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Observer-Structured PID Controller: Flight Controller Design Examples 4

Observer-Structured PID Controller: Flight Controller Design...

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4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Sato, Masayuki Sebe, Noboru Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9798350391183

This note shows two design examples of PID con-Trollers, whose state variables estimate the selected state vari-Ables of the controlled plant systems, for LTI Parametrically Dependent (LTIPD) systems. The authors have already proposed observer-structured controllers, whose structure is not the same as but is very similar to the well-established Luenberger observer-based controllers, and have also proposed a conversion method from a priori designed unstructured LTI or Linear Parameter-Varying (LPV) controllers to observer-structured LTI/LPV con-Trollers with the plant state estimation ability implemented. How-ever, the successful conversion has been reported only for full-order controllers. In this note, we show two examples, in which a priori designed LTI PID controllers are successfully converted to observer-structured PID controllers, and the designed observer-structured PID controllers well estimate the selected plant state variables in numerical simulations. © 2024 IEEE.

关键词： Three term control systems

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Target Identity Fusion Method Based on Improved DS Evidence Theory 3

Target Identity Fusion Method Based on Improved DS Evidence ...

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3rd international conference on Electronic Information engineering and computer, EIECT 2023

作者： Wang, Yi Liu, Desheng Yang, Yang Liu, Jiatong Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing101400 China Space Engineering University Beijing101400 China

ISBN: (纸本)9798350357707

Target data detected by multi-sensors often differ from each other due to different sensor performances, resulting in uncertainty of target identity information. Traditional Dempster-Shafer (DS) evidence theory may achieve counter-intuitive outcomes facing evidence conflicts caused by the uncertainty. Aiming for this, a novel target identity fusion method based on improved DS evidence theory is proposed in this paper. Specifically, the method considers the correlation between evidences and the importance of evidences integrally. First, an improved conflict measurement method based on Pearson correlation coefficient matrix is devised to measure the evidence credibility. Next the information volume of evidences is measured by Deng-entropy to indicate the evidence uncertainty. Then the credibility and uncertainty are weighted and combined to modify the basic probability assignment (BPA) of evidences. Ultimately, the modified evidences are fused by Dempster's combination rule to acquire the final results. Through three numerical analysis examples, this paper demonstrates the superiority of the proposed method over typical previous approaches, which can effectively process evidence conflicts in multi-sensors information fusion. © 2023 IEEE.

关键词： Numerical methods

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 international conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Short text classification based on convolutional upsampling feature enhancement

Short text classification based on convolutional upsampling ...

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2024 international conference on Computing, Machine Learning and Data science, CMLDS 2024

作者： Lian, Yukui Sun, Yanzhao Ding, Li Zhang, Han Lu, Jiahui Gu, Shuai School of Computer and Information Engineering Tianjin Normal University China

ISBN: (纸本)9798400716393

Short text can lead to sparse feature representation and classification inaccuracies due to noise and other issues. To address this, we propose a short text classification model that uses convolutional upsampling feature enhancement. Our approach involves using a multi-scale convolutional neural network to extract deep features of different dimensions. Secondly, we enhance the deep features by upsampling convolution and obtain more discriminative features for text classification by downsampling. Finally, we use an end-to-end approach to output the text categories. Experimental validation on a public dataset shows that our proposed feature enhancement approach significantly improves the model's performance in short text categorization. © 2024 ACM.

关键词： Convolution

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Change Detection of Building Remote Sensing Images Based on RMT-BIT 4

Change Detection of Building Remote Sensing Images Based on ...

引用

4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Lin, Ruixing Piao, Shunmei School of Engineering Yanbian University Civil Engineering and Water Conservancy Discipline Jilin China School of Engineering Yanbian University Architecture Jilin China

ISBN: (纸本)9798331531409

The detection of building changes through remote sensing image allows for the timely monitoring of urban development dynamics, providing a scientific basis for urban planning and management. Given the complexity of spatial features in remote sensing images of buildings, this paper introduces spatial prior knowledge in terms of both space and distance. Building features from remote sensing images are extracted using retentive networks meet vision transformers (RMT). The extracted feature maps from two temporal phases are processed by a bitemporal image transformer (BIT), leading to the output of the decoder, from which the areas of change between the two phases are computed. The proposed method is validated on the WHU-CD building change detection dataset, and the results show that the proposed method effectively improves the accuracy of building change detection in remote sensing images. © 2024 IEEE.

