Miracle-dz/UniFAR-FTUs huggingface dataset:
Rolling bearing fault diagnosis method based on enhanced knowledge distillation and lightweight convolution network

In order to solve the problems of high time ductility, low calculation accuracy and unfriendly hardware deployment in practical engineering in rolling bearing fault diagnosis methods under strong noise background, a new kind of Improved Knowledge Distillation was proposed. (IKD) lightweight convolutional Neural Network fault diagnosis method. In order to reduce the increase of computing time caused by channel addition in the process of convolution, standard pointwise convolution and channel shuffle are used to design the basic unit of the network. On this basis, the IKD algorithm is used to train the student network (IKDStu) based on the pre-trained teacher network (TeacherNet). Simulation analysis shows that under the background of the given data with strong noise, the student network trained by the proposed method can ensure the accuracy of diagnosis, reduce the size of the model and the number of parameters, and improve the efficiency of diagnosis.
Digital Transformation in Coal Mining: IoT-Based Air Quality Monitoring System

The coal mining industry, a crucial element of global energy production, faces enduring challenges concerning air quality and worker safety. This research explores the integration of Internet of Things (IoT) technologies to drive digital transformation in coal mining. Specifically, to investigate an IoT-based air quality monitoring system designed for coal mining environments. This system deploys a network of strategically positioned sensors to continuously gather air quality data, including particulate matter and gases. Real-time transmission of this data to a central control hub enables immediate analysis and visualization, empowering timely decision-making. This IoT solution enhances miner safety and contributes to environmental sustainability by facilitating emissions reduction measures. By facilitating remote monitoring and informed decision-making, the system optimizes mining operations. This research underscores the potential of digital transformation in the coal mining industry, offering valuable insights for coal mining stakeholders, regulatory bodies, and companies seeking to modernize while addressing safety and environmental concerns.
Transforming Last-Mile Logistics: A Simulation-Based Analysis of Collection Point Delivery Strategies

This research investigates the potential of collection point delivery (CPD) for delivering goods to designated collection points instead of individual addresses, offering an alternative for last-mile delivery from the perspective of logistics service providers. Our case study employs MATSim, a largescale agent-based transport simulation framework, to explore the possibility of increasing operational efficiency and mitigating environmental impacts through the implementation of CPDs in non-shared and shared scenarios. Results indicate that the integration of CPDs significantly enhances the delivery efficiency and reduces environmental impacts. While shared collection points enhance operational efficiencies with higher network penetration, non-shared points perform better at lower penetration rates, highlighting that the benefits of CPDs are maximized when tailored to specific network densities and regional characteristics.
Maritime Ship Target Detection Based on the YOLOv7 Model

Maritime ship target detection plays a significant role in the modern maritime domain. However, challenges such as low detection accuracy, false positives, and missed detections hinder its effectiveness due to the complexity of the maritime environment and the diversity and variability of ships. To address this challenge, we propose a sea ship target detection method based on YOLOv7. Firstly, we replace the main neural network with MobileNetv1, reducing model parameters and enhancing inference speed. Secondly, in the feature fusion section, we introduce a shallow feature fusion pathway to comprehensively capture image details and contextual information, improving the detection capability for objects of various scales. Experimental data demonstrates that the enhanced YOLOv7 model has achieved significant results, achieving an average precision of 97.16%, which represents a 5.53% improvement over the initial model. This validates the feasibility of our approach.
A Comparative Study Between EMD-MCKD and EMD-MEDA Techniques for the Bearing Faults Diagnosis in Induction Motors

The significance of induction motors has been greatly heightened due to the rapid advancements in modern industrial production. Therefore, a fault diagnosis based on vibration signals is therefore critical in industry and academia. In this context, we compared Empirical Mode Decomposition combined with Maximum Correlation Kurtosis Deconvolution (EMD-MCKD) to EMD with Minimum Entropy Deconvolution Adjusted (EMD-MEDA). In order to improve fault diagnosis, we apply to the reconstructed signal the technique of Maximum Correlation Kurtosis Deconvolution (MCKD). Simultaneously, subjected to Minimum Entropy Deconvolution Adjusted (MEDA). Finally, the outcomes are comprehensively analyzed and compared to identify the optimal technique for diagnosing bearing faults. This study is based upon experimental findings, and demonstrates the effectiveness and suitability of the EMD-MEDA technique, clearly promising in bearing fault diagnosis and extract the characteristics of the fault in the signal, by evaluating its accuracy and comparing its performance to EMD-MCKD.
Towards Large-Scale Video-Based Highway Traffic Monitoring

