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Soliton molecules for combined mKdV-type bilinear equation

Communications in Theoretical Physics 2023年第10期75卷 1-9页

作者： Zhang-Xuan Zhao Lu-Wei Zhang Wei Yang Xue-Ping Cheng School of Information Engineering Zhejiang Ocean UniversityZhoushan 316022China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhoushan 316022China

Starting from the multi-soliton solutions obtained by the Hirota bilinear method,the soli ton molecule structures for the combined mKdV-type bilinear equation(Dt+∑n=1NαnDx2n+1)f*·f=0 are investigated using the velocity resonance *** two-soliton molecules of the mKdV-35 equation and the three-soliton molecules of the mKdV-357 equation are specifically demonstrated in this *** particular selections of the involved arbitrary parameters,especially the wave numbers,it is confirmed that,besides the usual multi-bright soliton molecules,the multi-dark soliton molecules and the mixed multibright-dark soliton molecules can also be *** addition,we discuss the existence of the multi-soliton molecules for the combined mKdV-type bilinear equation with more higher order nonlinear terms and *** results demonstrate that when N≥4,the combined mKdVtype bilinear equation no longer admits soliton molecules comprising more than four solitons.

关键词： soliton molecule combined mKdV-type bilinear equation Hirota bilinear method velocity resonance mechanism

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Design and Implementation of Martime 5G Communication System Based on Software Defined Radio and Open Air Interface

Design and Implementation of Martime 5G Communication System...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Wang, Yushuo Yang, Kun Ding, Jianjun Chen, Xuyang Li, Qiang Peng, Tong Big Data Mining & Application Zhejiang Ocean University Key Laboratory of Oceanographic Zhoushan China

ISBN: (纸本)9798350316308

This article proposes an innovative maritime 5G communication system based on Software Defined Radio (SDR) and OpenAirInterface (OAI),which shows advantages of high scalability and low cost. By implementing with recent research on OAI-based software base station technology, we established a core network, 5G software base station (gNB), user terminal (UE), and uplink/downlink systems to test the stability and transmission rate of the entire platform based on the testing software such as Iperf. We obtained the communication performance indicators of the equipment in marine environments and compared them with the test results in terrestrial environments, which fills the gap in the accessible test solution for 5G communication systems in marine environments. The results show that the proposed test solution can be used for reliable and accurate performance eveluation of 5G communication systems in marine environments with the feature of easy-implmentation and easy-scalability. Future research can be exploited by increasing the complexity and diversity. © 2023 IEEE.

关键词： Radio

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LoRa Path-Loss Model and Verification for Maritime Environment

LoRa Path-Loss Model and Verification for Maritime Environme...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Zeng, Hao Yang, Kun Wu, Jianming Ding, Jianjun Wen, Chengyuan Li, Xin Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application Zhoushan China

ISBN: (纸本)9798350316308

Maritime communication faces the influence of many irresistible factors such as air pressure and waves, which leads to the degradation of the efficiency and quality of maritime communication, and increases the difficulty of channel modeling. In this paper, we conducted LoRa data testing, analyzed and simulated a LoRa path loss model for maritime environment, focusing on the influence of antenna height, sea surface roughness and other channel model parameters. According to the simulation results, it is concluded that changing the standard deviation of surface height distribution and the mean-square surface slope affects the influence of the reflection path on the large-scale fading. In addition, by appropriately increasing the antenna height on board, the communication coverage can be extended accordingly. © 2023 IEEE.

