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Identification of coexistence of biological and non-biological aerosol particles with DAPI (4′,6-diamidino-2-phenylindole) stain

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Particuology 2023年第1期72卷 49-57页

作者： Ting Liu Jiaquan Zhang Junji Cao Han Zheng Changlin Zhan Hongxia Liu Lili Zhang Kai Xiao Shan Liu Dong Xiang Daizhou Zhang School of Environmental Science and Engineering Hubei Polytechnic UniversityHubei Key Laboratory of Mine Environmental Pollution Control and RemediationHuangshi 435003China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430063China Faculty of Environmental and Symbiotic Sciences Prefectural University of KumamotoKumamoto 862-8502Japan

The fluorescent dye 4′,6-diamidino-2-phenylindole (DAPI) has been widely used to stain microorganisms in various environment media. We applied DAPI fluorescence enumeration to airborne microorganisms and found that non-biological particles, including organic compounds, minerals, and soot, were also visible upon exposure to UV excitation under fluorescence microscope. Using laboratory-prepared biological particles as the control, we investigated the feasibility of identifying both biological and non-biological particles in the same sample with DAPI staining. We prepared biological (bacterial, fungi, and plant detritus) and non-biological (biochar, soot, mineral, metal, fly ash, salt) particles in the laboratory and enumerated the particles and their mixture with DAPI. We found that mineral particles were transparent, and biochar, soot, metals and fly ash particles were black under a filter set at excitation 350/50 nm and emission 460/50 nm bandpass (DAPI-BP), while biological particles were blue, as expected. Particles of the water-soluble salts NaCl and (NH_(4))_(2)SO_(4) were yellow under a filter set at excitation 340–380 nm and emission 425 nm long pass (DAPI-LP). Case studies with samples of dustfall, atmospheric aerosols and surface soils could allow for the quantification of the relative number of different types of particles and particles with organic matter or salt coating as well. Fluorescence enumeration with DAPI stain is thus able to identify the co-existence of biological and non-biological particles in the air, at least to the extent of those examined in this study.

关键词： 4',6-diamidino-2-phenylindole(DAPI) Biological particles Nonbiological particles Atmospheric aerosols

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning 3

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Tian, Jilun Zhang, Jiusi Jiang, Yuchen Luo, Hao Huang, Congsheng Chow, Mo-Yuen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350324754

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks. © 2023 IEEE.

关键词： Long short-term memory

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OPENPATCH: A 3D PATCHWORK FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2023年

作者： Rabino, Paolo Alliegro, Antonio Borlino, Francesco Cappio Tommasi, Tatiana Department of Control and Computer Engineering Politecnico di Torino Italy Italian Institute of Technology Italy

Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further computational training effort at every new task. Out-of-distribution detection has attracted significant attention in the last years, however, the majority of the studies deal with 2D images ignoring the inherent 3D nature of the real-world and often confusing between domain and semantic novelty. In this work, we focus on the latter, considering the objects’ geometric structure captured by 3D point clouds regardless of the specific domain. We advance the field by introducing OpenPatch which builds on a large pretrained model and simply extracts from its intermediate features a set of patch representations that describe each known class. For any new sample, we obtain a novelty score by evaluating whether it can be recomposed mainly by patches of a single known class or rather via the contribution of multiple classes. We present an extensive experimental evaluation of our approach for the task of semantic novelty detection on real-world point cloud samples when the reference known data are synthetic. We demonstrate that OpenPatch excels in both the full and few-shot known sample scenarios, showcasing its robustness across varying pre-training objectives and network backbones. The inherent training-free nature of our method allows for its immediate application to a wide array of real-world tasks, offering a compelling advantage over approaches that need expensive retraining efforts. © 2023, CC BY.

关键词： Semantics

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A Rapid Finger Vein Recognition Approach Based on UNet++ 1

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39th CCF National Conference on computer Applications, CCF NCCA 2024

作者： Liu, Peng Jia, Yujiao Cao, Xiaofan Wang, Changjie Fan, Shanshan Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China Yangzhong Intelligent Electric Research Center North China Electric Power University Beijing China

ISBN: (数字)9789819796748

ISBN: (纸本)9789819796731

With the advancement of biometric technologies, finger vein recognition has garnered widespread attention within the academic community. This paper proposes a method based on the UNet++ model, wherein parameter adjustments are made to the UNet++ architecture to ensure precise pixelwise segmentation of finger vein images. Following the parameter optimization, model compression is applied through structural reparameterization to further increase the recognition speed of the trained model. The approach achieves recognition accuracies of 93.1% on two publicly available finger vein datasets, and it features a feature extraction time of 0.0131 s for a single finger vein image. The recognition time for a single fingerprint vein image is 0.0294 s. The results indicate that the use of the UNet++ model as a framework for finger vein feature extraction and subsequent model compression render it suitable for finger vein recognition tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Optimization Research on Pedestrian Multi Objects Tracking Model Based on TBD Strategy

Optimization Research on Pedestrian Multi Objects Tracking M...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Wang, Shi Liu, XiangJu Liu, XinShu Chen, JiaHui Wang, XiaoHong Qilu Institute of Technology College of Computer and Communication Engineering Shandong Jinan250200 China College of Artificial Intelligence and Software Liaoning Petrochemical University Liaoning Fushun113001 China College of Information and Control Engineering Liaoning Petrochemical University Liaoning Fushun113001 China

ISBN: (纸本)9781510672444

The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching. © 2024 SPIE.

