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| 1 | New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle显示文摘Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems. | DUAN HaiBin 1 ,SHAO Shan 2 ,SU BingWei 3 &ZHANG Lei 41 State Key Laboratory of Science and Technology on Holistic Flight Control,School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics,Beijing 100191,China 2 Flight Control Department,Shenyang Aircraft Design and Research Institute,Shenyang 110035,China 3 Beijing Institute of Near Space Vehicle’s System Engineering,Beijing 100076,China 4Integration and Project Section,Air Force Equipment Academy,Beijing 100085,China | 2010 | Science China(Technological Sciences)2010，53，8： | 32 |
| 2 | Comparative efficacy and safety of cognitive enhancers for treating vascular cognitive impairment: systematic review and Bayesian network meta-analysis显示文摘Objective: To assess and compare the clinical efficacy and safety of cognitive enhancers(donepezil, galantamine, rivastigmine, and memantine) on cognition, behavior, function, and global status in patients with vascular cognitive impairment.Data sources: The initial literature search was performed with PubMed, EMBASE, the Cochrane Methodology Register, the Cochrane Central Register of Controlled Trials, and Cumulative Index to Nursing & Allied Health(CINAHL) from inception to January 2018 for studies regarding donepezil, galantamine, rivastigmine, and memantine for treatment of vascular cognitive impairment.Data selection: Randomized controlled trials on donepezil, galantamine, rivastigmine, and memantine as monotherapy in the treatment of vascular cognitive impairment were included. A Bayesian network meta-analysis was conducted. Outcome measures: Efficacy was assessed by changes in scores of the Alzheimer's Disease Assessment Scale, cognitive subscale, Mini-Mental State Examination, Neuropsychiatric Inventory scores and Clinician's Interview-Based Impression of Change Scale Plus Caregiver's Input, Activities of Daily Living, the Clinical Dementia Rating scale. Safety was evaluated by mortality, total adverse events(TAEs), serious adverse events(SAEs), nausea, vomiting. diarrhea, or cerebrovascular accidents(CVAs). Results: After screening 1717 citations, 12 randomized controlled trials were included. Donepezil and rivastigmine(mean difference(e) = –0.77, 95% confidence interval(CI): 0.25–1.32; MD = 1.05, 95% CI: 0.18–1.79) were significantly more effective than placebo in reducing Mini-Mental State Examination scores. Donepezil, galantamine, and memantine(MD = –1.30, 95% CI: –2.27 to –0.42; MD = –1.67, 95% CI: –3.36 to –0.06; MD = –2.27, 95% CI: –3.91 to –0.53) showed superior benefits on the Alzheimer's Disease Assessment Scale–cognitive scores compared with placebo. Memantine(MD = 2.71, 95% CI: 1.05–7.29) improved global status(Clinician's Interview-Based Impression of Change Scale Plus Caregiver's Input) more than the placebo. Safety results revealed that donepezil 10 mg(odds ratio(OR) = 3.04, 95% CI: 1.86–5.41) contributed to higer risk of adverse events than placebo. Galantamine(OR = 5.64, 95% CI: 1.31–26.71) increased the risk of nausea. Rivastigmine(OR = 16.80, 95% CI: 1.78–319.26) increased the risk of vomiting. No agents displayed a significant risk of serious adverse events, mortality, cerebrovascular accidents, or diarrhea.Conclusion: We found significant efficacy of donepezil, galantamine, and memantine on cognition. Memantine can provide significant efficacy in global status. They are all safe and well tolerated. | Bo-Ru Jin Hua-Yan Liu | 2019 | Neural Regeneration Research2019，14，5： | 9 |
| 3 | Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma显示文摘BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS Patients with curatively resected advanced gallbladder adenocarcinoma(T3 and T4)were selected from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.A survival prediction model based on Bayesian network(BN)was constructed using the tree-augmented na?ve Bayes algorithm,and composite importance measures were applied to rank the influence of factors on survival.The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3.The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTS A total of 818 patients met the inclusion criteria.The median survival time was 9.0 mo.The accuracy of BN model was 69.67%,and the area under the curve value for the testing dataset was 77.72%.Adjuvant radiation,adjuvant chemotherapy(CTx),T stage,scope of regional lymph node surgery,and radiation sequence were ranked as the top five prognostic factors.A survival prediction table was established based on T stage,N stage,adjuvant radiotherapy(XRT),and CTx.The distribution of the survival time(>9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)>adjuvant radiation>adjuvant chemotherapy>surgery alone.For patients with node-positive disease,the larger benefit predicted by the model is adjuvant chemoradiotherapy.The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P<0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P<0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients.Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease. | Zhi-Min Geng Zhi-Qiang Cai Zhen Zhang Zhao-Hui Tang Feng Xue Chen Chen Dong Zhang Qi Li Rui Zhang Wen-Zhi Li Lin Wang Shu-Bin Si | 2019 | World Journal of Gastroenterology2019，25，37： | 9 |
| 4 | Structure learning on Bayesian networks by finding the optimal ordering with and without priors显示文摘Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets. | HE Chuchao GAO Xiaoguang GUO Zhigao | 2018 | Journal of Systems Engineering and Electronics2018，29，6： | 5 |
| 5 | Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network显示文摘Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors. | LIU Yang ZHANG Jian-jing ZHU Chong-hao XIANG Bo WANG Dong | 2019 | Journal of Mountain Science2019，16，8： | 5 |