Traffic safety on highways is supported by a variety of technical measures, including countless camera systems that are often only monitored by human operators. However, due to the sheer amount of data, safety monitoring and accident prevention are limited by human resources. In this paper, we present an efficient system capable of extracting accurate vehicle trajectories from the vast amount of video data generated by modern highway infrastructures. Our proposed system conveniently leverages bird's eye view transformations estimated from aerial data or street marker geometry to generate geo-localized trajectories. Utilizing existing infrastructure, we demonstrate that the central data for video-based highway traffic monitoring can be reliably extracted. Remarkably, this can be achieved solely relying on uncalibrated cameras and noisy video streams.
Here today, gone tomorrow : flow variability, larval dispersal and fisheries management in Hawaiʻi

A contemporary challenge in marine ecology is understanding population connectivity driven by transport during early life stages. Connectivity results from a complex combination of biological traits and physical mechanisms. This dissertation begins by investigating the influence of physical mechanisms on particle transport around the Island of Hawai‘i. Particle dispersion was determined by using an individual-based model (IBM) and the flow fields derived from a global and a regional implementations of an ocean circulation model. To understand the underlying physical processes of transport, coherent structures were located in the flow field, recurrent physical features were identified, and how particle transport related to them was observed. Results showed that the eddying flow increased connectivity and influenced retention, as well as that both the flow field and dispersal patterns were highly variable. In this scenario, eddy events influenced transport in distinct ways, and the timing of release played an important role in the dispersal and resulting connectivity. Differences were found in the transport of particles and emerging connectivity patterns when comparing the two model implementations, highlighting that modeling studies should use hydrodynamical model flows representing the scales of variability affecting dispersion. Then, the IBM was adapted for fish species, and connectivity patterns along the Hawaiian Archipelago of three bottomfish: E. coruscans, E. carbunculus, and P. filamentosus were investigated . These species appeared to share common features in their connectivity patterns driven by larval transport, as follows: i) limited connectivity between the Papahānaumokuākea Marine National Monument (PMNM) and the Main Hawaiian Islands (MHI); ii) greater vi connectivity among islands in the PMNM than in the MHI; iii) existence of four, mostly self-contained, dispersal zones along the Archipelago; and iv) islands from Kauai to Necker connected the PMNM and the MHI through larval dispersal, acting as ecological corridors. When the MHI’s bottomfish restricted fishing areas (BRFAs) were considered, it was found that these reserves are potentially replenishing depleted fishing areas by larval export, but rely on larval subsidy from the fishing sites to sustain their populations. Thus, the efficiency of the reserve network could be improved by the protection of additional bottomfish habitat, distributed in new or expanded reserves. Finally, dispersal characteristics of yellowfin tuna (T. albacares) around the MHI were investigated. The physical environment appeared favorable for retention of yellowfin tuna larvae. However, retention is not the main factor optimizing the spawning season of this species in the archipelago, and other factors, such as favorable water temperature and food availability, are likely contributing to spawning seasonality. The distribution of larvae by the time evolving eddying field was highly variable. Apart from nearshore regions, no additional persistent dispersal pathways or accumulation zones were observed.
Cross-lingual Sentiment Analysis of Code-Mixed Corpus based on Cross-lingual Word Embedding

Bilingualism is a common linguistic phenomenon that causes a challenge in opinion mining. The early methods in Cross-lingual Sentiment Analysis (CLSA), based on machine translation, parallel corpus, and bilingual sentiment lexicon, face issues in terms of translation error, vocabulary coverage, and dependence on extensive parallel data. Hence, this study examined the effectiveness of Cross-lingual Word Embedding (CLWE) for the sentiment analysis of code-mixed Filipino-English corpus. A large-scale manually annotated code-mixed dataset containing stakeholders' feedback on the Higher Education Institutions' services and infrastructure was developed to address resource scarcity. Several pre-trained transformer-based CLWE methods, such as mBERT, XLM-R, and XLM-T, were employed to represent the words from the two languages in the same vector space and obtain the cross-lingual embeddings. An Attention-based BiLSTM-CNN neural architecture, the baseline model from the previous work, was fine-tuned on these cross-lingual embeddings to perform the sentiment analysis of code-mixed Filipino-English corpus. The experimental results demonstrate that XLM-T has achieved the highest performance rate, with 91.30% accuracy, 90.36% precision, 90.92% recall, and 90.61% F1-score. Thus, employing cross-lingual word embedding was proven effective as it significantly increases the accuracy by up to 10.02% compared to the baseline model, which only uses word embedding having no cross-lingual alignment.
Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.
Single Underwater Image Enhancement Based on Transmission Map Weighted Fusion and Adaptive Color Correction

