Distributed Spectrum and Power Allocation for D2D-U Networks: A Scheme based on NN and Federated Learning

In this paper, a Device-to-Device communication on unlicensed bands (D2D-U) enabled network is studied. To improve the spectrum efficiency (SE) on the unlicensed bands and fit its distributed structure while ensuring the fairness among D2D-U links and the harmonious coexistence with WiFi networks, a distributed joint power and spectrum scheme is proposed. In particular, a parameter, named as price, is defined, which is updated at each D2D-U pair by a online trained Neural network (NN) according to the channel state and traffic load. In addition, the parameters used in the NN are updated by two ways, unsupervised self-iteration and federated learning, to guarantee the fairness and harmonious coexistence. Then, a non-convex optimization problem with respect to the spectrum and power is formulated and solved on each D2D-U link to maximize its own data rate. Numerical simulation results are demonstrated to verify the effectiveness of the proposed scheme

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Modeling and Optimization for Automobile Mixed Assembly Line in Industry 4.0

Industry 4.0 promotes the development of traditional manufacturing industry to digitization, networking, and intellectualization. Smart factory is composed of network that includes production equipment, robot, conveyor, and logistics system. According to the characteristics of the mixed flow assembly, a simulation platform of automobile mixed flow assembly is built based on industry 4.0 in the paper, which operates and manages automobile assembly, logistics warehouse, and CPS effectively. On this basis, FlexSim software is adopted to establish the auto-mixed assembly model that finds out the bottleneck of auto-mixed assembly problem. By means of parameter adjustment, rearrangement, and merger of process, the whole assembly time of the 500 automobiles dropped by 33 hours, the equipment utilization rate increased by 20.19%, and the average blocked rate decreased by 21.19%. The optimized results show that the proposed model can greatly increase manufacturing efficiency and practical application in industry 4.0

Experimental and Numerical Study on Motion and Resistance Characteristics of the Partial Air Cushion Supported Catamaran

The Partial Air Cushion Supported Catamaran (PACSCAT) is an innovative design which combines both the characteristics of hovercraft and catamaran. Further, it provides a high-speed and efficient solution with excellent performance, particularly for shallow water. In this paper, experimental and numerical method are carried out for research of motion attitude and resistance characteristics, which provide a reference for further research and hull optimization work. By model towing test and data interpretation, and the resistance, trim, and heave varying law with increasing speed is summarized. From the view of total resistance, the impacts of the cushion pressure and air flow on resistance performance of PACSCAT are analyzed. Based on the theory of viscous fluid mechanics, a numerical simulation method with high prediction accuracy is established. The flow field around and inside the hull is simulated, the simulating results show good agreements with the testing data. Finally, the effect of the cushion compartment improving the resistance performance is studied. The results show that the cushion compartment is significant for adjusting the pressure distribution of the air cushion. And the average resistance reduction ratio at the high-speed segment can even reach 22%

Learning-based WiFi traffic load estimation in NR-U systems

The unlicensed spectrum has been utilized to make up the shortage on frequency spectrum in new radio (NR) systems. To fully exploit the advantages brought by the unlicensed bands, one of the key issues is to guarantee the fair coexistence with WiFi systems. To reach this goal, timely and accurate estimation on the WiFi traffic loads is an important prerequisite. In this paper, a machine learning (ML) based method is proposed to detect the number of WiFi users on the unlicensed bands. An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users. Moreover, NN is trained online and the related parameters and learning rate of NN are jointly optimized to estimate the number of WiFi users adaptively with high accuracy. Simulation results demonstrate that compared with the conventional Kalman Filter based detection mechanism, the proposed approach has lower complexity and can achieve a more stable and accurate estimation