Electronics

32 pages, 6218 KiB

Open AccessArticle

Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach

by Kamta Nath Mishra, Alok Mishra, Paras Nath Barwal and Rajesh Kumar Lal

Electronics 2024, 13(21), 4331; https://doi.org/10.3390/electronics13214331 - 4 Nov 2024

Viewed by 803

Abstract

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, [...] Read more.

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy. Full article

(This article belongs to the Special Issue Innovations in Artificial Intelligence, Natural Language Processing and Big Data)

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19 pages, 3109 KiB

Open AccessArticle

Text Command Intelligent Understanding for Cybersecurity Testing

by Junkai Yi, Yuan Liu, Zhongbai Jiang and Zhen Liu

Electronics 2024, 13(21), 4330; https://doi.org/10.3390/electronics13214330 - 4 Nov 2024

Viewed by 518

Abstract

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for [...] Read more.

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses. Full article

(This article belongs to the Special Issue Data-Centric Artificial Intelligence: New Methods for Data Processing)

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18 pages, 688 KiB

Open AccessArticle

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

by Jiayi Yu, Yuliang Lu, Yongheng Zhang, Yi Xie, Mingjie Cheng and Guozheng Yang

Electronics 2024, 13(21), 4329; https://doi.org/10.3390/electronics13214329 - 4 Nov 2024

Viewed by 658

Abstract

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly [...] Read more.

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER. Full article

(This article belongs to the Special Issue New Challenges in Cyber Security)

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20 pages, 2042 KiB

Open AccessArticle

Computing Unit and Data Migration Strategy under Limited Resources: Taking Train Operation Control System as an Example

by Jianjun Yuan, Laiping Sun, Pengzi Chu and Yi Yu

Electronics 2024, 13(21), 4328; https://doi.org/10.3390/electronics13214328 - 4 Nov 2024

Viewed by 577

Abstract

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained [...] Read more.

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures. Full article

(This article belongs to the Special Issue Advanced Theories, Applications and Techniques in Cloud and Distributed Computing)

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21 pages, 12536 KiB

Open AccessArticle

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

by Yuanliang Fan, Zewen Li, Xinghua Huang, Dongtao Luo, Jianli Lin, Weiming Chen, Lingfei Li and Ling Yang

Electronics 2024, 13(21), 4327; https://doi.org/10.3390/electronics13214327 - 4 Nov 2024

Viewed by 574

Abstract

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, [...] Read more.

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform. Full article

(This article belongs to the Special Issue Emerging Technologies in DC Microgrids)

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18 pages, 3413 KiB

Open AccessArticle

Diagnosing and Characterizing Chronic Kidney Disease with Machine Learning: The Value of Clinical Patient Characteristics as Evidenced from an Open Dataset

by Juan Figueroa, Patrick Etim, Adithyan Karanathu Shibu, Derek Berger and Jacob Levman

Electronics 2024, 13(21), 4326; https://doi.org/10.3390/electronics13214326 - 4 Nov 2024

Viewed by 822

Abstract

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study [...] Read more.

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study employed reproducible validation of AI technology with public domain software applied to CKD diagnostics on a publicly available CKD dataset acquired from 400 patients. The approach presented includes patient-specific symptomatic variables and demonstrates performance improvements associated with this approach. Our best-performing AI models, which include patient symptom variables, achieve predictive accuracies ranging from 99.4 to 100% across both hold-out and 5-fold validation with the light gradient boosting machine. We demonstrate that the exclusion of patient symptom variables reduces model performance in line with the literature on the same dataset. We also provide an unsupervised learning cluster analysis to help interpret variability among, and characterize the population of, patients with CKD. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications)

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35 pages, 4769 KiB

Open AccessArticle

Balancing Security and Efficiency: A Power Consumption Analysis of a Lightweight Block Cipher

by Muhammad Rana, Quazi Mamun and Rafiqul Islam

Electronics 2024, 13(21), 4325; https://doi.org/10.3390/electronics13214325 - 4 Nov 2024

Viewed by 706

Abstract

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised [...] Read more.

