Pub Date : 2024-09-11DOI: 10.3390/electronics13183602
Konstantinos Koniavitis, Vassilis Alimisis, Nikolaos Uzunoglu, Paul P. Sotiriadis
This paper introduces a multiloop stabilized low-dropout regulator with a DC power supply rejection ratio of 85 dB and a phase margin of 80°. It is suitable for low-power, low-voltage and area-efficient applications since it consumes less than 100 μA. The dropout voltage is only 400 mV and the power supply rails are 1 V. Furthermore, a full mathematical analysis is conducted for stability and noise before the circuit verification. To confirm the proper operation of the implementation process, voltage and temperature corner variation simulations are extracted. The proposed regulator is designed and verified utilizing the Cadence IC Suite in a TSMC 90 nm CMOS process.
本文介绍了一种多环路稳定低压差稳压器，其直流电源抑制比为 85 dB，相位裕度为 80°。它适用于低功耗、低电压和节省面积的应用，因为其功耗低于 100 μA。压降电压仅为 400 mV，电源轨电压为 1 V。此外，在电路验证之前，还对稳定性和噪声进行了全面的数学分析。为确认实现过程的正常运行，还提取了电压和温度角变化模拟。所提议的稳压器是在 TSMC 90 纳米 CMOS 工艺中利用 Cadence IC Suite 设计和验证的。
{"title":"An Analog Integrated Multiloop LDO: From Analysis to Design","authors":"Konstantinos Koniavitis, Vassilis Alimisis, Nikolaos Uzunoglu, Paul P. Sotiriadis","doi":"10.3390/electronics13183602","DOIUrl":"https://doi.org/10.3390/electronics13183602","url":null,"abstract":"This paper introduces a multiloop stabilized low-dropout regulator with a DC power supply rejection ratio of 85 dB and a phase margin of 80°. It is suitable for low-power, low-voltage and area-efficient applications since it consumes less than 100 μA. The dropout voltage is only 400 mV and the power supply rails are 1 V. Furthermore, a full mathematical analysis is conducted for stability and noise before the circuit verification. To confirm the proper operation of the implementation process, voltage and temperature corner variation simulations are extracted. The proposed regulator is designed and verified utilizing the Cadence IC Suite in a TSMC 90 nm CMOS process.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183607
Bassam Al-Masri, Nader Bakir, Ali El-Zaart, Khouloud Samrouth
Malware attacks have a cascading effect, causing financial harm, compromising privacy, operations and interrupting. By preventing these attacks, individuals and organizations can safeguard the valuable assets of their operations, and gain more trust. In this paper, we propose a dual convolutional neural network (DCNN) based architecture for malware classification. It consists first of converting malware binary files into 2D grayscale images and then training a customized dual CNN for malware multi-classification. This paper proposes an efficient approach for malware classification using dual CNNs. The model leverages the complementary strengths of a custom structure extraction branch and a pre-trained ResNet-50 model for malware image classification. By combining features extracted from both branches, the model achieved superior performance compared to a single-branch approach.
{"title":"Dual Convolutional Malware Network (DCMN): An Image-Based Malware Classification Using Dual Convolutional Neural Networks","authors":"Bassam Al-Masri, Nader Bakir, Ali El-Zaart, Khouloud Samrouth","doi":"10.3390/electronics13183607","DOIUrl":"https://doi.org/10.3390/electronics13183607","url":null,"abstract":"Malware attacks have a cascading effect, causing financial harm, compromising privacy, operations and interrupting. By preventing these attacks, individuals and organizations can safeguard the valuable assets of their operations, and gain more trust. In this paper, we propose a dual convolutional neural network (DCNN) based architecture for malware classification. It consists first of converting malware binary files into 2D grayscale images and then training a customized dual CNN for malware multi-classification. This paper proposes an efficient approach for malware classification using dual CNNs. The model leverages the complementary strengths of a custom structure extraction branch and a pre-trained ResNet-50 model for malware image classification. By combining features extracted from both branches, the model achieved superior performance compared to a single-branch approach.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183615
Muhammad Furqan Javed, Muhammad Osama Imam, Muhammad Adnan, Iqbal Murtza, Jin-Young Kim
Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision.