The complex underwater environment and the absorption and scattering of light in the water lead to color degradation and loss of detail during the underwater imaging process. To address these problems, we propose a single-shot underwater image enhancement method based on the transfer graph normalization weighted fusion method and adaptive color correction, using a weighted fusion technique to combine the improved transfer graph with a bilateral filtering algorithm, which aims to defog underwater images and improve their contrast. Furthermore, by calculating the relative standard deviation between the three color channels, a novel adaptive correction method is designed to solve the image distortion problem due to light propagation underwater. Experimental results show that this comprehensive method performs well in underwater image enhancement.
Underwater Fish Image Enhancement Algorithm Based on Dark Channel Prior and Retinex

Due to the intricate underwater conditions and lighting challenges prevalent in marine farms, underwater fish images often suffer from issues like blurriness, low contrast, color distortion, and uneven illumination. In response to these hurdles, we present an underwater fish image enhancement algorithm that leverages the dark channel prior and Retinex. We commence by applying the dark channel prior to dehaze underwater fish images. To discriminate between foreground and background regions within the image, we employ a superpixels segmentation technique based on variations in image brightness. Consequently, we employ a multi-scale Retinex enhancement on each region and perform a secondary enhancement exclusively on the background region. Finally, a Poisson fusion method is utilized to amalgamate the enhanced foreground and background segments, resulting in a unified composite image. We conducted comprehensive experiments employing the Underwater Image Enhancement Benchmark Dataset (UIEBD). The experimental findings substantiate the efficacy of the proposed algorithm, with notable improvements observed across three key evaluation metrics: information entropy, average gradient, and standard deviation. These enhancements translate to an average percentage increase of 18.22%, 133.67%, and 59.29%, respectively. Additionally, the algorithm notably enhances underwater fish image clarity and ameliorates issues related to uneven illumination. It demonstrates superior aptitude in preserving image intricacies and elevating contrast when juxtaposed with alternative algorithms, substantiating its superior performance.
Bird's nest recognition method combining visual salience and deep learning

The identification of bird's nest targets on transmission lines based on drone inspection images is of great significance in reducing power inspection costs and improving inspection efficiency. This article conducts research on bird nest automatic recognition based on deep learning and visual saliency methods. Firstly, a saliency map is constructed to guide the segmentation of salient regions, and then feature enhancement is performed on the segmented salient regions. Next, the original image and enhancement results are fused and input into a deep learning model for training. The model constructs a feature pyramid to meet the needs of multi-scale bird nest target recognition. Finally, the classification and regression sub models are used to output the bird nest recognition results. Through conducting specific experiments, the results show that the proposed fusion saliency and deep learning bird nest recognition method performs well, with the average precision, Recall, and IoU accuracy indicators above 0.95.
Comparison Study and Performance Enhancement of Induction Motor using Optimized SynRM for Household Applications

Induction motors (IMs) are widely used in various industries and household applications. However, synchronous reluctance motors (SynRMs) are a perfect alternative to IM because of their structural simplicity, lowcost production, and higher torque density. This paper compares the efficacy of a SynRM and IM under same operating conditions and dimensions. First, the specifications of 0.5 hp 3-phase IM are extracted, and then the IM is simulated using finite element method (FEM). Afterwards, the IM is subjected to laboratory testing, and the results are compared to those of the simulation. Subsequently, a SynRM with the same specifications is analysed. In order to improve the average torque and minimize the torque ripple, the rotor of SynRM is optimized. Finally, the comparison results indicate that the improved model of SynRM achieve 0.8 N.m higher mean torque, 6.83% lower torque ripple as well as higher efficiency in a lighter weight that proves it is a perfect candidate for implementation in household applications.
Abstractive Bengali Text Summarization Using Transformer-based Learning

Text summarization serves the purpose of condensing extensive text documents into concise sentences that capture the core information. Given the exponential growth in textual data due to web-based applications, there is a pressing need for automated systems to streamline the summarization process, saving both time and resources. In response to this demand, this paper introduces an automated abstractive text summarization framework for Bengali, leveraging transformer-based models. The research evaluates the performance of five different transformer-based approaches using available datasets. The results demonstrate that the Bangla-T5 model, fine-tuned on a benchmark dataset, outperforms other methods, achieving the highest ROUGE-2 score of 13.83 on the test dataset.
Defect detection of photovoltaic modules based on improved SSD algorithm

