Intelligent vehicle classification system based on deep learning and multisensor fusion

In order to solve the problems of difficulty and long times to pick up cars in complex traffic scenes, this paper proposes an intelligent networked car-hailing system in complex scenes based on multi sensor fusion and Ultra-Wide-Band (UWB) technology. UWB positioning technology is adopted in the system, and the positioning data is optimized by the untraceable Kalman filter algorithm. Based on the environment perception technology of multi sensor fusion, such as machine vision and laser radar technology, an anti-collision warning algorithm was proposed in the process of car-hailing, which improved the safety factor of car-hailing. When the owner enters the parking lot, the intelligent vehicle can automatically locate the owner’s position and drive to the owner without human intervention, which provides a new idea for the development of intelligent networked vehicles and effectively improves the navigation accuracy and intelligence of intelligent vehicles.

Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.

Objective To evaluate the application value of an intelligent fundus assisted diagnosis system for detecting retinopathy of prematurity (ROP) based on deep learning. Methods A total of 38 895 fundus images for premature infants screening were collected from Renmin Hospital of Wuhan University Eye Center and were labeled by 10 licensed ophthalmologists.A deep learning network model was established to acquire automatic classification of disease stages and plus disease.The accuracy, sensitivity and specificity of the algorithm were calculated to evaluate the performance of the artificial intelligence system for ROP automatic diagnosis.This study protocol was approved by Ethic Committee of Renmin Hospital of Wuhan University (No.WDRY2019-K032). Written informed consent was obtained from the guardians of the children before entering the study cohort. Results The intelligent system achieved an accuracy of 0.931.Specifically, the accuracies in detecting demarcation line (stage Ⅰ) was 0.876, ridge (stage Ⅱ) was 0.942, ridge with extra retinal fibrovascular (stage Ⅲ) was 0.968, subtotal retinal detachment (stage Ⅳ) was 0.998, total retinal detachment (stage Ⅴ) was 0.999, vascular tortuosity and dilatation (plus disease) was 0.896, optic disc was 0.954, macular was 0.781, and laser scars were 0.974, respectively. Conclusions Deep learning algorithm can detect the stages and plus disease of ROP with excellent accuracy, and it provides the feasibility of applying the algorithm for ROP automated screening in clinical. Key words: Artificial intelligence; Deep learning; Retinopathy of prematurity; Assisted diagnosis

The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In the era of smart cities, smart tourism has emerged as a key innovation, enhancing travel experiences through digital technologies. Deep learning (DL) in particular has contributed to this field by analyzing complex patterns in user data. However, recommender systems using static deep learning models struggle to adapt to the rapidly changing preferences that define the tourism sector. Deep reinforcement learning (DRL)is a powerful solution, capable of evolving in response to user interactions. This study presents a deep Q-Learning model that outperforms traditional DL approaches in predicting tourist preferences, as evidenced by improved MSE and RMSE measures. Our work highlights the adaptability of DRL to provide more accurate and personalized tourism recommendations, representing a significant advance in smart tourism technologies. By incorporating sentiment analysis, the model gains a nuanced understanding of user preferences, improving the accuracy of its predictions. The results highlight the potential of DRL to transcend the limitations of conventional DL models, and argue in favor of a new wave of intelligent recommendation systems in the field of tourism.

Particle accelerators play a crucial role in scientific research and industrial applications, and enhancing their reliability, ensuring stable operation, and reducing downtime caused by faults are essential for achieving research goals. This paper introduces a novel particle accelerator fault diagnosis method based on deep learning and multi-sensor feature fusion. The approach employs one-dimensional convolution to extract signals from multiple sensors and achieves comprehensive feature fusion of multi-sensor data, effectively overcoming the limitations of individual sensors. It combines 1d convolutional neural networks (1D-CNN) and long short-term memory networks (LSTM) to enhance spatiotemporal feature extraction. This approach efficiently extracts features from multiple sensors while concurrently reducing data length and training time. The study uses real signals from particle accelerators and aims to achieve early detection of faults by identifying abnormal signals preceding accelerator malfunctions. Comparative analysis with other machine learning models and the use of multiple evaluation metrics validate the effectiveness and generalizability of this method. The research provides a better performance fault diagnosis model with significant implications for reducing downtime, troubleshooting faults, and improving the reliability of particle accelerators.