{"title":"Maritime Object Detection by Exploiting Electro-Optical and Near-Infrared Sensors Using Ensemble Learning","authors":"Muhammad Furqan Javed, Muhammad Osama Imam, Muhammad Adnan, Iqbal Murtza, Jin-Young Kim","doi":"10.3390/electronics13183615","DOIUrl":"https://doi.org/10.3390/electronics13183615","url":null,"abstract":"Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"101 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A single-phase grounding fault often occurs in 10 kV distribution networks, seriously affecting the safety of equipment and personnel. With the popularization of urban cables, the low-resistance grounding system gradually replaced arc suppression coils in some large cities. Compared to arc suppression coils, the low-resistance grounding system features simplicity and reliability. However, when a high-resistance grounding fault occurs, a lower amount of fault characteristics cannot trigger the zero-sequence protection action, so this type of fault will exist for a long time, which poses a threat to the power grid. To address this kind of problem, in this paper, a hybrid grounding system combining the low-resistance protection device and fully controlled power module is proposed. During a low-resistance grounding fault, the fault isolation is achieved through the zero-sequence current protection with the low-resistance grounding system itself, while, during a high-resistance grounding fault, the reliable arc extinction is achieved by regulating the neutral-point voltage with a fully controlled power module. Firstly, this paper introduces the principles, topology, and coordination control of the hybrid grounding system for active voltage arc extinction. Subsequently, a dual-loop-based control method is proposed to suppress the fault phase voltage. Furthermore, a faulty feeder selection method based on the Kepler optimization algorithm and convolutional neural network is proposed for the timely removal of permanent faults. Lastly, the simulation and HIL-based emulated results verify the rationality and effectiveness of the proposed method.
10 千伏配电网中经常发生单相接地故障，严重影响设备和人员的安全。随着城市电缆的普及，在一些大城市，低电阻接地系统逐渐取代了消弧线圈。与消弧线圈相比，低电阻接地系统具有简单可靠的特点。但是，当发生高阻接地故障时，较低的故障量特性无法触发零序保护动作，因此这类故障会长期存在，对电网造成威胁。针对此类问题，本文提出了一种低阻保护装置与全控功率模块相结合的混合接地系统。在低电阻接地故障中，通过低电阻接地系统本身的零序电流保护实现故障隔离；而在高电阻接地故障中，通过全控功率模块调节中性点电压实现可靠灭弧。本文首先介绍了主动电压灭弧混合接地系统的原理、拓扑结构和协调控制。随后，提出了一种基于双回路的控制方法来抑制故障相电压。此外，还提出了一种基于开普勒优化算法和卷积神经网络的故障馈线选择方法，以及时消除永久性故障。最后，仿真和基于 HIL 的模拟结果验证了所提方法的合理性和有效性。
{"title":"Neutral-Point Voltage Regulation and Control Strategy for Hybrid Grounding System Combining Power Module and Low Resistance in 10 kV Distribution Network","authors":"Yu Zhou, Kangli Liu, Wanglong Ding, Zitong Wang, Yuchen Yao, Tinghuang Wang, Yuhan Zhou","doi":"10.3390/electronics13183608","DOIUrl":"https://doi.org/10.3390/electronics13183608","url":null,"abstract":"A single-phase grounding fault often occurs in 10 kV distribution networks, seriously affecting the safety of equipment and personnel. With the popularization of urban cables, the low-resistance grounding system gradually replaced arc suppression coils in some large cities. Compared to arc suppression coils, the low-resistance grounding system features simplicity and reliability. However, when a high-resistance grounding fault occurs, a lower amount of fault characteristics cannot trigger the zero-sequence protection action, so this type of fault will exist for a long time, which poses a threat to the power grid. To address this kind of problem, in this paper, a hybrid grounding system combining the low-resistance protection device and fully controlled power module is proposed. During a low-resistance grounding fault, the fault isolation is achieved through the zero-sequence current protection with the low-resistance grounding system itself, while, during a high-resistance grounding fault, the reliable arc extinction is achieved by