关键词： Feature extraction

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Evaluation Metrics of Object Detection for Quantitative System-Level Analysis of Safety-Critical Autonomous Systems

Evaluation Metrics of Object Detection for Quantitative Syst...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Apurva Badithela Tichakorn Wongpiromsarn Richard M. Murray Control and Dynamical Systems California Institute of Technology Department of Computer Science Iowa State University

This paper proposes two metrics for evaluating learned object detection models: the proposition-labeled and distance-parametrized confusion matrices. These metrics are leveraged to quantitatively analyze the system with respect to its system-level formal specifications via probabilistic model checking. In particular, we derive transition probabilities from these confusion matrices to compute the probability that the closed-loop system satisfies its system-level specifications expressed in temporal logic. Instead of using object class labels, the proposition-labeled confusion matrix uses atomic propositions relevant to the high-level control strategy. Furthermore, unlike the traditional confusion matrix, the proposed distance-parametrized confusion matrix accounts for variations in detection performance with respect to the distance between the ego and the object. Empirically, these evaluation metrics, chosen by considering system-level specifications and control module design, result in less conservative system-level evaluations than those from traditional confusion matrices. We demonstrate this framework on a car-pedestrian example by computing the satisfaction probabilities for safety requirements formalized in Linear Temporal Logic.

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Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs

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IFAC-PapersOnLine 2022年第35期55卷 49-54页

作者： Lucian C. Iacob Roland Tóth Maarten Schoukens Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables. In the lifted space, the dynamics are linear and represented by a so-called Koopman operator. While the Koopman theory was originally introduced for autonomous systems, it has been widely used to derive linear time-invariant (LTI) models for nonlinear systems with inputs through various approximation schemes such as the extended dynamics mode decomposition (EDMD). However, recent extensions of the Koopman theory show that the lifting process for such systems results in a linear parameter-varying (LPV) model instead of an LTI form. As LTI Koopman model based control has been successfully used in practice and it is generally temping to use such LTI descriptions of nonlinear systems, due to the simplicity of the associated control tool chain, a systematic approach is needed to synthesise optimal LTI approximations of LPV Koopman models compared to the ad-hoc schemes such as EDMD, which is based on least-squares regression. In this work, we introduce optimal LTI Koopman approximations of exact Koopman models of nonlinear systems with inputs by using ℓ 2 -gain and generalized H 2 norm performance measures. We demonstrate the advantages of the proposed Koopman modelling procedure compared to EDMD.

关键词： Nonlinear systems Koopman operator Linear Parameter-Varying systems

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Joint Domain Alignment and Adversarial Learning for Domain Generalization 1

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2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Li, Shanshan Zhao, Qingjie Wang, Lei Liu, Wangwang Zhang, Changchun Zou, Yuanbing School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100190 China School of Technology Beijing Forestry University Beijing100083 China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

Domain generalization aims to extract a classifier model from multiple observed source domains, and then can be applied to unseen target domains. The primary challenge in domain generalization lies in how to extract a domain-invariant representation. To tackle this challenge, we propose a multi-source domain generalization network called Joint Domain Alignment and Adversarial Learning (JDAAL), which learns a universal domain-invariant representation by aligning the feature distribution of multiple observed source domains based on multi-kernel maximum mean discrepancy. We adopt an optimal multi-kernel selection strategy that further enhances the effectiveness of embedding matching and approximates different distributions in the domain-invariant feature space. Additionally, we use an adversarial auto-encoder to bound the multi-kernel maximum mean discrepancy for rendering the feature distribution of all observed source domains more indistinguishable. In this way, the domain-invariant representation generated by JDAAL can improve the adaptability to unseen target domains. Extensive experiments on benchmark cross-domain datasets demonstrate the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alignment

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Autonomous Hook-Based Grasping and Transportation with Quadcopters

arXiv

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arXiv 2023年

作者： Antal, Péter Péni, Tamás Tóth, Roland The Systems and Control Lab. HUN-REN Institute for Computer Science and Control Budapest Hungary The Control Systems Group The Eindhoven University of Technology Netherlands

Payload grasping and transportation with quadcopters is an active research area that has rapidly developed over the last decade. To grasp a payload without human interaction, most state-of-the-art approaches apply robotic arms that are attached to the quadcopter body. However, due to the large weight and power consumption of these aerial manipulators, their agility and flight time are limited. This paper proposes a motion control and planning method for transportation with a lightweight, passive manipulator structure that consists of a hook attached to a quadrotor using a 1 DoF revolute joint. To perform payload grasping, transportation, and release, first, time-optimal reference trajectories are designed through specific waypoints to ensure the fast and reliable execution of the tasks. Then, a two-stage motion control approach is developed based on a robust geometric controller for precise and reliable reference tracking and a linear–quadratic payload regulator for rapid setpoint stabilization of the payload swing. Furthermore, stability of the closed-loop system is mathematically proven to give safety guarantee for its operation. The proposed control architecture and design are evaluated in a high-fidelity physical simulator, and also in real flight experiments, using a custom-made quadrotor–hook manipulator platform. © 2023, CC BY-NC-SA.

关键词： Manipulators

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