Search Results (38)

Onboard Vehicle-to-Everything (V2X) communication technology is being widely implemented in domains such as intelligent driving, vehicle–road cooperation, and smart transportation. Nevertheless, time synchronization in V2X systems suffers from instability due to the random loss of Global Navigation Satellite System (GNSS) Pulse-Per-Second (PPS) signals. To address this challenge, a model-driven local clock correction approach is proposed. Leveraging probability theory and mathematical statistics, models for the randomly lost GNSS PPS signals are developed. High-order polynomials are used to model local clocks. An optimized Kalman-filter-based time compensation algorithm is then devised to compensate for time errors during PPS signal loss. A software-based task-scheduling solution for precision-time synchronization is developed. An experimental testbed was then built to measure both terminal clocks and PPS signals. The proposed algorithm was integrated into the V2X terminals. Results show that the full-value PPS signals follow an exponential distribution. The onboard clock correction algorithm operates stably across three V2X terminals and accurately predicts clock variations. Furthermore, the virtual clocks achieve an average absolute error of 1.1 μs and a standard deviation of 16 μs, meeting the time synchronization requirements for V2X communication in intelligent connected vehicles. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). First, the topology of the LVDG is represented as a graph structure using measurement data collected from intelligent terminals, with a feature matrix encoding the basic information of each entity. Secondly, the meta-path form of the heterogeneous graph is designed according to the connection characteristics of the LVDG, and the walking sequence is enhanced using a heterogeneous skip-gram model to obtain an embedded representation of the structural characteristics of each node. Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. Finally, case studies on five urban LVDGs in the Wuhan region are conducted to validate the effectiveness and practicality of the proposed F-GAT model. Full article

Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics of CNN algorithms, it is difficult to implement them at edge terminals with limited memory and computing resources. This paper uses a FPGA (Field-Programmable Gate Array) to accelerate CNNs to realize forest fire recognition in the field environment and solves the problem of the difficulty in giving consideration to the accuracy and speed of a forest fire recognition network in the implementation of edge terminal equipment. First, a simple seven-layer lightweight network, LightFireNet, is designed. The network is compressed using a knowledge distillation method and the classical network ResNet50 is used as the teacher network to supervise the learning of LightFireNet so that its accuracy rate reaches 97.60%. Compared with ResNet50, the scale of LightFireNet is significantly reduced. Its model parameter amount is 24 K and its calculation amount is 9.11 M, which are 0.1% and 1.2% of ResNet50, respectively. Secondly, the hardware acceleration circuit of LightFireNet is designed and implemented based on the FPGA development board ZYNQ Z7-Lite 7020. In order to further compress the network and speed up the forest fire recognition circuit, the following three methods are used to optimize the circuit: (1) the network convolution layer adopts a depthwise separable convolution structure; (2) the BN (batch normalization) layer is fused with the upper layer (or full connection layer); (3) half float or ap_fixed<16,6>-type data is used to express feature data and model parameters. After the circuit function is realized, the LightFireNet terminal circuit is obtained through the circuit parallel optimization method of loop tiling, ping-pong operation, and multi-channel data transmission. Finally, it is verified on the test dataset that the accuracy of the forest fire recognition of the FPGA edge terminal of the LightFireNet model is 96.70%, the recognition speed is 64 ms per frame, and the power consumption is 2.23 W. The results show that this paper has realized a low-power-consumption, high-accuracy, and fast forest fire recognition terminal, which can thus be better applied to forest fire monitoring. Full article

The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology. Full article

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects. Full article

Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers’ network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management. Full article

