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Computationally Efficient Hyperspectral Image Classification Using Deep autoencoder and Multistage 2 Dimensional Convolutional autoencoder Architectures

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Procedia Computer Science 2025年 260卷 1017-1025页

作者： Sneha R. Burnase Poonam N. Sonar Maitreya Shelare Rajiv Gandhi Institute of Technology

Hyper-spectral imaging (HSI) sensor technology enables extensive coverage with spatial, spectral, and temporal flexibility. However, the substantial volume of spectral-spatial information it provides poses significant challenges in data management and extraction. To address these challenges, various advancements have been made in hyperspectral imaging over recent decades. Key issues in hyperspectral image Classification include the large data size, insufficient training samples, particularly the scarcity of labeled samples and computational burden effective feature extraction methods and the appropriate selection of filters for classification are critical for achieving reliable results without data loss. In this paper, two methods named Deep autoencoder (DAE) and Multistage 2D- Convolutional autoencoder (2D-CAE) proposed with simplified and robust architecture which helped in reduced space and time complexity and efficient in dimentionality reduction. Analysed comparison for Support Vector Machine (SVM), K- nearest Neighbour (KNN), HybridSN, Wavelet-CNN is done with proposed methods (DAE) and Multistage 2D- Convolutional autoencoder (2D- CAE), evaluating their accuracy and computational efficiency with respect to Overall Accuracy (OA), Average Accuracy(AA), Kappa(k), FLOPs, Throughput and Test time required for prediction by performing extensive experiments on publically available dataset Indian Pines (IP), Salina (SA) and Pavia University (PU). FLOPs reduction in DAE is 99.97% and 2D-CAE is 99.95%, throughput reduction in DAE is 99.95% and 2D-CAE is 99.98% when compared individually with HybridSN and Wavelet-CNN, showing significant reduction in space complexity.

关键词： autoencoder Classification Convolution Complexity Hyper-spectral

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Enhanced instance selection for large-scale data using integrated clustering and autoencoder techniques

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年 1-18页

作者： Nazari, Mohammad Saadatfar, Hamid Islamic Azad Univ Fac Engn Dept Comp Engn Birjand Iran Univ Birjand Dept Comp Engn Fac Elect & Comp Engn Birjand Iran

Instance selection plays a crucial role in improving the efficiency of machine learning models, especially when dealing with large datasets. Traditional instance selection methods often struggle to balance data reduction with preserving essential information, particularly in high-dimensional and complex datasets. This paper introduces a novel approach, instance selection by combining clustering and autoencoders (CAIR), designed specifically for large-scale data. CAIR addresses key gaps in the literature by integrating clustering techniques to group similar data points and using autoencoders to reduce dimensionality while retaining critical boundary instances. Unlike conventional methods that focus primarily on either boundary or inner instances, CAIR effectively balances the removal of redundant data with the preservation of instances crucial for classification. Experimental results on 24 large datasets from the KEEL repository demonstrate that CAIR achieves superior data reduction while maintaining high classification accuracy compared to state-of-the-art methods, including k-nearest neighbor (KNN), edited nearest neighbors (ENN), DROP3, ATISA1, and RIS. CAIR fills a significant gap by providing an effective solution for large-scale data reduction without compromising performance.

关键词： Instance selection Large data Clustering autoencoder Classification

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MGFA : A multi-scale global feature autoencoder to fuse infrared and visible images

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2024年 128卷

作者： Chen, Xiaoxuan Xu, Shuwen Hu, Shaohai Ma, Xiaole Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing Peoples R China China Elect Technol Grp Corp Res Inst 3 Beijing Peoples R China

Since the convolutional operation pays too much attention to local information, resulting in the loss of global information and a decline in fusion quality. In order to ensure that the fused image fully captures the features of the entire scene, an end-to-end Multi-scale Global Feature autoencoder (MGFA) is proposed in this paper, which can generate fused images with both global and local information. In this network, a multi-scale global feature extraction module is proposed, which combines dilated convolutional modules with the Global Context Block (GCBlock) to extract the global features ignored by the convolutional operation. In addition, an adaptive embedded residual fusion module is proposed to fuse different frequency components in the source images with the idea of embedded residual learning. This can enrich the detailed texture of the fused results. Extensive qualitative and quantitative experiments have demonstrated that the proposed method can achieve excellent results in retaining global information and improving visual effects. Furthermore, the fused images obtained in this paper are more adapted to the object detection task and can assist in improving the precision of detection.

