Multi-view automatic translational correction (ATC) in coronary angiography (CAG) is critical for intraoperative automatic diagnosis, in which deep learning playing a key role. However, heartbeat-induced soft matching errors and costly annotations make it difficult to build high-quality, large-scale datasets for calibration algorithm training. The training of clinical models is difficult to fulfill, as existing datasets differ significantly from real CAG in both style and structure. To address this challenge, we propose a novel high-quality data synthesis method for annotation-free ATC. We fully automated the construction of a labeled, high-fidelity dataset for training matching models. An evolutionary algorithm is introduced for global optimization of translation estimation, mitigating epipolar constraint violations caused by vascular deformation and enabling reliable correction across large viewpoint differences. Furthermore, a theoretical analysis is presented, demonstrating that error propagation between adjacent views is more accurate than direct estimation across distant views. Our experiments on clinical datasets demonstrate that our method not only significantly outperforms weakly supervised learning approaches, but also performs comparably to fully supervised methods. Moreover, it exhibits remarkable multicenter generalizability.

As a core component of the maritime Internet of Things (IoT), the Automatic Identification System (AIS) continuously collects dynamic vessel navigation data, providing a solid foundation for addressing complex maritime traffic prediction tasks that support intelligent Vessel Traffic Services (VTS), such as vessel trajectory prediction and vessel arrival time (VAT) estimation. However, existing methods typically focus on single prediction objectives, falling short of meeting practical multi-task requirements. To address this gap, this study proposes a geospatial grid-constrained deep learning framework based on AIS data to simultaneously handle three key prediction tasks: vessel trajectory prediction, whether the vessel arrives within the specified time, and VAT. The framework incorporates a dynamic patch construction method and a Graph Soft Evolution (GSE) module to capture temporal correlations among observations under spatial grid constraints. An encoder-decoder architecture is introduced, where the encoder employs a Squeeze-and-Excitation (SE) block to adaptively select feature channels, and the decoder models dependencies across both variable and temporal dimensions. In a case study of New York Harbor, the model achieved an R² of 0.8386 and RMSE of 0.0329 for latitude increment prediction, and an R² of 0.8432 with RMSE of 0.0322 for longitude increment prediction. It also attained 99.83% accuracy in arrival status prediction and an R² of 0.9350 with RMSE of 0.0701 for VAT prediction. The framework demonstrated effectiveness in port scheduling and robust generalizability in cross-validation experiments at the Port of Los Angeles, thereby demonstrating its substantial potential to enhance the operational efficiency of VTS within maritime IoT systems.

Perimeter security system based on distributed optical fiber sensor network plays a key role in the monitoring and protection of restricted areas and large industrial areas. At present, most of the distributed intrusion signal recognition algorithms rely on manual feature extraction methods and traditional classifiers such as traditional support vector machines, which generally have low recognition efficiency and accuracy. To solve these problems, a convolutional prototype network DWTCPLnet is proposed in this paper. Firstly, the original onedimensional intrusion interference signal is decomposed into five approximate coefficients in the frequency domain by discrete wavelet transform (DWT), and then combined with the original signal to form a new two-dimensional data. This two-dimensional data is then entered into DWT-CPLnet for training. At the same time, the training process of the network is restricted by the metric space of prototype learning. The experimental results indicate that the average recognition accuracy of DWT-CPLnet in 6 types of common intrusion disturbance signals (three natural disturbances: wind blowing, light rain, heavy rain; three manmade disturbances: knocking, impacting and slapping) can reach 99.59%, and also has the ability to identify unknown classes to meet the actual monitoring needs.

Deep learning has achieved advancements across a variety of forefront fields. However, its inherent 'black box' characteristic poses challenges to the comprehension and trustworthiness of the decision-making processes within neural networks. To mitigate these challenges, we introduce InnerSightNet, an inner information analysis algorithm designed to illuminate the inner workings of deep neural networks through the perspectives of community. This approach is aimed at deciphering the intricate patterns of neurons within deep neural networks, thereby shedding light on the networks' information processing and decision-making pathways. InnerSightNet operates in three primary phases, 'neuronization-aggregation-evaluation'. Initially, it transforms learnable units into a structured network of neurons. Subsequently, these neurons are aggregated into distinct communities according to representation attributes. The final phase involves the evaluation of these communities' roles and functionalities, to unpick the information flow and decision-making. By transcending focus on single-layer or individual neuron, InnerSightNet broadens the horizon for deep neural network interpretation. InnerSightNet offers a unique vantage point, enabling insights into the collective behavior of communities within the overarching architecture, thereby enhancing transparency and trust in deep learning systems.