Since the integration of electronic devices and intelligent electronic devices into the power grid, power quality (PQ) has consistently remained a significant concern for system operators and experts. Maintaining high standards of power quality is crucial to preventing malfunctions and faults in electric assets and connected loads. Recently, PQ studies have shifted their focus to a specific frequency range, previously not considered problematic—the supraharmonic 2 kHz to 150 kHz range. This range is not populated by easily recognizable harmonic components of the 50 Hz to 60 Hz mains fundamental, but by a combination of intentional emissions, switching non-linearities and byproducts, and various types of resonances. This paper aims to provide a detailed analysis of the impact of supraharmonics (SHs) on power network operation and assets, focusing on the most relevant documented negative effects, namely power loss and the heating of grid elements, aging of dielectric materials, failure of medium voltage (MV) cable terminations, and interference with equipment and power line communication (PLC) technology in particular. Under some shareable assumptions, limits are derived and compared to existing ones for harmonic phenomena, providing a clear identification of the primary issues associated with supraharmonics and suggestions for the standardization process. Strictly related is the problem of grid monitoring and assessment of SH distortion, discussing the suitability of normative requirements for instrument transformers (ITs) with a specific focus on their accuracy. Full article

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios. Full article

Urban day-to-day travel systems generally exist in various types of cities. Their modeling is difficult due to the uncertainty of individual travelers in micro travel decision-making. Moreover, with the advent of the information age, intelligent connected vehicles, smartphones, and other types of intelligent terminals have placed urban day-to-day travel systems in an information environment. In such an environment, the travel decision-making processes of travelers are significantly affected, making it even more difficult to give theoretical explanations for urban day-to-day travel systems. Considering that analyzing urban day-to-day travel patterns in an information environment is of great significance for governing the constantly developing and changing urban travel system and, thus, of great importance for the sustainable development of cities, this paper gives a systematic review of the theoretical research on urban day-to-day travel and its development in an information environment over the past few decades. More specifically, the basic explanation of an information environment for urban day-to-day travel is given first; subsequently, the theoretical development of micro decision-making related to individual day-to-day travelers in an information environment is discussed, and the theoretical development related to changes in urban macro traffic flow, which can be recognized as the aggregation effect formed by individual micro decision-making, is also discussed; in addition, the development of understanding different types of traffic information that travelers may obtain in an information environment is discussed; finally, some important open issues related to the deep impact of information environment on urban day-to-day travel systems that require further research are presented. These valuable research directions include using information methods to fit day-to-day travel patterns of cities and implementing macro and micro integrated modeling for urban day-to-day travel systems based on complex system dynamics and even quantum mechanics. Full article

Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. Aimed to eliminate routing voids in traditional Euclidean geographic greedy routing strategies, we propose a hyperbolic-embedding-aided geographic routing strategy (HGR) in this paper. By embedding the network topology into a two-dimensional Poincaré hyperbolic disk, greedy forwarding is performed according to nodes’ hyperbolic coordinates. Simulation results demonstrated that the proposed HGR strategy can greatly enhance the routing success rate through a smaller stretch of the routing paths, with little sacrifice of routing computation time. Full article

A launch vehicle needs to adapt to a complex flight environment during flight, and traditional guidance and control algorithms can hardly deal with multi-factor uncertainties due to the high dependency on control models. To solve this problem, this paper designs a new intelligent flight control method for a rocket based on the deep reinforcement learning algorithm driven by knowledge and data. In this process, the Markov decision process of the rocket landing section is established by designing a reinforcement function with consideration of the combination effect on the return of the terminal constraint of the launch vehicle and the cumulative return of the flight process of the rocket. Meanwhile, to improve the training speed of the landing process of the launch vehicle and to enhance the generalization ability of the model, the strategic neural network model is obtained and trained via the form of a long short-term memory (LSTM) network combined with a full connection layer as a landing guidance strategy network. The proximal policy optimization (PPO) is the training algorithm of reinforcement learning network parameters combined with behavioral cloning (BC) as the reinforcement learning pre-training imitation learning algorithm. Notably, the rocket-borne environment is transplanted to the Nvidia Jetson TX2 embedded platform for the comparative testing and verification of this intelligent model, which is then used to generate real-time control commands for guiding the actual flying and landing process of the rocket. Further, comparisons of the results obtained from convex landing optimization and the proposed method in this work are performed to prove the effectiveness of this proposed method. The simulation results show that the intelligent control method in this work can meet the landing accuracy requirements of the launch vehicle with a fast convergence speed of 84 steps, and the decision time is only 2.5 ms. Additionally, it has the ability of online autonomous decision making as deployed on the embedded platform. Full article

