACT FROM AUTHOR]