Abstract An important data in intelligent ocean is the sea surface temperature (SST). Most of the previous works on SST prediction deal with independent spatial point, ignoring the spatial correlation of two‐dimensional intelligent ocean, which leads to instability of prediction accuracy. Therefore, in this paper, a deep learning model based on convolutional gated recurrent unit is proposed for the SST prediction of two‐dimensional intelligent ocean. Input and output of the proposed model are both spatiotemporal SST data, which means the model directly process spatiotemporal data. This method adds the spatial information of the intelligent ocean temperature data, thereby improving the accuracy. From the experiments, it turns out that the proposed model shows superior performance compared to another three prevailing deep learning models.

With the rapid development of Industrial Internet of Things (IIoT), the emergence of credible federated learning provides a more effective solution for it. In this article, we use the credible collaboration between large language models (LLMs) and reinforcement learning (RL) model to improve the autonomous decision-making efficiency of autonomous underwater vehicle (AUV), reduce resource and power consumption, and solve robust decision-making problem in open environments. First, considering the complex terrain and hydrodynamic environment in the ocean, we construct a 3-D ocean simulation environment with high accuracy and high reliability to simulate the behavioral constraints of AUV in the real ocean. Second, we integrate LLaMA model into the decision-making process of AUV, utilizing its powerful information processing capability for environmental analysis and action selection, so as to improve the decision-making generalization ability of AUV in dynamic ocean environments. Finally, we propose proximal policy advantage estimation (PPAE) method and achieve safe and efficient path planning for AUV based on LLMs decision output and dynamic field environment information. The experimental results show that our method achieves a good effect in improving the decision accuracy and robustness of the AUV, which proves the effectiveness of the LLMs in the application of underwater intelligent agent control decision.

Existing active learning methods for object detection face challenges, such as the lack of ground truth labels for regression loss, insufficient representation of unlabeled instance samples information, and discrepancies in information quality between image-level and multiple anchor-level instances. To address these issues, we propose an active learning method for object detection with vectorized dual pseudo loss and multiple instance offset constraint. This method implements a two-stage framework. The first stage focuses on evaluating the information quality of detection images. We first pioneer a dual pseudo loss formulation that provides theoretically grounded regression loss estimation. The regression loss is calculated as the norm of the offset discrepancy loss vector between the enhanced and original base box vector, further constrained by the cosine value of the angle between the anchor box feature and regressor parameters vector. The distance entropy from the base box feature vector to each category's feature prototype vector is used as a weighting factor for the regression and classification information quality of instance samples. Subsequently, the second stage employs diversity-driven sampling on high-information images, leveraging instance-level cosine similarity to effectively remove redundant images. The proposed method outperforms state-of-the-art active learning approaches for object detection on PASCAL VOC and MS COCO datasets. Additionally, the proposed dual pseudo regression loss robustly captures regression information quality, demonstrating its effectiveness for active learning in object detection.

In the decade, artificial intelligence has achieved great popularity and applications in medicine and healthcare. Various AI-based algorithms have shown astonishing performance. However, in various data-driven smart healthcare algorithms, the problem of incomplete dataset remains a huge challenge. In this paper, we propose a data completeness enhancement algorithm based on generative AI (i.e., GenAI-DAA) to solve the problems of the in-sufficient data for model training, the data imbalance, and the biases of the training samples. We first construct the cognitive field of the generative models and effectively understand the state of incomplete cognition in generative models. Secondly, on this basis, we propose a quest algorithm for abnormal samples in the cognitive field based on local outlier factor. By fine-grained value evaluation, abnormal samples are given more refined attention. Finally, integrating the above process through multiple cognitive adjustments, GenAI-DAA gradually improves the cognitive ability. GenAI-DAA can be summarized as "Quest $ \longrightarrow$ Estimate$ \longrightarrow$Tune-up". We have conducted extensive experiments to demonstrate the effectiveness of our proposed algorithm, and shown widely applications to some typical data-driven smart healthcare algorithms.