Automated inspection of unmanned aerial vehicles (UAVs) offers an effective solution to address the operational and maintenance requirements of large-scale distributed photovoltaic systems. Due to the abundance of image data generated by UAVs, a robust algorithm is needed to achieve higher recognition accuracy and faster processing speed. In this regard, we propose an enhanced Single Shot MultiBox Detector (SSD) algorithm for detecting defects in photovoltaic (PV) modules. The novel algorithm incorporates an attention mechanism into the original SSD algorithm and leverages transfer learning to enhance both detection speed and accuracy. It is capable of automatically identifying and classifying common PV module defects such as glass breakage, yellowing of the light receiving surface, and dust accumulation. Experimental results demonstrate that the improved SSD algorithm outperforms other state-of-the-art algorithms including Faster-RCNN, YOLO3, and VGG16-SSD in terms of recognition accuracy, recall rate, and detection speed. Consequently, the proposed algorithm significantly enhances the efficiency of defect recognition in photovoltaic modules.
Towards an Automatic Ensemble Methodology for Explainable Reinforcement Learning

Nowadays, the performances of Deep Reinforcement Learning algorithms have surpassed human capabilities at the cost of losing transparency. For this reason, the attention has been moved to methodologies related to Interpretable and Explainable Reinforcement Learning. A fundamental aspect in generating reliable explanations for Reinforcement Learning agents consists of the identification of important states. Therefore, a multitude of metrics have been developed to characterize specific situations and exigencies. In this paper, we propose a methodology for the recognition of the states helpful for getting insights into the choices of the agent through the analysis of the importance measures of stored trajectories. Moreover, we couple this mechanism with a pipeline for the creation of explanations through Bayesian Networks and Recurrent Neural Networks. In this way, we are able to extend the predicted information provided to human users by including a risk-awareness report. Finally, this advanced approach is evaluated in the Taxi grid world environment to underline the efficiency in identifying fundamental states and the generation of extensive explanations.
Goal-Directed Target Discovery in Social Network

Traditional information-retrieving technology in open networks mainly focused on crawling certain webpages or extracting certain attributes from webpages, however, very few studies have been done on the user scale. In the paper, we propose an innovative goal-directed target discovery approach to find the users concerned automatically in social networks. Different from the traditional methods of crawling social networks blindly and exhaustively, our method leveraged deep learning technology to guide the crawlers to cleverly find the target users with certain political stances. Finally, experiments and studies were conducted to demonstrate the effectiveness of our method.
Case Study of 360 Image Viewer Software Utilization in Interior Design Presentation to Improve Product Immersion

This case study investigates the use of 360 Image Viewer software to improve product immersion in interior design presentations. The goal is to determine how the utilization of 360-degree photography technology may enhance the immersive experience and user engagement with items for interior design. This study uses a qualitative method to analyze observation. During design presentations, 360 Image Viewer software was evaluated by a sample of interior design experts and prospective clients. Users were able to move around in virtual environments thanks to the software, which gave them a sensation of presence and interaction. Observation was used to gather qualitative data with an emphasis on the participants’ impressions of immersion and participation. According to the investigation, using 360 Image Viewer software greatly improved product immersion and gave users a realistic and dynamic portrayal of the interior design. Participants noted a stronger understanding of the design concept and an improved sense of spatial presence. This case study illustrates the advantages of using 360 Image Viewer software and adds to the increasing body of information on immersive technology in interior design. The results show how such technology can improve the display and communication of products used in interior design, leading to greater client knowledge and involvement. In conclusion, this study promotes the use of 360- degree imaging as a technique to enhance product immersion and the quality of the entire interior design experience.
Enhancing Museum Visitor Engagement: Personalized Learning with Adaptive Robot Tutor

The present study describes the development and evaluation of an adaptive question selection system for a robot deployed in a museum that aims to test visitors’ familiarity with various robot types. The robot initially asked visitors questions randomly and recorded their responses; the analysis of more than 1,000 interactions showed that different robot types were recognized at different success rates. A Q-learning-based reinforcement learning model was then trained, enabling the robot to select questions adaptively depending on perceived difficulty. Initial findings point to the possibility of adaptive question selection to improve game outcomes and visitor engagement, with an almost 43% increase in the number of desired game outcomes.