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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Mosselaar, Matthijs Malbasic, Zoran Fu, Aihui Lekic, Aleksandra Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Power conversion & hybrid solutions Alewijnse Netherlands

With the maritime industry poised on the cusp of a hybrid revolution, the design and analysis of advanced vessel systems have become paramount for engineers. This paper presents AC and DC electrical hybrid power system models in ETAP, the simulation software that can be adapted to engineer future hybrid vessels. These models are also a step towards a digital twin model that can help in troubleshooting and preventing issues, reducing risk and engineering time. The testing of the models is focused on time domain analysis, short-circuit currents, and protection & coordination. The models are based on actual vessels and manufacturer parameters are used where available. © 2024 IEEE.

关键词： Short circuit currents

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A Comparison of Various Deep Learning Methods for Household Load Forecasting

A Comparison of Various Deep Learning Methods for Household ...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Deivamani, Karthikeyan Norouzi, Farshid Shekhar, Aditya Bauer, Pavol Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of DC Systems Energy Conversion and Storage Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350396782

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bidirectional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE;however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive. © 2023 IEEE.

关键词： deep learning electric load forecasting smart grid time-series forecasting univariate

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Mapping of Cores onto Network on Chip Architectures by Hierarchical Genetic Evolution Algorithm 17th

Mapping of Cores onto Network on Chip Architectures by Hiera...

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Pan, Wenxin Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)9789819904075

The Network on Chip (NoC) is a communication architecture of system on chip, which mainly adopts data routing and packet switching techniques. The NoC has the advantages of low delay, high bandwidth and low power consumption. The design of NoC includes topology structure generation, core mapping, and communication mechanism. The core mapping decides the position of each core in a specified topology structure, it has an important impact on the communication delay, scalability, and reliability. The core mapping needs to be carried out under the specified constraints to achieve the best system performance. A new core mapping method is proposed in this paper, the method makes use of hierarchical genetic evolution strategy, which contains following two-level evolutions: high-level evolution and low-level evolution. In each level of evolutions, its own populations and evolutionary operations are used. The experimental results show that compared with the conventional genetic algorithm, the proposed method in this paper can reduce the total computing time of the core mapping. The obtained core mapping scheme can not only meet the requirement of communication bandwidth, but also reduce the total communication power consumption. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Network-on-chip

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Architecture Design of Network on Chip for Reducing Network Delay by Cellular Genetic Algorithm 10th

Architecture Design of Network on Chip for Reducing Network ...

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10th Frontier Academic Forum of Electrical engineering, FAFEE 2022

作者： Pan, Wenxin Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)9789819934034

The network on chip(NoC) makes use of sharing segmented network structure, data transmission link is no longer unique. Compared with bus communication, NoC makes a good trade-off between design cost and connection complexity, and can achieves better scalability. A NoC mainly includes interconnect topology, a lot of cores, routers, and data communication between these cores. The topology architecture of NoC has an important effect on its performance. A new method is proposed in this paper to minimize network delay for topology architecture design of NoC, the method employs cellular genetic algorithm for the placement design of cores and routers, such that the communication bandwidth requirement is satisfied and the delay of NoC is minimal. The computational efficiency of cellular genetic algorithm is enhanced by choosing one of the five neighborhood topologies at random in the selection and crossover operations, and using two selection modes for the selection of individuals. The experimental results show that the NoC topology architecture with lower network delay can be obtained by using the method in this paper, the delay of NoC is reduced, and the total time required by this method is shorter compared with the traditional genetic algorithm. © Beijing Paike Culture Commu. Co., Ltd. 2023.

关键词： Network-on-chip

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Pulsed Electromagnetic Field Interaction With a Transmission Line: An Analytical Traveling-Wave Approach Based on Reciprocity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3783-3791页

作者： Stumpf, Martin Antonini, Giulio Lager, Ioan E. Ekman, Jonas The Czech Republic Lerch Laboratory of em Research Department of Radio Electronics Feec Brno University of Technology Technická Brno3082/12 616 00 Czech Republic Luleå University of Technology Department of Computer Science Electrical and Space Engineering Eislab Luleå971 87 Sweden University of l'Aquila UAq Emc Laboratory Department of Industrial and Information Engineering and Economics L'Aquila671 00 Italy Delft University of Technology Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

Pulsed electromagnetic (EM) field signal transfer from a general EM source distribution to a transmission line (TL) is analyzed with the aid of Lorentz's reciprocity theorem. In this fashion, the transient voltage induced by the impulsive EM source is expressed through the EM fields as radiated by the TL. These transmitted EM fields are expressed in closed form using an analytical procedure that resembles the Cagniard-DeHoop (CdH) technique. The validity of the proposed reciprocity-based methodology is verified with the aid of an alternative analytical solution describing the EM field signal transfer excited by an impulsive vertical electric dipole (VED). Illustrative numerical examples are presented. © 1963-2012 IEEE.

关键词： Time domain analysis

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A FRAMEWORK FOR INVESTIGATING EDUCATIONAL THEORIES IN engineering EDUCATION RESEARCH 51

A FRAMEWORK FOR INVESTIGATING EDUCATIONAL THEORIES IN ENGINE...

