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High Accuracy Individual Identification Model of Crested Ibis <italic>(Nipponia Nippon)</italic>Based on autoencoder With Self-Attention

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IEEE ACCESS 2020年 8卷 41062-41070页

作者： Xie, Jiangjian Yang, Jun Ding, Changqing Li, Wenbin Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab Natl Forestry & Grass Land Adm Forestry E Beijing 100083 Peoples R China Beijing Forestry Univ Sch Ecol & Nat Conservat Beijing 100083 Peoples R China

As the population and the distribution of Crested Ibis (Nipponia nippon) become larger, it is necessary to propose a highly efficient census method to estimate the population size of the Crested Ibis. Passive acoustic monitoring (PAM) has a very good prospect for the Crested Ibis monitoring. To realize the automatic census of the Crested Ibis with PAM, the automatic individual identification method based on the vocalization is the key technology. A novel individual identification model was proposed in this paper, which built the autoencoder based on LSTM to obtain the meaningful latent representation from the raw recording directly, further, embedded self-attention and putted forward a combined training mode to achieve distinctive latent representation. With this model, nine Crested Ibis individuals were identified accurately, the highest accuracy is 0.971, and the average accuracy reaches 0.958. As for other three species, Little owl (Athene noctua), Chiffchaff (Phylloscopus collybita) and Tree pipit (Anthus trivialis), the better performances were achieved than the existing method, which means the proposed model can provide an alternative method for the individual identification of other bird species.

关键词： Nipponia nippon individual identification autoencoder self-attention LSTM

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Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

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INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023年第3期58卷 1284-1298页

作者： Munyendo, Leah Njoroge, Daniel Zhang, Yanyan Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci Garbenstr 23 D-70599 Stuttgart Germany Dedan Kimathi Univ Technol Inst Food Bioresources Technol Dedan Kimathi 10143 Nyeri Kenya Univ Hohenheim Dept Flavor Chem Fruwirthstr 12 D-70599 Stuttgart Germany

Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta-adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

关键词： adulteration autoencoder chicory coffee geographical origin NIR spectroscopy

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Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 247卷 108114-108114页

作者： Dai, Peishan Shi, Yun Lu, Da Zhou, Ying Luo, Jialin He, Zhuang Chen, Zailiang Zou, Beiji Tang, Hui Huang, Zhongchao Liao, Shenghui Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Dept Psychiat Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Natl Clin Res Ctr Mental Disorders Changsha 410083 Hunan Peoples R China Cent South Univ Sch Basic Med Sci Dept Biomed Engn Changsha 410083 Hunan Peoples R China

Background and objective: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. Methods: We developed a Residual Denoising autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over -sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. Results: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. Conclusions: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

关键词： Recurrent major depressive disorder Functional magnetic resonance imaging Functional connectivity autoencoder Residual network Deep learning

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An interpretable health indicator for bearing condition monitoring based on semi-supervised autoencoder latent space variance maximization

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125135-125135页

作者： Chen, Xieyi Wang, Yi Meng, Lihua Qin, Yi Tang, Baoping Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Effective health indicator (HI) construction can help equipment managers detect the abnormal state of rotating machinery quickly. However, although the current deep learning-based HI construction methods have good life prediction value, most of them lose the ability to detect device anomalies and little work has been done on model interpretability. Therefore, an interpretable HI construction method based on semi-supervised autoencoder (AE) latent space variance maximization (SSALSVM) was proposed to monitor the health status of bearings. In order to fully excavate degradation features inside the device and make the model focus on the encoding process, a deep convolutional neural network (DCNN) is used as the encoding layer, while only a layer of fully-connected layer is used as the decoding layer. In addition, to enable the latent space to capture the device early degradation point (EDP) successfully, an auxiliary layer is added to the output of the encoder layer. Simultaneously, for improving the sensitivity of the indicator to capture equipment abnormal state and highlight the difference between equipment health state and degradation state, the constraint of variance maximization is added into the latent space. The model optimizing process was presented by observing the projected variance of the test set in latent space of each epoch model. The validity of the proposed HI was verified by comparison experiments on two datasets.

关键词： interpretable health indicator semi-supervised autoencoder hidden space variance maximization autoencoder early degradation point

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Disentangled-Multimodal Adversarial autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第12期39卷 4137-4149页

作者： Hu, Dan Zhang, Han Wu, Zhengwang Wang, Fan Wang, Li Smith, J. Keith Lin, Weili Li, Gang Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated o

关键词： Functional magnetic resonance imaging Brain modeling Predictive models Data models Biological system modeling Biomarkers Infant age prediction autoencoder multimodal machine learning magnetic resonance imaging

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Signal-to-noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder

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JOURNAL OF RAMAN SPECTROSCOPY 2021年第4期52卷 890-900页

作者： Fan, Xian-guang Zeng, Yingjie Zhi, Yu-Liang Nie, Ting Xu, Ying-jie Wang, Xin Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361102 Fujian Peoples R China Xiamen Key Lab Optoelect Transducer Technol Fujian Key Lab Univ & Coll Transducer Technol Xiamen Peoples R China

The signal-noise ratio plays a key role in acquiring plentiful chemical structural information in the Raman spectrometer. The miniature spectrometer is generally compact at the expense of performance. In this work, we proposed a compact, signal-to-noise ratio (SNR) enhancement of the Raman spectrometer by the optimization of optical structure and a noise reduction method. Concerning its optical structure, the Raman spectrometer is increasing the intensity by adding an off-the-shelf cylindrical lens. On the other side of the algorithm, a relevant automatic denoising method of convolutional denoising autoencoder (CDAE) is proposed to further advance the SNR in Raman spectra without manual intervention. The results indicate the performance of the compact Raman spectrometer could increase to a certain extent by testing with 785 nm laser and Ne/Ar source. Besides, by using CDAE to deal with contaminated Raman spectra, a higher SNR is obtained. The results demonstrate that the improvement of the hardware and algorithm is effective for removing the noisy Raman signal and achieving higher SNR. This result may be helpful in further improving the performance of integrated Raman spectrometers and research on miniaturized instruments.