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications. Full article

(This article belongs to the Special Issue Recent Advances in Software Security and Cryptographic Protocols Using Machine Learning)

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37 pages, 796 KiB

Open AccessArticle

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

by Dionisios N. Sotiropoulos, Gregory Koronakos and Spyridon V. Solanakis

Electronics 2024, 13(21), 4324; https://doi.org/10.3390/electronics13214324 - 4 Nov 2024

Viewed by 621

Abstract

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study [...] Read more.

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models. Full article

(This article belongs to the Special Issue Explainability in AI and Machine Learning)

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22 pages, 762 KiB

Open AccessArticle

BTIP: Branch Triggered Instruction Prefetcher Ensuring Timeliness

by Wenhai Lin, Yiquan Lin, Yiquan Chen, Shishun Cai, Zhen Jin, Jiexiong Xu, Yuzhong Zhang and Wenzhi Chen

Electronics 2024, 13(21), 4323; https://doi.org/10.3390/electronics13214323 - 4 Nov 2024

Viewed by 626

Abstract

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) [...] Read more.

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) has been widely adopted in commercial processors to reduce Icache misses, our analysis reveals that FDIP still suffers from Icache misses caused by branch mispredictions and late prefetch, leaving considerable opportunity for performance optimization. Priority-Directed Instruction Prefetching (PDIP) has been proposed to reduce Icache misses caused by branch mispredictions in FDIP. However, it neglects Icache misses due to late prefetch and suffers from high storage overhead. In this paper, we proposed a branch-triggered instruction prefetcher (BTIP), which aims to prefetch Icache lines that FDIP cannot efficiently handle, including the Icache misses due to branch misprediction and late prefetch. We also introduce a novel Branch Target Buffer (BTB) organization, BTIP BTB, which stores prefetch metadata and reuses information from existing BTB entries, effectively reducing storage overhead. We implemented BTIP on the Champsim simulator and evaluated BTIP in detail using traces from the 1st Instruction Prefetching Championship (IPC-1). Our evaluation shows that BTIP outperforms both FDIP and PDIP. Specifically, BTIP reduces Icache misses by 38.0% and improves performance by 5.1% compared to FDIP. Additionally, BTIP outperforms PDIP by 1.6% while using only 41.9% of the storage space required by PDIP. Full article

(This article belongs to the Special Issue Computer Architecture & Parallel and Distributed Computing)

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17 pages, 2057 KiB

Open AccessArticle

Fake Review Detection Model Based on Comment Content and Review Behavior

by Pengfei Sun, Weihong Bi, Yifan Zhang, Qiuyu Wang, Feifei Kou, Tongwei Lu and Jinpeng Chen

Electronics 2024, 13(21), 4322; https://doi.org/10.3390/electronics13214322 - 4 Nov 2024

Viewed by 556

Abstract

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. [...] Read more.

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants. Full article

(This article belongs to the Special Issue Data Mining Applied in Natural Language Processing)

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17 pages, 1111 KiB

Open AccessArticle

Conditional Community Search Based on Weight Information

by Mengxiang Wang, Dong Ma, Qiang Fu and Chuanyu Zong

Electronics 2024, 13(21), 4321; https://doi.org/10.3390/electronics13214321 - 4 Nov 2024

Viewed by 494

Abstract

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some [...] Read more.

