Miracle-dz/UniFAR-FTUs huggingface dataset:
Electricity prediction model based on improved Gaussian process regression

Power system forecasting assumes a crucial role in ensuring the safe operation of oil and gas stations, encompassing three pivotal facets: load forecasting, output forecasting and health status forecasting. Health status prediction is mainly used for equipment maintenance. Through the continuous monitoring of equipment status, potential issues can be detected promptly, thereby guaranteeing the stable operation of the power system. In this process, machine learning technology plays a key role. It has the ability to analyze and predict features and can efficiently process power data. In addition, the research conclusion points out that machine learning regression methods can be used to predict the power output of oil and gas stations. This paper constructs an optimized Gaussian process regression model, named GPRS, for predicting electricity in cyclic power data. To optimize the hyperparameters of GPR, a Bayesian estimation method is introduced. In order to reduce the adverse effects of noise in complex environments, wavelet theory is also introduced to optimize data processing. Experimental results also prove that our GPRS model has excellent performance, with the accuracy of R2 reaching 0.976.
Detection of Traffic Signs in Complex Weather Conditions Based on YOLOv5

Accurate and real-time detection of traffic signs in real-world scenarios is of paramount importance for traffic safety. To address the low precision issue when detecting small target traffic signs using YOLOv5 algorithm, this study proposes a detection algorithm specifically designed for small target traffic sign detection. Firstly, an SE module is introduced into the main network to focus on learning critical information within features. Secondly, a CoT module is incorporated into Neck network to facilitate comprehensive feature extraction of contextual information. Finally, a small target detection layer is introduced to enhance the model’s ability to detect small targets. Experimental results on TT100K dataset demonstrate that the proposed improved model achieves a 1.9% increase in accuracy, reaching 86.1%; a 2.3% increase in recall, reaching 79.5%; a 2.4% increase in mAP, reaching 85.5%; and an FPS of 33.4, meeting real-time requirements.
Water and Heat Transfer Mechanics in the Soil-Plant-Atmosphere Continuum and Regional Evapotranspiration Model

Three models were constructed for estimating water and heat transfer in the Soil-Plant-Atmosphere Continuum (SPAC) in different spatial and temporal scales. Three kinds of experiments have been conducted for the models at two Agro-ecosystem Stations, Chinese Academy of Sciences in the North China Plain, since 1998. The first model is the Coupled canopy Photosynthesis-Conductance-Evapotranspiration (ET) Model (CPCEM), which was applied to simulate CO 2 and water fluxes above plant canopies well, compared with the measurement. The second one is the one-dimension and three-layer Soil Water Balance (SWB) model, which was successfully used to simulate soil water deep percolation and ET under different irrigation schedules in 1998—2003. The third one is the regional energy fluxes and daily ET model, which is based on remotely sensed and synchronous surface data. It was successfully used to estimate surface energy fluxes and daily ET in a regional scale.
Fault diagnosis method of rolling bearing based on Xception-CNN model

A fault diagnosis method based on Xception-CNN model was proposed to accurately diagnose the fault types of rolling bearings of belt conveyor transmission rollers under noise interference and variable load conditions. Firstly, based on Xception module, a CNN model body which integrates feature recognition and feature classification is designed, which can effectively reduce the dependence on expert knowledge. Secondly, an MP module is designed to replace the traditional maximum pooling layer, which can not only reduce the model training parameters, but also extract more feature information. Then, by introducing attention mechanism and residual learning mechanism into the model, the attention module can screen out more critical feature information, and the residual module can avoid gradient disappearance and network degradation to a certain extent. Finally, the proposed method and related deep learning methods are simulated and compared on the bearing data set of Case Western Reserve University. The experiments show that the average accuracy rate of the proposed model is 97.39% in the variable noise experiment and 94.58% in the variable load experiment, both of which are higher than other comparison methods, and have good universality and generalization ability.
State-Based Dynamic Graph with Breadth First Progression For Autonomous Robots

