Accelerating federated learning via momentum gradient descent

Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD) and do not consider the preceding iterations to gradient update which can potentially accelerate convergence. In this article, we consider momentum term which relates to the last iteration. The proposed momentum federated learning (MFL) uses momentum gradient descent (MGD) in the local update step of FL system. We establish global convergence properties of MFL and derive an upper bound on MFL convergence rate. Comparing the upper bounds on MFL and FL convergence rates, we provide conditions in which MFL accelerates the convergence. For different machine learning models, the convergence performance of MFL is evaluated based on experiments with MNIST and CIFAR-10 datasets. Simulation results confirm that MFL is globally convergent and further reveal significant convergence improvement over FL

Development of NCOVID-19 Colloidal Gold Immunochromatographic Test Strip

Purpose: To establish a fast, simple and accurate method and immunoassay test card for the detection of new coronavirus (nCOVID-19) antigen. Methods: In this study, colloidal gold immunochromatography technology was used to detect nCOVID-19 virus antigens through the sandwich method. At the same time, the preparation plan of colloidal gold was improved, and the application of rapid immune-diagnosis technology in other fields was developed. In this study, purified recombinant nCOVID-19 nucleocapsid protein is used as the antigen to prepare murine monoclonal antibodies. The BN02 antibody produced by the mouse is used as the detection antibody to couple with colloidal gold, forming a gold-labeled complex probe. BN9m1 is used as the coating antibody for the C-line, and ProA is used for the T-line. The polymerization of colloidal gold particles enables us to detect the new coronavirus antigen’s appearance. Thus an in vitro rapid detection kit for virus detection can be made. Results: The positive detection rate of the antigen quality control serum with this colloidal gold reagent was 100%. The specificity was 100%, and the sensitivity was 1ng/ml.  Conclusion: The nCOVID-19 antigen detection reagent (colloidal gold method) developed in this research has high specificity and sensitivity, and can be used in conjunction with nucleic acid detection. As a means of detecting nCOVID-19, it can achieve qualitative and rapid screening of samples with advantage such as accuracy, repeatability, and low cost

An Efficient Probabilistic Deep Learning Model for the Oral Proficiency Assessment of Student Speech Recognition and Classification

Natural Language Processing is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. Speech recognition systems utilize machine learning algorithms and statistical models to analyze acoustic features of speech, such as pitch, duration, and frequency, to convert spoken words into written text. The Student English Oral Proficiency Assessment and Feedback System provides students with a comprehensive evaluation of their spoken English skills and offers tailored feedback to help them improve. It can be used in language learning institutions, universities, or online platforms to support language education and enhance oral communication abilities. In this paper constructed a framework stated as Latent Dirichlet Integrated Deep Learning (LDiDL) for the assessment of student English proficiency assessment. The system begins by collecting a comprehensive dataset of spoken English samples, encompassing various proficiency levels. Relevant features are extracted from the samples, including acoustic characteristics and linguistic attributes. Leveraging Latent Dirichlet Allocation (LDA), the system uncovers latent topics within the data, enabling a deeper understanding of the underlying themes present in the spoken English. To further enhance the analysis, a deep learning model is developed, integrating the LDA topics with the extracted features. This model is trained using appropriate techniques and evaluated using performance metrics. Utilizing the predictions made by the model, the system generates personalized feedback for each student, focusing on areas of improvement such as vocabulary, grammar, fluency, and pronunciation. Simulation mode uses the native English speech audio for the LDiDL training and classification. The experimental analysis stated that the proposed LDiDL model achieves an accuracy of 99% for the assessment of English Proficiency

Extremum Seeking Control for An Air-source Heat Pump Water Heating System with Flash Tank Cycle based Vapor Injection

Vapor injection (VI) techniques have been well received as an effective technology for improving the performance of air-source heat pump (ASHP) under very low ambient temperature, for which the flash tank cycle (FTC) and the internal heat exchanger cycle (IHXC) are the two majaor configurations. In principle, FTC has higher achievable performance than IHXC because that the saturated vapor from the flash tank has a lower temperature which helps reduce the compressor discharge temperature and thus power consumption [1]. However, development of the FTC technology has been hampered by the lack of proper control/operational strategy that can optimize the thermodynamic characteristics of the vapor injection channel under variable ambient and load conditions. In the flash tank, the refrigerant is separated into the liquid and saturated vapor phase. The liquid refrigerant enters the lower-stage expansion valve and then circulates through the evaporator before entering the suction side of compressor, while the saturated-vapor refrigerant is injected into the intermediate pressure port of compressor. As saturated vapor is in principle the best choice for the vapor injection channel, superheat adjustment via the upper electronic expansion valve (EEV) is no longer viable. A liquid level measurement for the flash tank has been considered as feedback for the EEV control, however, determination of the optimum liquid level is rather difficult for practical operation due to the complexity of the underlying process and diversity in operating condition. In this paper, we propose an extremum seeking control (ESC) based strategy for efficient operation of the FTC-VI based ASHP heating systems [3]. ESC is a model-free real-optimization strategy, which is a dynamic gradient search with the online gradient estimation realized by a dither-demodulation scheme. For this problem, the setpoint for the intermediate pressure of injected saturated vapor is adopted as the manipulated input of the ESC, which is adjusted by the opening of the upper EEV via an inner-loop proportional-integral (PI) controller; while the total power consumption of the system is the only feedback needed. The heating load is regulated by the compressor capacity. To evaluate the proposed ESC strategy, a Modelica based dynamic simulation model of an FTC-VI based ASHP water heater is developed with Dymola and TIL Library. The hot-water outlet temperature is regulated by the compressor capacity, while the upper-EEV opening is used to regulate the intermediate pressure and liquid level of the flash tank. Simulation study is performed under different scenarios of ambient and thermal load conditions. The results show that the ESC is able to find the optimum intermediate pressure (corresponding to the optimum flash tank liquid level) by adjusting the upper EEV, which minimizes the total power consumption without sacrifice of heating load regulation and thus maximizes the system COP. To the authors’ best knowledge, the proposed strategy is a novel control solution to the optimal operation of FTC-VI ASHP systems, which does not require plant models or sensor measurements beyond power consumption. The presented results promises a great potential for the proposed strategy to facilitate the adoption of FTC technology

Fuzzy Logic and Its Application in Football Team Ranking

Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range

Complexation With Polysaccharides Enhanced Polyphenol Gastrointestinal Stability and Activity

Fruits and vegetables contain dietary polyphenols and polysaccharides. Accumulating evidence suggests that polyphenol- containing whole foods are protective against inflammation-promoted chronic colonic diseases. However, isolated polyphenols are less stable and may not confer the same gastrointestinal health benefits as that of the whole food matrix. Therefore, we hypothesized that the complex- ation of anthocyanins, a class of polypheonols, with polysaccharides would enhance colonic concentration and stability of anthocyanins, and attenuate impaired barrier function