Development Of Intelligent System Research Articles

TinySpiking: a lightweight and efficient python framework for unsupervised learning spiking neural networks

Abstract Neural computation frameworks are essential for advancing computational neuroscience and artificial intelligence, offering a robust platform for simulating intricate brain-like processes and fostering the growth of intelligent systems. This study presents TinySpiking, a novel, lightweight, and energy-efficient Python framework designed for the simulation and learning of Spiking Neural Networks (SNNs). Unlike traditional frameworks, TinySpiking does not depend on third-party libraries, thereby reducing computational overhead and energy consumption. The framework's innovation is that it implements unsupervised learning through Spike-Time Dependent Plasticity (STDP), a biologically inspired learning rule that adjusts synaptic weights based on the precise timing of neuronal spikes. This mechanism is crucial for constructing complex neural architectures and effectively processing spatio-temporal data, which is vital for the dynamic and intricate demands of real-world data analysis. Demonstrating the practical utility of TinySpiking, we applied it to image reconstruction tasks using the MNIST and landmark datasets. The results not only validate the network's ability to autonomously identify and enhance key visual features without external supervision but also highlight its efficiency in learning from data, mirroring the adaptability of biological neural systems. In conclusion, TinySpiking's innovative, lightweight design and its proven effectiveness in unsupervised learning tasks make it a standout computational tool for the fields of computational neuroscience and machine learning. Its low time consumption, biological plausibility, and independence from third-party libraries position it as a compelling platform for future research and applications, promising to drive advancements in unsupervised learning and intelligent system development.

Design and Implementation of Intelligent Traffic Signal Light Control System based on LoRa Technology

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs

With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments.

Programmable assemblies of photothermal anisotropic micromotors for multimodal motion.

Light-driven micromotors with multiple motion modes offer significantly greater application potential than single-mode micromotors. However, achieving such versatility often requires complex structural designs and precise light focusing on specific micromotor regions, presenting challenges for dynamic operations and microscale precisions. This study introduces programmable assemblies of anisotropic micromotors driven by the photothermal Marangoni effect, produced in bulk via microfluidic technology. Under full-area near-infrared (NIR) irradiation, the micromotor exhibits multiple motion modes, including translation and revolution, while micromotor assemblies display additional rotational motion. Self-assembly of these micromotors is highly controllable and programmable, enabling easy customization of assembled structures to achieve desired motion modes. These features are expected to advance the development of various intelligent self-propelling systems, using multimodal individual micromotors as foundational building blocks.

Research on Road Traffic Sign Detection and Recognition Technology

With the development of intelligent transportation systems (ITS) and autonomous driving technologies, road traffic sign detection and recognition have become increasingly important in the fields of traffic safety, road management, and vehicle automation. Road traffic signs not only provide crucial information for drivers but also offer essential perception data for autonomous vehicles within intelligent transportation systems. This paper systematically reviews the current research progress in road traffic sign detection and recognition technology, analyzing the classification, visual features of signs, and the challenges faced in complex traffic environments. By comparing traditional image processing methods with advanced deep learning-based technologies, this paper discusses the advantages and disadvantages of sign detection and recognition in different application scenarios, and explores future technological development trends. Finally, the paper provides an outlook on the research directions in this field, aiming to provide theoretical support for future technological improvements and application promotion.

Design and Simulation of an Automatic Alignment System for Ship Multi-Support Shafting

The current shafting alignment process primarily relies on manual adjustments, resulting in low efficiency and limited precision. This study designs an automatic alignment system for multi-support shafting to improve alignment efficiency and accuracy, advancing the development of intelligent systems. The overall design of the automatic alignment system is proposed, including the establishment of shafting and actuator models. The alignment performance of two control strategies under different shafting deviations is validated through simulations. The results indicate that both the full-state feedback control strategy and the model predictive control strategy can regulate bearing load errors to within 1%. Compared to manual alignment, both strategies demonstrate significantly higher efficiency. The proposed automatic alignment system is feasible and lays a foundation for the development of high-precision shafting alignment systems.

An Efficient Cluster-Based Mutual Authentication and Key Update Protocol for Secure Internet of Vehicles in 5G Sensor Networks.

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system's resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments.

