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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Shreepad, Sarange Deena, S. Raman, Ramakrishnan Shastri, Dhiresh S. Rajesh, A.S. Krishnakumar, S.K. Department of Mechanical Engineering Ajeenkya DY Patil School of Engineering Maharashtra Pune412105 India Department of Computer Science Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai601103 India University Maharashtra Pune412115 India Department of Mechanical Engineering Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune411038 India Department of Mechanical Engineering JSS Science & Technology University Karnataka Mysuru570006 India Department of Mechanical Engineering Academy of Maritime Education and Training Tamil Nadu Chennai600119 India

ISBN: (纸本)9781665460842

Military, naval, army, etc., rely heavily on aircraft as their primary mode of transportation. In comparison to other types of transportation, aircraft have more freedom of motion. Tracking an aircraft's trajectory in the sky, is an extremely non-linear process. Movement control is crucial to the success of trajectory tracking. The prevention of crashes, damage, and other mishaps relies heavily on the accurate direction and movement controls in the aviation sector. Both stability and control are crucial in aircraft. Yaw, roll, and pitch are the three angles that can be employed to stabilize and control the movement. In this study, an Adaptive Fuzzy PID Controller is developed to regulate all three parameters. The study begins with a model of the aircraft, then moves on to the design of a conventional PID controller and an Adaptive Fuzzy PID controller, and finally concludes with an evaluation of the two controllers through an examination of their output responses at varying degrees of set angle and error metrics like the Integral Squared Error (ISE) and the Integral Absolute Error (IAE). This comparison highlights the advantage of the Adaptive Fuzzy PID controller in trajectory tracking with minimal rising, settling, and overshoot. Among the three angle-tracking systems, Adaptive Fuzzy PID gives the lowest error values. © 2022 IEEE

关键词： Controllers

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Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation

Introducing Shape Prior Module in Diffusion Model for Medica...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Zhiqing Zhang Guojia Fan Tianyong Liu Nan Li Yuyang Liu Ziyu Liu Canwei Dong Shoujun Zhou Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Shenzhen China College Of Information Science and Engineering Northeastern University (Of Affiliation) Shenzhen China College Of Geosciences Northeast Petroleum University Shenzhen China Department of Computer Science City University of Hong Kong Shenzhen China Shenyang Institute of Automation University of Chinese Academy of Sciences Shenzhen China Faculty of Science and Engineering Chinese Academy of Sciences University of Nottingham Ningbo China Ningbo China College Of Information Science And Engineering Northeastern University (Of Affiliation) Shenzhen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, inter-class similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy. The generality and validity of our proposed model are confirmed through extensive experiments. By enabling more precise segmentation of anatomical structures, our framew

关键词： Image segmentation Visualization Accuracy Shape Semantics Noise reduction Anatomical structure Diffusion models Probabilistic logic X-ray imaging

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Causality-driven hierarchical structure discovery for reinforcement learning 22

Causality-driven hierarchical structure discovery for reinfo...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shaohui Peng Xing Hu Rui Zhang Ke Tang Jiaming Guo Qi Yi Ruizhi Chen Xishan Zhang Zidong Du Ling Li Qi Guo Yunji Chen SKL of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences and Cambricon Technologies SKL of Processors Institute of Computing Technology CAS SKL of Processors Institute of Computing Technology CAS and Cambricon Technologies Department of Computer Science and Engineering Southern University of Science and Technology SKL of Processors Institute of Computing Technology CAS and Cambricon Technologies and University of Science and Technology of China University of Chinese Academy of Sciences and SKL of Computer Science Institute of Software CAS SKL of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences

ISBN: (纸本)9781713871088

Hierarchical reinforcement learning (HRL) effectively improve agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e.g., subgoals or options). However, how to automatically discover high-quality hierarchical structures is still a great challenge. Previous HRL methods can hardly discover the hierarchical structures in complex environments due to the low exploration efficiency by exploiting the randomness-driven exploration paradigm. To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments. The key insight is that the causalities among environment variables are naturally fit for modeling reachable subgoals and their dependencies and can perfectly guide to build high-quality hierarchical structures. The results in two complex environments, 2D-Minecraft and Eden, show that CDHRL significantly boosts exploration efficiency with the causality-driven paradigm.

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Improving Iris Recognition Systems With CNN Based Image Processing and Machine Learning Approach

Improving Iris Recognition Systems With CNN Based Image Proc...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Shaleen Bhatnagar S.Pravinth Raja Amirtha Preeya Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of CSE & IS Presidency University Bengaluru Karnataka India School of CSE & IS Presidency University Bangalore India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The principal advancement in iris biometric acknowledgment is the extraction of these striking highlights. The outcome of profound learning innovations can assist us with settling the issues we had with PC vision. The Iris acknowledgment framework has viewed the elements found through CNN as of extraordinary use. In most of validation and personality situations, biometric security confirmation is fundamental. Because of its predictable and astounding surface variety, iris acknowledgment is believed to be the most reliable biometric acknowledgment. While utilizing iris acknowledgment to recognize the people who need an elevated degree of safety, the particular examples are utilized. To work on the viability of acknowledgment, this examination researches a valuable strategy that utilizes support vector machines (SVM) and convolutional brain organizations (CNN) for highlight extraction and characterization, separately. The uniqueness and security of the iris design have made iris acknowledgment frameworks a hotly debated issue in biometric confirmation. This theoretical spotlights on creating iris distinguishing proof frameworks using machine learning and CNN-based image processing.

