Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting of solar power generation in Vietnam deploying a simple GRU model

Solar energy is a renewable energy source that is widely used in the world. It is characterized by its instability and susceptibility to weather changes. Forecasting the power output of solar energy sources is crucial to help grid operators manage the power system according to supply and demand. Nowadays, Artificial Neural Network (ANN) models simplify and improve the accuracy of power forecasting by utilizing historical data. In short-term solar power forecasting, the Gate Recurrent Unit (GRU) model is simple, reliable, and highly accurate. This paper uses a simple GRU model for forecasting solar power output based on a two-year historical dataset collected from a solar power plant in the central region of Vietnam.
Adaptive Dynamic Programming-Based Distributed Coordinated Control for Dual-Arm Reconfigurable Manipulators

In this paper, a distributed coordinated control method of dual-arm reconfigurable manipulators based on adaptive dynamic programming is proposed. Through kinematic analysis, the dynamic models of the reconfigurable manipulators and the grasped object are established, respectively. According to the load distribution method, the motion induction force is effectively distributed on each arm. Then, the dynamic model of a single reconfigurable manipulator is obtained. The performance index function is defined to reflect the joint position tracking error and internal force tracking error. The neural network is utilized to approximate the performance index function to obtain the optimal control strategy through the Policy Iteration (PI) algorithm. Thus, a distributed optimal coordinated controller is acquired. The tracking error of the manipulator system is proved be ultimately uniformly bounded (UUB) through Lyapunov's stability. Finally, the effectiveness of the developed control method is verified by simulations.
Comparative Analysis of Intrusion Detection Mechanism for WSN Using ML

To protect the network system from attacks across the network, effective intrusion detection is required. Since intruders have tried to break into or abuse the system over the past 20 years, intrusion detection has become quite popular. IDS employs a variety of ways to defend computers and networks from host-based and network-based assaults. As the Internet of Things (IoT) expands, sensor nodes are utilised more frequently and are more likely to be connected to other sensor nodes that have security vulnerabilities. Hence, it is essential to utilize intrusion detection methods to prevent attacks that take advantage of vulnerabilities in sensor systems. However, conventional intrusion detection systems were ineffective in WSN situations because of the specific protocols used and the constrained capabilities of Sensor nodes.
An Adaptive Driving Beam System with Integrated Automatic Lamp Control Function

The abuse of automotive high beam can cause dazzling to the driver and enormously increase the traffic accident risk at night. To improve driving safety, The Adaptive Driving Beam (ADB) technique is proposed in recent years. According to the traffic information gathered from the frontal camera, the ADB system can form a proper high beam shadow area by turn off specific LED groups. The shadow area can track the preceding vehicle until the vehicle leave the sights of the high beams. In this paper, An ADB system integrated with automatic headlight control strategy is presented. The principle of ADB is established on the coordinate transformation from the frontal camera to the left and right high beams. The status of high beam and ADB function is determined by the vehicle real-time status and the setting of the Head Unit (HU). The hardware architecture of ADB system is also discussed. The ADB system proposed can provide a full function of high beam control, and reduce the abuse risk of high beam effectively.
Research Status of Condition Monitoring Data Cleaning Methods for Power Equipment

As the intelligence and scale of power equipment continue to increase, the requirements for reliability and safety are also getting higher and higher. Therefore, condition monitoring and data cleaning of power equipment status have become the focus of attention. In this paper, the current research status of power equipment condition monitoring data cleaning methods is reviewed. First, the extraction method and characteristics of monitoring data are introduced, and then the basic cleaning process and common cleaning methods of monitoring data are given. Finally, two types of data cleaning algorithms based on mathematical statistics and artificial intelligence are compared, and it is pointed out that existing problems and future development trends in this field. This review provides a reference for the research on power equipment condition monitoring data cleaning methods, and provides guidance and inspiration for researchers and engineers in related fields.
Research On The Training Path Of Big Data Management And Application Talents Based On Text Mining

In order to solve the problems of asymmetry between supply and demand of data science talents and slow construction of discipline system, this study analyses 85 texts of curriculum system of big data management and application majors collected through social network analysis, and at the same time, constructs TOPSIS-LDA model of job qualities based on more than 10,000 job competence demands of the big data industry in 2 mainstream recruitment websites, and draws the focus of attention and logical links of talent cultivation and talent demand side. The focus of attention and logical links of talent demand side were obtained. Finally, based on BERT-LSTM-CRF depth model, entity extraction was carried out on data science classic books and thesis texts, and the knowledge map was constructed for the big data management and application speciality in combination with the logical links of supply and demand, which provided the theoretical and practical basis for the construction of the speciality.
Monte Carlo Simulation Framework for the Evaluation of PET Detector Designs in Charged Particle Therapy Applications