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some complex search conditions based on Boolean expressions. For example, certain desired nodes should be contained in the communities, while certain undesired nodes cannot exist in the communities, which is called conditional community search. However, existing solutions for conditional community search often introduce some undesired nodes into the identified communities and exhibit relatively low search efficiency. To overcome these drawbacks, therefore, this paper investigates the problem of conditional community search based on weight information. First, we refine the original problem definition of conditional community search and outline the need for an improved algorithm for calculating the weights of the nodes. Then, we explore two novel algorithms for searching conditional communities based on calculated weight information. Finally, we conduct extensive experiments on several real-world datasets to verify the accuracy and efficiency of our proposed searching algorithms. Full article

(This article belongs to the Section Computer Science & Engineering)

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19 pages, 4437 KiB

Open AccessArticle

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

by Tianyu Zhang, Xianglian Xu, Fangqing Zhang, Yifeng Gu, Kaitian Deng, Yuli Xu, Tunzhen Xie and Yuanqing Song

Electronics 2024, 13(21), 4320; https://doi.org/10.3390/electronics13214320 - 3 Nov 2024

Viewed by 547

Abstract

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading [...] Read more.

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance. Full article

(This article belongs to the Section Power Electronics)

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13 pages, 2312 KiB

Open AccessArticle

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

by Xi Li, Zongsheng Zheng, Ziyuan Shao and Yuhang Han

Electronics 2024, 13(21), 4319; https://doi.org/10.3390/electronics13214319 - 3 Nov 2024

Viewed by 502

Abstract

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant [...] Read more.

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control. Full article

(This article belongs to the Section Circuit and Signal Processing)

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17 pages, 6266 KiB

Open AccessArticle

Adaptive Virtual Synchronous Generator Control Strategy Based on Frequency Integral Compensation

by Kaixuan Li, Yongqing Wei and Jingru Zhang

Electronics 2024, 13(21), 4318; https://doi.org/10.3390/electronics13214318 - 2 Nov 2024

Viewed by 652

Abstract

With the increasing proportion of power electronic equipment in the power system, improving the inertia and damping characteristics of the system through virtual synchronous generator (VSG) control technology has become a hot research topic. In terms of adjusting the output frequency, traditional primary [...] Read more.

With the increasing proportion of power electronic equipment in the power system, improving the inertia and damping characteristics of the system through virtual synchronous generator (VSG) control technology has become a hot research topic. In terms of adjusting the output frequency, traditional primary frequency modulation control cannot ensure that the output frequency is maintained within the safe operating range when the load disturbance is large, so secondary frequency modulation with the integral link is usually adopted. However, the fixed integral coefficient cannot solve the contradiction between system regulation time and frequency oscillation. In this paper, a strategy of coordinated adaptive control based on the integral coefficient, moment of inertia, and damping coefficient is proposed. According to the offset of frequency and the change rate of the frequency offset, the value of parameters is determined, and the specific parameter setting method is determined. Finally, the correctness of the proposed control strategy is verified on a simulation platform and a semi-physical experimental platform. Full article

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15 pages, 779 KiB

Open AccessArticle

BWSAR: A Single-Drone Search-and-Rescue Methodology Leveraging 5G-NR Beam Sweeping Technologies for Victim Localization

by Ming He, Keliang Du, Haoran Huang, Qi Song and Xinyu Liu

Electronics 2024, 13(21), 4317; https://doi.org/10.3390/electronics13214317 - 2 Nov 2024

Viewed by 765

Abstract

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for [...] Read more.

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone’s navigation to emergency areas. Simulation results underscore BWSAR’s efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes. Full article

(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)

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34 pages, 23234 KiB

Open AccessArticle

Empowering Consumer Decision-Making: Decoding Incentive vs. Organic Reviews for Smarter Choices Through Advanced Textual Analysis

by Kate Kargozari, Junhua Ding and Haihua Chen

Electronics 2024, 13(21), 4316; https://doi.org/10.3390/electronics13214316 - 2 Nov 2024

Viewed by 548

Abstract

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in [...] Read more.

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement. Full article

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23 pages, 7224 KiB

Open AccessArticle

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

by Mintong Zhao, Yuling He, Yunfeng Tian, Kai Sun, Lingyu Jiao and Haipeng Wang

Electronics 2024, 13(21), 4315; https://doi.org/10.3390/electronics13214315 - 2 Nov 2024

Viewed by 707

Abstract

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar [...] Read more.