关键词： Support vector machines Technological innovation Reviews Machine learning Trademarks Safety Convolutional neural networks

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Efficient Reconstruction of Spatial Features for Remote Sensing Image-Text Retrieval

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Transactions of Nanjing University of Aeronautics and Astronautics 2025年第1期42卷 101-111页

作者： ZHANG Weihang CHEN Jialiang ZHANG Wenkai LI Xinming GAO Xin SUN Xian Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190P.R.China Key Laboratory of Target Cognition and Application Technology(TCAT) Chinese Academy of SciencesBeijing 100190P.R.China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Artificial Intelligence Aerospace Information Technology UniversityJinan 250299P.R.China

Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

关键词： remote sensing cross-modal image-text retrieval(RSCIR) spatial features channel features contrastive learning parameter effective transfer learning

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Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber

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Advanced Photonics Nexus 2023年第6期2卷 40-50页

作者： Shengfu Cheng Xuyu Zhang Tianting Zhong Huanhao Li Haoran Li Lei Gong Honglin Liu Puxiang Lai The Hong Kong Polytechnic University Department of Biomedical EngineeringHong KongChina The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina Chinese Academy of Sciences Shanghai Institute of Optics and Fine MechanicsKey Laboratory for Quantum OpticsShanghaiChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina University of Science and Technology of China Department of Optics and Optical EngineeringHefeiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina The Hong Kong Polytechnic University Photonics Research InstituteHong KongChina

Transmission matrix(TM)allows light control through complex media,such as multimode fibers(MMFs),gaining great attention in areas,such as biophotonics,over the past *** have been taken to retrieve a complex-valued TM directly from intensity measurements with several representative phase-retrieval algorithms,which still see limitations of slow or suboptimum recovery,especially under noisy ***,we propose a modified nonconvex optimization *** numerical evaluations,it shows that the optimum focusing efficiency is approached with less running time or sampling *** comparative tests under different signal-to-noise levels further indicate its improved ***,the superior focusing performance of our algorithm is collectively validated by single-and multispot focusing;especially with a sampling ratio of 8,it achieves a 93.6%efficiency of the gold-standard holography *** on the recovered TM,image transmission through an MMF is realized with high *** to parallel operation and GPU acceleration,our nonconvex approach retrieves a 8685×1024 TM(sampling ratio is 8)with 42.3 s on average on a regular *** proposed method provides optimum efficiency and fast execution for TM retrieval that avoids the need for an external reference beam,which will facilitate applications of deep-tissue optical imaging,manipulation,and treatment.

关键词： transmission matrix phase retrieval multimode fiber imaging wavefront shaping

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Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

arXiv

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arXiv 2023年

作者： Jin, Guangyin Liang, Yuxuan Fang, Yuchen Shao, Zezhi Huang, Jincai Zhang, Junbo Zheng, Yu National Innovative Institute of Defense Technology Beijing China National University of Defense Technology Changsha China Guangzhou China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Institute of Computing Technology University of Chinese Academy of Sciences Beijing China College of Systems Engineering National University of Defense Technology Changsha China JD Intelligent Cities Research and JD iCity JD Technology Beijing China

With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work. © 2023, CC BY.

关键词： Complex networks

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An Innovation Development Of Bandwidth Management In 4g Wireless Local Area Network (4g-Wlan) Using Smart Machine Learning Algorithm

An Innovation Development Of Bandwidth Management In 4g Wire...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： B. Gopinathan Papiya Mukherjee Naveen Kumar N R. Nandhini S. Shanmugapriya S D Lalitha Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur Tamilnadu India Department of CSE Education Koneru Lakshmaiah Foundation Green Fields Guntur Andhra Pradesh India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of ECE Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India Department of CSE R.M.K. Engineering College Kavaraipettai Tamilnadu India

Currently, the vast use of existing fourth generation networks is further increasing its users. A major problem with this process is the allocation of equal bandwidth to everyone on the network. This varies depending on the secondary users who are aging. But the time difference between them allows both of them to be switched to get the required bandwidth utilization. An advanced machine learning algorithm is proposed in this paper. It is designed to reduce the hassle for the primary user by calculating the login times of the primary user and the secondary user. Also the switching schedule data for the secondary user is given to improve and fix them so that they do not have immediate connection problems. And the most important thing in this system is time management for primary and secondary users. Two are even better designed to manage this effectively.

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Retraction Note: Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Alqahtani, Abdullah Saleh Pathak, Pankaj Pandiaraj, Saravanan Indrapriyadarsini, H. Arun Kumar, U. Nataraju, Susheela Department of Self-Development Skills-IT CFY Deanship King Saud University Riyadh Saudi Arabia Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) Lavale Pune India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to Be University) Coimbatore India Department of Computer Science Engineering G Madegowda Institute of Technology Bharathinagara (K M Doddi) India

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