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22nd IEEE International Conference on industrial Informatics, INDIN 2024

作者： Zhu, Ningyu Xie, Wen-Fang Toker, Onur Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland United States

ISBN: (纸本)9798331527471

The efficient control of serial robots in the presence of dynamic uncertainties and external disturbances is significant in many industrial applications. In this work, an adaptive sliding mode control (ASMC) approach with deep recurrent neural network (DRNN) is proposed for a 6-degree-of-freedom (6-DOF) industrial serial robot in joint space. A model-based sliding mode controller is developed to maintain the strong robustness of the robotic system. A deep recurrent neural network is designed to estimate the lumped system uncertainties in the controller. It consists of a feedforward structure through two hidden layers and a feedback loop from the output layer to the input layer, which exhibits more powerful online learning ability and dynamic property than shallow feedforward neural networks. According to Lyapunov theorem, the adaptation laws of the neural network parameters are derived, and the stability of the controller can be guaranteed. Simulation results demonstrate the effectiveness and superiority of the DRNN-based ASMC strategy regarding estimation convergence speed and trajectory tracking accuracy. © 2024 IEEE.

关键词： Recurrent neural networks

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Future Heat Risk Index: The Case of Attica Regions, Greece 15

Future Heat Risk Index: The Case of Attica Regions, Greece

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Dimitriou, Iason C. Trachanas, Georgios P. Arsenopoulos, Apostolos Sarmas, Elissaios School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece

ISBN: (纸本)9798350368833

Extreme heat has become a common phenomenon especially the summer months. Dense urban environments suffer greatly from extreme heat events, which makes the everyday life of citizens difficult. For the more vulnerable population, such as the elderly, extreme heat waves can become dangerous for their health and even lethal. The study employs a Future Heat Risk Index to evaluate potential heat-related vulnerabilities under different climate change scenarios, particularly Representative Concentration Pathway 8.5 for the year 2040. Through the integration of climate change projections and spatial analysis, the research identifies areas at high risk of heat-related impacts and prioritizes mitigation strategies. Key findings underscore the importance of proactive planning and adaptation measures in enhancing resilience to extreme heat events, particularly in Mediterranean cities like those in Attica, Greece. The proposed mitigation strategies include implementation of vegetation enhancement and cold materials installation. The effects of both strategies are compared and discussed, highlighting the importance of incorporating climate change scenarios into decision-making processes to safeguard public health and well-being in a warming climate. © 2024 IEEE.

关键词： Geographic Information systems Heat Risk IPCC RCP Remote sensing Urban Planning

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Enhanced PTE and Throughput in Wireless Capacitive Power and Data Transfer via Biocompatible Dielectric Shielding 46

Enhanced PTE and Throughput in Wireless Capacitive Power and...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Hossain, A. N. M. Shahriyar Mohseni, Pedram Lavasani, H.M. Case Western Reserve University Department of Electrical Computer and Systems Engineering ClevelandOH United States

ISBN: (纸本)9798350371499

This study explores the use of biocompatible dielectric shielding to enhance the power transfer efficiency (PTE) and improve the data rates in capacitive links designed for biomedical applications. A dielectric shielding structure that surrounds the capacitive patches on all sides except the main signal path is used to minimize the energy leakage, ensuring high PTE and improved signal-to-noise ratio (SNR). Ex vivo measurements align well with the simulations, showing maximum PTE > 74% for the capacitive link. The link is also capable of supporting data transfer rate exceeding 10 Mbps using a quadrature phase shift keying modulation (QPSK) scheme. These performance metrics represent the highest reported values among tuned capacitive links intended for implantable medical devices. © 2024 IEEE.

关键词： Quadrature phase shift keying

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Junior Design for electrical and computer Engineers: A Novel Course for Discipline-Specific Instruction of the engineering Design Process 54

Junior Design for Electrical and Computer Engineers: A Novel...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Dickerson, Samuel J. Clark, Renee M. University of Pittsburgh Department of Electrical and Computer Engineering PittsburghPA United States Unversity of Pittsburgh Department of Industrial Engineering PittsburghPA United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a novel design course for electrical and computer engineering students. In the class, students are taught the basics of the engineering design process in the context of electrical and computer engineering applications. Students are guided through a series of three design (ECE) projects, increasing in scope and difficulty, that involve the design of various electronic hardware and software components. In addition to teaching students the design process, well before their capstone design experience, the class is also designed to provide industry relevant exposure to ECE design tools and methodologies. Several examples of student work are provided as well as results from assessment of student learning. The results show that the course excels at preparing junior-level students to tackle the challenge of complex system design. © 2024 IEEE.