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model. Full article

(This article belongs to the Topic Control and Optimization of Networked Microgrids)

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16 pages, 6180 KiB

Open AccessArticle

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

by Syed Ali Hassan, Michail J. Beliatis, Agnieszka Radziwon, Arianna Menciassi and Calogero Maria Oddo

Electronics 2024, 13(21), 4314; https://doi.org/10.3390/electronics13214314 - 2 Nov 2024

Viewed by 865

Abstract

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to [...] Read more.

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP). Full article

(This article belongs to the Special Issue Applications of Computer Vision, 3rd Edition)

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16 pages, 15088 KiB

Open AccessArticle

Impact of Air Gaps Between Microstrip Line and Magnetic Sheet on Near-Field Magnetic Shielding

by Hyun Ho Park, Eakhwan Song, Jiseong Kim and Cheolsoo Kim

Electronics 2024, 13(21), 4313; https://doi.org/10.3390/electronics13214313 - 2 Nov 2024

Viewed by 519

Abstract

This study experimentally analyzed the impact of air gaps between a magnetic sheet and a test board with a microstrip line, which is used to measure the near-field magnetic shielding effectiveness (NSE) of magnetic sheets made of metallic powder. To conduct the measurements, [...] Read more.

This study experimentally analyzed the impact of air gaps between a magnetic sheet and a test board with a microstrip line, which is used to measure the near-field magnetic shielding effectiveness (NSE) of magnetic sheets made of metallic powder. To conduct the measurements, a material fixture equipped with a microstrip line to generate the near magnetic field, a rectangular loop probe, and an automatic probe positioning system capable of moving the loop probe along three axes were designed and fabricated. In addition, to systematically vary the thickness of the gaps, three paper spacers with a thickness of 0.11 mm per paper were used, and a 1.0 mm thick acrylic sheet, along with a specially designed material fixture, was used to press down the magnetic sheets during measurement. The magnetic shielding properties were measured and compared under various air gap conditions using a near-field magnetic loop probe. The effect of the gaps on the shielding performance of the magnetic sheets was quantitatively evaluated for three different magnetic sheets. The experimental results showed that as the gap thickness increased, NSE tended to improve up to a frequency around 1 GHz, while in the higher frequency range of a few GHz, NSE tended to decrease. The physical background of this phenomenon was explained using an equivalent magnetic circuit represented by reluctances for the structure, where the magnetic sheet is placed above the microstrip line with an air gap. This model helps to elucidate how the presence of the air gap affects the near-field magnetic shielding performance. Full article

(This article belongs to the Special Issue Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition)

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27 pages, 18482 KiB

Open AccessArticle

Current Compensation for Faulted Grid-Connected PV Arrays Using a Modified Voltage-Fed Quasi-Z-Source Inverter

by Abdullah Abdurrahman Al-Saloli and Faris E. Alfaris

Electronics 2024, 13(21), 4312; https://doi.org/10.3390/electronics13214312 - 2 Nov 2024

Viewed by 589

Abstract

Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary [...] Read more.

Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary obstacles are the occurrence of direct current (DC) faults and shading in a large array of PV panels, where any malfunction in a single panel can have a detrimental impact on the overall output power of the entire series-connected PV string and therefore the PV array. Due to the abrupt and frequent fluctuations in power, beside the low-PV systems’ moment of inertia, various technical problems may arise at the point of common coupling (PCC) of grid-connected PV generations, such as frequency and voltage stability, power efficiency, voltage sag, harmonic distortion, and other power quality factors. The majority of the suggested solutions were deficient in several crucial transient operating features and cost feasibility; therefore, this paper introduces a novel power electronic DC–DC converter that seeks to mitigate these effects by compensating for the decrease in current on the DC side of the system. The suggested solution was derived from the dual-source voltage-fed quasi-Z-source inverter (VF-qZSI), where the PV generation power can be supported by an energy storage element. This paper also presents the system architecture and the corresponding power switching control. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate the feasibility and capability of the proposed scheme, which contributes in suppressing the peak of the transient power-to-time variation (dP/dt) by 72% and reducing its normalized root-mean-square error by about 38%, with an AC current total harmonic distortion (THD) of only 1.04%. Full article