Charged particle therapy uses ionizing radiation beams to deliver dose precisely, while sparing nearby healthy tissue. Positron emission tomography (PET) is an imaging technique used to perform in-vivo range verification in charged particle therapy by detecting where the therapeutic particles have actually interacted, through the detection of annihilation radiation following the decay of positron emitters induced in the patient by the incident beam. The use of dedicated PET detectors placed close to the patient is preferable to using whole body scanners as it maximizes the detection efficiency when radioinduced activity is at its maximum, reduces blurring from biological washout and allows imaging of short-lived 15O, particularly important in oxygen-rich tissue organs the brain. In this work, we propose a framework to evaluate the performance of PET detector designs in charged particle therapy applications, by Monte Carlo simulations of real patient treatments with artificially-induced range shifts by modulating beam energy or scaling tissue densities. Compared to irradiating phantoms of different materials with pencil beams, the use of CT images and treatment plans from real patients allows one to simulate in a more realistic way the real clinical conditions in which the PET detectors will be used. We have evaluated the proposed method by simulating real patient treatments with arbitrary range shifts by modulating beam energy, detecting the positron annihilation radiation with a dedicated PET detector design of our group. Results show that range variations as small as 1 mm can be identified, validating the use of the proposed method in the design and validation of novel PET detector designs for range verification in particle therapy.
A Supervised Deep Learning Approach for Estimating Image-to-Image Motion

In the clinical imaging setting, a single scanning session often comprises multi-modality and/or multiple time-point imaging data, which are used jointly to improve image processing and radiologic interpretation. It is generally desirable for these images to be spatially aligned, and in most cases, this is assumed. As such, subject motion between imaging points can lead to inconsistencies in image quantification, incompatibility with automated analyses pipelines and an overall reduction in diagnostic confidence. Image registration approaches have been used successfully for specific applications, typically for intra-modal data, but preform less reliably when handling data with weak correlations between pixel intensities, e.g. for multi-modal images. This paper presents a deep learning technique for dense motion field estimation between images, applicable to both intra-and inter-modal datasets. The foundation of this work is a supervised training framework, designed to avoid many of the problems associated with related unsupervised approaches. The feasibility of the approach is demonstrated using clinical PET/CT data for characterizing intra-modal motion between various time-point PET frames and for characterizing inter-modal motion between accompanying PET and CT images.
Hybrid Deep Neural Network for Alpaca Animal Recognition: Achieving High Accuracy in Detection and Classification

Developing a reliable and efficient model of animal recognition is challenging. There are numerous factors to consider, such as the model's accuracy, the number of variables, the complexity of the calculations, etc. We propose the Hybrid Deep Neural Network (HDNN) to make the technology more applicable to detect and a set of classifiers to classify. This new HDNN attempts to establish a balance between detection accuracy and classifying. It comprises two modules-feature extraction and classification modules-that collaborate to achieve its objectives. Yolo v5 comprises the feature extraction module's hybrid deep neural network architecture. Here, two neural networks are employed: one for extracting image characteristics and the other for identifying objects within an image. The classification component employs two completely interconnected layers as a classifier. It achieves an outstanding balance between recognition accuracy and model size compared to other methods. The proposed method has been shown to be effective in experiments, with an accuracy of 98.02% in classification and high precision values. The proposed method can automatically categorize animals while accounting for various dataset difficulties.
Systematic Review of AIS Based Trajectory Prediction Models

This paper presents a comprehensive review of Automatic Identification System (AIS) trajectory prediction models. AIS is a vital technology used in maritime domains for vessel tracking and collision avoidance. Accurate trajectory prediction is crucial for ensuring maritime safety, efficient navigation, and resource management. This study systematically analyzes various AIS trajectory prediction models proposed in the literature, categorizing them based on their underlying methodologies, data sources, prediction horizons, and evaluation metrics. The review discusses the strengths, limitations, and potential applications of each model, highlighting their performance and suitability for different maritime scenarios. The findings of this review contribute to the understanding of AIS trajectory prediction and provide valuable insights for future research and development in this field.
NMPC-based Path Tracking Control Strategy for 4WID Autonomous Vehicle Considering Handling Stability Under Extreme Conditions