The integration of multi-sensor imaging and deep learning techniques has emerged as a pivotal innovation in advancing structural mechanics, particularly in the prediction of stress and strain distributions. This study falls within the thematic scope of multi-sensor imaging and fusion methods, emphasizing their crucial role in assessing material behavior under complex conditions. Traditional methodologies, such as finite element methods and classical constitutive models, often fall short in capturing the intricacies of heterogeneous materials and nonlinear stress–strain relationships. These limitations necessitate a more robust computational framework capable of addressing material variability and computational efficiency challenges. To this end, we propose the Stress–Strain Adaptive Predictive Model (SSAPM), which synergizes mechanistic modeling with data-driven corrections. Leveraging hybrid representations, adaptive optimization strategies, and modular architectures, SSAPM ensures precision by embedding physics-informed constraints and reduced-order modeling for computational scalability. Experimental validations underscore the model’s capability to generalize across diverse structural scenarios, outperforming conventional approaches in accuracy and efficiency. This work establishes a transformative pathway for incorporating multi-sensor data fusion into structural analysis, advancing the predictive power and applicability of stress–strain models.

Bolt loosening is a significant concern in structural health monitoring (SHM), and the nonlinear wave modulation (NWM) technique shows promise for its detection. However, temperature variations affect piezoelectric sensors, complicating detection. This study examines thermal stress effects on NWM-based damage detection by simulating a beam with boundary loosening under thermal stress. A temperature compensation method was developed to mitigate these effects. A multibolt structure was also analyzed using a hybrid deep learning model combining convolutional neural networks (CNNs) and Dezert-Smarandache Theory (DSmT) for multisensor data fusion. Results show that thermal stress significantly reduces classification accuracy. At 60 °C, the Damage Index dropped to zero, and CNN classification accuracy fell from 87.5% (at 25 °C) to 27.08%. Applying the compensation algorithm restored CNN accuracy to 87.27%. Statistical tests confirmed the compensated results at 60 °C were comparable to those at 25 °C. The DSmT-based fusion model achieved 98.92% accuracy on average, and even at 60 °C, maintained 98.80% accuracy with compensation for a multibolted plate. It combines the probabilistic outputs of multiple CNNs using the PCR6 rule, which redistributes conflicting evidence to improve decision reliability. This fusion strategy effectively handles uncertainty across sensors and significantly enhances classification robustness under temperature variations. In conclusion, thermal stress adversely impacts SHM damage detection, but the proposed compensation method effectively restores performance. Moreover, integrating multisensor fusion with DSmT greatly enhances accuracy, offering a robust solution for SHM in varying thermal environments.

Fire hazards pose a significant risk to human lives, infrastructure, and the environment, necessitating the development of efficient fire detection and localization systems. Traditional methods, including smoke and heat sensors, suffer from high false alarm rates and delayed response times. In recent years, deep learning has emerged as a transformative approach, leveraging computer vision to enhance accuracy in fire detection and localization. This paper provides a comprehensive survey of deep learning techniques employed for fire detection, including Convolutional Neural Networks (CNNs), object detection models such as YOLO and Faster R-CNN, and hybrid approaches integrating multimodal data. The study further explores publicly available fire datasets, preprocessing techniques, and performance evaluation metrics. Additionally, key challenges such as real-time processing constraints, environmental variability, and model generalization are discussed. The paper concludes with an outlook on future advancements, including lightweight AI models, multi-sensor fusion, and synthetic dataset generation for robust fire detection systems.

