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Prevalence and factors associated with belief in COVID-19 vaccine efficacy in Indonesia:A cross-sectional study

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Asian Pacific Journal of Tropical Medicine 2022年第7期15卷 308-313页

作者： Diyan Ermawan Effendi Agung Dwi Laksono Setia Pranata Zainul Khaqiqi Nantabah Research Center for Public Health and Nutrition National Research and Innovation Agency Republic of IndonesiaIndonesia Research Center for Hydrodynamics Technology National Research and Innovation Agency Republic of IndonesiaIndonesia

Objective:To investigate the prevalence of belief in COVID-19 vaccine efficacy and its associated factors.Methods:Due to mobility restriction,this study was conducted cross-sectionally via online platforms.The included factors were age,gender,religious identity,marital status,education level,occupation,and living with health workers.Logistic regression was used to assess the association between belief in COVID-19 vaccine with the predictors.Results:A total of 5397 responses were taken into analysis.The prevalence of belief in COVID-19 vaccine efficacy was 62.3%.Whereas factors associated with belief in COVID-19 vaccines were being in the age of 45-54(aOR 1.767;95%CI 1.219-2.562),55-64(aOR 1.703;95%CI 1.219-2.562),and>64(aOR 2.136;95%CI 1.128-4.047),completing education until the secondary level(aOR 1.354;95%CI 1.111-1.650),working as health practitioners(aOR 2,353;95%CI 1.655-3.344),and living with health workers(aOR 1.278,95%CI 1.079-1.514).All religious identities including Muslim(aOR 2.447;95%CI 1.183-5.062),Protestant(aOR 3.615;95%CI 1.703-7.677),Catholic(aOR 4.486;95%CI 2.015-9.987),and Hindu(aOR 3.286;95%CI 1.410-7.655)showed significant association with belief in COVID-19 vaccine efficacy.Conclusions:A high prevalence of belief in COVID-19 vaccine efficacy was evident.Since vaccine compliance is determined by an individual's risk-benefit perception,this study emphasizes the need of raising awareness of the benefits of COVID-19 immunization.

关键词： COVID-19 belief Vaccine Efficacy Muslim

Multiple Deep-belief-Network-Based Spectral-Spatial Classification of Hyperspectral Images

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Tsinghua Science and Technology 2019年第2期24卷 183-194页

作者： Atif Mughees Linmi Tao Pervasive Computing Division Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University

A deep-learning-based feature extraction has recently been proposed for HyperSpectral Images(HSI)classification. A Deep belief Network(DBN), as part of deep learning, has been used in HSI classification for deep and abstract feature extraction. However, DBN has to simultaneously deal with hundreds of features from the HSI hyper-cube, which results into complexity and leads to limited feature abstraction and performance in the presence of limited training data. Moreover, a dimensional-reduction-based solution to this issue results in the loss of valuable spectral information, thereby affecting classification performance. To address the issue, this paper presents a Spectral-Adaptive Segmented DBN(SAS-DBN) for spectral-spatial HSI classification that exploits the deep abstract features by segmenting the original spectral bands into small sets/groups of related spectral bands and processing each group separately by using local DBNs. Furthermore, spatial features are also incorporated by first applying hyper-segmentation on the HSI. These results improved data abstraction with reduced complexity and enhanced the performance of HSI classification. Local application of DBN-based feature extraction to each group of bands reduces the computational complexity and results in better feature extraction improving classification accuracy. In general, exploiting spectral features effectively through a segmented-DBN process and spatial features through hyper-segmentation and integration of spectral and spatial features for HSI classification has a major effect on the performance of HSI classification. Experimental evaluation of the proposed technique on well-known HSI standard data sets with different contexts and resolutions establishes the efficacy of the proposed techniques,wherein the results are comparable to several recently proposed HSI classification techniques.

关键词： hyperspectral image classification segmentation deep belief network support vector machine

Improved Deep belief Network and Model Interpretation Method for Power System Transient Stability Assessment

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Journal of Modern Power Systems and Clean Energy 2020年第1期8卷 27-37页

作者： Shuang Wu Le Zheng Wei Hu Rui Yu Baisi Liu State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Stanford University StanfordCalifornia 94305USA Southwest Branch State Grid Corporation of ChinaChengdu 610041China

The real-time transient stability assessment(TSA)and emergency control are effective measures to suppress accident expansion,prevent system instability,and avoid large-scale power outages in the event of power system failure.However,real-time assessment is extremely demanding on computing speed,and the traditional method is not competent.In this paper,an improved deep belief network(DBN)is proposed for the fast assessment of transient stability,which considers the structural characteristics of power system in the construction of loss function.Deep learning has been effective in many fields,but usually is considered as a black-box model.From the perspective of machine learning interpretation,this paper proposes a local linear interpreter(LLI)model,and tries to give a reasonable interpretation of the relationship between the system features and the assessment result,and illustrates the conversion process from the input feature space to the high-dimension representation space.The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China.The result demonstrates that the proposed method has rapidity,high accuracy and good interpretability in transient stability assessment.

