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检索条件"机构=Dept. of Applied Computer Engineering"

共 1549 条记录，以下是1-10 订阅

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PIR-R Control for Three-Phase Grid-Connected Inverter with Unbalanced Grid Current Correction 14

PIR-R Control for Three-Phase Grid-Connected Inverter with U...

引用

14th Annual IEEE applied Power Electronics Conference and Exposition, APEC 2025

作者： Ghanayem, Haneen Yang, Xingyu Alathamneh, Mohammad Nelms, R.M. Al-Huson University College Al-Balqa Applied University Dept. of Electrical Engineering Irbid Jordan Auburn University Dept. of Electrical and Computer Engineering AuburnAL United States

ISBN: (纸本)9798331516116

Proposed in this paper a proportional-integral-resonant-resonant (PIR-R) control method for a three-phase grid-connected inverter. Unbalanced loads at the point of common coupling (PCC) can disrupt grid current balance and adversely impact other PCC connections, impairing the inverter's ability to accurately track active and reactive power commands. The PIR-R control integrates PI and PR components for fundamental reference current tracking and second-order harmonic rejection. The first resonant controller component targets the grid frequency, while the second addresses the double-frequency oscillations caused by unbalanced grid currents. Without the need for additional correction devices such as shunt active power filters at the PCC, the PIR-R method ensures effective reference command tracking and corrects unbalanced grid currents. Simulation and experimental results confirm that the PIR-R control provides improved transient response and reduced steady-state error compared to conventional methods. © 2025 IEEE.

关键词： Proportional control systems

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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InterACT: Enhancing Human-AI Interaction 2

InterACT: Enhancing Human-AI Interaction

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katta, Jithendra Kodumuru, Nikhil Reddy Katta, Swathi School of Engineering and Applied Sciences The George Washington University WashingtonDC United States Vignana Bharathi Institute of Technology Computer Science & Engineering dept. Hyderabad India

ISBN: (纸本)9798331505745

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools. © 2025 IEEE.

关键词： Object detection

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Estimation of the Position of Loaded Packages in a Roll Cage Cart: Estimating the Placement Layout of Multiple Packages in a Flat Space

Estimation of the Position of Loaded Packages in a Roll Cage...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Kako, Takaya Yokota, Sho Chugo, Daisuke Muramatsu, Satoshi Hashimoto, Hiroshi Toyo University Dept. of Mechanical Engineering Saitama Japan School of Engineering Kwansei Gakuin University Sanda Japan Tokai University Dept. of Applied Computer Eng. Hiratsuka Japan Adv. Institute of Industrial Tech Shinagawa Japan

ISBN: (纸本)9798331533892

The purpose of this research is to develop a system that shows the loading position of deliveries in a roll cage cart. This study solves the problem of the difficulty of locating individual deliveries in piles. In the proposed system, it is possible to determine the location and size of the package without tearing down the pile of deliveries. The system uses a combination of RFID readers and IMUs to estimate the location of deliveries based on RSSI and azimuth. In addition, the system incorporates the known information of the size of the deliveries and the size of the cart to estimate the position of the delivery in the cart. The proposed algorithm using the constraints of this roll cage cart and the size and coordinates of the packages is used to estimate the location of the packages in the roll cage cart and present it to the delivery workers. This paper reports on the estimation of the positional relationship of multiple packages arrangements in a two-dimensional plane. © 2025 IEEE.

关键词： Deployment estimation Radio strength RFID

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Automatic Emoticons Insertion System Based on Acoustic Information of User Voice: 4th Report on Silence Removal for Generalized Emotion Estimation and Improved Visual Representations

Automatic Emoticons Insertion System Based on Acoustic Infor...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Senuma, Ryo Yokota, Sho Matsumoto, Akihiro Chugo, Daisuke Muramatsu, Satoshi Hashimoto, Hiroshi Toyo University Dept. of Mechanical Engineering Saitama Japan School of Engineering Kwansei Gakuin University Sanda Japan Tokai University Dept. of Applied Computer Eng. Hiratsuka Japan Adv. Institute of Industrial Tech Shinagawa Japan

