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Intelligent anomaly detection for wind turbines using deep-learning methods has been extensively researched and yielded significant results. However, supervised learning necessitates sufficient labeled data to establish the discriminant boundary, while unsupervised learning lacks prior knowledge and heavily relies on assumptions about the distribution of anomalies. A long short-term memory-based variational autoencoder Wasserstein generation adversarial network (LSTM-based VAE-WGAN) was established in this paper to address the challenge of small and noisy wind turbine datasets. The VAE was utilized as the generator, with LSTM units replacing hidden layer neurons to effectively extract spatiotemporal factors. The similarity between the model-fit distribution and true distribution was quantified using Wasserstein distance, enabling complex high-dimensional data distributions to be learned. To enhance the performance and robustness of the proposed model, a two-stage adversarial semi-supervised training approach was implemented. Subsequently, a monitoring indicator based on reconstruction error was defined, with the threshold set at a 99.7% confidence interval for the distribution curve fitted by kernel density estimation (KDE). Real cases from a wind farm in northeast China have confirmed the feasibility and advancement of the proposed model, while also discussing the effects of various applied parameters. [ABSTRACT FROM AUTHOR]

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A semi-supervised, one-dimensional recurrent neural network (RNN) approach called RVS has been proposed in this paper for video salient object detection. The proposed RVS approach involves the processing of each frame independently without explicitly considering temporal information. The RNN is trained using one-dimensional superpixel features to classify the salient object regions into salient foreground and non-salient background superpixels. Deep learning algorithms generally exhibit heavy dependence on training data size and often take extremely long time for training. On the contrary, the proposed RVS approach involves the training of an RNN using a small data which results in significant reduction in training time. The RVS approach has been extensively evaluated and its results are compared with those of several state-of-the-art methods using the public-domain VideoSeg, SegTrack v1 and SegTrack v2 benchmark video datasets. Further, the RVS approach has been tested using the authors' own video dataset and the complex DAVIS and video object segmentation datasets to evaluate the impact of motion and blur on its performance. The RVS approach delivers results superior to those of several approaches that strongly rely upon spatio-temporal features in detecting the salient objects from the video sequences. [ABSTRACT FROM AUTHOR]

The feedforward neural network based on randomization has been of great interest in the scientific community, particularly extreme learning machines, due to its simplicity, training speed, and levels of accuracy comparable to traditional learning algorithms. Extreme learning machines (ELMs) are a type of artificial neural network (ANN) with one or more hidden layers that are trained under supervised, unsupervised, or semi-supervised learning approaches. These networks are widely used in various research areas, such as medical image processing (MI). This research work presents an exhaustive review of extreme learning machines (ELM) and medical image processing (MI), due to the high impact that these networks have had on the scientific community and the importance of MI for physicians who use them to diagnose different injuries and diseases. First, the theoretical construct of ELMs is developed based on the types of supervised, unsupervised, and semi-supervised learning. Then, the importance of MI for the diagnosis of a disease or classification of the most commonly used imaging modalities is analyzed for articles concerning radiography, computed tomography (CT), magnetic resonance (MR), ultrasound (US), and mammography (MG). Next, the reference data sets linked to various human body organs, such as the brain, lungs, skin, eyes, breasts, and cervix are described. Then, a review, analysis, and classification of the development of the last 6 years (2017–2022) of ELMs, based on learning types and MI, is performed. With the information obtained above, a construction of summary tables of the articles, classified according to the type of learning, is performed, highlighting the organ, reference, year, methodology, database, modality, and results. Finally, the discussion, conclusions and challenges related to this topic are presented. The findings indicate that the review articles reported in the literature have not addressed the relationship between ELMs and medical imaging in depth and have excluded key aspects, which are developed in this article. These aspects include a comprehensive analysis of the most popular imaging modalities, a detailed description of both the most popular databases and the most relevant databases for the machine learning community and, finally, the incorporation of schemes that explain the fundamentals of the main learnings considered when generating ELM-based trained smart models, which can be useful for medical image processing. • Papers on Extreme Learning Machine medical imaging applications are discussed. • Supervised, unsupervised and semi-supervised learning are considering. • The evolution of pseudo-inverse idea is examined. • Image repositories using in medical imaging are reviewed • Research trends on Extreme Learning Machine medical imaging are presented. [ABSTRACT FROM AUTHOR]

Emerging federated learning (FL) is able to train a global machine learning (ML) model by using decentralized data from various clients, without exposing the privacy data of clients. Traditional FL assumes that the training data are labeled, but in reality the data captured by the clients are usually unlabeled. Nowadays, the manual data labeling, a common method, is very expensive in practical operation. To solve the above problems, in this paper, we propose a semi-supervised federated learning scheme (FedEntropy) to improve the model performance in the case that unlabeled data dominates the datasets. Specifically, our proposed FedEntropy firstly utilizes information entropy to jointly compute the loss of labeled and unlabeled data. Subsequently, assisted with inverse-trigonometric-based adaptive proportional adjustment algorithm, FedEntropy is able to dynamically set the ratio between loss of labeled and unmarked data. In particular, we prove the effectiveness of the information entropy function on unlabeled data training and reducing the probability distribution gap of datasets. Extensive experiments results demonstrate that, compared with state of art methods, our FedEntropy not only achieves accuracy improvement of up to 6.42% on two common datasets, but also simultaneously reduces the approximately half of the computation overheads in semi-supervised FL training. [ABSTRACT FROM AUTHOR]

Discriminative language modeling aims to reduce the error rates by rescoring the output of an automatic speech recognition (ASR) system. Discriminative language model (DLM) training conventionally follows a supervised approach, using acoustic recordings together with their manual transcriptions (reference) as training data, and the recognition performance is improved with increasing amount of such matched data. In this study we investigate the case where matched data for DLM training is limited or is not available at all, and explore methods to improve ASR accuracy by incorporating acoustic and text data that come from separate sources. For semi-supervised training, we utilize a confusion model to generate artificial hypotheses instead of the real ASR N-bests. For unsupervised training, we propose three target output selection methods to take over the missing reference. We handle this task both as a structured prediction and a reranking problem and employ two different variants of the WER-sensitive perceptron algorithm. We show that significant improvement over baseline ASR accuracy is obtained even when there is no transcribed acoustic data available to train the DLM. [ABSTRACT FROM AUTHOR]