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autoencoder-Based Eggshell Crack Detection Using Acoustic Signal

JOURNAL OF FOOD PROCESS ENGINEERING 2024年第11期47卷

作者： Yabanova, Ismail Balci, Zekeriya Yumurtaci, Mehmet Unler, Tarik Manisa Celal Bayar Univ HFT Technol Fac Elect Engn Dept Manisa Turkiye Van Yuzuncu Yil Univ Caldiran Vocat Sch Elect & Automat Dept Van Turkiye Afyon Kocatepe Univ Dept Elect & Elect Engn Afyon Turkiye Necmettin Erbakan Univ Aeronaut & Astronaut Fac Avionics Dept Konya Turkiye

Breaks or cracks in eggshells offer substantial food safety issues. Bacteria and viruses, in particular, are more likely to enter the egg through breaks and cracks, increasing the risk of food poisoning. Furthermore, deformations in the shell may compromise the integrity of the protective shell, exposing the egg to more external variables and causing it to lose freshness and decay faster. To reduce such hazards, this research created an innovative crack detection system based on an autoencoder (AE) that uses acoustic signals from eggshells. A system that creates an acoustic effect by hitting the eggshell without damaging it was designed, and these effects were recorded through a microphone. Acoustic signal data of size 1 x 1000 was fed into k nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM) classifiers. AE was employed to reduce data size in order to accommodate the raw data's unique features. This AE model, which reduces data size, was used with many classifiers and was able to accurately distinguish between intact and cracked eggs. The built AE-based classifier model completed the classification procedure with 100% accuracy, including microcracks that are invisible to the naked eye.

关键词： acoustic signal autoencoder classification crack eggshell

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autoencoder-Based Recommender System Exploiting Natural Noise Removal

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IEEE ACCESS 2023年 11卷 30609-30618页

作者： Park, Hyeseong Jeong, Jaeik Oh, Kyung-Whan Kim, Hongseok Sogang Univ Dept Comp Sci & Engn Seoul 04107 South Korea Sogang Univ Dept Elect Engn Seoul 04107 South Korea

Collaborative filtering (CF) is a widely used technique in recommender systems by automatically predicting the user's latent interests based on many users' historical rating data. To improve the performance of the CF-based recommender systems, users' rating data should be pre-processed to avoid noise and enhance data reliability. Many researchers studied anomaly detection to remove malicious noise caused by shilling attacks, but anomalies can still exist in non-attacked real user data, which is called natural noise, as the ratings of users can be impacted by unpredictable factors such as other users' ratings and anchoring bias. In this paper, we propose an autoencoder-based recommendation system for exploiting the ability of both anomaly detection and CF. The proposed system detects the natural noise in the rating data based on the reconstruction errors after training. By removing the detected natural noise, CF can predict the unrated ratings with noise-free data. Our experiments show that the proposed model showed better performance than the traditional method by reducing the error by up to 5% compared to the method that does not consider natural noise detection and reducing the error by up to 4% compared to the conventional rating classification based natural noise detection methods.

关键词： Recommender systems Encoding Training Anomaly detection Decoding Prediction algorithms Feature extraction Collaborative filtering recommender system natural noise autoencoder anomaly detection

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autoencoder asset pricing models

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JOURNAL OF ECONOMETRICS 2021年第1期222卷 429-450页

作者： Gu, Shihao Kelly, Bryan Xiu, Dacheng Univ Chicago Booth Sch Business Chicago IL 60637 USA Yale Univ AQR Capital Management New Haven CT 06520 USA NBER Cambridge MA 02138 USA

We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature - autoencoder neural networks - to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Stock returns Conditional asset pricing model Nonlinear factor model Machine learning autoencoder Neural networks Big data

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autoencoder-Based MIMO Cooperative Communications With Quantize-Forward Relaying

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19229-19239页

作者： Shin, Juin Jin, Xianglan Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea Jeonbuk Natl Univ IT Convergence Res Ctr Jeonju 54896 South Korea

This paper investigates an autoencoder-based quantize-forward (QF) relay system that includes a source, a destination, and a relay, each equipped with multiple antennas. The existing phase quantization (PQ) algorithm at the relay has limitations in capturing the amplitude differences of received signals, leading to performance saturation with increasing quantization bits. To address these limitations, we propose a novel relay algorithm, amplitude-phase quantization (APQ), which quantizes both the phase and the amplitude. Moreover, we introduce neural networks into the relay process, resulting in PQ with neural networks (PQNN) and APQ with neural networks (APQNN), which is expected to further improve system performance at the expense of additional computational load at the relay. We also propose a sub-message one-hot encoding method and a retraining approach for the worst-performing sub-message to reduce computational complexity and improve performance in autoencoder-based systems. Simulation results demonstrate that the autoencoder-based QF relay system, with various relay algorithms and the sub-message one-hot encoding method, achieves excellent performance with reduced memory usage at the relay and significantly reduced complexity at the source and destination.