ISBN: (纸本)9798331533892

This study proposes an automatic emoticon insertion system based on acoustic information from user voice. The system estimates emotions from acoustic features using Support Vector Machine (SVM) and inserts corresponding emoticons to enhance emotional communication in text-based social media. Key advancements include the removal of silent segments from training model voice data, improving emotion estimation accuracy and generalizability, as well as a refined visual representation method to address the polysemous nature of emoticons. Experimental results show a significant performance improvement, with accuracy rising to 75.12% compared to the previous model. These findings lay the groundwork for real-time applications, supporting enhanced user communication efficiency and emotion expressiveness in digital contexts. © 2025 IEEE.

关键词： Emotion Recognition

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of Science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Work in Progress: Enhancing Students' Learning Experience in Formal Methods through Practical Assignments

Work in Progress: Enhancing Students' Learning Experience in...

引用

IEEE World engineering Education Conference (EDUNINE)

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on passing formal methods courses with minimal effort. Performance in compulsory formal methods courses is usually below average, with students perceiving the subject as overly mathematical and lacking practical relevance. To address these challenges and enrich the learning experience, we have integrated mandatory group homework assignments into our teaching framework. Students are required to work collaboratively on case studies and present their solutions during class. This work-in-progress paper provides an experience report on enhancing the learning possibilities of master’s students in a model checking course at the Frankfurt University of applied Sciences (FRA-UAS).

关键词： Model checking engineering education

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EEG-Based Brain-computer Interface for Robotic System Control Using Right-Left Hand Movements with Ensemble Model

EEG-Based Brain-Computer Interface for Robotic System Contro...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Biplov Paneru Bipul Thapa Bishwash Paneru Krishna Bikram Shah Dept. of Eleectronics and Communication Engineering Nepal Engineering College Pokhara University Nepal Dept. of Computer Science & Engineering Kathmandu University Nepal Dept. of Applied Sciences and Chemical Engineering Institiute of Engineering Tribhuvan University Nepal Dept. of Computer Science and Engineering Pokhara University Nepal

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In this work, machine learning models are utilized to interpret EEG data for the development of a brain-computer interface (BCI) system that controls a robotic car. Voluntary hand motor actions are employed for interface controls, with each hand corresponding to a different direction. EEG data is recorded using a 14-channel EMOTIV EPOC+ device during motor activities such as moving the car left, right, or stopping. The raw EEG signals are preprocessed using Fast Fourier Transform (FFT) before training a range of models, including Random Forest, XGBoost, CatBoost, Bi-Directional GRU, Transformer-based, and a Stacking Ensemble Technique (SET). The highest accuracy is achieved by SET (87%), followed by XGBoost (82%). These results demonstrate the robustness of ensemble approach SET for EEG-based tasks. Although further advancements in real-time processing are necessary, the system's ability to successfully control the robotic car underscores the potential of BCIs in robotic control and assistive systems development.

关键词： Hands Fast Fourier transforms Computational modeling Brain modeling Motors Transformers Electroencephalography Brain-computer interfaces Automobiles Robots

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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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Work in Progress: Adapting eduScrum to the Online Teaching Format

Work in Progress: Adapting eduScrum to the Online Teaching F...

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IEEE World engineering Education Conference (EDUNINE)

作者： Christina Andersson Gerald Kroisandt Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Germany School of Engineering Htw Saar Germany

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

The main idea of agile processes is that they focus on short loops with fast feedback, instead of lengthy projects only evaluated in the end. The agile principles show a lot of promising features, which could be used also in the teaching-learning process, e.g. in the engagement of students. Therefore, the agile manifesto has been successfully mapped to the educational context, giving rise to the pedagogical approach eduScrum. Early applications of eduScrum took place in schools, but it has also been adapted to suit tuition at university level as well. In this paper, we discuss the application of eduScrum in a data science course at university level, both with respect to the face-to-face setting and the current online tuition. We especially focus on challenges and opportunities with the transition of eduScrum to the online environment. Findings in the first in-class usage of the online-taught eduScrum approach are presented.

关键词： Distance learning Data science engineering education

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