关键词： Antennas

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Ship Early Warning System Based on Nine Axis Sensors 10

Ship Early Warning System Based on Nine Axis Sensors

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Ding, Jianjun Yang, Kun Liu, Yupeng Lin, Jinglong Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application Zhoushan China

ISBN: (纸本)9798350309379

In view of the various dangerous situations that ships may face the sea, such as sea wind, waves, and ocean currents, which can cause problems such as tilting, ascending and descending, and swaying of the ship's body, this paper proposes a ship attitude recognition method based on the MPU9250 nine-axis sensor. The method aims to achieve real-time and accurate monitoring and warning of ship movements, with technical advantages such as instant response, continuous monitoring, and low energy consumption. The system utilizes wireless communication technology for terminal data uploading and interaction, and employs 2G/3G/4G/5G technology for remote data transmission and warning functionality, enabling real-time data transmission and remote warning capabilities. Additionally, a machine learning-based algorithm is designed to analyze sensor datas. Experimental results demonstrate that the system can accurately monitor ship attitude changes and abnormal movements, and issue timely warning signals, which are of significant importance for ship safety and operational efficiency. © 2023 IEEE.

关键词： 5G component Monitoring System MPU9250

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A novel Multi-Label Leaming Approach for Missing and Noisy Labels 5

A novel Multi-Label Leaming Approach for Missing and Noisy L...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Yang, Xiaolei Liu, Kai Gu, Shenming Tan, Anhui Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350357950

Recently, many scholars have shown a strong interest in weak-supervised multi-label learning, to solving which idealizing label information is a common approach. However, in real life, we cannot obtain the complete labels of instances. In addition, the obtained labels may contain noise, which can reduce the performance of the algorithms. To address the above issues, we introduce MLML, a novel multi-label learning method which is designed specifically to handle missing labels and noisy labels simultaneously. Our method involves the following several steps. Firstly, we utilize the global information of multi-label data to construct a label correlation matrix. Through this matrix, we can better understand the association and influence between different labels. Secondly, in order to mitigate the impact of noisy labels, we introduce label popularity normalization, which improves the performance of the algorithm by considering the frequency of labels in the data. Finally, we improve the method by integrating positive and negative label information. We also consider the relationship between feature similarity and label similarity, which ensures that our method not only considers the information of a single label, but also the mutual influence between labels and the association between features and labels. Based on extensive experiments conducted on multiple datasets, our algorithm MLML has demonstrated comparable performance to state-of-the-art algorithms. © 2023 IEEE.

关键词： feature collaboration label collaboration multi-label noisy label optimization objective

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Potato Disease Identification Method based on Improved DeeplabV3+ Network 24

Potato Disease Identification Method based on Improved Deepl...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Duan, Xiaoyue Gu, Shenming School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9798400716720

In recent years, the advancement of deep learning technology and convolutional neural networks has presented novel solutions for the swift and precise detection of crop diseases. To address issues such as low segmentation accuracy in plant leaf images amidst complex backgrounds, inadequacies in recognition and detection models, insufficient data sets, and sluggish training speed, we propose a method for potato leaf image segmentation and recognition based on an enhanced DeeplabV3+ network. Specifically, we substitute the model encoder's backbone feature extraction network with MobileNetV2 to significantly reduce computational requirements while enhancing calculation speed. Then, the attention mechanism module was incorporated into the backbone feature extraction network and decoder to further enhance the model's edge recognition capability and improve its segmentation accuracy. Simultaneously, this study devised a classification model to achieve potato leaf disease segmentation and detection amidst complex backgrounds. In the initial stage, an enhanced DeeplabV3+ was employed for segmenting potato leaves in intricate environments. Subsequently, ResNet101, ResNet50, and ShuffleNetV2 classification models were utilized in the second stage for accurately classifying potato leaves. Finally, the two-stage model was integrated to effectively segment and detect potato leaf diseases amidst intricate backgrounds. The experimental findings demonstrate that the enhanced DeeplabV3+ segmentation model achieves a remarkable Mean Intersection over Union (MIoU) of 82.6%, an impressive Average Pixel Accuracy (MPA) of 90.65%, and a commendable accuracy rate of 94.72% for the second stage classification model. In conclusion, this approach presents a novel research avenue for accurately segmenting and detecting potato leaf diseases in challenging background scenarios, thereby contributing to advancements in this field. © 2024 ACM.