关键词： Image fusion Object detection autoencoder Global information Multi-scale feature

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Kernel functions embed into the autoencoder to identify the sparse models of nonlinear dynamics

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2024年 131卷

作者： Dong, Xin Bai, Yu -Long Wan, Wen -Di Northwest Normal Univ Coll Phys & Elect Engn Lanzhou Peoples R China Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

Numerous researches have shown that there are three main challenges in data-driven model identification methods: high-dimensional measurements, system complexity and unknown underlying dynamical properties. For most nonlinear dynamics, the feature space defined by the coefficients of their control equations is sparse. Therefore, sparse regression methods are used to learn the sparse coefficients of the control equations of nonlinear dynamics. However, this method strongly depends on the appropriate selection of the sparse basis vectors. In this essay, the autoencoder is combined with the sparse regression method to simultaneously identify the sparse coordinate and a parsimonious, interpretable and generalizable model of the specified system. It also integrates kernel functions to map the intractable measurements in the hidden space of the autoencoder into a linearly distinguishable kernel space, which kernelizes the candidate function library of the sparse identification of nonlinear dynamics (SINDy) model as the sparse dictionaries. Therefore, the flexible representation of neural networks, the simplicity of sparse regression methods and the implicit non-linear representation of kernel functions are consolidated in this article. To inspect the reliability of the proposed model in this paper, a set of nonlinear dynamics formulated by ordinary differential equations (ODEs), second-order trigonometric functions and partial differential equations (PDEs) are utilized as test cases. And the comparisons between the proposed model and other model identification methods illustrate that the performance of the former is the best.

关键词： Model identification SINDy Kernel functions autoencoder

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Multi-classification in-vehicle intrusion detection system using packet- and sequence-level characteristics from time-embedded transformer with autoencoder

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KNOWLEDGE-BASED SYSTEMS 2024年 299卷

作者： Le, Tien-Dat Truong, Hoang Bao Huy Pham, Van Phu Kim, Daehee Soonchunhyang Univ Dept Mobil Convergence Asan 31538 Chungcheongnam South Korea Soonchunhyang Univ Dept Future Convergence Technol Asan 31538 Chungcheongnam South Korea

In recent years, the increased application of controller area network (CAN) protocols has made it the de facto standard for communication between electronic control units (ECUs) in the automotive and transportation fields. This widely used protocol was designed as a reliable and straightforward broadcast-based protocol that connects ECUs without considering security concerns such as node authentication or traffic encryption. Despite its efficiency, this tradeoff makes the CAN bus vulnerable to attacks. Implementing intrusion detection systems (IDSs) based on machine learning (ML) can address these security challenges effectively. However, existing ML-based IDSs have limited classification capabilities, lack adaptability and time sensitivity, incomprehensive analysis, and produce high false-negative rates (FNR), while attack schemes are becoming increasingly complex, resulting in insufficient capability of intrusion detection in real-time and insufficient ability to offer reliable protection. Therefore, our study proposes a novel in-vehicle IDS for multiclass classification using both packet- and sequence-level characteristics extracted from an autoencoder and a variant transformer (Time-embedded Transformer) with an improved position encoding mechanism, which analyses CAN traffic in-depth from various perspectives to overcome the existing challenges above. Both standard (Car-Hacking) and advanced (ROAD) datasets are used to evaluate the capabilities of our proposed IDS. The evaluation results demonstrated 100 % detection accuracy and 0 % FNR for both the Car-Hacking and ROAD Masquerade datasets, which also peaked at the highest F1 score for the ROAD Fabrication dataset, emphasizing superior intrusion detection to minimize FNR of the proposed model with high adaptability through its multi-dimensional analysis at packet- and sequencelevel.

