The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data.

Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways.

Intelligent ships represent a crucial trend in the development of the maritime industry and will become the predominant vessel form in the future. Intelligent ships must have efficient perception and situational analysis capabilities in complex navigation environments to achieve intelligent decision-making and safe navigation. Maritime traffic safety is a critical issue for the global shipping industry, and maritime situational awareness is essential for ensuring safe navigation in waterways. This paper addresses the problem of intelligent identification of potential navigational risks in ship navigation environments and proposes a Transformer-based approach for ship encounter situation recognition. This method utilizes Automatic Identification System (AIS) data to extract encounter features. Contextual Position Encoding and Coordinate Attention mechanisms are introduced into the model to capture spatial correlation and directional features, enhancing the accuracy of determining encounter situations. Through experimentation in the research area and comparison with other models, the results demonstrate that the proposed model achieves higher recognition accuracy in ship encounter situation identification. This research provides a beneficial new idea and technical approach for studying intelligent environmental perception and situational analysis for ships.

Since water scenes are highly susceptible to environmental factors, in the actual process of collecting and storing pictures, it is easy to cause noise pollution of image samples. This requires that the feature extraction algorithm can reduce the impact of noise on the sample data when dealing with data containing noise, i.e.That is to say, the algorithm model is required to have high robustness. The high-speed corner detection algorithm FAST, scale-invariant feature transform SIFT, etc. are traditional feature point detection methods, but the advantages in terms of computational speed and robustness are mixed. In this paper, we focus on the SuperPoint network, which has better robustness, and modify the network accodrding to the requirements of real-time and accuracy. To address the problems of gradient vanishing and gradient explosion, a residual connection structure is added between each convolutional layer and activation function. Meanwhile, to ensure the convergence speed of the model, a normalisation layer is added between the convolutional layers and the activation function. Finally, in order to improve the representation and generalisation ability of the model, the SE-Net channel attention mechanism module is added after the residual connection structure. Some of the Seaship ships, navigation beacons and other datasets are transformed into feature point datasets in the training dataset and strengthen its feature extraction capability for water scenes.Experimental analysis is conducted based on the water scenario, and the experimental results show that the detection and matching effects of feature points are improved under the guarantee of a slight increase in the computational speed, the detection effect of the number of feature points is improved by about 15.4%, the matching effect of feature points is improved by about 9.7%, the nearest-neighbour accuracy of the NN mAp is improved by about 9%, the repeatability of the Rep. is improved by about 11.4%, and the average positioning error is reduced by about 2.1%.

This paper presents an improved VHF Speech Recognition Model (VHFSR) based on the modified Conformer architecture, aiming to address the issue of poor communication quality in very high- frequency (VHF) voice communication within the navigation domain. Speech recognition is a technology that transforms acoustic features into text sequences, and VHFSR demonstrates accurate identification of maritime VHF voice communication content. We selected the WeNet speech recognition model as the baseline model, and through a series of systematic studies, identified that the Conformer architecture's design choices were suboptimal. Consequently, we modified the Conformer encoder in WeNet to propose the VHFSR model, consistently outperforming the WeNet model under the same training scheme. Specifically, VHFSR introduces improvements to the multi-head attention module in Conformer by incorporating an additional multi-head attention module, transforming the Conformer architecture into an FMCMF form rather than the Macaron structure proposed in Conformer. This model effectively addresses VHF speech recognition challenges in the navigation domain and achieves promising results by combining a custom VHF dataset with the AISHELL dataset when using MFCC as the acoustic feature. The final achieved Character Error Rate (CER) for speech stream recognition is 2.53%.

In order to solve the problems of low targets resolution obtained by traditional ship radar and less intuitive ship navigation mode, meanwhile, further improve navigation safety, a ship "video radar" enhanced navigation system based on multi-camera is proposed in this paper. Firstly, stitching the navigation environment video captured by multi-camera by video stitching technology, to provide 360° navigation environment for drivers during navigation; Secondly, detecting the targets (ships and beacons) in the stitched navigation video with improved YOLOv5; Finally, presenting an augmented reality navigation interface through target information filtering and matching. In addition, according to the characteristics of single environment and few feature points during navigation, proposing a feature points detection method named SRSuperpoint based on deep learning, which can greatly improve the stitching effect. The system can present a more intuitive and comprehensive navigation environment, assist ship drivers to make driving decisions, and then to improve navigation safety. It also makes an experimental exploration for the development of intelligent ships.

Recognizing the structural states of different navigation marks from camera images is an important field for efficient navigation marks maintenance. This paper focuses on the classification of structural damaged navigation marks image, and uses the navigation marks image in the middle and lower reaches of the Yangtze River to construct a navigation marks image dataset with structural damage. An improved Efficientnet is proposed for processing classification of structural damaged navigation marks image. In the improved EfficientNet-b0, Fused-MBConv is used to speed up the training. And the SE module is retained to improve the extraction of subtle features. Experimental results based on a dataset with 7894 images of damaged navigation marks and 34695 images of normal navigation marks show that the improved EfficientNet-b0 has an accuracy about 95% to classify damaged navigation marks, which had a faster training speed than the original EfficientNet-b0.

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.