This paper introduces a novel method for enhancing robotic systems using Large Language Models (LLMs). We focus on leveraging LLMs to substantially improve robots’ decision-making and interaction with their environment. Our proposed framework employs an agent-based approach, where robots utilize LLMs for sophisticated pattern recognition, environmental understanding, and autonomous decision-making. The main innovation of this research is the integration of LLMs into a robotic system, enabling robots to process large volumes of unstructured data, recognize complex patterns, and make informed decisions with increased precision and efficiency. This integration marks a significant leap in robotic cognitive abilities, surpassing the constraints of traditional programming. Our methodology transforms LLMs into dynamic elements within robotic systems, fostering enhanced and responsive interactions with the environment. Robots equipped with LLMs are thus capable of advanced autonomous operations, including navigating complex environments, solving intricate problems, and interacting more naturally with humans. The primary contribution of this work is the creation of an agent-based graph framework, designed to facilitate collaborative problem-solving in robotic systems. This framework consists of multiple agents working collaboratively, spanning from data ingestion to comprehensive world understanding and decision-making. These agents include modules responsible for various operational aspects, such as environmental analysis, data processing, and specialized LLMs for data interpretation and summarization. Positioning LLMs as proactive, inquisitive agents, our approach enables them to actively seek information and efficiently collaborate with other agents to complete tasks. The dynamic nature of this graph search and inter-agent communication model is a considerable innovation in robotics, offering a more integrated and effective approach for robots to tackle complex tasks, thereby enhancing their ability to operate autonomously and intelligently in diverse environments.
Construction and Implementation of Electric Power Deep Learning Computing Service Platform

In order to accurately and effectively identify the types and locations of abnormal information in the power industry, an electric power deep learning computing service platform is constructed to provide intelligent inspection services. The platform adopts hierarchical technology architecture, based on Docker technology, realizes the abstract pooling of many kinds of computing resources, and provides the management interface of heterogeneous resources such as GPU, NPU and so on according to the specific requirements of deep learning field, so as to realize the unified management and scheduling of heterogeneous resources, and provide an efficient, flexible and scalable distributed computing service supporting environment for deep learning computing tasks. The platform layer provides data center, development center, training center, model center, service center and other subsystems, and built-in a variety of learning frameworks and model algorithms to support external algorithm model integration. It really realizes the one-stop development ability of deep learning tasks. In the process of intelligent inspection of electric power, the online recognition and detection of a variety of abnormal information is realized, which provides a technical basis for on-line identification, supervision, prevention and control, comprehensive management of abnormal behavior.
Improved Remote Sensing Classification and Detection Model Based on YOLOv7-Tiny

Aiming at the problem that due to the complex background of optical remote sensing images, non-rotating frame target detection is easy to introduce background information, resulting in image classification that is prone to errors and missed detections, a classification model based on improved YOLOv7-tiny was proposed. On the one hand, this method introduces rotating frame detection to YOLOv7-tiny, which avoids the prediction frame containing background information and effectively improves the classification recognition rate of the model. On the other hand, it proposes a dual-scale loss function, which effectively solves the problem of the rotation angle of the square prediction frame. The problem cannot be optimized, and the high-precision coverage target of the rotation prediction frame is achieved. This method has been tested on remote sensing ship, aircraft, and car classification data sets. The experimental results show that the average detection accuracy of the improved network is increased by about 8 %.
Dual Unsupervised Luminance-Space Pyramid-Constrained Network for Image Deraining in Real-World Scenarios

In the field of image processing, addressing the deleterious impact of rain streaks on images captured in real-world scenarios poses a formidable challenge. Previously developed deep learning-based deraining methods have demonstrated remarkable effectiveness when assessed using standardized benchmark datasets. Nonetheless, their applicability is often limited to pairwise training samples and domain-specific discrepancies. Moreover, these methods experience significant performance degradation when applied to genuine real-world rainy scenarios. Some researchers have observed the influence of luminance but have not proposed targeted solutions or have even disregarded it. In real rain-incorporated scenes, luminance exerts a noticeable influence on the visual characteristics of the images. The primary aim of this study is to alleviate the challenges related to dataset limitations and performance constraints inherent in real-world image deraining processes. To address this, we introduce a dual unsupervised Luminance-space Pyramid constrained Generative Adversarial Network (LP-GAN) designed to eliminate rain streak artifacts from real-world images. More specifically, the LP-GAN architecture comprises two interrelated loops: the first loop is responsible for mapping rainy input images to their corresponding rain-free counterparts, while the second loop operates in the reverse direction. In addition to the standard generative adversarial network (GAN) structure and the application of consistency constraints that align the initial rainy input with the predicted rainy image, we introduce an innovative luminance constraint. This constraint acts as a guiding mechanism to enhance the synthesis of rain-free images with improved naturalness and heightened contrast. Empirical evaluations conducted on diverse and challenging real-world datasets highlight the superior performance of our proposed model when compared to existing unsupervised deraining methodologies.
Analytical Modeling of Synchronous Reluctance Machines with Finite Rotor Ferromagnetic Permeability

