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autoencoder-CatBoost Model for Accurate Hyperspectral Quality Assessment of Yunling Snowflake Beef

IEEE ACCESS 2024年 12卷 184701-184713页

作者： Gao, Lutao Zhang, Lilian Chen, Jian Peng, Lin Guo, Lujiale Yang, Linnan Yunnan Agr Univ Coll Food Sci & Technol Kunming 650201 Yunnan Peoples R China Yunnan Agr Univ Coll Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Technol Res Ctr Agr Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Res Ctr Big Data Intelligent Informat Kunming 650201 Yunnan Peoples R China Fudan Univ Zhongshan Hosp Shanghai 200032 Peoples R China

Snowflake beef is highly valued for its nutritional benefits and exquisite taste, yet the inconsistent quality in the market poses challenges for consumers in distinguishing genuine products, leading to economic losses and trust issues. This study aims to develop a rapid, accurate, and non-destructive method for evaluating the quality of snowflake beef to enhance consumer satisfaction and confidence. We propose a novel autoencoder-Catboost model based on an autoencoder and CatBoost classifier, utilizing hyperspectral imaging (HSI) technology to assess the quality grades of Yunling Cattle snowflake beef. A total of 250 beef samples, scanned in the 900-2500 nm wavelength range, were processed using seven data preprocessing techniques and two feature extraction methods. The autoencoder, comprising three layers of Transformer units, effectively extracted features from the hyperspectral data, which were then classified by the CatBoost classifier. The model demonstrated superior accuracy, precision, recall, and F1-score compared to traditional machine learning methods, achieving an average accuracy of 82.42%. This research introduces an innovative approach by integrating Transformer-based autoencoders with CatBoost for hyperspectral data analysis, providing a new methodology for non-destructive snowflake beef quality evaluation and offering valuable insights for future research in food quality assessment.

关键词： Hyperspectral imaging Feature extraction Accuracy Spectroscopy Cows Transformers Noise Information filters Data models Data mining Snowflake beef quality hyperspectral imaging autoencoder CatBoost Yunling cattle

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autoencoder networks extract latent variables and encode these variables in their connectomes

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NEURAL NETWORKS 2021年 141卷 330-343页

作者： Farrell, Matthew Recanatesi, Stefano Mihalas, Stefan Reid, R. Clay Shea-Brown, Eric Univ Washington Appl Math Dept Seattle WA 98195 USA Univ Washington Computat Neurosci Ctr Seattle WA 98195 USA Allen Inst Brain Sci Seattle WA USA

Advances in electron microscopy and data processing techniques are leading to increasingly large and complete microscale connectomes. At the same time, advances in artificial neural networks have produced model systems that perform comparably rich computations with perfectly specified connectivity. This raises an exciting scientific opportunity for the study of both biological and artificial neural networks: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that - even with well constrained neural dynamics - there are in principle many different connectomes that could support a given computation. Here, we define a tractable setting in which the problem of inferring circuit function from circuit connectivity can be analyzed in detail: the function of input compression and reconstruction, in an autoencoder network with a single hidden layer. Here, in general there is substantial ambiguity in the weights that can produce the same circuit function, because largely arbitrary changes to input weights can be undone by applying the inverse modifications to the output weights. However, we use mathematical arguments and simulations to show that adding simple, biologically motivated regularization of connectivity resolves this ambiguity in an interesting way: weights are constrained such that the latent variable structure underlying the inputs can be extracted from the weights by using nonlinear dimensionality reduction methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： autoencoder Connectome Dimensionality reduction Identifiability Latent variable encoding

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autoencoder-bank based design for adaptive channel-blind robust transmission

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2021年第1期2021卷 1-15页

作者： Safi, Hossein Akbari, Mohammad Vaezpour, Elaheh Parsaeefard, Saeedeh Shubair, Raed M. Shahid Beheshti Univ Dept Elect Engn Tehran Iran ICT Res Inst ITRC Dept Commun Technol Tehran Iran Univ Toronto Dept Elect & Comp Engn Toronto ON Canada MIT 77 Massachusetts Ave Cambridge MA 02139 USA New York Univ NYU Abu Dhabi Abu Dhabi 129188 U Arab Emirates

The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to 10-4, our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

关键词： autoencoder Blind estimation Deep learning

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autoencoder-Based fault detection using building automation system data

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： El Mokhtari, Karim McArthur, J. J. Toronto Metropolitan Univ Dept Architectural Sci Toronto ON Canada

This paper explores the application of autoencoder algorithms in Automated Fault Detection (AFD) for Heating, Ventilation, and Air Conditioning (HVAC) systems, specifically focusing on Fan Coil Units (FCUs). The begins by reviewing the current state of Fault Detection and Diagnostics (FDD), emphasizing the limitations the potential of unsupervised learning techniques like autoencoders and transfer learning to fill these gaps. data from a full-scale building case study featuring five Fan Coil Units (FCUs), the research develops and uates autoencoder-based AFD models that models effectively compress multivariate inputs into a reduced space, enabling accurate and efficient fault detection. The paper makes two novel contributions: (1) It introduces a methodology to distinguish between equipment-level and system-level faults;and (2) It demonstrates generalizability of the approach across different types of FCUs through cross-testing and transfer learning. results indicate that autoencoders outperform other dimensionality reduction algorithms and separate predictors in fault detection accuracy and efficiency. The paper concludes by discussing the implications of these findings for future research and practical applications in building management.