At night, buoys and other navigation marks disappear to be replaced by fixed or flashing lights. Navigation marks are seen as a set of lights in various colors rather than their familiar outline. Deciphering that the meaning of the lights is a burden to navigators, it is also a new challenging research direction of intelligent sensing of navigation environment. The study studied initiatively the intelligent recognition of lights on navigation marks at night based on multilabel video classification methods. To capture effectively the characteristics of navigation mark's lights, including both color and flashing phase, three different multilabel classification models based on binary relevance, label power set, and adapted algorithm were investigated and compared. According to the experiment's results performed on a data set with 8000 minutes video, the model based on binary relevance, named NMLNet, has highest accuracy about 99.23% to classify 9 types of navigation mark's lights. It also has the fastest computation speed with least network parameters. In the NMLNet, there are two branches for the classifications of color and flashing, respectively, and for the flashing classification, an improved MobileNet-v2 was used to capture the brightness characteristic of lights in each video frame, and an LSTM is used to capture the temporal dynamics of lights. Aiming to run on mobile devices on vessel, the MobileNet-v2 was used as backbone, and with the improvement of spatial attention mechanism, it achieved the accuracy near Resnet-50 while keeping its high speed.

综述电子海图导航系统的标准化历程和应用现状,从多维化、一体化、开放化、服务化以及移动化等维度分析电子海图导航系统的进化,介绍多维电子海图、船联网、S-100标准、E-Navigation、智能服务和移动导航APP等相关关键技术.结合智能船舶的发展趋势,分析环境感知、增强现实、VDES和自动驾驶等技术与电子海图导航系统相结合的应用前景.

The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 × 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 × 320, ShipYOLO, compared to YOLO-V4.

In this paper, we propose a tooth brushing monitoring system based on acoustic inputs through an asymmetrical sound-field detector. This detector consists of a throat microphone and a Bluetooth earphone equipped on the user's neck and ear, respectively. This system can capture unique acoustic signals generated by the movement of the toothbrush on the surfaces of teeth via the detector. The throat microphone captures the brushing sound travelling through gums, bones, and muscles, which forms unique patterns with less attenuation than the sound travelling through the air. The Bluetooth earphone captures the brushing sound through the air. The tooth surface is divided into 16 parts for detection. By adopting machine learning models with the input of acoustic features from both time and frequency domains, we build a high accuracy detector to distinguish the brushing events happened at each of the 16 parts of the tooth surface. We employ Support Vector Machine (SVM), Hidden Markov Model (HMM), K-Means, C4.5 and Random Forest (RF) to evaluate the performance of our detection system. Experiments show that the RF model performs the best and achieves an average accuracy of 85.69\%. Based on the pre- trained model, we develop an Android-based APP to monitor the user's daily tooth brushing time and help the user form a good habit of tooth brushing.

The paper designed and implemented a mobile AIS data display system based on Android platform .The system can connect Shipborne AIS equipment by Bluetooth , and ena-ble an intelligent mobile device ( phone or tablet ) to receive and decode AIS data to obtain realtime information of vessels around and transmit them onto electronic nautical charts . With the functions of vessel dynamic monitoring and vessel in-formation querying , the system can act as a simple portable navigator for the ship handlers of small inland vessels by run-ning it in their mobile devices .

In order to develop an open source and cross-platform application to replace costly commercial Web-GIS softwares for Web electronic chart service , the paper studied the key technologies for data access and cartographic expression of electronic chart data based on MapServer and its heart Mapfile , and opened up a new way for researching Web electronic chart service , which can help to reach a low-cost solution for Web electronic chart service and accelerate its popularization in the shipping information field .

利用Android移动操作系统平台,设计并实现了一种移动电子海图显示系统.对系统的组织结构,以及S-57数据的解析与转换、坐标投影与坐标转换、海图的检索与物标显示等关键技术进行重点论述.实现的系统显示海图准确美观,在智能手机和平板上运行快速稳定,在船舶导航、船舶监控等领域具有广阔的推广应用前景.

The Low-cost chaos radar with coherent reception was proposed, and the range measurement method was also introduced in 2010, where the coherent reception is achieved by a matched filter. In this paper, the chaos radar concept is simulated to verify the target range measurement method. A velocity measurement is introduced. Simulation results show that it is feasible to use the low cost chaos radar to measure the velocity. The new chaotic signal generator is successfully implemented on an electronic circuit. (5 pages)

In accordance with the requirements of the maintenance electrician syllabus,an economical,practical,nice,small footprint practice teaching equipment for PLC is designed.The device allows students intuitively and fully manifest the control process of PLC control,practice design thinking of PLC,and increase their interest in learning PLC.

In our daily life people used to charge their mobile phones all the night,it violates the principle of low carbon life.In addition,Continuous charging tends to cause speedy aging of the charger.So far,there is no any mobile phone charger which can be called as a "green" one.Aiming at saving energy in charging mobile phones this paper proposes a practical circuit which uses 555 Manifold to realize delay power outages.

中职学生在进行电力拖动控制线路装接实习当中,安全生产实习问题成为困扰老师和学生的突出问题。本人对电力拖动控制线路装接实习当中使用的交流接触器的结构和使用提出几点改进意见,以提高实习效率,减少安全隐患。