关键词： Intrusion detection Controller area network Transformer autoencoder Feature extraction

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Mobile Robot Control for Agriculture Using autoencoder Sensor Fusion 19

Mobile Robot Control for Agriculture Using Autoencoder Senso...

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Latin American Robotics Symposium (LARS) / Brazilian Symposium on Robotics (SBR) / Workshop on Robotics in Education (WRE)

作者： Negreiros Ventura, Maria Leticia Mazoni, Alysson Fernandes Colon, Diego Univ Sao Paulo Dept Telecom & Control Engn Sao Paulo Brazil Univ Estadual Campinas InSySPo Inst Geosci Campinas Brazil

ISBN: (纸本)9781665462808

This work proposes a mobile robot control applied to a seeding task using inertial sensors. The position estimation through these sensors is solved using an autoencoder neural network trained in simulation. The control strategy is tested in the Webots simulation environment. The control strategy uses two PID controllers, one for position and other for direction. Theses controllers are optimized during trajectory using the bat algorithm to update its parameters aiming limited energy cost. The optimization objective involves position and energy cost estimated using the data fusion autoencoder as well as speed imposed to the wheels. The results present a trajectory tracking for a straight path in a rectangular environment.

关键词： Mobile robot navigation Extended state observer Simulation Autonomous navigation autoencoder Agricultural mobile robots Bat algorithm

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Snapshot-to-video autoencoder for compressed ultrahigh-speed imaging 3

Snapshot-to-video autoencoder for compressed ultrahigh-speed...

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Conference on AI and Optical Data Sciences III

作者： Liu, Xianglei Monteiro, Joao Albuquerque, Isabela Lai, Yingming Jiang, Cheng Zhang, Shian Falk, Tiago H. Liang, Jinyang Inst Natl Rech Sci Ctr Energie Mat Telecommun 1650 Blvd Lionel Boulet Varennes PQ J3X 1P7 Canada East China Normal Univ State Key Lab Precis Spect 3663 North Zhongshan Rd Shanghai 200062 Peoples R China

ISBN: (数字)9781510649101

ISBN: (纸本)9781510649101;9781510649095

Single-shot two-dimensional (2D) optical imaging of transient scenes is indispensable for numerous areas of study. Among existing techniques, compressed optical-streaking ultrahigh-speed photography (COSUP) uses a cost-efficient design to endow ultra-high frame rates with off-the-shelf CCD and CMOS cameras. Thus far, COSUP's application scope is limited by the long processing time and unstable image quality in existing analytical-modeling-based video reconstruction. To overcome these problems, we have developed a snapshot-to-video autoencoder (S2V-AE)-a new deep neural network that maps a compressively recorded 2D image to a movie. The S2V-AE preserves spatiotemporal coherence in reconstructed videos and presents a flexible structure to tolerate changes in input data. Implemented in compressed ultrahigh-speed imaging, the S2V-AE enables the development of single-shot machine-learning assisted real-time (SMART) COSUP, which features a reconstruction time of 60 ms and a large sequence depth of 100 frames. SMART-COSUP is applied to wide-field multiple-particle tracking at 20 thousand frames-per-second. As a universal computational framework, the S2V-AE is readily adaptable to other modalities in high-dimensional compressed sensing. SMART-COSUP is also expected to find wide applications in applied and fundamental sciences.

关键词： compressed sensing deep learning optical-streaking ultrahigh-speed autoencoder generative adversarial network

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Deep Learning autoencoder based Anomaly Detection Model on 4G Network Performance Data

Deep Learning Autoencoder based Anomaly Detection Model on 4...