In this paper, the analytical modeling of Joukowsky-barrier SynRe machines is proposed. The rotor is modeled using the magnetic equivalent circuit (MEC), and the Fourier-based (FB) method is used to estimate the magnetic field distribution in the stator slots, slot openings, and the airgap. The developed MEC and FB models are directly coupled through the coupling boundary conditions on the rotor’s outer surface. The stator iron is assumed to be infinitely permeable and the rotor iron has a finite permeability. The obtained results are validated using the finite element method.
3-GWD : A Textural and AI-Based Approach for Real-Time Detection of Weather Disturbances Applied to Autonomous Vehicle

We propose 3-GWD, a new and innovative solution for the particle atmospheric disturbers' detection that can be used to weight the data in a fusion process and adjust the sensor reliability. The proposed approach, based on 3D Gray-Level Co-occurrence Matrices (3D-GLCMs), is generic and could be used for a significant number of sensors (LiDAR, IR camera, Neuromorphic camera…). We demonstrate that this method leads to the identification of very relevant patterns in the symbolic space that shallow Artificial Neural Network (ANN) can classify with a great accuracy. We also discuss the importance of all-environment dataset by creating a reality- enriched simulated database with controlled disturbances and numerous environments. Using a single optic camera produces promising results with a high prediction score. We end our study with an attempt to compare our approach with previous works using the DAWN and the MCWCD datasets.
Optimization Algorithm Research and Design of Chip Manufacturing Virtual System Based on SECS/GEM

In order to improve the production efficiency, improve the product quality and reduce the cost in the field of chip manufacturing, this paper designs a virtual system based on SECS/GEM protocol to simulate the real production process of chip manufacturing, so as to optimize each stage of chip manufacturing. The system includes a equipment-side program that simulates the equipment and a host-side program that interacts with it. The host-side can collect state data from the equipment and monitor the equipment. Secondly, in order to improve production efficiency, an optimization algorithm of job shop scheduling based on reinforcement deep learning is designed. Experiments show that the system can simulate the process of chip manufacturing to some extent, optimize and improve the manufacturing process, and improve the overall efficiency of the equipment.
Enhancing Emotion Recognition using Contrastive Learning Representation of Paired Peripheral Physiological and EEG Signal

Human emotions play a fundamental role in human-computer interaction, and accurately recognizing emotions is essential for enhancing user-friendly experiences in HCI applications. Emotions are complex and cannot be simply identified by a single modality, making multimodal approaches crucial. In this study, we propose a cross-modal representation learning approach for emotion recognition using peripheral physiological signals (PPS) and electroencephalogram (EEG). We focus on leveraging PPS, which is easier to obtain in real-life scenarios due to wearable technologies’ availability. Through contrastive learning with paired EEG signals, we aim to enhance the capabilities of PPS representation and achieve comparable performance to EEG-based models in downstream unsegmented emotion recognition tasks. We utilized the DEAP dataset and low or high arousal category for the downstream task. The results of the study demonstrate the successful learning of PPS representations that closely resemble EEG signals representations, leading to comparable performance on downstream tasks.
Image Inpainting Using PatchGAN

A system designed with an accurate and efficient model for image painting aims to help anyone fill in the missing parts of the image. Designing such a system is a challenging task, involving various aspects such as model accuracy, computational complexity, and more. Therefore, this research will utilize the Generative Adversarial Network (GAN) method for inpainting the damaged areas in the images. The Generative Adversarial Network (GAN) method is a technique known for generating high-quality data, including images. The GAN method is an architecture model consisting of a Generator and a Discriminator. The Generator generates images, while the Discriminator distinguishes between the images generated by the Generator and the original images. The Generator in the model is liable for generating images that closely resemble the images in the training data. The Discriminator is responsible for attempting to differentiate between real images and images generated by the Generator. The inpainting results will be evaluated by calculating the Peak Signal to Noise Ratio (PSNR). Based on the results of these studies, the GAN method can perform image inpainting on images that have damaged areas. Result of the evaluation, the average PSNR value in the CelebA dataset was 39,312 dB.
Deep Segment-Attentive Network for Altered-Engine Recognition

Altered engine recognition is of significant research and application prospects since it is a sub-task of environmental sound classification and helps detect altered vehicles for law enforcement. In this work, we propose a simple but effective framework based on a sound embedding network with an attention mechanism to identify noise emitted from vehicles with altered engines. Real-world vehicle sound data were collected at a number of streets with different amounts of traffic in Hong Kong. In particular, we developed a proprietary dataset consisting of the environmental and engine sounds through manual segmentation and annotation, and we found that the attention mechanism can help emphasize the informative segments in the frame-level embeddings outputted by the embedding network. We also demonstrate the effectiveness of the proposed attention mechanism on the ESC-50 and UrbanSound8K datasets. The experimental results show that our method achieves remarkable performance on UrbanSound8K and outperforms the baseline on ESC-50 by more than 10%.
Dense Video Captioning Based on Memory Enhanced Attention and Guided Learning