With the advancement of communication technology from 5G to 6G, future communication networks will no longer be limited to land and air, and the ocean will also become the battlefield for 6G networks. The expansion of the network has expanded the scope of Intelligent Autonomous Transport Systems (IATS). As a new type of underwater transport system, Autonomous Underwater Vehicle (AUV) has gained popularity due to their advantages of autonomy, endurance, and concealment. In practical applications, it is necessary to fully consider the impact of uncertain marine environments on AUV’s motion, and also design stable control unit to achieve AUV formation. The core of the control unit is the AUV formation control algorithm, which should enable AUV to complete path planning and obstacle avoidance while ensuring formation control. In order to solve the above problems, an Intelligent Multi-agent path planning and formation control algorithm based on Value-decomposition networks (IMV) is proposed in this paper. Specifically, a three-dimensional high-resolution marine simulation environment located in the Mariana Trench is established, the state transition function and reward function are well designed under uncertain conditions for stable Multi-Agent Reinforcement Learning (MARL) mechanism, a Value-Decomposition Networks (VDN) based training framework is constructed to improve the convergence speed of the proposed method. The experimental results verify the excellent performance of the IMV method proposed in this paper, demonstrating that our method can outperform other methods in the aspect of stability, adaptability, intelligence, and timeliness.

By combining transportation information with physical elements, transportation cyber-physical systems (T-CPS) take advantage of the strengths of information technology and show great potential in terms of efficiency, safety, and control. T-CPS covers land, air, and underwater domains involving vehicles, drones, and autonomous underwater vehicles (AUVs), facilitating our lives and creating productivity. However, underwater T-CPS faces greater difficulties and challenges than the first two areas. On the one side, underwater equipment is generally expensive and thus requires a high level of safety. On the other side, the complexity of the marine environment causes uncertainty in the control. To address these challenges, this paper proposes an expert experience-enhanced control approach designed to enhance AUV reliability and safety. Firstly, we model AUV cluster control, including the complex underwater environment and cooperative control strategy, and refine this problem into a Markov decision problem (MDP) model based on the leader-follower strategy. Subsequently, a multi-agent reinforcement learning cluster control algorithm is developed on the framework of Centralized Training Distributed Execution (CTDE) to improve the learning and exploration capabilities of AUVs. Finally, we propose an expert experience-enhanced strategy that reduces the impact of non-smooth environments and also ameliorates the limitation of relying exclusively on rule-based experience. Experiments compare the linear and triangular AUV formation control tasks, and the proposed approach shows promising superiority and possesses sound stability in dynamically changing environments.

An autonomous underwater vehicle (AUV) has shown impressive potential and promising exploitation prospects in numerous marine missions. Among its various applications, the most essential prerequisite is path planning. Although considerable endeavors have been made, there are several limitations. A complete and realistic ocean simulation environment is critically needed. As most of the existing methods are based on mathematical models, they suffer from a large gap with reality. At the same time, the dynamic and unknown environment places high demands on robustness and generalization. In order to overcome these limitations, we propose an information-assisted reinforcement learning path planning scheme. First, it performs numerical modeling based on real ocean current observations to establish a complete simulation environment with the grid method, including 3-D terrain, dynamic currents, local information, and so on. Next, we propose an information compression (IC) scheme to trim the mutual information (MI) between reinforcement learning neural network layers to improve generalization. A proof based on information theory provides solid support for this. Moreover, for the dynamic characteristics of the marine environment, we elaborately design a confidence evaluator (CE), which evaluates the correlation between two adjacent frames of ocean currents to provide confidence for the action. The performance of our method has been evaluated and proven by numerical results, which demonstrate a fair sensitivity to ocean currents and high robustness and generalization to cope with the dynamic and unknown underwater environment.

How to balance the diversity and quality of results from generative models through perception rectification poses a significant challenge. Abnormal perception in generative models is typically caused by two factors: inadequate model structure and imbalanced data distribution. In response to this issue, we propose the dynamic model perception rectification algorithm (DMPRA) for generalized generative models. The core idea is to gain a comprehensive perception of the data in the generative model by appropriately highlighting the low-density samples in the perception space, also known as the minor group samples. The entire process can be summarized as "search-evaluation-adjustment". To identify low-density regions in the data manifold within the perception space of generative models, we introduce a filtering method based on extended neighborhood sampling. Based on the informational value of samples from low-density regions, our proposed mechanism generates informative weights to assess the significance of these samples in correcting the models' perception. By using dynamic adjustment, DMPRA ensures simultaneous enhancement of diversity and quality in the presence of imbalanced data distribution. Experimental results indicate that the algorithm has effectively improved Generative Adversarial Nets (GANs), Normalizing Flows (Flows), Variational Auto-Encoders (VAEs), and Diffusion Models (Diffusion).