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IEEE World AI IoT Congress (AIIoT)

作者： Ahasan, Md Rakibul Akram, Mohammad Rubbyat Alam, Md Golam Rabiul Haque, Mirza Sanita Momen, Mohammed Fahim BRAC Univ Dhaka Bangladesh Robi Axiata Ltd Dhaka Bangladesh Miami Univ Oxford OH 45056 USA

ISBN: (纸本)9781665484534

A 4G network stands for a fourth-generation mobile network that enables 4G capable mobile phones to connect with the internet faster than ever. It is possible because of faster authentication between mobile phone and network entity. The network entities are sophisticated and require constant monitoring in terms of fault management and performance management. However, the fault is very rare in that network nodes, but a deviation of performance is normal. This deviation is known as an anomaly, and machine learning is useful for detecting an anomaly. In this paper, deep neural network autoencoder-based anomaly detection is discussed over 4G network performance data. An autoencoder can mimic an output from its input and provide superior performance when the data properties are similar. Further elaboration in this paper is how different properties of autoencoder hidden layer count, variable threshold measurement etc influence the anomaly detection outcome of 4G network performance data. At last, an autoencoder configuration is recommended for anomaly detection of 4G network performance data.

关键词： Anomaly Detection Unsupervised Learning autoencoder Network Performance Data

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Physics-Informed deep autoencoder for fault detection in New-Design systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 215卷

作者： Lai, Chenyang Baraldi, Piero Zio, Enrico Politecn Milan Energy Dept Milan Italy MINES Paris PSL Univ CRC Sophia Antipolis France

The industrial application of data-driven methods for fault detection of new-design systems is limited by the inevitable scarcity of real data. Physics-Informed Neural Networks (PINNs) can mitigate this problem by integrating data and physical knowledge. In this work, we develop a novel fault detection method that combines physics-based simulations for data generation with a Physics-Informed Deep autoencoder (PIDAE) for reproducing the system behaviour in normal conditions;the Sequential Probability Ratio Test (SPRT) is, then, used for detecting abnormal conditions. The proposed method is applied to new-design electro-hydraulic servo actuators used in turbofan engine fuel systems. The results show that it can provide more satisfactory fault detection performance, in terms of false and missed alarms, than state-of-the-art methods based on traditional autoencoders only and pure physics-based models only. Furthermore, the PIDAE outcomes are physically consistent and, therefore, more acceptable and trustworthy.

关键词： Prognostics and health management Fault detection Physics-informed neural network (PINN) autoencoder Electro-Hydraulic Servo Actuator (EHSA) Turbofan engine

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A study on ship hull form transformation using convolutional autoencoder

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第1期11卷 34-48页

作者： Seo, Jeongbeom Kim, Dayeon Lee, Inwon Pusan Natl Univ Dept Naval Architecture & Ocean Engn Busan 46241 South Korea

The optimal ship hull form in contemporary design practice primarily consists of three parts: hull form modification, performance prediction, and optimization. Hull form modification is a crucial step to affect optimization efficiency because the baseline hull form is varied to search for performance improvements. The conventional hull form modification methods mainly rely on human decisions and intervention. As a direct expression of the three-dimensional hull form, the lines are not appropriate for machine learning techniques. This is because they do not explicitly express a meaningful performance metric despite their relatively large data dimension. To solve this problem and develop a novel machine-based hull form design technique, an autoencoder, which is a dimensional reduction technique based on an artificial neural network, was created in this study. Specifically, a convolutional autoencoder was designed;firstly, a convolutional neural network (CNN) preprocessor was used to effectively train the offsets, which are the half-width coordinate values on the hull surface, to extract feature maps. Secondly, the stacked encoder compressed the feature maps into an optimal lower dimensional-latent vector. Finally, a transposed convolution layer restored the dimension of the lines. In this study, 21 250 hull forms belonging to three different ship types of containership, LNG carrier, and tanker, were used as training data. To describe the hull form in more detail, each was divided into several zones, which were then input into the CNN preprocessor separately. After the training, a low-dimensional manifold consisting of the components of the latent vector was derived to represent the distinctive hull form features of the three ship types considered. The autoencoder technique was then combined with another novel approach of the surrogate model to form an objective function neural network. Further combination with the deterministic particle swarm optimization method led t

关键词： Artificial intelligence machine learning autoencoder dimension reduction hull form design

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