In the task of captioning multiple events in video content, traditional models using self-attention mechanisms often suffer from the problem of missing fine-grained visual semantic details due to the complexity and diversity of the content. The long-tail distribution of words in the video corpus further exacerbates the insufficient training of words related to visual content, which in turn affects the accuracy of video captions. Therefore, this paper proposes a Memory-Enhanced Attention Network that utilizes a memory module to capture the importance of frame-level features. It combines the historical visual semantic information and performs weighted fusion to select and control the flow, enabling the model to capture complex visual semantic information and better capture long-range inter-frame correlations, thus addressing the problem of missing visual semantic details.Additionally, the paper introduces a guided learning cross-entropy loss function that incorporates rich linguistic knowledge into the model using a pre-trained language model (ELM). By minimizing the KL divergence between the predicted word distribution and the ELM probability distribution at the sentence level, the model's caption accuracy is improved, alleviating the long-tail word distribution problem in the video corpus. Experimental results on the ActivityNet Captions and YouCook2 datasets demonstrate that the proposed approach outperforms other state-of-the-art models.
Overview of the Operational Design Domain Monitoring for Safe Intelligent Vehicle Navigation

The operational safety of intelligent vehicles (IV) is a central topic and a complex issue on which many research projects are focused. While there is no consensus yet on the most reliable way to validate driving automation systems, several methods exist such as scenario-based testing and real-time operational domain (OD) restriction based on the specification of the operational design domain (ODD). In our case, we focus on the role of the ODD concept as a safety guarantee for IVs and how, combined with real-time monitoring of their operational domain (OD) can act as a safeguard for complex mobile systems. We propose to analyze the related literature, standards, works and proposals made in the field of OD/ODD monitoring for operational safety and to assemble them into 3 categories: the first concerns the use of taxonomies and ontologies to represent the elements of the vehicle driving environment; the second concerns the formal definition of the ODD through a dedicated language; the third concerns techniques allowing the monitoring of the vehicles' OD to improve operational safety. By taking a step back from the identified methods, we highlight how they can be combined in a coherent manner, to progress towards a framework enabling ODD definition and OD/ODD monitoring.
Mesh Model Codec Based on Fusion Graph Convolution and Parallel Pooling

Three-dimensional shape generation is an important research direction of computer graphics. In this paper, a graph convolutional neural network is introduced to obtain local features for a three-dimensional mesh model data set with the same connectivity relationship. A parallel pooling function is proposed to jointly extract richer global features, improve network generation performance, and ensure the robustness of the model during network training. Experiments are carried out on datasets such as Swing. The experimental results show that the mesh model codec network based on fusion graph convolution and parallel pooling proposed in this paper can effectively obtain global and local features in grid data and achieve a higher-quality mesh generation effect.
Large Kernel Convolutional Neural Networks for Action Recognition Based on RepLKNet

To improve the classification accuracy of human actions in videos, two algorithms based on RepLKNet, a large kernel neural network, are proposed. By combining RepLKNet with Temporal Segment Networks (TSN) and incorporating the Temporal Shift Module (TSM), the models can better extract spatiotemporal information from videos, leading to improved classification accuracy. Experimental results demonstrate that the proposed models achieve higher classification accuracy compared to previous convolutional neural network (CNN) models. Moreover, these models outperform transformer-based models such as TimeSformer in classification accuracy while requiring significantly fewer parameters.
Enhancing Cloud-Based Application Component Placement with AI-Driven Operations

The cloud-based application component placement problem is complex and often tackled with heuristics to find near-optimal solutions maintaining the application’s performance and avoiding resource over-provisioning. Mapping application components into virtual machines in potentially unpredictable cloud conditions is challenging and lacks performance guarantees. To address this challenge, we present an Artificial Intelligence (AI)-based resource and workload-aware mechanism, formulated as a dynamic decision-making problem solver based on Markov Decision Process (MDP). By leveraging Deep Reinforcement Learning (DRL) models, Depth-Search-First, and Dancing Links algorithms, our approach provides workload-dependent solutions ensuring keeping a balance between the application’s overall performance and the virtual machines’ resource allocation. In our study, we conducted experiments utilizing simulated multi-component-cloud-web-based applications, employing both the Deep-Q-Network (DQN) and Proximal-Policy-Optimization (PPO) architectures. Our evaluations show that the PPO outperforms the DQN, by predicting near-optimal sets of application components within virtual machines in less than 10-episode steps(on average).