With the development of society and the economy, low-carbon and low-energy means of exploiting marine resources are receiving increasing attention. Autonomous path planning is a fundamental capability for IoT Autonomous Underwater Vehicle (AUV) to carry out ocean exploration tasks. Currently, the main issue lies in the numerous disturbances and uncertainties present in the marine environment during practical applications, which can significantly impact path planning, leading to high energy consumption and carbon emissions. To address this challenge, this paper presents a sustainable reinforcement learning algorithm for handling time-varying current disturbances to achieve low-carbon AUV path planning, which is delineated into three steps. Firstly, a three-dimensional time-varying current environment is established as the environmental framework for reinforcement learning, and the dynamic model of the AUV is formulated. Secondly, to enhance training efficiency and reduce AUV’s energy consumption, this paper puts forth the OCDRP (Ocean Current Disturbance Rejection PPO) algorithm, which incorporates tidal current information to enhance the AUV’s resilience to time-varying currents. Lastly, expectile regression methods are introduced to facilitate the algorithm’s convergence. Experimental results confirm the efficacy of the proposed algorithm and its adaptability to time-varying currents, making it an efficient, adaptable, and low-carbon sustainable path planning approach.

The Unmanned Aerial Vehicles (UAVs) are competent to perform a variety of applications, possessing great potential and promise. The Deep Neural Network (DNN) technology has enabled the UAV-assisted paradigm, accelerated the construction of smart cities, and propelled the development of the Internet of Things (IoT). UAVs play an increasingly important role in various applications, such as surveillance, environmental monitoring, emergency rescue, supplies delivery, for which a robust path planning technique is the foundation and prerequisite. However, existing methods lack comprehensive consideration of the complicated urban environment and do not provide an overall assessment of the robustness and generalization. Meanwhile, due to the resource constraints and hardware limitations of UAVs, the complexity of deploying the network needs to be reduced. This paper proposes a lightweight, reinforcement learning-based real-time path planning method for UAVs, Adaptive Soft Actor-Critic algorithm (ASAC), which optimizing training process, network architecture, and algorithmic models. First of all, we establish a framework of global training and local adaptation, where the structured environment model is constructed for interaction, and local dynamically varying information aids in improving generalization. Secondly, ASAC introduces a cross-layer connection approach that passes the original state information into the higher layers to avoid feature loss and improve learning efficiency. Finally, we propose an adaptive temperature coefficient, which flexibly adjusts the exploration probability of UAVs with the training phase and experience data accumulation. In addition, a series of comparison experiments have been conducted in conjunction with practical application requirements, and the results have fully proved the favorable superiority of ASAC.

With the rapid development of information technology, social network has become an indispensable part of daily life. People have been able to get news from all over the world through social networks for a long time. People spend more time online than they do in real life. However, the information we get in the world of social network is not purely benign. Due to the development of artificial intelligence technology, more and more tampered media information appears in social networks, some for entertainment, while others become the dark side of social networks, of which the most harmful is to people in the media tamper. For fake news and misinformation caused by media tampering, we need to trace the source and clearly distinguish the truth from the manipulated. This article proposes an image media forgery classification method of multiorder attribute nodes. First, we use different methods to extract the edge, texture, grayscale, and color attributes of the image. Second, according to the characteristics of different attributes, we calculate the first-order entropy of edge attributes, the second-order entropy of texture attributes, local entropy of grayscale, and color properties. Finally, we represent each image with some nodes and build a graph convolutional network (GCN) to classify real and fake images. Experimental results on mainstream media manipulation datasets show that our method is the state-of-the-art compared with similar methods.

Research in psychology demonstrates that visual features and semantic content can convey various emotions. Furthermore, studies have proved that image emotion and aesthetics are inextricably linked. During the image aesthetic assessment process (IAA), images elicit emotional responses from individuals, leading to emotional resonance and influencing the evaluation of images. This article proposes an image aesthetics assessment method based on hypernetwork of emotion fusion (HNEF). Our method incorporates the emotions depicted in images into the process of IAA. To accomplish this, we extract both aesthetic and emotional features from the images. Additionally, we employed the self-attention mechanism of the transformer to comprehensively investigate the intimate connection between aesthetics and emotion. Additionally, the hypernetwork is designed to establish perception rules governing the high-level semantic information in images. The experimental results validate the strong correlation between emotion and aesthetics. Furthermore, the proposed method exhibits a significantly competitive advantage when compared to existing methods on the Aesthetic Visual Analysis (AVA) dataset.