关键词： Fault detection autoencoder Building automation system HVAC

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autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

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IEEE ACCESS 2023年 11卷 18868-18886页

作者： Kohne, Jonas Henning, Lars Guhmann, Clemens Tech Univ Berlin Chair Elect Measurement & Diagnost Technol D-10623 Berlin Germany IAV GmbH D-10587 Berlin Germany

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.

关键词： Clustering algorithms Mechatronics Labeling Data models Transient analysis Maintenance engineering Terminology Condition-based maintenance multivariate time-series data change point detection unsupervised clustering autoencoder segmentation subsequence clustering

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autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms

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JOURNAL OF BIG DATA 2023年第1期10卷 14页

作者： Garouani, Moncef Ahmad, Adeel Bouneffa, Mourad Hamlich, Mohamed Univ Littoral Cote dOpale LISIC Lab Informat Signal & Image Cote Opale UR 4491 F-62100 Calais E France Univ Hassan 2 CCPS Lab ENSAM Casablanca Morocco HESTIM Study & Res Ctr Engn & Management CERIM Casablanca Morocco

The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.

关键词： Algorithm selection AutoML Meta-learning Meta-features Data representation kNN autoencoder

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autoencoder-like semi-NMF multiple clustering

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INFORMATION SCIENCES 2021年 572卷 331-342页

作者： Yao, Shihong Hu, Chuli Wang, Tao Cui, Xinyou China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Peoples R China Cent China Normal Univ Sch Educ Informat Technol Wuhan 430079 Peoples R China Army Engn Univ PLA Ordnance NCO Coll Wuhan 430075 Peoples R China

Clustering is performed to partition samples into disjoint groups for facilitating the discovery of hidden patterns in the data. Many real-world applications involve various clustering methods, most of which only produce a single clustering. As a response to this issue, multiple clustering that aims to generate diverse and high-quality clustering, has emerged recently. This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive experimental results demonstrate that the ASNMFMC is superior to the existing multiple clustering methods and can explore diverse high-quality clustering. (c) 2021 Elsevier Inc. All rights reserved.

关键词： autoencoder Semi-NMF Multiple clustering

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autoencoder Constrained Clustering With Adaptive Neighbors

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第1期32卷 443-449页

作者： Li, Xuelong Zhang, Rui Wang, Qi Zhang, Hongyuan Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously.

关键词： Kernel Adaptive systems Clustering methods Clustering algorithms Sparse matrices Learning systems Neural networks Adaptive neighbors autoencoder deep clustering parameter-free similarity structured graph

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autoencoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第8期131卷 1995-2014页

作者： Cao, Bing Bi, Zhiwei Hu, Qinghua Zhang, Han Wang, Nannan Gao, Xinbo Shen, Dinggang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China Shanghai Clin Res & Trial Ctr Shanghai Peoples R China

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods.

关键词： Multimodal collaborative learning Medical image synthesis autoencoder Self-representation

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autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第3期34卷 1427-1444页

作者： Yun, Huitaek Kim, Hanjun Jeong, Young Hun Jun, Martin B. G. Purdue Univ Sch Mech Engn W Lafayette IN 47906 USA Purdue Univ Indiana Mfg Competitiveness Ctr MaC W Lafayette IN 47906 USA Kyungpook Natl Univ Sch Mech Engn Daegu 41566 South Korea

Sound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the machine status to detect machine faults. However, it is difficult to collect enough amount of fault data as much as normal status data for training general NN models. To resolve the issue, this paper proposes the autoencoder-based anomaly detection framework for industrial robot arms using an internal sound sensor. The autoencoder uses signals in the normal state of the robots for training the model. It reconstructs the input signals as output, and anomalous states are found from high reconstruction error. Two stethoscopes were attached to the surface of the robot joint as sensors, and the sounds were recorded by USB microphone attached to the outlet of the stethoscopes. Features were extracted from STFT spectrogram images of the gathered sound, then used to train and test an autoencoder model. The reconstruction errors of the autoencoder were compared to distinguish the abnormal status from normal one. The experimental results suggest that the stethoscopes prevent the interference of noise, and the collected sound signals can be utilized for detecting machine anomalies.

关键词： Sound spectrogram autoencoder Neural network Stethoscope Industrial robot arm

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