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51st Annual Conference of the European Society for engineering Education, SEFI 2023

作者： van der Werf, V. van Helden, G. Schleiss, J. Saunders-Smits, G.N. LDE Centre for Education and Learning Delft University of Technology Delft Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Aerospace Engineering Delft University of Technology Delft Netherlands 4TU Centre for Engineering Education Delft University of Technology Delft Netherlands Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9782873520267

Grounding the design of educational interventions and their analysis in theory allows us to understand and interpret results of interventions and advance educational theories. Moreover, building an understanding of which educational theories are used and how they are used can build a consensus among researchers and mature the research in a field. In this paper, we investigate the extent to which educational theories are used to ground the design, analysis, and evaluation of learning activities in engineering education. For this purpose, we developed a coding instrument to determine: (1) which educational theories are expressed in studies investigating learning activities and interventions, and (2) the extent to which these theories inform (a) the design of an intervention and (b) the analysis of that intervention. The instrument was applied to a sample of 12 studies from an existing literature review on collaborative engineering design activities to demonstrate the relevance of the developed framework. Results reveal that most studies refer to educational theory, primarily pedagogical approaches such as project-based learning. Furthermore, half of the time, the design of learning interventions is grounded in theory, however, the evaluation of those interventions is often not connected to educational theories. © 2023 SEFI 2023 - 51st Annual Conference of the European Society for engineering Education: engineering Education for Sustainability, Proceedings. All Rights Reserved.

关键词： engineering education

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Behavior-based Feedback Loop for Attentive E-reading (BFLAe): A Real-Time computer Vision Approach 1

Behavior-based Feedback Loop for Attentive E-reading (BFLAe)...

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1st IJCAI Workshop and Challenge on Micro-Gesture Analysis for Hidden Emotion Understanding, MiGA 2023

作者： Lee, Yoon Migut, Gosia Specht, Marcus Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

This study is built upon a behavior-based framework for real-time attention evaluation of higher education learners in e-reading. Significant challenges in AI model developments for learning analytics have been 1) defining valid indicators and 2) connecting the analytics results to interventions, balancing the generalization and personalization needs. To address this, we utilized a public multimodal WEDAR dataset and trained a neural network model based on real-time features of learners, aiming at predicting learners' moment-to-moment distractions. Real-time features for model training include 30 learners' attention regulation behaviors annotated every second, reaction times to blur stimuli, and page numbers indicating various reading phases. Our preliminary model based on a neural network has achieved 66.26% accuracy in predicting self-reported distractions. Based on the model, we suggest a framework of a Behavior-based Feedback Loop for Attentive e-reading (BFLAe). It has text blur as feedback, a mechanism responsive to learners' distractions that also works as data for next-round feedback. The general feedback implementation rules are established on a statistical analysis conducted on all learners. In addition, we propose a strategy for personalizing feedback using a quartile analysis of individual data, promoting learner-specific feedback. Our framework addresses the high demand for an automated e-learning assistant with non-intrusive data collection based on real-world settings and intuitive feedback provision. The feedback system aims to help learners with longer attention spans and less frequent distractions, leading to more engaging e-reading. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Modal analysis

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Analysis of Conductance Variability in RRAM for Accurate Neuromorphic Computing 25

Analysis of Conductance Variability in RRAM for Accurate Neu...

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25th IEEE Latin American Test Symposium, LATS 2024

作者： Aziza, H. Postel-Pellerin, J. Fieback, M. Hamdioui, S. Xun, H. Taouil, M. Coulie, K. Rahajandraibe, W. Aix-Marseille University Cnrs IM2NP France Delft University Of Technology Computer Engineering Laboratory Delft2628CD Netherlands

ISBN: (纸本)9798350365559

While Resistive RRAM (RRAM) offers attractive features for artificial neural networks (NN) such as low power operation and high-density, its conductance variation can pose significant challenges when the storage of synaptic weights is concerned. This paper reports an experimental evaluation of the conductance variations of manufactured RRAMs at the memory array level. Working at the memory array level allows to catch cycle-to-cycle (C2C) as well as device-to-device (D2D) variability and, hence, to propose a realistic evaluation of the conductance variation. Variability is evaluated with respect to the RRAM low resistance state (LRS) and high resistance state (HRS) conductance ratio. This ratio is selected as the parameter of interest as it guarantees the proper operation of the RRAM: the larger the ratio, the more reliable and robust the RRAM cell is in storing and retrieving data. The measurement results show that the conductance ratio is heavily affected by variability. Large spatial and temporal variations are reported, making challenging RRAM-based analog weight storage. © 2024 IEEE.

关键词： RRAM

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Guided Malware Sample Analysis Based on Graph Neural Networks

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IEEE Transactions on Information Forensics and Security 2023年 18卷 4128-4143页

作者： Chen, Yi-Hsien Lin, Si-Chen Huang, Szu-Chun Lei, Chin-Laung Huang, Chun-Ying National Taiwan University Department of Electrical Engineering Taipei106319 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan Delft University of Technology Faculty of Technology Policy and Management Delft2628 BX Netherlands

Malicious binaries have caused data and monetary loss to people, and these binaries keep evolving rapidly nowadays. With tons of new unknown attack binaries, one essential daily task for security analysts and researchers is to analyze and effectively identify malicious parts and report the critical behaviors within the binaries. While manual analysis is slow and ineffective, automated malware report generation is a long-term goal for malware analysts and researchers. This study moves one step toward the goal by identifying essential functions in malicious binaries to accelerate and even automate the analyzing process. We design and implement an expert system based on our proposed graph neural network called MalwareExpert. The system pinpoints the essential functions of an analyzed sample and visualizes the relationships between involved parts. We evaluate our proposed approach using executable binaries in the Windows operating system. The evaluation results show that our approach has a competitive detection performance (97.3% accuracy and 96.5% recall rate) compared to existing malware detection models. Moreover, it gives an intuitive and easy-to-understand explanation of the model predictions by visualizing and correlating essential functions. We compare the identified essential functions reported by our system against several expert-made malware analysis reports from multiple sources. Our qualitative and quantitative analyses show that the pinpointed functions indicate accurate directions. In the best case, the top 2% of functions reported from the system can cover all expert-annotated functions in three steps. We believe that the MalwareExpert system has shed light on automated program behavior analysis. © 2005-2012 IEEE.

关键词： Feature extraction

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