Cross Domain Research Articles

A meta transfer learning method for gearbox fault diagnosis with limited data

Intelligent diagnosis of mechanical faults is an important means to guarantee the safe maintenance of equipment. Cross domain diagnosis may lack sufficient measurement data as support, and this bottleneck is particularly prominent in high-end manufacturing. This paper presents a few-shot fault diagnosis methodology based on meta transfer learning for gearbox. To be specific, firstly, the subtasks for transfer diagnosis are constructed, and then joint distribution adaptation is conducted to align the two domain distributions; secondly, through adaptive manifold regularization, the data of target working condition is further utilized to explore the potential geometric structure of the data distribution. Meta stochastic gradient descent is explored to dynamically adjust the model’s parameter based on the obtained task information to obtain better generalization performance, ultimately to achieve transfer diagnosis of gearbox faults with few samples. The effectiveness of the approach is supported by the experimental datasets of the gearbox.

Narration with Graphics: An AI Generated Story Teller

Storytelling has always been a powerful tool to convey information, connect with people, and make an impact. The aim of our project is to generate story by using the concept of AI and Machine learning .AI generated storytelling has apotential to transform a variety of industries, for marketing and education to entertainment and beyond. By utilizing the capabilities of ML and NLP, stories generated by artificial intelligence can be tailored to meet the specific need of each industry like personalized narratives, interactive and dynamic storytelling, multimodal storytelling, cross domain storytelling. The introduction of GEN-AI can lead to both opportunities and potential challenges among different creator communities which required collaboration from both academic and industry. This topic gathers and analyse vast amount of data from various sources including literature, movies, and social media we are using natural language processing which understand and process human language enabling them to communicate with the user effectively. To use the facial recognition technology, we are using DALL-E, OpenCV which uses model like SVM, decision tree, and knn etc. The objective of the research is to investigate the power of AI software and automating the process of story generation. This exploration involves utilizing various AI models, such as natural language generation (NLG) algorithms, deep learning architectures, and reinforcement learning frameworks, to create compelling and coherent stories across different genres and themes. We're showcasing an innovative system that empowers users to effortlessly create short stories accompanied by relevant images with minimal input. Our final project is accessible through a user-friendly web page interface, allowing individuals to craft their narratives seamlessly. Key Words: Storytelling, Artificial intelligence, Natural language processing, Ai system, Narratives, Automatic, DALL -E, OpenCV.

A Comparative Analysis of Feature Selection Algorithms in Cross Domain Sentiment Classification

Background: Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. Method: The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. Results: From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Conclusion: Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.

A multi scale meta-learning network for cross domain fault diagnosis with limited samples

A multi scale meta-learning network for cross domain fault diagnosis with limited samples

Unsupervised cross domain semantic segmentation with mutual refinement and information distillation

Unsupervised cross domain semantic segmentation recently has gained much attention, due to its powerful ability of solving the segmentation problem on unlabeled domains. Traditional methods often employ an adversarial network to confuse the source and target inputs, so as to align them in a new feature space. However, these methods cannot well fuse the source and target domain information because the information of the two domains does not really interact with each other and are passed through separate network branches. To tackle this problem, we propose a real interactive learning framework, named Mutual Refinement and Information Distillation (MURID), to align the two domains. Concretely, MURID introduces a Mutual Refinement module in shallow network layers to enhance information sharing and integration between the source and target domains, which can effectively transfer knowledge from source domain to target domain. In addition, in order to avoid using the same structure for testing as for training, which would result in huge computational requirements, we exploit an Information Distillation module to simplify the testing network while maintaining the powerful inference capability of the training. Moreover, we incorporate Curriculum Learning, a self-training mechanism that iteratively trains the network using pseudo-labels obtained from the target domain, to further improve performance. Extensive experiments were conducted on three popular datasets, i.e., GTA5→Cityscapes and Synthia→Cityscapes, and the results demonstrate the state-of-the-art performance of our method. Additionally, detailed analysis and ablation studies are also carried out to validate the effectiveness of each designed module.