regulating the neutral-point voltage with a fully controlled power module. Firstly, this paper introduces the principles, topology, and coordination control of the hybrid grounding system for active voltage arc extinction. Subsequently, a dual-loop-based control method is proposed to suppress the fault phase voltage. Furthermore, a faulty feeder selection method based on the Kepler optimization algorithm and convolutional neural network is proposed for the timely removal of permanent faults. Lastly, the simulation and HIL-based emulated results verify the rationality and effectiveness of the proposed method.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"47 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183605
An Zhao, Wenzhong Yang, Danny Chen, Fuyuan Wei
Remote-sensing image captioning (RSIC) aims to generate descriptive sentences for ages by capturing both local and global semantic information. This task is challenging due to the diverse object types and varying scenes in ages. To address these challenges, we propose a positional-channel semantic fusion transformer (PCSFTr). The PCSFTr model employs scene classification to initially extract visual features and learn semantic information. A novel positional-channel multi-headed self-attention (PCMSA) block captures spatial and channel dependencies simultaneously, enriching the semantic information. The feature fusion (FF) module further enhances the understanding of semantic relationships. Experimental results show that PCSFTr significantly outperforms existing methods. Specifically, the BLEU-4 index reached 78.42% in UCM-caption, 54.42% in RSICD, and 69.01% in NWPU-captions. This research provides new insights into RSIC by offering a more comprehensive understanding of semantic information and relationships within images and improving the performance of image captioning models.
{"title":"Enhanced Transformer for Remote-Sensing Image Captioning with Positional-Channel Semantic Fusion","authors":"An Zhao, Wenzhong Yang, Danny Chen, Fuyuan Wei","doi":"10.3390/electronics13183605","DOIUrl":"https://doi.org/10.3390/electronics13183605","url":null,"abstract":"Remote-sensing image captioning (RSIC) aims to generate descriptive sentences for ages by capturing both local and global semantic information. This task is challenging due to the diverse object types and varying scenes in ages. To address these challenges, we propose a positional-channel semantic fusion transformer (PCSFTr). The PCSFTr model employs scene classification to initially extract visual features and learn semantic information. A novel positional-channel multi-headed self-attention (PCMSA) block captures spatial and channel dependencies simultaneously, enriching the semantic information. The feature fusion (FF) module further enhances the understanding of semantic relationships. Experimental results show that PCSFTr significantly outperforms existing methods. Specifically, the BLEU-4 index reached 78.42% in UCM-caption, 54.42% in RSICD, and 69.01% in NWPU-captions. This research provides new insights into RSIC by offering a more comprehensive understanding of semantic information and relationships within images and improving the performance of image captioning models.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"32 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation.
{"title":"Digital Twin for Modern Distribution Networks by Improved State Estimation with Consideration of Bad Date Identification","authors":"Huiqiang Zhi, Rui Mao, Longfei Hao, Xiao Chang, Xiangyu Guo, Liang Ji","doi":"10.3390/electronics13183613","DOIUrl":"https://doi.org/10.3390/electronics13183613","url":null,"abstract":"With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"411 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183612
Paulo H. N. Gonçalves, Hendrio Bragança, Eduardo Souto
Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model.