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21 pages, 1278 KiB

Open AccessArticle

An Automated Penetration Testing Framework Based on Hierarchical Reinforcement Learning

by Hongri Liu, Chuhan Liu, Xiansheng Wu, Yun Qu and Hongmei Liu

Electronics 2024, 13(21), 4311; https://doi.org/10.3390/electronics13214311 - 2 Nov 2024

Viewed by 471

Abstract

Given the large action space and state space involved in penetration testing, reinforcement learning is widely applied to enhance testing efficiency. This paper proposes an automatic penetration testing scheme based on hierarchical reinforcement learning to reduce both action space and state space. The [...] Read more.

Given the large action space and state space involved in penetration testing, reinforcement learning is widely applied to enhance testing efficiency. This paper proposes an automatic penetration testing scheme based on hierarchical reinforcement learning to reduce both action space and state space. The scheme includes a network intelligence responsible for specifying the penetration host and a host intelligence designated to perform penetration testing on the selected host. Specifically, within the network intelligence, an action-masking mechanism is adopted to shield unenabled actions, thereby reducing the explorable action space and improving the penetration testing efficiency. Additionally, the host intelligence employs an invalid discrimination mechanism, terminating testing after actions that do not alter system states, thereby preventing sudden increases in the number of neural network training steps for an action. An optimistic estimation mechanism is also introduced to select penetration strategies suited to various hosts, preventing training crashes due to value confusion between different hosts. Ablation experiments demonstrate that the host intelligence can learn different penetration strategies for varying penetration depths without significant fluctuations in training steps, and the network intelligence can coordinate with the host intelligence to perform network penetration steadily. This hierarchical reinforcement learning framework can detect network vulnerabilities more quickly and accurately, significantly reducing the cost of security policy updates. Full article

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21 pages, 5596 KiB

Open AccessArticle

EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network

by Yudie Hu, Lei Sun, Xiuqing Mao and Shuai Zhang

Electronics 2024, 13(21), 4310; https://doi.org/10.3390/electronics13214310 - 2 Nov 2024

Viewed by 602

Abstract

Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promising alternative owing to its advantages such as resistance to coercion, non-forgeability, [...] Read more.

Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promising alternative owing to its advantages such as resistance to coercion, non-forgeability, and revocability. Nevertheless, the scarcity of high-quality electroencephalogram (EEG) data limits the performance of brainprint recognition systems, necessitating the use of shallow models that may not perform optimally in real-world scenarios. Data augmentation has been demonstrated as an effective solution to address this issue. However, EEG data encompass diverse features, including temporal, frequency, and spatial components, posing a crucial challenge in preserving these features during augmentation. This paper proposes an end-to-end EEG data augmentation method based on a spatial–temporal generative adversarial network (STGAN) framework. Within the discriminator, a temporal feature encoder and a spatial feature encoder were parallelly devised. These encoders effectively captured global dependencies across channels and time of EEG data, respectively, leveraging a self-attention mechanism. This approach enhances the data generation capabilities of the GAN, thereby improving the quality and diversity of the augmented EEG data. The identity recognition experiments were conducted on the BCI-IV2A dataset, and Fréchet inception distance (FID) was employed to evaluate data quality. The proposed method was validated across three deep learning models: EEGNET, ShallowConvNet, and DeepConvNet. Experimental results indicated that data generated by STGAN outperform DCGAN and RGAN in terms of data quality, and the identity recognition accuracies on the three networks were improved by 2.49%, 2.59% and 1.14%, respectively. Full article