A novel decomposition and hybrid transfer learning-based method for multi-step cutterhead torque prediction of shield machine

As a core operating parameter of shield machine, the high-precision multi-step prediction of cutterhead torque can effectively help the driver adjust the operating parameters in advance to ensure the safe operation of shield machine. Due to data distribution discrepancy, it is challenging to accurately predict the cutterhead torque under different geological conditions. Currently, the existing methods cannot precisely predict cutterhead torque in cross domain. In order to accurately predict the cutterhead torque under different geological environments, we propose a novel decomposition and hybrid transfer learning-based method for multi-step cutterhead torque transfer prediction. First, original cutterhead torque signal is decomposed into some sub-signals by variational mode decomposition (VMD), which can effectively reduce the complexity. Then hybrid transfer learning-based method (HTLM) is established to predict each sub-signal and add the prediction results to realize multi-step cutterhead torque prediction in cross domain. HTLM has three network branches. The first one is that multi-layer GRU network is used to extract time-varying characteristics of source domain data. The second is that the automatic encoder is used to extract the deep common features between source domain and target domain. The third is the adversarial network and the maximum mean discrepancy (MMD) metric for domain adaptation. Then, the extracted features are integrated to predict the cutterhead torque in cross domain. To verify the proposed VMD-HTLM, several transfer prediction experiments were carried out. From the first step to fifth step transfer prediction, the proposed method achieved higher prediction accuracy than existing methods. Especially in the fifth step, the accuracy of proposed method is on average 7.09 % higher than existing methods, the RMSE and the MAE decreased on average 40.00 % and 39.92 %, respectively. Furthermore, the average accuracy of proposed method is 93.79 %, 93.58 %, 93.55 %, 92.25 % and 91.79 % from first step to fifth step, respectively. Hence, the proposed VMD-HTLM can accurately predict the cutterhead torque under different geological conditions.

Cross Domain Deterrence in Asia Pacific and Indian Ocean Region

Deterrence as a concept is deeply rooted in human nature and for a considerable time it was contested and jilted, yet it resurfaced in one way or the other. Nuclear weapons became the backbone of the concept of Deterrence. Due to the inherent dynamism of Revolution in Military Affairs, the supremacy of Nuclear is being challenged with the advent of the latest technologies which are acting as enablers and disruptors. The concept of Cross DomainDeterrence emerged a decade ago, took shape, and entered the strategic lexicon. It has enablers and drivers, while a gradual shift in this direction is visible. While remaining within the ambit of complex interdependence theory given by Robert O Keohane and Joseph S Nye. This article examines the applicability of Cross-Domain Deterrence in the Asia Pacific and Indian Ocean Region focusing on the US, China, India, and Pak to identify the need to look beyond the horizon and embrace the change.

A Method for Ancient Book Named Entity Recognition Based on BERT-Global Pointer

Correct identification of entities in ancient books and documents is the basic step of analyzing ancient Chinese texts, and provides an important prerequisite for in-depth mining of humanistic knowledge in ancient books and documents. In CCL2023 named entity recognition task of ancient books, according to the task definition and the re-quirements of the task organizer, this paper proposes the BERT Global Pointer named entity recognition model; Fine tune the field adaptation training based on the unlabeled 24 history ancient book text data; SWA, FGM, cross validation and post-processing are used to improve the recognition accuracy of the model. The experimental results show that the model and the strategy proposed in this paper have good recognition effect in the multi dynasties, cross domain ancient book entity recognition scene. F1 value on the final line reaches 95.083%.

Sparse Bayesian Deep Learning for Cross Domain Medical Image Reconstruction

Cross domain medical image reconstruction aims to address the issue that deep learning models trained solely on one source dataset might not generalize effectively to unseen target datasets from different hospitals. Some recent methods achieve satisfactory reconstruction performance, but often at the expense of extensive parameters and time consumption. To strike a balance between cross domain image reconstruction quality and model computational efficiency, we propose a lightweight sparse Bayesian deep learning method. Notably, we apply a fixed-form variational Bayes (FFVB) approach to quantify pixel-wise uncertainty priors derived from degradation distribution of the source domain. Furthermore, by integrating the uncertainty prior into the posterior sampled through stochastic gradient Langevin dynamics (SGLD), we develop a training strategy that dynamically generates and optimizes the prior distribution on the network weights for each unseen domain. This strategy enhances generalizability and ensures robust reconstruction performance. When evaluated on medical image reconstruction tasks, our proposed approach demonstrates impressive performance across various previously unseen domains.