Path tracking for autonomous vehicles may encounter vehicle stability problem under extreme conditions. To tackle this challenge, this paper proposes a control strategy considering the path tracking and handling stability comprehensively. An NMPC controller is designed for path tracking, in which the tire vertical load transfer is taken into consideration to reduce model mismatch. Orthogonal collocation discretization method is adopted to enhance the solving of NMPC problem. In the stability controller, active rear-wheel steering is applied to avoid the excessive increase of sideslip angle of vehicle and ensure handling stability. The proposed controller is verified in a high-speed and low-adhesion road scene by CarSim/Simulink co-simulation. The results demonstrate the effectiveness of the proposed controller in enhancing path tracking performance and maintaining handling stability even under extreme conditions.
Spam Detection Using Machine Learning Algorithms and AdaBoost Technique

- E-mail has become an integral part of every individual's life. All sorts of communication formal and informal are carried out through this channel. As it is relatively cheaper and reliable, it has recently become a target for marketing agencies and hackers. A large number of unsolicited emails, called spam, containing either malicious content or unwanted advertisements, are sent to the individuals on a daily basis. This makes it difficult for e-mail service providers to segregate spam from non-spam (ham) e-mails. Consequently, important e-mails are often lost in spam, causing losses consumers and wasting valuable resources. The trust of consumers is compromised when their private information is accessed. This is where the need for improved performance of machine learning classifiers is felt. The aim of this paper is to classify spam emails by using supervised machine learning classifiers with and without Adaptive Boosting, and to conduct a deep analysis by using performance metrics. A significant improvement in the accuracy of SVM and Decision Tree has been observed.
Plant Disease Detection Using Deep Learning and Machine Learning Algorithms

Understanding the economic sector of India's agricultural productivity relies heavily on the ability to identify diseases in plants. Although numerous diseases pose a significant threat to the food industry, prompt detection remains challenging due to a lack of infrastructure. Categorizing the diseases in plants at an early stage can help in the proper growth and development of the species. The increased use of computers has made deep learning possible and convenient. This research paper explores the use of deep learning algorithms, specifically ResNet model for the automated classification of plant diseases found in the images and many more have been explored to detect the diseases in plants using a dataset of plant leaves with diseases. The goal is to check how well the algorithm works and figure out which one is best for classifying plant diseases. This paper proposes a model which includes image filters and deep learning for plant diseases and it is found that ResNet is a powerful architecture for plant disease classification and achieves 87.5 % accuracy. Approaching the methods of deep learning on images of leaves shows a clear path towards the disease in the plant on a global level.
Automatic Number Plate Recognition System Using Deep Learning YOLOv5 Algorithm

A computer vision system called Automatic Number Plate Recognition (ANPR) automates the process of reading licence plates from moving objects. Because to its capacity for precise and effective object recognition, deep learning-based object detection algorithms like YOLO (You Only Look Once) have recently demonstrated promising results in ANPR systems. This study suggests a YOLOv5 object detection model-based architecture for an ANPR system. The suggested architecture consists of a preprocessing stage for image enhancement, a YOLOv5 detector for licence plate detection, post-processing methods like NMS to fine-tune the detection results, an Optical Character Recognition (OCR) algorithm to extract characters from the licence plate, and a decoding stage to ascertain the actual plate number. Real-world datasets are used to assess the proposed design. and displays encouraging accuracy and productivity outcomes. The potential uses for the proposed ANPR system include traffic monitoring, parking management, and law enforcement.
A Low-quality Data User Identification Method Based on Blockchain

Federated learning completes the training of the global model only by exchanging model parameters or intermediate results under the condition that the user data is not stored locally, which not only ensures the privacy of user data, but also maximizes the value of the data, becoming the first choice for machine learning technology in distributed scenarios. But federated learning itself is data sensitive, and low-quality data will affect model performance. This paper proposes an efficient identification and repair algorithm based on training logs and influence functions for erroneous data. By analyzing the parameters of training logs to identify low-quality users, and then repairing the erroneous data of low-quality users based on the influence function, the convergence speed of the model is accelerated. Aiming at the problems that the existing data quality assessment schemes use third-party platforms or the assessment process is dominated by servers, resulting in opaque assessment processes and unverifiable assessment results, this paper proposes a blockchain-based data quality assessment scheme with verifiable results. Based on the decentralized, non-tamperable, and traceable characteristics of the blockchain, the solution ensures that there is no third party in the data quality assessment process and the calculation is safe and reliable.
Managing Smart Urban Transportation with the integration of Big Data Analytic Platform