(This article belongs to the Section Artificial Intelligence)

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21 pages, 8238 KiB

Open AccessArticle

Object Tracking Algorithm Based on Integrated Multi-Scale Templates Guided by Judgment Mechanism

by Jing Wang, Yanru Wang, Yuxiang Que, Weichao Huang and Yuan Wei

Electronics 2024, 13(21), 4309; https://doi.org/10.3390/electronics13214309 - 2 Nov 2024

Viewed by 560

Abstract

The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object’s appearance, the algorithm exhibits [...] Read more.

The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object’s appearance, the algorithm exhibits issues such as insufficient tracking accuracy and drift, which directly affect the stability of the algorithm. In order to overcome this problem, this paper demonstrates how to expand scene information at the scale level during the fusion process and, on this basis, achieve accurate recognition and positioning. The predicted results are promptly fed back to the subsequent tracking process, from which temporal templates are embedded. Starting from both location and time can effectively improve the adaptive ability of the tracking model. In the final experimental comparison results, the algorithm proposed in this paper can adapt well to the situation of object deformation, and the overall performance of the tracking model has also been improved. Full article

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15 pages, 6981 KiB

Open AccessArticle

Noncontact Monitoring of Respiration and Heartbeat Based on Two-Wave Model Using a Millimeter-Wave MIMO FM-CW Radar

by Mie Mie Ko and Toshifumi Moriyama

Electronics 2024, 13(21), 4308; https://doi.org/10.3390/electronics13214308 - 1 Nov 2024

Viewed by 625

Abstract

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of [...] Read more.

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of long-term measurements due to the discomfort and skin irritation they cause. Therefore, the use of non-contact sensors, such as radars, is desirable. In this study, we obtained heartbeat and respiration information from phase data measured using a millimeter-wave MIMO FM-CW radar. We propose a two-wave model based on a Fourier series expansion and extract respiration and heartbeat information as a minimization problem. This model makes it possible to produce respiration and heartbeat waveforms. The produced heartbeat waveform can be used for estimating the interbeat interval (IBI). Experiments were conducted to confirm the usefulness of the proposed method. Moreover, the estimated results were compared with the contact sensor’s results. The results for both types of sensors were in good agreement. Full article

(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)

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20 pages, 521 KiB

Open AccessArticle

SafeMD: Ownership-Based Safe Memory Deallocation for C Programs

by Xiaohua Yin, Zhiqiu Huang, Shuanglong Kan and Guohua Shen

Electronics 2024, 13(21), 4307; https://doi.org/10.3390/electronics13214307 - 1 Nov 2024

Viewed by 360

Abstract

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system that enforces the automatic deallocation of unused resources without using a garbage collector. In light of Rust’s promise of safety, a [...] Read more.

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system that enforces the automatic deallocation of unused resources without using a garbage collector. In light of Rust’s promise of safety, a natural question arises about the possible benefits of exploiting ownership to ensure the memory safety of C programs. In our previous work, we developed a formal ownership checker to verify whether a C program satisfies exclusive ownership constraints. In this paper, we further propose an ownership-based safe memory deallocation approach, named SafeMD, to fix memory leaks in the C programs that satisfy exclusive ownership defined in the prior formal ownership checker. Benefiting from the C programs satisfying exclusive ownership, SafeMD obviates alias and inter-procedural analysis. Also, the patches generated by SafeMD make the input C programs still satisfy exclusive ownership. Usually, a C program that satisfies the exclusive ownership constraints is safer than its normal version. Our evaluation shows that SafeMD is effective in fixing memory leaks of C programs that satisfy exclusive ownership. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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18 pages, 718 KiB

Open AccessArticle

Dynamic Black-Box Model Watermarking for Heterogeneous Federated Learning

by Yuying Liao, Rong Jiang and Bin Zhou

Electronics 2024, 13(21), 4306; https://doi.org/10.3390/electronics13214306 - 1 Nov 2024

Viewed by 555

Abstract

Heterogeneous federated learning, as an innovative variant of federated learning, aims to break through the constraints of vanilla federated learning on the consistency of model architectures to better accommodate the heterogeneity in mobile computing scenarios. It introduces heterogeneous and personalized local models, which [...] Read more.