SBIT-Fuse: Infrared and visible image fusion based on Symmetrical Bilateral interaction and Transformer

Infrared and visible image fusion, as a typical image enhancement task, aims to generate an image containing complementary information. Existing deep learning-based methods usually use a dual-stream model to extract features, which suffers from insufficient feature interaction in the feature extraction process. In order to effectively utilize information, this work proposes a Symmetrical Bilateral Interaction and Transformer fusion method (SBIT-Fuse) that is simple and efficient for constructing a dual-stream interaction network. Specifically, inspired by the problem of dead ReLU, we propose a Symmetrical Bilateral Interaction (SBI) module which consists of a certain number of cascaded cross domain activation interaction (CDAI) layers, where the deactivation information of the ReLU rectifier is transferred from one flow to another instead of being discarded. The proposed module exploits the drawbacks of ReLU to efficiently build the interaction of two streams and combine spatial and channel attention mechanisms, achieving the enhancement of feature extraction. Besides, our network is end-to-end and a transformer module is employed for extracting long-range semantic information. Moreover, combining a novel designed loss function, the proposed method can generate higher-quality fusion images. The comparative experiments with existing advanced methods and ablation studies on publicly datasets demonstrate the effectiveness of the proposed approach. Our code can be found at https://github.com/ljx111790/SBIT-Fuse.

Transfer learning-based English translation text classification in a multimedia network environment.

In recent years, with the rapid development of the Internet and multimedia technology, English translation text classification has played an important role in various industries. However, English translation remains a complex and difficult problem. Seeking an efficient and accurate English translation method has become an urgent problem to be solved. The study first elucidated the possibility of the development of transfer learning technology in multimedia environments, which was recognized. Then, previous research on this issue, as well as the Bidirectional Encoder Representations from Transformers (BERT) model, the attention mechanism and bidirectional long short-term memory (Att-BILSTM) model, and the transfer learning based cross domain model (TLCM) and their theoretical foundations, were comprehensively explained. Through the application of transfer learning in multimedia network technology, we deconstructed and integrated these methods. A new text classification technology fusion model, the BATCL transfer learning model, has been established. We analyzed its requirements and label classification methods, proposed a data preprocessing method, and completed experiments to analyze different influencing factors. The research results indicate that the classification system obtained from the study has a similar trend to the BERT model at the macro level, and the classification method proposed in this study can surpass the BERT model by up to 28%. The classification accuracy of the Att-BILSTM model improves over time, but it does not exceed the classification accuracy of the method proposed in this study. This study not only helps to improve the accuracy of English translation, but also enhances the efficiency of machine learning algorithms, providing a new approach for solving English translation problems.

SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification.

Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs. Nevertheless, the majority of current approaches disregard the prior information of spectral coordinates with limited interpretability, leading to inadequate robustness and knowledge transfer. In this paper, we propose an asymmetric encoder-decoder architecture, Spectral Coordinate Transformer (SCFormer), for the CDFSL HSIC task. Several dense Spectral Coordinate blocks (SC blocks) are embedded in the backbone of the encoder to establish feature representation with better generalization, which integrates spectral coordinates via Rotary Position Embedding (RoPE) to minimize spectral position disturbance caused by the convolution operation. Due to a large amount of hyperspectral image data and the high demand for model generalization ability in cross-domain scenarios, we design two mask patterns (Random Mask and Sequential Mask) built on unexploited spectral coordinates within the SC blocks, which are unified with the asymmetric structure to learn high-capacity models efficiently and effectively with satisfactory generalization. Besides, from the perspective of the loss function, we devise an intra-domain loss function founded on the Orthogonal Complement Space Projection (OCSP) theory to facilitate the aggregation of samples in the metric space, which promotes intra-domain consistency and increases interpretability. Finally, the strengthened class expression capacity of the intra-domain loss function contributes to the inter-domain loss function constructed by Wasserstein Distance (WD) for realizing domain alignment. Experimental results on four benchmark data sets demonstrate the superiority of the SCFormer.

Cross domain and adversarial learning based deep learning approach for web recommendation

Cross domain and adversarial learning based deep learning approach for web recommendation

Cross domain and Adversarial Learning based Deep Learning approach for Web Recommendation

Cross domain and Adversarial Learning based Deep Learning approach for Web Recommendation

Cross Domain Solution With Stateful Correlation of Outgoing and Incoming Application- Layer Packets

Cross Domain Solution With Stateful Correlation of Outgoing and Incoming Application- Layer Packets

Cross Domain Optimization for Speech Enhancement: Parallel or Cascade?

Cross Domain Optimization for Speech Enhancement: Parallel or Cascade?

Cross Domain Early Crop Mapping Using CropSTGAN

Cross Domain Early Crop Mapping Using CropSTGAN

Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation

Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation

Geographical dual-prior guided few shot network for cross domain hyperspectral image classification

Geographical dual-prior guided few shot network for cross domain hyperspectral image classification

Implicit Discriminator Domain Adversarial Residual Network for Cross Domain Rolling Bearing Fault Diagnosis

Implicit Discriminator Domain Adversarial Residual Network for Cross Domain Rolling Bearing Fault Diagnosis