Navigating China’s regulatory approach to generative artificial intelligence and large language models

Abstract The rapid development of generative artificial intelligence (AI) systems, particularly those fuelled by increasingly advanced large language models, has raised concerns of their potential risks among policymakers globally. In July 2023, Chinese regulators enacted the Interim Measures for the Management of Generative AI Services (“the Measures”). The Measures aim to mitigate various risks associated with public-facing generative AI services, particularly those concerning information content safety and security. China’s approach to regulating AI to date has sought to address the risks associated with rapidly advancing AI technologies while fostering innovation and development. Tensions between these policy objectives are reflected in the provisions of the Measures. As Beijing moves towards establishing a comprehensive legal framework for AI governance, there will be growing interest in how China’s approach may influence AI governance and regulation at a global level.

Development of intelligent shape meter system

Development of intelligent shape meter system

Development of Artificial Intelligence Systems for Chronic Kidney Disease

Development of Artificial Intelligence Systems for Chronic Kidney Disease

Medicine Recommendation System Using ML

Abstract: The growing demand for personalized healthcare solutions has led to the development of intelligent systems that assist in medical decision-making. This project focuses on creating a Medicine Recommendation System that utilizes machine learning techniques to recommend suitable medicines based on user inputs such as symptoms or medical conditions. The system leverages a well-structured medical dataset to train a machine learning model capable of accurately predicting medicine recommendations.By analyzing user-provided symptoms, the system identifies potential diagnoses and suggests relevant medicines, ensuring improved healthcare accessibility and support. The frontend of the system is designed to be interactive and user-friendly, utilizing HTML, CSS, and jQuery, while the backend integrates a robust Python-based framework, such as Flask or Django, to process user inputs and interact with the machine learning model.The implementation incorporates essential features like data preprocessing, symptom encoding, and model optimization to enhance the accuracy of predictions. Additionally, the system includes a feedback mechanism for continuous improvement and warns users about potential medicine interactions to ensure safety.This project has the potential to revolutionize patient care by offering real-time, data-driven medicine recommendations, thereby empowering users to make informed healthcare decisions. Future developments may include advanced personalization based on patient history and natural language processing to understand user inputs more effectively.

Анализ применения технологий Индустрии 4.0 в интеллектуальных транспортных системах

The intensive development of digital technologies, their active implementation in the field of transport and logistics necessitates the study of their impact on the transport system, in particular on the level of its intellectualization. This will allow us to determine which technologies of Industry 4.0 need to be implemented first of all in order to achieve the best parameters for the development of intelligent transport systems. In this regard, the paper analyzes various tech-nologies of Industry 4.0, identifies possible ways of their application to solve problems in the context of the functioning of intelligent transport systems and their elements. Based on the analysis, possible parameters of the impact of technologies on the development of intelligent transport systems are identified, which include physical and environmental safety of transport, information security, speed and accuracy of decision-making, physical safety of transport, resource efficiency, convenience and comfort, the presence of an entertainment component, etc. Based on the compari-son of the identified parameters with specific types of digital technologies, it is determined which technologies have a greater impact on the intellectualization of transport systems. These technologies include the Internet of Things, augmented reality AR and cloud solutions. The parameters that are more susceptible to the effects of digital technologies are also determined: the safety and efficiency of transport systems. The results obtained are of practical importance for the development of strategic solutions in the implementation of the concept of Industry 4.0 in the field of transport and logistics.

Intelligent Gesture Recognition Gloves for Real-Time Monitoring in Wireless Human-Computer Interaction.

Flexible sensors mimic the sensing ability of human skin, and have unique flexibility and adaptability, allowing users to interact with intelligent systems in a more natural and intimate way. To overcome the issues of low sensitivity and limited operating range of flexible strain sensors, this study presents a highly innovative preparation method to develop a conductive elastomeric sensor with a cracked thin film by combining polydimethylsiloxane (PDMS) with multiwalled carbon nanotubes (MCNT). This novel design significantly increases both the sensitivity and operating range of the sensor (strain range 0-50%; the maximum tensile sensitivity of this sensor reaches 4.97), marking a breakthrough in flexible sensor technology. The sensor can also be fabricated easily and inexpensively which is suitable for large-scale production, indicating its significant application potential. Combined with a dedicated hardware and software system, the sensors enable real-time monitoring of the finger status and accurate gesture recognition. Furthermore, machine-learning algorithms were used to identify American Sign Language gestures with an accuracy of up to 97%. This innovative human-machine interface provides important support for the future development of intelligent interactive systems, and is of great significance in promoting the progress of advanced human-computer interaction technologies.