关键词： Relays Quantization (signal) Neural networks Communication systems System performance Encoding MIMO communication autoencoder deep learning multiple-input multiple-output (MIMO) phase quantization relay

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autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis

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JOURNAL OF PROCESS CONTROL 2021年 101卷 24-34页

作者： Li, Yanxia Chai, Yi Yin, Hongpeng Chongqing Univ Coll Automat Chongqing 400044 Peoples R China State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China

Industrial processes usually exhibit great nonlinearity generated from the effects of complex mechanisms, system integrations and multiple working conditions. Although a variety of dictionary learning algorithms have been proposed in recent years for industrial process fault diagnosis, most of them only model the process data via a linear combination of a few dictionary atoms, which cannot effectively characterize the nonlinear relationships among variables and may lead to limited diagnosis performance. Recent improvements in multilayer neural networks, especially the autoencoders, offer opportunities to tackle the nonlinear problem. However, the overall limited availability of fault samples poses great challenges in achieving satisfactory performance. To address the mentioned issues simultaneously, the present study proposes an autoencoder Embedded Dictionary Learning approach (AEDL) for nonlinear industrial process fault diagnosis. First, an autoencoder is employed to learn a nonlinear mapping that maps the linearly inseparable industrial process data to a high-dimensional space, where a desired dictionary is learned according to the basic dictionary learning algorithm. Next, two supervised graphs, leveraging the priors of industrial process data, are introduced into the learning process to make the proposed approach robust to training samples. After obtaining the dictionary, the coding coefficients of the process data over the dictionary can be used for fault diagnosis via a simple classifier. As revealed from the encouraging experimental results on the Tennessee Eastman process, the developed approach outperforms several dictionary learning approaches and some other nonlinear fault diagnosis methods. (C) 2021 Published by Elsevier Ltd.

关键词： Fault diagnosis Nonlinear industrial process Dictionary learning autoencoder

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autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第12期20卷 21098-21119页

作者： Zhu, Shuwei Wang, Wenping Fang, Wei Cui, Meiji Jiangnan Univ Sch Artificial Intelligence & Comp Sci Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Peoples R China Nanjing Univ Sci & Technol Sch Intelligent Mfg Nanjing 210094 Peoples R China

Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multiomics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AEassisted multi-omics clustering method can identify clinically significant cancer subtypes.

关键词： multi-omic data cancer subtyping multi-view clustering autoencoder latent space data integration

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autoencoder-based part clustering for part-in-whole retrieval of CAD models

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COMPUTERS & GRAPHICS-UK 2019年第0期81卷 41-51页

作者： Muraleedharan, Lakshmi Priya Kannan, Shyam Sundar Muthuganapathy, Ramanathan Indian Inst Technol Madras Dept Engn Design Adv Geometr Comp Lab Chennai 600036 Tamil Nadu India

Part-in-whole retrieval (PWR) is an important problem in the field of computer-aided design (CAD) with applications in design reuse, feature recognition and suppression and so on. Initially, we present a non parametric (and hence threshold independent) algorithm for segmenting CAD models (represented as meshes) which does not require any user intervention. As there is no labelled segmented dataset available for part clustering, we propose the use of autoencoders, one of the approaches used in deep networks along with hierarchical clustering. The features for autoencoder is derived from the Gauss map of the segments. The autoencoder network is then trained and validated using a hierarchical clustering-based approach that generates a dictionary of labels for each segment. PWR is then done by testing a query model with the network that retrieves models having the query as their subset. Comparison of the segmentation algorithm with the state-of-the-art approaches indicate that it performs better or on par. The algorithm was also tested for noisy models. Results of the part clustering and PWR are also presented for models from a CAD dataset along with the discussions. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Segmentation Part-based CAD mesh model Hyperbolic points autoencoder Unsupervised part clustering