关键词： autoencoder convolutional neural network Czerny-Turner denoising Raman spectrometer

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Radar pre-sorting algorithm based on autoencoder and LSTM

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2024年 187卷

作者： Jiang, Yilin Shi, Shaoxiong Zhang, Fangyuan Huang, Wuqi Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Univ Sci & Technol Harbin Peoples R China AVIC United Technol Ctr Electromagnet Spectrum Col Harbin Peoples R China

As the electromagnetic environment becomes increasingly complex, most current radar signal sorting methods are unsustainable. They often perform poorly when dealing with unknown radar types and low-frequency radar pulse data. This paper introduces a radar pre-sorting algorithm based on autoencoder and LSTM. The algorithm utilizes multi-dimensional information such as pulse width, carrier frequency, and time of arrival. The autoencoder network is employed to achieve automatic feature extraction and clustering, enhancing the extraction of latent features in the data. The proposed network model mainly consists of three parts: an encoding module composed of a convolutional neural network (CNN), a feature aggregation module composed of long short-term memory (LSTM), and a decoding module obtained through a convolutional autoencoder, referred to as CLDE (CNN-LSTM-Decode). The encoding module extracts features from multi-dimensional data to obtain compressed features, the feature accumulation module processes the compressed features, further extracting hidden features between pulses. Subsequently, the decoding module determines the pulse modulation type of each pulse, achieving the purpose of radar pulse signal pre-sorting. Simulation results show that this network structure effectively pre-classifies unknown radar signals and has a high recognition rate for low-frequency pulses. Additionally, CLDE exhibits high reliability and stability in environments with pulse loss.

关键词： CLDE Low-frequency autoencoder LSTM

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Itakura-Saito distance based autoencoder for dimensionality reduction of mass spectra

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 167卷 63-68页

作者： Nozaki, Yuji Nakamoto, Takamichi Tokyo Inst Technol Midori Ku 4259 Nagatsuta Yokohama Kanagawa 2268503 Japan

Small signals may contain important information. Mass spectra of chemical compounds are usually given in a format of sparse high-dimensional data-of large dynamic range. As peaks at high m/z (mass to charge ratio) region of a mass spectrum contribute to sensory information, they should not be ignored during the dimensionality reduction process even if the peak is small. However, in most of dimensionality reduction techniques, large peaks in a dataset are typically more emphasized than tiny peaks when Euclidean space is assessed. autoencoders are widely used nonlinear dimensionality reduction technique, which is known as one special form of artificial neural networks to gain a compressed, distributed representation after learning. In this paper, we present an autoencoder which uses IS (Itakura-Saito) distance as its cost function to achieve a high capability of approximation of small target inputs in dimensionality reduction. The result of comparative experiments showed that our new autoencoder achieved the higher performance in approximation of small targets than that of the autoencoders with conventional cost functions such as the mean squared error and the cross-entropy.

关键词： Dimensionality reduction Mass spectra autoencoder Itakura-Saito distance

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Efficient Blind Source Separation Method for fMRI Using autoencoder and Spatiotemporal Sparsity Constraints

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IEEE ACCESS 2023年 11卷 50364-50381页

作者： Khalid, Muhammad Usman Khawaja, Bilal A. Nauman, Malik Muhammad Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia Islamic Univ Madinah Fac Engn Dept Elect Engn Madinah 42351 Saudi Arabia Univ Brunei Darussalam Fac Integrated Technol Bandar Seri Begawan BE1410 Brunei

Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals. This leads to some inefficient BSS methods that are derived from either a mixing matrix (mm), sparse weight vectors (sw), or sparse code (sc). In contrast, the proposed efficient method, sparse spatiotemporal BSS (ssBSS), avoids computational complications associated with lag sets, deflation strategy, and repeated error matrix computation using the whole dataset. It solves the spatiotemporal data reconstruction model (STEM) with l(1)-norm penalization and l(0)-norm constraints using Neumann's alternating projection lemma and block coordinate descent approach to yield the desired bases. Its specific solution allows incorporating a three-step autoencoder and univariate soft thresholding for a block update of the source/mixing matrices. Due to the utilization of both spatial and temporal information, it can better distinguish between the sources and yield interpretable results. These steps also make ssBSS unique because, to the best of my knowledge, no mixing matrix based BSS method incorporates sparsity of both features and a multilayer network structure. The proposed method is validated using synthetic and various functional magnetic resonance imaging (fMRI) datasets. Results reveal the superior performance of the proposed ssBSS method compared to the existing methods based on mmBSS and swBSS. Specifically, overall, a 14% increase in the mean correlation value and 91% reduction in computation time over the ssICA algorithm was discovered.

关键词： Sparse representation autoencoder BSS fMRI activation maps PCA l(0)-norm l(1)-norm

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4517-4533页

作者： Al-Andoli, Mohammed Cheah, Wooi Ping Tan, Shing Chiang Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Melaka 75450 Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Ningbo Peoples R China

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

关键词： Complex networks community detection autoencoder particle swarm optimization continuation method

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