This paper investigates the issue of adaptive fuzzy fixed-time control of non-strict feedback nonlinear multi-intelligent systems. In this regard, a fuzzy logic system (FLS) is utilized to tackle the unknown nonlinear dynamics, and a non-singular fixed-time consensus control approach based on the adding power integral technique is then developed by building a novel integral-type Lyapunov function. Technically, a fixed-time adaptive fuzzy consensus control solution is formulated by engineering an integral-type Lyapunov function, which means that the tracking errors can convergence to zero in a fixed time. Finally, the effectiveness of the proposed asymptotic consensus control method is verified by a simulation example.
Objective: To explore the application value of artificial intelligence-assisted diagnosis system for TBS report in cervical cancer screening. Methods: A total of 16 317 clinical samples and related data of cervical liquid-based thin-layer cell smears, which were obtained from July 2020 to September 2020, were collected from Southern Hospital, Guangzhou Huayin Medical Inspection Center, Shenzhen Bao'an People's Hospital(Group) and Changsha Yuan'an Biotechnology Co., Ltd. The TBS report artificial intelligence-assisted diagnosis system of cervical liquid-based thin-layer cytology jointly developed by Southern Medical University and Guangzhou F. Q. PATHOTECH Co., Ltd. based on deep learning convolution neural network was used to diagnose all clinical samples. The sensitivity,specificity and accuracy of both artificial intelligence-assisted diagnosis system and cytologists using artificial intelligence-assisted diagnosis system were analyzed based on the evaluation standard(2014 TBS). The time spent by the two methods was also compared. Results: The sensitivity of artificial intelligence-assisted diagnosis system in predicting cervical intraepithelial lesions and other lesions (including endometrial cells detected in women over 45 years old and infectious lesions) under different production methods, different cytoplasmic staining and different scanning instruments was 92.90% and 83.55% respectively, and the specificity of negative samples was 87.02%, while that of cytologists using artificial intelligence-assisted diagnosis system was 99.34%, 97.79% and 99.10%, respectively. Moreover, cytologists using artificial intelligence-assisted diagnosis system could save about 6 times of reading time than manual. Conclusions: Artificial intelligence-assisted diagnosis system for TBS report of cervical liquid-based thin-layer cytology has the advantages of high sensitivity, high specificity and strong generalization. Cytologists can significantly improve the accuracy and work efficiency of reading smears by using artificial intelligence-assisted diagnosis system.
<div class="section abstract"><div class="htmlview paragraph">With the acceleration of urbanization, developing public transportation is an important means to alleviate travel pressure and traffic congestion in cities. Work zones that occupy urban road resources affect normal vehicle operations, leading to reduced vehicle efficiency. Based on this, the paper conducts research on traffic flow modeling and simulation analysis for work zones in a vehicle-road coordination environment. Based on the Gipps model and the SCAT model, optimizations and improvements were made to the following and lane-changing rules for three types of vehicles: human-driven vehicles (HVs), autonomous and connected vehicles (CAVs), and buses. Using cellular automata theory, it constructs a running model suitable for mixed traffic flow vehicles in work zones. MATLAB software is utilized to simulate the operation process of vehicles under work zone scenarios, analyzing changes in traffic flow from two directions: road geometric conditions (speed limits) and traffic flow states (volume, vehicle type ratios, etc.). The study analyzes the impact of vehicle motion behavior on mixed traffic flow under different road scenarios, and examines the effect of work zones on time-space diagrams. It concludes that, at the same density, the higher the proportion of CAVs, the lower the probability of congestion. When the traffic flow is in a free-flow state, the speeds of vehicles under different speed limit conditions are not the same. When the traffic density is around 45 veh/km, the traffic volume reaches its maximum. At the same density, the higher the proportion of CAVs, the greater the overall traffic flow speed and the higher the capacity of the road section.This research provides support for the improvement of theories related to traffic flow operations in work zones under a vehicle-road coordination environment.</div></div>
With the development of 5G communication and smart devices, the prosperity of online content has boosted the research of Recommender System (RS). Due to the data scarcity problem, researchers employ knowledge transfer techniques to improve the accuracy of RS. Data sharing or data augmentation are promising methods for such a problem, but the data suffers from privacy leakage during sharing. Thus, Federated Learning (FL) has been adopted to collaboratively train recommender models while preserving data privacy. Federated Recommender System (FRS) combines FL and RS to provide distributed recommendation services to the users. However, the existing FRS suffers from massive communication and system heterogeneity, where the necessity of model transmission and the diversity of the clients bring significant communication overhead to the system. In this paper, we propose Efficient Federated Recommender System with Adaptive Model Pruning and Momentum-based Batch Adjustment ( eFRSA 2 ) to reduce the communication overhead of FRS. eFRSA 2 contains two modules. Adaptive Model Pruning utilizes magnitude pruning to reduce the communication volume and adaptively modifies the compression ratios of different clients to maintain the model accuracy. Momentum-based Batch Adjustment adjusts the local training batch number by a similar method of gradient descent with momentum to align the local computation time of the clients and reduce the communication overhead. The experimental results demonstrate that eFRSA 2 can reduce up to 90% communication volume and mitigate the system heterogeneity by over 75%, demonstrating the priority of eFRSA 2 in training efficiency. Source code can be found at https://github.com/shhjwu5/eFRSA2.
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