Heterogeneous federated learning, as an innovative variant of federated learning, aims to break through the constraints of vanilla federated learning on the consistency of model architectures to better accommodate the heterogeneity in mobile computing scenarios. It introduces heterogeneous and personalized local models, which effectively accommodates the heterogeneous data distributions and hardware resource constraints of individual clients, and thus improves computation and communication efficiency. However, it poses a challenge to model ownership protection, as watermarks embedded in the global model are corrupted to varying degrees when they are migrated to a user’s heterogeneous model and cannot continue to provide complete ownership protection in the local models. To tackle these issues, we propose a dynamic black-box model watermarking method for heterogeneous federated learning, PWFed. Specifically, we design an innovative dynamic watermark generation method which is based on generative adversarial network technology and is capable of generating watermark samples that are virtually indistinguishable from the original carriers. This approach effectively solves the limitation of the traditional black-box watermarking technique, which only considers static watermarks, and makes the generated watermarks significantly improved in terms of stealthiness and difficult to detect by potential model thieves, thus enhancing the robustness of the watermarks. In addition, we design two watermark embedding strategies with different granularities in the heterogeneous federated learning environment. During the watermark extraction and validation phase, PWFed accesses watermark samples claiming ownership of the model through an API interface and analyzes the differences between their output and the expected labels. Our experimental results show that PWFed achieves a 99.9% watermark verification rate with only a 0.1–4.8% sacrifice of main task accuracy on the CIFAR10 dataset. Full article

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9 pages, 2576 KiB

Open AccessArticle

Reducing Interface Resistance in Semiconductor System Through the Integration of Graphene

by Tae Yeong Hong, Jong Kyung Park and Seul Ki Hong

Electronics 2024, 13(21), 4305; https://doi.org/10.3390/electronics13214305 - 1 Nov 2024

Viewed by 526

Abstract

In the quest to improve overall semiconductor system performance as scaling down continues, reducing resistance in interconnects and bonding interfaces has become a critical focus. This study explores the use of graphene, a highly conductive 2D material, as an interfacial layer between metal [...] Read more.

In the quest to improve overall semiconductor system performance as scaling down continues, reducing resistance in interconnects and bonding interfaces has become a critical focus. This study explores the use of graphene, a highly conductive 2D material, as an interfacial layer between metal and dielectric layers to enhance adhesion and stability while reducing contact resistance. Graphene’s excellent adhesion properties make it a promising candidate for improving bonding strength at metal–dielectric interfaces. We investigated the following two approaches: direct growth of graphene via chemical vapor deposition and the transfer of pre-grown graphene onto the metal surface. The contact resistance characteristics of both methods were analyzed, with results indicating that graphene effectively enhances the bonding interface while significantly lowering contact resistance. These findings suggest that incorporating graphene as an interfacial material could lead to improved performance in advanced semiconductor devices, particularly in applications like hybrid bonding and interconnect technology. Full article

(This article belongs to the Special Issue Feature Papers in Semiconductor Devices)

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22 pages, 7000 KiB

Open AccessArticle

A Multidimensional Financial Data Model for User Interface with Process Mining Systems

by Audrius Lopata, Daina Gudonienė, Rimantas Butleris, Ilona Veitaitė, Vytautas Rudžionis and Saulius Gudas

Electronics 2024, 13(21), 4304; https://doi.org/10.3390/electronics13214304 - 1 Nov 2024

Viewed by 704

Abstract

Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their [...] Read more.

Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their attributes. There are data mining (DM) and process mining (PM) methods for analyzing enterprise operational data and identifying deficiencies in business process management. There is a need to find new user experience (UX)-driven methods for user interface with the specification of DM and PM tools on the level of business process management concepts. The paper presents the UX design-based approach to designing the user interface (UI) of process mining and data mining systems and is based on a conceptual semantic model named financial data space (FDS). The peculiarity of FDS is that it can include the characteristics of financial data and other UX-related characteristics (events, environmental and internal changes, business location) that may have an impact on changes in the values of financial objects (FO). The presented multidimensional financial data model helps increase the possibility of uncovering management weaknesses by identifying anomalies in large amounts of financial data. The prototypes of components of the financial data analysis system are described and developed using the process mining tool. The presented method of a multidimensional representation of financial data and transformation into a PM project is a user-friendly solution that allows to increase the analytical capabilities of the auditor’s work with large amounts of data, providing a more flexible view of the financial indicators of the company’s activity. Full article

(This article belongs to the Special Issue The Role of Digital Technologies in Sustainability Accounting and Reporting)

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16 pages, 5072 KiB

Open AccessArticle

Double Counterfactual Regret Minimization for Generating Safety-Critical Scenario of Autonomous Driving

by Yong Wang, Pengchao Sun, Liguo Shuai and Daifeng Zhang

Electronics 2024, 13(21), 4303; https://doi.org/10.3390/electronics13214303 - 1 Nov 2024

Viewed by 522

Abstract

Developing a high-quality scenario library is crucial for evaluating more reliable autonomous driving systems. A fundamental prerequisite for constructing such a library is the generation of safety-critical scenarios. In this paper, we propose a nested game algorithm to assign trajectories and their time [...] Read more.

Developing a high-quality scenario library is crucial for evaluating more reliable autonomous driving systems. A fundamental prerequisite for constructing such a library is the generation of safety-critical scenarios. In this paper, we propose a nested game algorithm to assign trajectories and their time series to multiple background vehicles. This method aimed to generate safety-critical scenarios with varying degrees of interference for the tested vehicle. To ensure the realism of the generated scenarios, we extracted a series of natural trajectories from an existing dataset as the input for the algorithm. We then analyzed multiple types of scenarios generated by this method and evaluated their danger using generalized metrics, such as the Time-to-Collision (TTC) and Minimum Safe Distance Factor (MSDF), which demonstrated the effectiveness of our approach. The experimental results demonstrate that the nested game-based approach could efficiently construct safety-critical scenarios, contributing to the development of high-quality test scenario libraries. Full article

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18 pages, 15722 KiB

Open AccessArticle

PANDA: A Polarized Attention Network for Enhanced Unsupervised Domain Adaptation in Semantic Segmentation

by Chiao-Wen Kao, Wei-Ling Chang, Chun-Chieh Lee and Kuo-Chin Fan

Electronics 2024, 13(21), 4302; https://doi.org/10.3390/electronics13214302 - 31 Oct 2024

Viewed by 679

Abstract

Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. [...] Read more.

Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. To address this, we propose Polarized Attention Network Domain Adaptation (PANDA), a novel approach that leverages Polarized Self-Attention (PSA) to capture the intricate relationships between the source and target domains, effectively mitigating domain discrepancies. PANDA integrates both channel and spatial information, allowing it to capture detailed features and overall structures simultaneously. Our proposed method significantly outperforms current state-of-the-art unsupervised domain adaptation (UDA) techniques for semantic segmentation tasks. Specifically, it achieves a notable improvement in mean intersection over union (mIoU), with a 0.2% increase for the GTA→Cityscapes benchmark and a substantial 1.4% gain for the SYNTHIA→Cityscapes benchmark. As a result, our method attains mIoU scores of 76.1% and 68.7%, respectively, which reflect meaningful advancements in model accuracy and domain adaptation performance. Full article

(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)

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Electronics, Volume 13, Issue 21 (November-1 2024) – 190 articles

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