Supramolecular assembly of amphiphilic molecules in aqueous solutions to form stimuli-responsive entities is attractive for developing intelligent supramolecular materials for bioapplications. Here we report on the supramolecular chiral assembly of amphiphilic dendronized tetraphenylethylenes (TPEs) in aqueous solutions. Hydrophobic TPE moieties were connected to the hydrophilic three-fold dendritic oligoethylene glycols (OEGs) through a tripeptide proline–hydroxyproline–glycol (POG) to afford the characteristic topological structural effects of dendritic OEGs and the peptide linker. Both ethoxyl- and methoxyl-terminated dendritic OEGs were used to modulate the overall hydrophilicity of the dendronized TPEs. Their supramolecular aggregates exhibited thermoresponsive behavior that originated from the dehydration and collapse of the dendritic OEGs, and their cloud point temperatures (T_cps) were tailored by solution pH conditions. Furthermore, aggregation-induced fluorescent emission (AIE) from TPE moieties was used as an indicator to follow the assembly, which was reversibly tuned by temperature variation at different pH conditions. Supramolecular assemblies from these dendronized amphiphiles exhibited enhanced supramolecular chirality, which was dominated mainly by the interaction balance between TPE with dendritic OEG and TPE with POG moieties and was modulated through different solvation by changing solution temperature or pH conditions. More interestingly, ethoxyl-terminated dendritic OEG provided a much stronger shielding effect than its methoxyl-terminated counterpart to prevent amino groups within the peptide from protonation, even in strong acidic conditions, resulting in different responsive behavior to the solution temperature and pH conditions for these supramolecular aggregates. Full article

The rapid development of cloud computing and deep learning makes the intelligent modes of applications widespread in various fields. The identification of Raman spectra can be realized in the cloud, due to its powerful computing, abundant spectral databases and advanced algorithms. Thus, it can reduce the dependence on the performance of the terminal instruments. However, the complexity of the detection environment can cause great interferences, which might significantly decrease the identification accuracies of algorithms. In this paper, a deep learning algorithm based on the Dense network has been proposed to satisfy the realization of this vision. The proposed Dense convolutional neural network has a very deep structure of over 40 layers and plenty of parameters to adjust the weight of different wavebands. In the kernel Dense blocks part of the network, it has a feed-forward fashion of connection for each layer to every other layer. It can alleviate the gradient vanishing or explosion problems, strengthen feature propagations, encourage feature reuses and enhance training efficiency. The network’s special architecture mitigates noise interferences and ensures precise identification. The Dense network shows more accuracy and robustness compared to other CNN-based algorithms. We set up a database of 1600 Raman spectra consisting of 32 different types of liquid chemicals. They are detected using different postures as examples of interfered Raman spectra. In the 50 repeated training and testing sets, the Dense network can achieve a weighted accuracy of 99.99%. We have also tested the RRUFF database and the Dense network has a good performance. The proposed approach advances cloud-enabled Raman spectra identification, offering improved accuracy and adaptability for diverse identification tasks. Full article

Recently, smart port systems connected to autonomous ships have attracted increasing interest. Thus, an intelligent port system can automatically berth to create an intelligent port system. To ensure the safety of large ships moored in open coastal terminals, which are often subjected to bad weather such as strong winds, it is necessary to calculate and evaluate their mooring security on a case-by-case basis. In this study, the mooring capacities of the large ships were estimated according to the port and fishing port design criteria of the Ministry of Ocean and Fisheries. Under the wind speed of 14 m/s, the longitudinal and lateral forces acting on the JBC, KCS, and KVLCC ships are 41.2 and 340 kN, 38.7 and 837 kN, and 77.2 and 222 kN, while under the wind speed of 30 m/s, they are 43 and 1674 kN, 132.7 and 4118 kN, and 159.2 and 1091 kN, respectively, for the mooring forces. Full article