关键词： Change detection

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Augmented Reality for Next Generation Connected and Automated Vehicles 5

Augmented Reality for Next Generation Connected and Automate...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Sachin Bhadula, Shuchi Juyal, Amit Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

Augmented Reality (AR) may be an innovation that upgrades real-world situations with computer-generated substance. AR for Another Era Associated and Mechanized Vehicles includes joining AR advances into the plan and operation of vehicles. This paper points to supply drivers with data and interaction in a more instinctive and intuitively way, and to upgrade the driving encounter in associated and computerized vehicles. This incorporates utilizing AR for route, vehicle control, infotainment, and support. Also, AR seem offer assistance move forward street security by giving real-time data to drivers and upgrading their situational mindfulness. The integration of AR in next-gen vehicles has the potential to revolutionize the way we drive and associated with vehicles, making the encounter more secure, more helpful, and more agreeable. © 2024 IEEE.

关键词： Augmented Reality Connected and Automated Vehicles Next Generation

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An Active Reconfigurable Mixer in GaN Technology for 5G Power Amplifier Wi-Fi Communications 6

An Active Reconfigurable Mixer in GaN Technology for 5G Powe...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Kumar, Rajesh Babu, Tosif Abdul Gaffar Kanaujia, Binod Kumar Tiwari, Devesh Department of Computer Science & Engineering Galgotias College of Engineering &Technology Greater Noida India Department of Electronics & Communication of Engineering PIET Gujarat Vadodara India Department of Electronics & Communication of Engineering National Institute of Technology Jalandhar Jawaharlal Nehru University New Delhi110067 India Department of Computer Science & Engineering Graphic Science and Engineering Graphic Era Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

The mixer's key attributes in this article are its high power output, high conversion gain, high efficiency, and good isolation between board positions. We have designed and implemented a mixer that can be reconfigured dynamically. Using a voltage gain of 12.3 dB, the local oscillator (LO) signal is amplified. The purpose of the active reconfigurable mixer is to enhance the cross-driver platform's performance. Alongside, with the occurrence of a mixer a high-power level is attained. It is noteworthy to highlight that the third-order intermodulation (IIP3) of the new configuration is -9 dBm, which is regarded as negligible. The mixer runs at 6 GHz and is powered by 0.15-µm GaN technology. © 2024 IEEE.

关键词： Gallium nitride

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Classification of multi-view mammogram images using a parallel pre-trained models system 8

Classification of Multi-view mammogram images using a parall...

引用

8th IEEE international conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kacher, Abdelhafidh Merati, Medjeded Mahmoudi, Saïd University of Tiaret LIM Research Laboratory Computer Science Department Algeria University of Mons Faculty of Engineering ILIA Department Belgium

ISBN: (纸本)9798350309249

Breast cancer is the main lead of women's cancer-related mortalities. Therefore, early detection is imperative for preventing breast cancer from developing to advanced stages. Moreover, the emergence of computer-aided diagnosis systems combined with Deep Learning techniques improve breast cancer diagnosis at its early stages. This study proposes a system for classifying mammogram images scanned from different views based on Deep learning techniques. In this paper, we propose combining four pre-trained models in one system employing the Transfer Learning technique with different input images of the same lesion. This proposed system provides a binary classification of the lesion into malignant or benign classes. The Mini-DDSM dataset images were used in this study;the images were pre-processed and augmented to achieve the best results. Several well-known pre-trained models for image classification (ResNet, Xception, DenseNet, MobileNet, and Inception) were experimented with for the proposed model construction. The study results showed that our proposed model achieved an accuracy of 94.18%, a precision of 94.26%, a recall of 94.16%, and an F1-Score of 94.21%, outperforming the pre-trained model on classifying mammogram images of the same dataset. © 2024 IEEE.

关键词： computer aided diagnosis

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Joint Optimization of Model Splitting and Device Task Assignment for Private multi-hop Split Learning 59

Joint Optimization of Model Splitting and Device Task Assign...

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59th Annual conference on Information sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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Improving Student Learning Risk Detection: A multi-Level Early Warning Framework with Adaptive Mechanisms 6

Improving Student Learning Risk Detection: A Multi-Level Ear...

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6th international conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Zhu, Xuyang Yang, Wenjun Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798331541750

Early warning of learning is important for reminding students to improve themselves and helping teachers to optimize their teaching plans. Researches on early warning of learning have not effectively modeled the fluctuations in students' learning states and have not provided multi-level warnings for students at different levels. To address these issues, a new early warning method is proposed, combining cognitive diagnosis with learning behavior analysis to predict risks in the learning envi-ronment. We use convolutional neural networks and long short-term memory networks to explore students' potential learning features, and attention mechanisms are used to enhance feature extraction. We use Adaboost algorithm to predict students' learning performance. To prevent overfitting and improve data accuracy, an adaptive mechanism is added to the output layer. Based on data analysis results, multi-level early warning of learning are provided for students. Finally, experiments show that the proposed method can effectively and accurately predict at-risk students. © 2024 IEEE.

关键词： Adversarial machine learning

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