关键词： Feature extraction

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Spatial matching and flow in supply and demand of water provision services: A case study in Xiangjiang River Basin

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Journal of Mountain Science 2022年第1期19卷 228-240页

作者： DENG Chu-xiong ZHU Da-mei LIU Yao-jun LI Zhong-wu College of Resources and Environmental Sciences Hunan Normal UniversityChangsha 410081China Hunan Key Laboratory of Geospatial Big Data Mining and Application Changsha 410081China College of Environmental Science and Engineering Hunan UniversityChangsha 410082China

Global climate change and increased human consumption have aggravated the uneven spatiotemporal distribution of watershed water resources, affecting the water provision supply and demand state. However, this problem has often been ignored. The present study used the Xiangjiang River basin(XRB) as the study area, and the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST) model, demand quantification model,supply–demand ratio, and water flow formula were applied to explore the spatial heterogeneity, flow, and equilibrium between water supply and demand. The results demonstrated significant spatial heterogeneity in the upstream, midstream, and downstream *** areas of water shortage were mainly located the downstream of the Changsha–Zhuzhou–Xiangtan urban agglomeration, and the Hengyang basin was the most scarcity area. Affected by terrain gradients and human needs, water flow varied from-16.33 × 10^(8) m^(3) to 13.69 × 10^(8) m^(3)from the upstream to the downstream area, which provided a possibility to reduce spatial heterogeneity. In the future, measures such as strengthening water resource system control,sponge city construction, and dynamic monitoring technology should be taken to balance the supply and demand of water in different river sections of the basin. This study can provide references for regulating water resources allocation in different reaches of the basin.

关键词： Water provision services Supply and demand Spatiotemporal dislocation Water flow Water management and saving policy Xiangjiang River basin

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Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

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Intelligent Automation & Soft Computing 2023年第8期37卷 2059-2075页

作者： Changshun Liu Yan Liu Jingjing Song Taihua Xu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhejiang Ocean UniversityZhoushan316022China

Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from *** granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute *** information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is *** fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information *** first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence *** second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information *** results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the *** strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

关键词： Attribute reduction information granulation mapping-equiva-lence relation neighborhood rough set

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Low-light image enhancement combining dehazing and auxiliary channels 3

Low-light image enhancement combining dehazing and auxiliary...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wu, Yiting Yang, Chuansheng Wang, Chao Chen, Hongming Ye, Qihong School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province China

ISBN: (纸本)9781510668522

Image enhancement tasks have garnered significant attention in recent years, particularly regarding images captured in low-light conditions. Due to their inherent under-illumination characteristics, these images often suffer from challenges such as low contrast, low brightness, and noise that hinder their quality. With the recent advancements in deep learning, convolutional neural networks (CNNs) have demonstrated a high potential in addressing these challenges. In this paper, we propose a deep CNN-based approach that enhances color components a and b through Lab-space decomposition of images, utilizing a deep unsupervised dehazing network model as a baseline. Our approach considers both algorithmic and image decomposition, splitting the low-light image enhancement task into two parallel subtasks. Extensive experiments demonstrate that our proposed approach outperforms previous methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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LIGI: low-light image enhancement via gray image

LIGI: low-light image enhancement via gray image

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Sun, Yue Ye, Qihong Chen, Hongming Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510671881

Low-light images are usually affected by problems such as low illumination, noise, and color distortion, resulting in unsatisfactory image enhancement. Low-light image enhancement aims to improve the quality and visibility of low-light images. In this paper, a gray image based low-light image enhancement deep learning method is proposed, which converts the low-light image to gray image and then extracts the important information in the image as branches to assist the main network. To achieve better enhancement performance, we proposed denoising center block (DC block), which focusing on color restoration and information preservation. The experimental results show that the method proposed in this paper outperforms the existing low-light image enhancement methods in both objective and subjective evaluation metrics, and can effectively improve the clarity, contrast, and color reproduction of low-light images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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