{"title":"Efficient Human Activity Recognition on Wearable Devices Using Knowledge Distillation Techniques","authors":"Paulo H. N. Gonçalves, Hendrio Bragança, Eduardo Souto","doi":"10.3390/electronics13183612","DOIUrl":"https://doi.org/10.3390/electronics13183612","url":null,"abstract":"Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183616
Viorel-Costin Banța, Ștefan Bunea, Daniela Țuțui, Raluca Florentina Crețu
Higher education institutions are increasingly concerned with providing students with sustainable education by developing the necessary competencies for various roles in the business environment. To be more effective, courses must develop technological, organizational and environmental (TOE) competencies in an integrated manner. SAP is a tool that yields this possibility through the diversity of IT solutions by ensuring a significant increase in employability rates. Learning SAP is a competitive advantage because it helps with all aspects of digital transformation within the concept of Industry 4.0. Our research aims to investigate to what extent students perceive that they have acquired the knowledge and competencies specific to the three dimensions of the TOE framework within the SAP course. We have added a fourth dimension to the TOE framework: the learning context (L) considering the impact of the educational environment on perceived learning outcomes. Data collection was based on a questionnaire distributed to students enrolled in the SAP course in the academic year 2023–2024 at Bucharest University of Economic Studies (BUES). The data were processed using correlation and regression analysis. Reconfiguring the content elements of SAP courses based on the TOE framework would ensure greater effectiveness in the learning process.
高等教育机构越来越关注通过培养学生在商业环境中扮演各种角色所需的能力，为学生提供可持续教育。为了提高效率，课程必须以综合方式培养技术、组织和环境（TOE）能力。SAP 是一种工具，可通过信息技术解决方案的多样性实现这种可能性，确保显著提高就业率。学习 SAP 是一种竞争优势，因为它有助于实现工业 4.0 概念中数字化转型的各个方面。我们的研究旨在调查学生在多大程度上认为他们已经掌握了 SAP 课程中 TOE 框架三个维度所特有的知识和能力。考虑到教育环境对感知学习成果的影响，我们在 TOE 框架中增加了第四个维度：学习环境（L）。数据收集基于向布加勒斯特经济研究大学（BUES）2023-2024 学年 SAP 课程学生发放的调查问卷。数据处理采用了相关分析和回归分析。根据 TOE 框架重新配置 SAP 课程的内容要素将确保学习过程更加有效。
{"title":"Challenges in Information Systems Curricula: Effectiveness of Systems Application Products in Data Processing Learning in Higher Education through a Technological, Organizational and Environmental Framework","authors":"Viorel-Costin Banța, Ștefan Bunea, Daniela Țuțui, Raluca Florentina Crețu","doi":"10.3390/electronics13183616","DOIUrl":"https://doi.org/10.3390/electronics13183616","url":null,"abstract":"Higher education institutions are increasingly concerned with providing students with sustainable education by developing the necessary competencies for various roles in the business environment. To be more effective, courses must develop technological, organizational and environmental (TOE) competencies in an integrated manner. SAP is a tool that yields this possibility through the diversity of IT solutions by ensuring a significant increase in employability rates. Learning SAP is a competitive advantage because it helps with all aspects of digital transformation within the concept of Industry 4.0. Our research aims to investigate to what extent students perceive that they have acquired the knowledge and competencies specific to the three dimensions of the TOE framework within the SAP course. We have added a fourth dimension to the TOE framework: the learning context (L) considering the impact of the educational environment on perceived learning outcomes. Data collection was based on a questionnaire distributed to students enrolled in the SAP course in the academic year 2023–2024 at Bucharest University of Economic Studies (BUES). The data were processed using correlation and regression analysis. Reconfiguring the content elements of SAP courses based on the TOE framework would ensure greater effectiveness in the learning process.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"91 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183609
Hong Zhang, Kunzhong Miao, Huangzhi Yu, Yifeng Niu
The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.
{"title":"Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets with ACD-NSGA-II Algorithm","authors":"Hong Zhang, Kunzhong Miao, Huangzhi Yu, Yifeng Niu","doi":"10.3390/electronics13183609","DOIUrl":"https://doi.org/10.3390/electronics13183609","url":null,"abstract":"The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"28 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.3390/electronics13183601
Brunel Rolack Kikissagbe, Meddi Adda
The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem.
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