Face-swapping technology has been widely used in people's life, and people also put forward higher requirements for it. Most of the current face-swapping methods are difficult to generate a high-definition face image. Through StyleGAN, we can generate high-definition face images. However, face-swapping with StyleGAN is still challenging. Firstly, we need to map the target image to the latent space of StyleGAN. Many tasks need to map the input image to a new latent space for face-swapping, because identity features are complex and challenging to map to specific latent space layers directly. So face-swapping is completed in the remapping process, which consumes excess computing resources for reconstruction. And the generated image is difficult to maintain the original image color, face attributes, background and other attributes. We propose a new method, which only edits the code of w+ latent space of StyleGAN to complete the face-swapping and generate high-definition face images. We propose the GAN inversion method to improve the effect of face swapping, which combines convolution networks' advantages in extracting texture features and the benefits of transformers in extracting structure features. In the latent space of StyleGAN, the low-level feature layer is dominated by structure information, and the high-level feature layer is overwhelmed by texture information. Furthermore, we propose latent space selection, through which the neural network can learn disentangled representations of identity information in the latent space. Finally, we improved the post-processing process of face swapping to keep the image's background. Our method can complete face-swapping by editing the w+ space. Thus, high-quality face image can be generated and a lot of computing resource is saved on image reconstruction. At the same time, our method can keep other attributes better in the face-swapping process.

The usual active learning is to sample unlabeled set by designing efficient sample information evaluation algorithms. However, information redundancy between candidate sets is often overlooked. This can cause similar data to be labeled repeatedly, producing ineffective gains for the model. In this paper, we proposed an Unsupervised Redundant Feature Elimination Active Learning module (URFEAL), which utilizes the information feature coincidence of the unlabeled set to eliminate information redundant data, thus guaranteeing the validity of each candidate data. URFEAL consists of feature clusterer and eliminator. The feature clusterer computes class boundaries based on feature densities to discretize each class of the candidate set, and the eliminator judges data similarity by overlapping degree to eliminate redundant data features. Furthermore, we propose an anti-noise sampling strategy Outlier Feature Elimination (OFE) in URFEAL to filter mislabeled sets for relabeling in the data sampling stage. We extensively evaluate our method by image classification and perform experimental validation on CIFAR-10, CIFAR-100 and CALTECH-101. The experimental results show that the improvements we make are especially significant for most existing active learning algorithms in the low data stage, which demonstrates the effectiveness and generality of URFEAL.

Generative adversarial networks have shown impressive results in the modeling of movies and games, but what if such powerful image generation capability is used to harm the Multimedia? The face replacement methods represented by Deepfakes are becoming a threat to everyone, so the development of image authenticity detection methods has become a top priority. For achieving accurate detection resistant to compression effects, we propose a weighted complementary dual-stream detection method. First, to alleviate the influence of image compression on manipulation detection, we propose the concept of pixel-wise saliency invariance. We map fake images onto saliency maps via Quaternary Fourier Transform, which discovers the invariant properties of image phase spectra on different compressions. Meanwhile, to capture boundary traces more easily, we propose the concept of pixel-wise detail enhancement. We apply Bilateral Filtering to preserve the texture edges of fake images and amplify the fake boundaries. Finally, to take full advantage of the two proposed concepts, a weighted complementary dual-stream network is designed as a classifier to fuse features and identify real and fake. On different benchmarks like FaceForensics++ (FF++), Celeb-DF, and DFDC, the experimental results show that the proposed method has the average best detection accuracy compared to existing methods.

Internet of Things (IoT) has extended its coverage to various spatial domains and has established interconnection to serve widespread applications of a larger spatial scale. Such IoT is called the space–air–ground–ocean-integrated network (SAGOI-Net), which consists of multiple battery-powered heterogeneous devices. Hence, energy efficiency is the key point of SAGOI-Net to be stably operated for a long time without manual maintenance. This article proposes a novel scheme of energy-efficient autonomous and decentralized SAGOI-Net establishment using an intelligent autonomous underwater glider (AUG) to serve marine applications. The proposed SAGOI-Net is energy efficient because the energy consumption is minimized by: 1) employing nonpropeller-driven AUG; 2) navigating AUG under water without acoustic sensor or extra energy-consuming vision sensors; and 3) equipping the self-navigation (SN) system based on lightweight neural network model to save the energy consumption of onboard computing resource. Moreover, assuming the AUG navigation problem as time-series regression, the proposed scheme designs SAGOI-Net to be autonomous and decentralized with the aid of lightweight long short-term memory (LSTM) network-based SN (SN-LSTM) system of AUG. The lightweight SN-LSTM model is trained end-to-end on dynamically modeled AUG motion information along with numerically modeled ocean environment data to quantitatively analyze the impact of the ocean environment on AUG. The simulation results demonstrate a superior performance of the AUG SN along with energy efficiency of the proposed SAGOI-Net.