Due to society’s practical demands, the legal profession’s growth within the context of the new liberal arts is necessary to improve teaching mechanisms and the comprehensive development of interdisciplinarity. In this paper, from the two major dilemmas of the specificity of legal instruments and the correlation between sentencing tasks, to examine the relationship between sentencing prediction, charge prediction, and law suggestion of legal instruments, respectively, the Bi-LSTM-attention model, end-to-end memory network model, and CNN-GRU network model are employed, to build an intelligent legal learning system. The outcomes demonstrate that, compared to the conventional machine learning algorithm, the intelligent legal learning system based on deep learning can increase prediction performance by 5.2% to 6%, global accuracy can reach 93.3%, and accuracy of legal documents processing by 7.9%. The deep learning-based intelligent legal learning system suggested in this study can assist law students in completing legal paperwork duties and increase their learning effectiveness.

Automatic detonator code recognition via deep neural network

This paper focuses on the systematic research of intelligent control and multi-sensor fusion technology for maze escape robots, aiming to solve the problems of inaccurate environmental perception, complex dynamic path planning, and easy navigation without GPS positioning. In terms of intelligent control systems, a hierarchical architecture of "master-slave collaboration" is constructed, with MCU responsible for high-frequency real-time tasks at the bottom layer, Raspberry Pi responsible for data fusion and path planning at the middle layer, AI modules enhancing the adaptability to complex scenes, and comparing the applicable scenarios and performance differences between rule-based control (such as wall following method) and AI control (such as deep reinforcement learning) algorithms. In terms of multi-sensor fusion technology, the complementary functions of laser radar, ultrasonic, and infrared sensors are clearly defined, and data fusion is achieved using algorithms such as Kalman filtering and random forest. Combined with error correction technology, the ranging accuracy is improved to within 1cm. Experiments have shown that multi-sensor fusion schemes are more accurate than single sensor positioning, and the enhanced Q-learning algorithm improves convergence speed by 40%. The Q-learning and A * hybrid algorithm reduces escape time by 20% -40%. Finally, summarise the technological progress and propose future research directions such as AI control optimisation and sensor fusion upgrade, providing theoretical and technical references for the multi-scenario application of maze escape robots.

The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system

An integration of deep learning and classical filtering techniques has been developed for achieving an accurate and robust positioning system. Despite significant technological advances, positioning systems often face fundamental challenges such as signal obstruction, multipath interference, and error drift. Although 3D map matching techniques using LiDAR sensors can mitigate these challenges, their practical implementation is limited by high costs, computational burdens, and dependency on pre-modeled environments. In response, recent research has increasingly focused on enhancing positioning systems through deep learning–based sensor fusion and adaptive filtering methods, emphasizing improvements in accuracy and operational robustness. In this paper, a seamless hierarchical context-aware intelligent positioning system (SH-CAIPS) is proposed by integrating sensor fusion, two-stage outlier mitigation gates, and deep reinforcement learning (DRL)-based noise scaling with an innovation-based adaptive Kalman filter (IAKF). As a result, the proposed positioning system can enhance robustness by adaptively adjusting measurement noise and dynamically updating the position based on the confidence score between model predictions and sensor measurements. From simulation results, it is confirmed that the positioning performances can be significantly improved based on the proposed SH-CAIPS compared with conventional positioning systems.

Nowadays, science and technology has rapidly increasing due to the specific requirements of education. The main aim of this technique is to enhance the physical education activities and helps sports man to finest teaching facilities of these activities. Moreover, the body changes with sports activities based on Deep Learning (DL) frameworks and classifier technology are examined to promote the practical application. Therefore, this paper Deep Reinforcement Learning based Chimp optimization (DRLbCO) algorithm was developed to improve the teaching efficiency of physical education system through the intelligent computer aided assessment models. Also, this model improving the performance, enhancing coaching effectiveness, and gaining competitive advantages in sports. Consequently, the proposed system is based on DL based image detection model DRL, which can analyse and provides proper actions in teaching students and enhance the ability of motion analysis in sports video training quality. Moreover, the DL model is integrated with optimization algorithm to optimize the performance. This could involve reinforcement learning, and other classifiers such as Support Vector Machine (SVM). Based on this provides feedback to students and teachers during physical education classes. The developed model is significantly enhances the physical education teaching student action in virtual sports instruction. Overall intention of the video assessment system of the sports training performance in analyzed.