Deep deterministic policy gradients with a self-adaptive reward mechanism for image retrieval

Traditional image retrieval methods often face challenges in adapting to varying user preferences and dynamic datasets. To address these limitations, this research introduces a novel image retrieval framework utilizing deep deterministic policy gradients (DDPG) augmented with a self-adaptive reward mechanism (SARM). The DDPG-SARM framework dynamically adjusts rewards based on user feedback and retrieval context, enhancing the learning efficiency and retrieval accuracy of the agent. Key innovations include dynamic reward adjustment based on user feedback, context-aware reward structuring that considers the specific characteristics of each retrieval task, and an adaptive learning rate strategy to ensure robust and efficient model convergence. Extensive experimentation with the three distinct datasets demonstrates that the proposed framework significantly outperforms traditional methods, achieving the highest retrieval accuracy having 3.38%, 5.26%, and 0.21% improvement overall as compared to the mainstream models over DermaMNIST, PneumoniaMNIST, and OrganMNIST datasets, respectively. The findings contribute to the advancement of reinforcement learning applications in image retrieval, providing a user-centric solution adaptable to various dynamic environments. The proposed method also offers a promising direction for future developments in intelligent image retrieval systems.

Dynamic Multi-Energy Optimization for Unit Commitment Integrating PEVs and Renewable Energy: A DO3LSO Algorithm

The global energy crisis and the pursuit of carbon neutrality have introduced significant challenges to the optimal dispatch of power systems. Despite advancements in optimization techniques, existing methods often struggle to efficiently handle the uncertainties introduced by renewable energy sources and the dynamic behavior of plug-in electric vehicles (PEVs). This study presents a multi-energy collaborative optimization approach based on a dynamic opposite level-based learning optimization swarm algorithm (DO3LSO). The methodology explores the impact of integrating PEVs and renewable energy sources, including photovoltaic and wind power, on unit commitment (UC) problems. By incorporating the bidirectional charging and discharging capabilities of PEVs and addressing the volatility of renewable energy, the proposed method demonstrates the ability to reduce reliance on traditional fossil fuel power generation, decrease carbon emissions, stabilize power output, and achieve a 7.01% reduction in costs. Comparative analysis with other optimization algorithms highlights the effectiveness of DO3LSO in achieving rapid convergence and precise optimization through hierarchical learning and dynamic opposite strategies, showcasing superior adaptability in complex load scenarios. The findings underscore the importance of multi-energy collaborative optimization as a pivotal solution for addressing the energy crisis, facilitating low-carbon transitions, and providing essential support for the development of intelligent and sustainable power systems.

A “Pipeline”-Based Approach for Automated Construction of Geoscience Knowledge Graphs

With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral deposits, and relationships. Efficient management of these Earth Science data is crucial for the development of digital earth systems, rational planning of resource industries, and resource security. By representing entities, relationships, and attributes through graph structures, knowledge graphs capture and present concepts and facts about the real world, facilitating efficient data management. However, due to the highly specialized and complex nature of Earth Science data and disciplinary differences, the methods used to construct general-purpose knowledge graphs cannot be directly applied to building knowledge graphs in the field of geological science. Therefore, this paper summarizes a “pipeline” approach to constructing an Earth Science knowledge graph in order to clarify the complete construction process and reduce barriers between data and technology. This approach divides the construction of the Earth Science knowledge graph into two parts and designs functional modules under each part to specify the construction process of the knowledge graph. In addition to proposing this approach, a knowledge graph of iron ore deposits is automatically constructed by integrating geographic and geological data related to iron ore deposits using deep learning techniques. The systematic approach presented in this paper reduces the threshold for constructing geological science knowledge graphs, provides methodological support for specific disciplines or research objects in Earth Science, and also lays the foundation for the construction of large-scale Earth Science knowledge graphs that combine crowdsourcing and expert decision-making, as well as the development of intelligent question-answering systems and intelligent decision-making systems covering the entire field of Earth Science.