As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots.Note to Practitioners—The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.

Considering the efficiency and security of healthcare data processing, indiscriminate data collection, annotation, and transmission are unwise. In this article, we propose the normalized double entropy (NDE) method to assess image data quality in the form of metatask. In specific, the probability entropy and distance entropy are both adopted and normalized to evaluate the data quality. The experimental results show the stable ability of the NDE to distinguish good and bad data in terms of information contribution. Furthermore, the model's diagnostic performances driven by selected good and bad data are compared, and a clear gap exists between them under the premise of the same amount of data. Screening 70% of the dataset can achieve almost the same accuracy as that based on all data. This article focuses on healthcare data quality and data redundancy and provides a practical evaluation tool to facilitate the identification and collection of valuable data, which is beneficial to improve efficiency and protect cybersecurity in healthcare systems.

The shaking table has been used extensively in the structure test field to verify the structure’s performance against various vibrations, e.g., earthquakes. In order to replicate the vibrations, which are measured by the acceleration signal specifically, the model of the shaking table should be thoroughly constructed to design the controller. However, parametric uncertainty and strong nonlinearity, such as the nonlinear friction, make it an obstacle to obtaining an accurate model. A neural network-based controller is designed in this paper to address this issue, and the nonlinear systems are estimated by the neural network’s universal approximation characteristics. Furthermore, a robust sliding mode controller is utilized to compensate for the residual error of the neural network and other uncertainties. The semi-global asymptotic stability of the controller is proved by Lyapunov analysis. Comparative experimental results indicate the superiority of the proposed controller.

The path planning of the autonomous underwater vehicle (AUV) has shown great potential in various Internet of Underwater Things (IoUT) applications. Although considerable efforts had been made, prior studies are confronted with some limitations. For one thing, existing work only uses the ocean current simulation model without introducing real ocean information, having not been supported by real data. For another, traditional path planning algorithms have strong environment dependence and lack flexibility: once the environment changes, they need to be remodeled and replanned. To overcome these challenges, this article proposes comprehensive ocean information D3QN (COID), an AUV path planning scheme exploiting comprehensive ocean information and reinforcement learning (RL), which consists of three steps. First, we introduce the comprehensive real ocean data, including weather, temperature, thermohaline, current, etc., and apply them into the regional ocean modeling system to generated reliable ocean current. Next, through well-designed state transition function and reward function, we build a 3-D grid model of ocean environment for RL. Furthermore, based on the framework of the double dueling deep$Q$network (D3QN), COID integrates local ocean current and position features to provide state input and uses priority sampling to accelerate network convergence. The performance of COID has been evaluated and proved by numerical results, which demonstrate efficient path planning and high flexibility for expansion into different ocean environments.

The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This article proposes a semisupervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this article is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.

The deepening of ocean measurement work requires higher transmission bandwidth and information calculation efficiency, which provides an opportunity for fog computing. Compared with cloud computing, fog computing shows distribution because it concentrates data processing and application on devices at the edge of the network. In this article, the Ocean of Things (OoT) framework is designed for marine environment monitoring based on the Internet of Things technology. The OoT is divided into three layers: 1) data acquisition layer; 2) fog layer; and 3) cloud layer. In the fog layer, in order to complete the quality control of the sensor measurement data, we use the numerical gradient-based method to process the original acquisition data. An improved D-S algorithm is designed for multisensor information fusion, reducing the data capacity and improving data quality. In the cloud layer, we build ocean information change models based on the fog layer data to predict the dynamic ocean environment. The designed fog layer is evaluated based on marine multisensor information. The results have shown that fog-based multisensor data processing shows low time consumption and high reliability. Moreover, this article uses real temperature data sets to evaluate the prediction accuracy of the cloud model. Finally, we tested the performance of the designed OoT framework with multiple data sets. The simulation results show that the framework can improve the efficiency of data utilization at sea and improve the efficiency of information utilization.