关键词： Students

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Subspace Deep Learning of Independent Component Regression Model for Ensemble Spectroscopic Calibration

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IEEE Sensors Journal 2025年第12期25卷 22317-22325页

作者： Zheng, Junhua Zhou, Hansong Liu, Xinyu Lyu, Yuting Yang, Zeyu Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Independent component regression (ICR) has become as an important spectroscopic calibration modeling method, due to its advantages in extracting non-Gaussian and high-order statistic features. While the calibration performance of ICR was recently improved by its deep learning form, the result is unstable which is subjected to different random seeds in the model initialization stage. This, however, will make the model less trustworthy in practical industrial applications. In this paper, a subspace deep learning framework is formulated to combine the ideas of both deep learning and ensemble learning, in order to improve the prediction stability of the ICR model. Meanwhile, with an appropriate design of the ensemble learning strategy in the subspace modeling framework, the calibration performance can be further enhanced. For performance evaluation, a detailed benchmark case study on the Corn dataset is carried out. Compared to ordinary and deep residual ICR models, both of the calibration performance and prediction stability have been significantly improved by the subspace deep learning approach. © 2001-2012 IEEE.

关键词： Linear regression

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review 17

Latest Innovations in Intelligent Network-on-Chip Architectu...

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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331506421

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures. © 2024 IEEE.

关键词： Federated learning

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images 27

Classifying Bicycle Infrastructure Using On-Bike Street-Leve...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Backman, Kal Beck, Ben Kulic, Dana School of Public Health and Preventive Medicine Monash University Department of Electrical and Computer Systems Engineering Australia

ISBN: (纸本)9798331505929

While cycling offers an attractive option for sus-tainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hi-erarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis. © 2024 IEEE.

关键词： Bicycles

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Intelligent reflecting surface-assisted cognitive radio-inspired rate-splitting multiple access systems

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Digital Communications and Networks 2023年第3期9卷 655-666页

作者： Peixu Liu Gang Jing Hongwu Liu Liang Yang Theodoros A.Tsiftsis School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan250357China School of Transportation and Logistics Engineering Shandong Jiaotong UniversityJinan250357China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhai519070China

Intelligent reflecting surface(IRS)has been widely regarded as a promising technology for configuring wireless propagation *** this paper,we utilize IRS to assist transmission of a secondary user(SU)in a cognitive radio-inspired rate-splitting multiple access(CR-RSMA)system in which a primary user's(PU's)quality of service(QoS)requirements must be *** introducing intolerable interference to deteriorate the PU's outage performance,the SU conducts rate-splitting to transmit its signal to the base-station through the direct link and IRS reflecting *** the IRS-assisted CR-RSMA(IRS-CR-RSMA)scheme,we derive the optimal transmit power allocation,target rate allocation,and successive interference cancellation decoding order to enhance the outage performance of the *** closed-form expression for the SU's outage probability achieved by the IRS-CR-RSMA scheme is *** simulation results are presented to clarify the enhanced outage performance achieved by the proposed IRS-CR-RSMA scheme over the CR-RSMA scheme.

关键词： Rate-splitting multiple access(RSMA) Intelligent reflecting surface(IRS) Successive interference cancellation(SIC) Cognitive radio

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A Mathematical Optimization Approach for Preference Learning in Movie Recommender systems with Shared Accounts 15

A Mathematical Optimization Approach for Preference Learning...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Khademali, Milad Aghamohammadi, Fazlollah Kaedi, Marjan Nasiri, Alireza Industrial Engineering Department Faculty of Engineering University of Hormozgan Bandar Abbas Iran Faculty of Computer Engineering University of Isfahan Isfahan Iran Electrical Engineering Department Faculty of Engineering University of Hormozgan Bandar Abbas Iran

ISBN: (纸本)9798331522254

A recommender system typically assumes that each row of the user-item rating matrix reflects the preferences of a single user. However, in many cases, an account is shared among multiple household members, resulting in mixed ratings data that do not accurately represent individual preferences. As a consequence, the recommendations will fail to align with the specific interests of each user. To address this issue, we introduce the concept of a "user character," which represents a common latent factor in both movie and account features. By establishing a movie feature matrix based on these character representations, we can identify the presence of different characters in shared accounts. This is achieved by factoring the account feature binary matrix from the rating matrix, a process that can be modeled as a binary quadratic optimization problem. For scalability, we relax the binary constraint using a penalty function and approximate the solutions through the gradient descent method. Additionally, we apply a thresholding function to obtain binary solutions that reveal the user characters within each account. Once we identify the characters associated with each account, we can learn users' distinct preferences through a demixing procedure. This allows us to reconstruct the rating matrix so that each row accurately represents a single user's preferences. To evaluate our method, we generated a shared account dataset from MovieLens ratings based on the CAMRa2011 dataset. Experiments conducted on this dataset demonstrate the effectiveness of our proposed approach. © 2024 IEEE.

关键词： Convex optimization

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