Urban traffic management is a major challenge for cities worldwide, but with the help of technology, such as big data analytics and intelligent transport systems, cities are working to improve their situations. An integrated big data analytics platform has the potential to change transportation networks for the better, providing real-time insights that can be used for decisions. By collecting and analyzing real-time data from various sources, such as traffic sensors and GPS, we can predict traffic flow and create plans more efficiently. This paper will explore how an integrated big data analytics platform can be used to improve urban transportation management. It discusses some of the challenges that urban transportation systems face and provides insights from government officials and industry experts. The importance of this paper reviews fundamental concepts such as big data analytics and smart cities so that you have a better understanding of what this technology can do. Afterwards, it identifies opportunities for improving urban transportation networks using big data analytics.
Template Reinforcement Learning for Automated Driving with Scenario Switching Inference Labeling

Decision making in dense traffic uncertainty scenarios is challenging for autonomous vehicles. Compare with costly manually designed driving policy, deep Reinforcement Learning (DRL) offers considerable potential by automatically learning and improving strategies. However, automated driving systems that only rely on the physical state space can hardly perform well in complex scenarios. In this paper, a framework is present to implement model-free reinforcement learning in complex uncertainty scenarios. Supervised learning is used to infer the latent states of other drivers through subtle cues and combine it with reinforcement learning. The performance of traditional reinforcement learning algorithms is enhanced by considering the surrounding vehicle types as well as the latent states of other drivers. Some challenging tasks are demonstrated, such as merging and overtaking in dense traffic in the Simulation of Urban Mobility (SUMO) simulation platform. The results show that our approach solves the tasks well and significantly better than the baseline.
Motion Planning and Control of Wheel-legged Vehicle Compound Jumping

This paper carries out related research work about the jumping motion control of the wheel-legged vehicle. Firstly, based on the Lagrangian method, the single-leg flying-phase and landing-phase dynamics models of the wheel-legged vehicle are established, and a basic wheel-legged compound jumping motion is proposed. The jumping process is divided into the take-off stage, the flying stage and the landing stage, the trajectory planning of the center of mass in each stage is completed. Secondly, a jump control strategy with attitude and joint control as the core is designed, and the attitude control module converts the center of mass attitude adjustment problem into joint trajectory tracking problem, the joint control adopts the inverse dynamics feedback control method. Finally, the working condition indicators are selected, and the wheel-legged vehicle jumping simulation work is completed based on Simulink, which verifies the rationality of jumping trajectory planning and the effectiveness of jumping motion control strategy.
An Approach for Diabetic Retinopathy Using GNN

The early diagnosis and prevention of blindness can both benefit from finding diabetic retinopathy. For this task, Graph Neural Networks (GNNs) can be applied. Making a graph representation of the retina image is the first step in utilizing GNN to detect diabetic retinopathy. To do this, one strategy is to display the picture as a graph in which the pixels serve as the nodes and the connections between them serve as the edges. GNNs can be used to learn the characteristics of the nodes and edges after the graph has been built. A huge collection of retinal pictures with labels that denote the presence or absence of diabetic retinopathy can then be used to train the GNN. The GNN will learn things throughout the training procedure.
Multimodal Relationship-aware Attention Network for Fake News Detection

Given the problems that multimodal fake news detection methods were difficult to simultaneously learn the intra-modality and cross-modal dependencies between images and texts, lack of mining for complete hierarchical semantics of text, resulting in ineffective utilization of multimodal information and affecting detection performance, a multimodal relationship-aware attention network for fake news detection was proposed. First, the proposed multi-level encoding network was used to extract hierarchical semantic feature representations of text, and the visual feature extractor VGG19 is used to capture image features. Next, the extracted features are fed into the relationship-aware attention network to combine the image and text representations. This is achieved by learning the similarity between segments of information within each modality and taking into account their cross-modal similarity, resulting in the generation of fusion features. Finally, the fake news detector is utilized to identify fake news. The experiments on two common real datasets show that the proposed method outperforms the benchmark models and has good detection performance.