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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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Rethinking CNN Models for Audio Classification

arXiv

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arXiv 2020年

作者： Palanisamy, Kamalesh Singhania, Dipika Yao, Angela Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India School of Computing National University of Singapore Singapore

In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification. Even though there is a significant difference between audio Spectrogram and standard ImageNet image samples, transfer learning assumptions still hold firmly. To understand what enables the ImageNet pretrained models to learn useful audio representations, we systematically study how much of pretrained weights is useful for learning spectrograms. We show (1) that for a given standard model using pretrained weights is better than using randomly initialized weights (2) qualitative results of what the CNNs learn from the spectrograms by visualizing the gradients. Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model. This variance in performance is due to the random initialization of linear classification layer and random mini-batch orderings in multiple runs. This brings significant diversity to build stronger ensemble models with an overall improvement in accuracy. An ensemble of ImageNet pretrained DenseNet achieves 92.89% validation accuracy on the ESC-50 dataset and 87.42% validation accuracy on the UrbanSound8K dataset which is the current state-of-the-art on both of these datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrographs

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Multi-method analysis of emotional and affective states

Multi-method analysis of emotional and affective states

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Progress in Applied Electrical engineering (PAEE)

作者： Adrian Luckiewicz Bogdan Ruszczak Mariusz Pelc Dariusz Mikołajewski Adam Sudoł Barbara Grochowicz Edward J. Gorzelanczyk Patryk Mendon Aleksandra Kawala-Sterniu Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

关键词： Electrical engineering Brain Data processing Hemodynamics Functional near-infrared spectroscopy

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Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

arXiv

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arXiv 2024年

作者： Katharria, Aashu Rajwar, Kanchan Pant, Millie Velásquez, Juan D. Snášel, Václav Deep, Kusum Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Statistical Quality Control and Operations Research Unit Indian Statistical Institute Hyderabad India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Department of Industrial Engineering University of Chile Santiago Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mathematics Indian Institute of Technology Roorkee Roorkee India

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on Computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Anti-disturbance control based on uncertain data 20

Anti-disturbance control based on uncertain data

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3rd International Conference on Software engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd International Conference on Big Data and Smart computing, ICBDSC 2020

作者： Cheng, Peng Pan, Feng Yin, Yanyan Kong, Lingshuang Wang, Song Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia College of Electrical and Information Engineering Hunan University of Technology Zhuzhou412007 China

ISBN: (纸本)9781450376907

In this paper, we describe a novel method for constructing probabilistic robust disturbance rejection control for systems contain uncertain data in which a scenario optimization method is used to deal with the nonlinear and unbounded uncertainties. For anti-disturbance, a reduced order disturbance observer is considered and a state-feedback controller is designed. Sufficient conditions are presented to ensure that the resulting closed-loop system is stable and a prescribed H performance index is satisfied. A numerical example is presented to illustrate the effectiveness of the techniques proposed and analyzed. © 2020 Association for computing Machinery.

关键词： Closed loop systems

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Wireless Acoustic Sensor Networks and Edge computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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Binding of the three-hadron DD*K system from the lattice effective field theory

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Physical Review D 2025年第3期111卷 036002-036002页

作者： Zhenyu Zhang Xin-Yue Hu Guangzhao He Jun Liu Jia-Ai Shi Bing-Nan Lu Qian Wang Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Graduate School of China Academy of Engineering Physics Beijing 100193 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China

We employ the nuclear lattice effective field theory (NLEFT), an efficient tool for nuclear ab initio calculations, to solve the asymmetric multihadron systems. We take the DD*K three-body system as an illustration to demonstrate the capability of the method. Here the two-body chiral interactions between D, D*, and K are regulated with a soft lattice regulator and calibrated with the binding energies of the Tcc+, Ds0*(2317), and Ds1(2460) molecular states. We then calculate the three-body binding energy using the NLEFT and analyze the systematic uncertainties due to the finite volume effects, the sliding cutoff, and the leading-order three-body forces. Even when the three-body interaction is repulsive (even as large as the infinite repulsive interaction), the three-body system has a bound state unambiguously with binding energy no larger than the Ds1(2460)D threshold. To check the renormalization group invariance of our framework, we extract the first excited state. We find that when the ground state is fixed, the first excited states with various cutoffs coincide with each other when the cubic size goes larger. In addition, the standard angular momentum and parity projection technique is implemented for the quantum numbers of the ground and excited states. We find that both of them are S-wave states with quantum number JP=1−. Because the three-body state contains two charm quarks, it is easier to be detected in the Large Hadron Collider.

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

引用

Progress in Applied Electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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Fingertip interactive tracking registration method for AR assembly system

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Advances in Computational Intelligence 2022年第2期2卷 1-26页

作者： Jiu, Yong Jianguo, Wei Yangping, Wang Jianwu, Dang Xiaomei, Lei The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Lanzhou China College of Intelligence and Computing Tianjin University Tianjin China The National Experimental Teaching Demonstration Center of Computer Science and Technology Lanzhou Jiaotong University Lanzhou China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China Gansu Meteorological Service Meteorological Information and Technological Supporting Center Lanzhou China

Aiming at the problems of single input mode and lack of naturalness in the assembly process of existing AR systems, a tracking registration method of mobile AR assembly system is proposed based on multi-quantity and multi-degree of freedom natural fingertip interaction. Firstly, the real-time and stable tracking of hand area in complex environment is realized based on the hand region tracking; secondly, the fingertip detection and recognition based on K-COS and parallel vector is used to improve the precision and stability of fingertip recognition; thirdly, the special movement track of fingertip is recognized based on improved DTW algorithm, which has strong compatibility and feature gradient transformation for complex fingertip trajectory recognition; finally, through the real-time transformation of projection relationship between fingertip and virtual object, the interaction between fingertip and virtual object is made more natural and realistic. The experimental results show that in the complex environment of background, illumination, scale and rotation, the precision of fingertip detection and recognition is about 93%, and the precision of fingertip motion template matching is about 91%. The translation error of the registration method based on visual feature recognition is reduced by about 100pix compared with fingertip tracking registration method, and the efficiency of mobile AR-guided assembly method is improved by about 24.77% compared with the traditional manual assisted assembly method. These data verifies the strong interaction and practicability of the fingertips based on the user's multi-quantity and multi-degree of freedom features in the process of mobile AR guided assembly.

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