关键词： Transient stability assessment(TSA) representation learning deep belief network(DBN) local linear interpretation(LLI) visualization emergency control

An improved α-cut approach to transforming fuzzy membership function into basic belief assignment

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Chinese Journal of Aeronautics 2016年第4期29卷 1042-1051页

作者： Yang Yi X.Rong Li Han Deqiang SKLSVMS School of Aerospace Xi'an Jiaotong University Department of Electrical Engineering University of New Orleans Center for Information Engineering Science Research Xi'an Jiaotong University

In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory(DST) framework, transformations from the other type of uncertainty representation into the basic belief assignment are needed. a-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional a-cut approach caused by its normalization step are pointed out in this paper. An improved a-cut approach is proposed, which can counteract the drawbacks of the traditional a-cut approach and has good properties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved a-cut approach.

关键词： belief functions Evidence theory Fuzzy sets Membership functions Uncertainty

Convergence Rate Analysis of Gaussian belief Propagation for Markov Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 668-673页

作者： Zhaorong Zhang Minyue Fu School of Electrical Engineering and Computer Science The University of Newcastle.University DriveNSW 2308Australia IEEE School of Automation Guangdong University of Technologyand Guangdong Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhou 510006China

Gaussian belief propagation algorithm(GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability(or generalised diagonal dominance). This paper extends this convergence result further by showing that the convergence is exponential under the generalised diagonal dominance condition,and provides a simple bound for the convergence rate. Our results are derived by combining the known walk summability approach for asymptotic convergence analysis with the control systems approach for stability analysis.

关键词： belief propagation distributed algorithm distributed estimation Gaussian belief propagation Markov networks

A Real-Time and Ubiquitous Network Attack Detection Based on Deep belief Network and Support Vector Machine

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 790-799页

作者： Hao Zhang Yongdan Li Zhihan Lv Arun Kumar Sangaiah Tao Huang IEEE National Engineering Laboratory for Educational Big Data Central China Normal UniversityWuhan 430072China Lanzhou Central Sub-branch of The People’s Bank of China Lanzhou 730000China School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China School of Computing Science and Engineering Vellore Institute of Technology UniversityTamil Nadu 632014India

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

关键词： Deep belief network(DBN) flow calculation frequent pattern intrusion detection sliding window support vector machine(SVM)

Distributed Resource Allocation in Ultra-Dense Networks via belief Propagation

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China Communications 2015年第11期12卷 79-91页

作者： CHEN Siyi XING Chengwen FEI Zesong Beijing Institute of Technology

Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.

关键词： resource allocation distributed optimization belief propagation(BP) ultradense network

A belief-Dependent Utility Model

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Acta Mathematicae Applicatae Sinica 2021年第4期37卷 682-696页

作者： Yu-lian FAN North China University of Technology Beijing100144China

This paper proposes a belief-dependent utility function(BDU).The BDU is based on the fact that the beliefs of acts have inconsistent impacts on utilities.The existence and uniqueness result of the BDU function is provided.It is then proved that the BDU function can provide risk attitude analysis and general comparative analysis as powerful as those presented by the classical expected utility theories.

关键词： belief utility function risk aversion

Spectrometry analysis based on approximation coefficients and deep belief networks

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Nuclear Science and Techniques 2018年第5期29卷 65-74页

作者： Jian-Ping He Xiao-Bin Tang Pin Gong Peng Wang Zhen-Yang Han Wen Yan Le Gao Department of Nuclear Science and Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Jiangsu Key Laboratory of Nuclear Energy Equipment Materials Engineering Nanjing 210016China

A method of spectrometry analysis based on approximation coefficients and deep belief networks was developed. Detection rate and accurate radionuclide identification distance were used to evaluate the performance of the proposed method in identifying radionuclides. Experimental results show that identification performance was not affected by detection time, number of radionuclides, or detection distance when the minimum detectable activity of a single radionuclide was satisfied. Moreover, the proposed method could accurately predict isotopic compositions from the spectra of moving radionuclides. Thus, the designed method can be used for radiation monitoring instruments that identify radionuclides.

关键词： Approximation coefficient Deep belief network Spectrometry analysis Radionuclide identification Detection rate