Analysis of the Transformation of the New Energy Vehicle Industry under the Background of the Digital Economy

Today, the digital economy has become the main theme. The digital economy includes digital industrialization, industrial digitization, digital governance, and data value. In this context, data has become the kinetic energy of factors for economic development. In recent years, China's new energy vehicle industry has risen rapidly. In the 2024 "Global Top 100 Auto Supply Chain", three Chinese enterprises have entered the top 20: CATL, Weichai Group, and Huayu Automotive. This represents the profound accumulation and innovation ability of Chinese component enterprises and shows the indispensable role and status of Chinese enterprises in the global auto supply chain to the world. The development of the new energy vehicle industry has promoted the reconstruction of the auto industry chain, including the production and manufacturing of core components such as batteries, motors, and electronic controls, as well as the development of fields such as intelligent driving and intelligent interconnection. This transformation has not only promoted technological innovation and business model innovation within the industry but also driven the construction and improvement of related infrastructure, such as the construction of charging piles and battery swapping stations, as well as the development of intelligent transportation systems, providing convenience for the popularization and use of new energy vehicles.

High-Precision Traffic Sign Detection and Recognition Using an Enhanced YOLOv5

High-precision traffic sign detection plays an important role in enhancing road traffic safety, ensuring traffic smoothness, supporting the development of intelligent transport systems and promoting the standardization and normalization of traffic facilities. To overcome the limitations of traditional methods, this study proposes an enhanced YOLOv5 algorithm for complex road environments. First, the K-Means clustering algorithm is used to cluster and analyze the boundary boxes of traffic signs in the training dataset, obtaining anchor box sizes that are closer to the distribution of the dataset to improve detection accuracy. Then, a genetic algorithm was used to further optimize the initial anchor box, and a global search strategy was used to find the optimal anchor box configuration, further improving detection performance. In terms of model structure, a bidirectional feature pyramid network (Bi-FPN) is introduced, which effectively utilizes multi-scale feature information and enhances the adaptability of the model to traffic signs of different sizes through top-down and bottom-up feature fusion paths, as well as cross-scale connections. In addition, a global attention mechanism (GAM) was introduced to recalibrate the feature maps through channel attention and spatial attention dimensions, improving the robustness and detection accuracy of the model for complex environments. Finally, a loss function called Focal-EIoU was used to solve the problems of class imbalance and sample imbalance, which improved the stability and performance of the object detection model. Experiments on a Chinese traffic sign dataset demonstrate significant improvements, with an increase in mAP by 10.03%, precision by 4.7%, recall by 2.6% and F1-score by 3.48%, respectively. The results prove the validity of the proposed traffic sign recognition method, especially in complex road environments. This study provides new ideas and methods for the field of traffic sign detection, which has important theoretical significance and application value.

An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN.

Book localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional object detection algorithms. To address these challenges, this study proposes an enhanced oriented R-CNN algorithm for book spine detection. First, we replace the standard 3 × 3 convolutions in ResNet50's residual blocks with deformable convolutions to enhance the network's capacity for modeling the geometric deformations of book spines. Additionally, the PAFPN (Path Aggregation Feature Pyramid Network) was integrated into the neck structure to enhance multi-scale feature fusion. To further optimize the anchor box design, we introduce an adaptive initial cluster center selection method for K-median clustering. This allows for a more accurate computation of anchor box aspect ratios that are better aligned with the book spine dataset, enhancing the model's training performance. We conducted comparison experiments between the proposed model and other state-of-the-art models on the book spine dataset, and the results demonstrate that the proposed approach reaches an mAP of 90.22%, which outperforms the baseline algorithm by 4.47 percentage points. Our method significantly improves detection accuracy, making it highly effective for identifying book spines in real-world library environments.

Optimization and Safety Research on Autonomous Vehicles Based on Blockchain, Quantum Computing, and Artificial Intelligence

The field of autonomous vehicles is undergoing rapid development, with research focused on enhancing safety, efficiency, and intelligence to drive the full realization and transformation of intelligent transportation systems. This paper explores the potential applications and challenges of blockchain technology, quantum computing, and artificial intelligence in the realm of autonomous vehicles. The study finds that integrating blockchain with autonomous vehicles significantly improves data encryption security through quantum hash algorithms. However, the advancement of quantum computers still faces challenges in technological maturity, stability, and cost. In the operating mechanisms of autonomous vehicles, quantum reinforcement learning demonstrates unique advantages in path optimization, while quantum annealing and quantum optimization algorithms improve decision-making efficiency and precision. This research highlights the innovative opportunities brought by blockchain, quantum computing, and artificial intelligence to the field of autonomous vehicles, while also addressing the challenges of technology integration, providing valuable insights for the development of intelligent transportation systems.