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autoencoder-based feature construction for IoT attacks clustering

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 127卷 487-502页

作者： Haseeb, Junaid Mansoori, Masood Hirose, Yuichi Al-Sahaf, Harith Welch, Ian Victoria Univ Wellington POB 600 Wellington 6140 New Zealand

Variations in commands executed as part of the attack process can be used to determine the behavioural patterns of IoT attacks. Existing approaches rely on the domain knowledge of security experts to identify the behavioural patterns, categorise and classify cyber attacks. We proposed an autoencoder (AE)-based feature construction approach to remove the dependency of manually correlating commands and generate an efficient representation by automatically learning the semantic similarity between input features extracted through commands data. We applied three clustering algorithms, i.e., K-means, Gaussian Mixture Models and Density-based spatial clustering of applications with noise, on our data set of AE features. We discussed the clustering arrangements for understanding the impact of changes in commands on behavioural patterns of attacks and how attacks are grouped in the same or different clusters. Evaluation of our feature construction approach shows that the clustering algorithm grouped attacks with more common features values compared to clustering with original features. Moreover, we performed a comparative analysis of two existing feature extraction approaches on our data set considering the type of analysis in the process, generalisability of applying features, coverage to the data set and clustering arrangements. We found that challenges identified in applying existing approaches can be addressed with our proposed approach and improving features with AE resulted in providing meaningful clustering interpretations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： IoT attacks Feature construction autoencoder Clustering

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autoencoder for Semisupervised Multiple Emotion Detection of Conversation Transcripts

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2021年第3期12卷 682-691页

作者： Duc-Anh Phan Matsumoto, Yuji Shindo, Hiroyuki Nara Inst Sci & Technol Nara 6300192 Japan Nara Inst Sci & Technol Informat Sci Nara 6300192 Japan

Textual emotion detection is a challenge in computational linguistics and affective computing study as it involves the discovery of all associated emotions expressed within a given piece of text. It becomes an even more difficult problem when applied to conversation transcripts, as we need to model the spoken utterances between speakers, keeping in mind the context of the entire conversation. In this paper, we propose a semisupervised multilabel method of predicting emotions from conversation transcripts. The corpus contains conversational quotes extracted from movies. A small number of them are annotated, while the rest are used for unsupervised training. We use the word2vec word-embedding method to build an emotion lexicon from the corpus and to embed the utterances into vector representations. A deep-learning autoencoder is then used to discover the underlying structure of the unsupervised data. We fine-tune the learned model on labeled training data, and measure its performance on a test set. The experiment result suggests that the method is effective and is only slightly behind human annotators.

关键词： Motion pictures Correlation Social network services Neural networks Context modeling Data models Training data Emotion recognition semisupervised learning multilabel word2vec autoencoder

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autoencoder-based deep metric learning for network intrusion detection

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INFORMATION SCIENCES 2021年 569卷 706-727页

作者： Andresini, Giuseppina Appice, Annalisa Malerba, Donato Univ Bari Aldo Moro Dept Informat Via Orabona 4 I-70125 Bari Italy Consorzio Interuniv Nazl Infomat CINI Rome Italy

Nowadays intrusion detection systems are a mandatory weapon in the war against the ever-increasing amount of network cyber attacks. In this study we illustrate a new intrusion detection method that analyses the flow-based characteristics of the network traffic data. It learns an intrusion detection model by leveraging a deep metric learning methodology that originally combines autoencoders and Triplet networks. In the training stage, two separate autoencoders are trained on historical normal network flows and attacks, respectively. Then a Triplet network is trained to learn the embedding of the feature vector representation of network flows. This embedding moves each flow close to its reconstruction, restored with the autoencoder associated with the same class as the flow, and away from its reconstruction, restored with the autoencoder of the opposite class. The predictive stage assigns each new flow to the class associated with the autoencoder that restores the closest reconstruction of the flow in the embedding space. In this way, the predictive stage takes advantage of the embedding learned in the training stage, achieving a good prediction performance in the detection of new signs of malicious activities in the network traffic. In fact, the proposed methodology leads to better predictive accuracy when compared to competitive intrusion detection architectures on benchmark datasets. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Network intrusion detection Deep metric learning Triplet network autoencoder

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