Improved Denoising Autoencoder for Maritime Image Denoising and Semantic Segmentation of USV

Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely studied.The former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of USV.In this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising performance.Furthermore,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different backbone.Subsequently,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation performance.Case studies are provided to prove the feasibility of our proposed denoising and segmentation method.Finally,a simple integrated communication system combining image denoising and segmentation for USV is shown.

Impact of Bifidobacterium longum NSP001 on DSS-induced colitis in conventional and humanised mice

Most scientific investigations regarding inflammatory bowel disease(IBD)pathogenesis or therapeutic strategies use dextran sulfate sodium(DSS)-induced models performed on mice.However,differences between human and animal microbiota may confound the data reproducibility from rodent experiments to clinical trials.In this study,the intervention effects of Bifidobacterium longum NSP001 on DSS-induced colitis were investigated using mice colonized with either native or humanised microbiota.Disorders in disease activity index(DAI),morphology and histology of colon tissue,intestinal permeability,and secretion of MPO,TNF-αand IL-6 were ameliorated by daily intake of live B.longum NSP001 cells in both conventional and humanised colitis mice.But the abnormal thymus index,and colonic production of ZO-1 and iNOS were improved only in colitis mice treated with B.longum NSP001 and humanised microbiome.The accumulation of acetic acid and propionic acid in colon microbiome,and the optimization of primary bile acid biosynthesis and glycerophospholipid metabolism pathways in cecum commensals were likely to explain the beneficial effects of B.longum NSP001.These data revealed that intestinal microbiome baseline would possibly affect the manifestation features of interventions by probiotics or dietary components and highlighted the necessity to include humanised microbiome while investigating potential therapeutic strategies based on rodent models.

Knowledge-Based Efficient N-1 Analysis Calculation Method for Urban Distribution Networks with CIM File Data

The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks.However,the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners.To address this issue,we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling.Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis.We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP.We also take into account the characteristics of medium voltage distribution networks after a line failure and select the feeder section at the exit of each substation with a high load rate to improve the efficiency of N-1 analysis.We validate our approach through a series of case studies and demonstrate its scalability and superiority over traditional N-1 analysis and heuristic optimization algorithms.By enabling online N-1 analysis,our approach significantly improves the work efficiency of distribution network planners.In summary,our proposed method provides a valuable tool for distribution network planners to enhance the accuracy and efficiency of their N-1 analyses.By leveraging the advantages of CIM file data analysis and MILP-based mathematical modeling,our approach contributes to the development of more resilient and reliable electric power distribution networks.

Literature on the law of Miao medicine in treating liver cancer

Aims:Through the literature review of Miao medicine for liver cancer,this paper discussed the law of Miao medicine for liver cancer.Methods:Using"Zhonghua Bencao Miao medicine"as the source of literature,the search term was prepared to search the related literature of CNKI database and Wanfang database for the treatment of liver cancer in the Miao medicine from May 2018.The qualified drugs were used to conduct statistical analysis on their family genus,medicinal property,medicinal taste,regression and efficacy.Results:A total of 132 flavors of Miao medicine for liver cancer were included,and the high-frequency family was compositae,liliaceae,and rosaceae,accounting for 22.8%.The medicine was mainly cold medicine,accounting for 75.8%.The main taste of medicine was bitter,hot and sweet,accounting for 75.5%.The main parts of longitude are hot longitude and cold longitude,accounting for 90.5%.Drugs are mainly used to cure poison,tonifying body and vent the blood,a total of 80.4%.Conclusions:Miao medical treatment of liver cancer is based on the dialectic of cold and heat,with proper use of tonifying body and vent the blood drugs.

VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

Visualization and artificial intelligence(AI)are well-applied approaches to data analysis.On one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration.On the other hand,AI is able to learn from data and implement bulky tasks for humans.In complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and AI.Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI.However,how can AI and visualization complement each other and be integrated into data analysis processes are still missing.In this paper,we define three integration levels of visualization and AI.The highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces.We also summarize future directions of VIS+AI to inspire related studies.