| 6 | Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm显示文摘In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data isvery important to understand the underlying biological system,and it has been a challenging task in bioinformatics.TheBayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determinethe network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithmwhich integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use ofboth simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of theknown real regulatory relationships from literature and predict the others unknown with high validity and accuracy. | Gui-xia Liu, Wei Feng, Han Wang, Lei Liu, Chun-guang ZhouCollege of Computer Science and Technology, Jilin University, Changchun 130012,P.R. China | 2009 | Journal of Bionic Engineering2009，6，1： | 4 |
| 7 | Parallel ADR Detection Based on Spark and BCPNN显示文摘Adverse Drug Reaction(ADR) is one of the major challenges to the evaluation of drug safety in the medical field. The Bayesian Confidence Propagation Neural Network(BCPNN) algorithm is the main algorithm used by the World Health Organization to monitor ADRs. Currently, ADR reports are collected through the spontaneous reporting system. However, with the continuous increase in ADR reports and possible use scenarios, the efficiency of the stand-alone ADR detection algorithm will encounter considerable challenges. Meanwhile, the BCPNN algorithm requires a certain number of disk I/O, which leads to considerable time consumption. In this study,we propose a Spark-based parallel BCPNN algorithm, which speeds up data processing and reduces the number of disk I/O in BCPNN, and two optimization strategies. Then, the ADR data collected from the FDA Adverse Event Reporting System are used to verify the performance of the proposed algorithm and its optimization strategies.Experiments show that the parallel BCPNN can significantly accelerate data processing and the optimized algorithm has a high acceleration rate and can effectively prevent memory overflow. Finally, we apply the proposed algorithm to a dataset provided by a real medical consortium. Experiments further prove the performance and practical value of the proposed algorithm. | Li Sun Shan Sun Tianlei Wang Jiyun Li Jingsheng Lin | 2019 | Tsinghua Science and Technology2019，24，2： | 4 |
| 8 | Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor显示文摘Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. Faults are inevitable during the operation of modified sequencing batch reactors(MSBRs) because of the uncertainty of various factors. Abnormal MSBR results require fault diagnosis to determine the cause of failure and implement appropriate measures to adjust system operations. Bayesian network(BN) is a powerful knowledge representation tool that deals explicitly with uncertainty. A BN-based approach to diagnosing wastewater treatment systems based on MSBR is developed in this study. The network is constructed using the knowledge derived from literature and elicited from experts, and it is parametrized using independent data from a pilot test.A one-year pilot study is conducted to verify the diagnostic analysis. The proposed model is reasonable, and the diagnosis results are accurate. This approach can be applied with minimal modifications to other types of wastewater treatment plants. | 李丹 王鸿东 梁晓锋 | 2019 | Journal of Shanghai Jiaotong university(Science)2019，24，4： | 2 |
| 9 | Finding optimal Bayesian networks by a layered learning method显示文摘It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper proposes an approach to layer nodes of a BN by using the conditional independence testing.The parents of a node layer only belong to the layer,or layers who have priority over the layer.When a set of nodes has been layered,the number of feasible structures over the nodes can be remarkably reduced,which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms.Integrating the dynamic programming(DP)algorithm with the layering approach,we propose a hybrid algorithm—layered optimal learning(LOL)to learn BN structures.Benefitted by the layering approach,the complexity of the DP algorithm reduces to O(ρ2^n?1)from O(n2^n?1),whereρ | YANG Yu GAO Xiaoguang GUO Zhigao | 2019 | Journal of Systems Engineering and Electronics2019，30，5： | 2 |
| 10 | Forecasting Winning Bid Prices in an Online Auction Market - Data Mining Approaches显示文摘To solve information asymmetry problem on online auction, this study suggests and validates a forecasting model of winning bid prices. Especially, it explores the usability of data mining approaches, such as neural network and Bayesian network in building a forecasting model. This research empirically shows that, in forecasting winning bid prices on online auction, data mining techniques have shown better performance than traditional statistical analysis, such as logistic regression and multivariate regression. | KIM Hongil BAEK Seung | 2004 | Journal of Electronic Science and Technology of China2004，2，3： | 1 |
| 11 | 基于贝叶斯网络的水源涵养服务空间格局优化(英文)显示文摘Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using Bayesian belief networks not only helps develop a better understanding of water conservation processes but also increases the rationality of scenario design and pattern optimization. This study establishes a water conservation network model. The model, based on Bayesian belief networks, forecasts the distribution probability of the water conservation projected under different land use scenarios for the year 2050 with the CA-Markov model. A key variable subset method is proposed to optimize the spatial pattern of the water conservation. Three key findings were obtained. First, among the three scenarios, the probability of high water conservation value was the largest under the protection scenario, and the design of this scenario was conducive to the formulation of future land use policies. Second, the key influencing factors impacting the water conservation included precipitation, evapotranspiration and land use, and the state set corresponding to the highest state of water conservation was mainly distributed in areas with high annual average rainfall and evapotranspiration and high vegetation coverage. Third, the regions suitable for optimizing water conservation were mainly distributed in the southern part of Maiji District in Tianshui, southwest of Longxian and south of Weibin District in Baoji, northeast of Xunyi County and northwest of Yongshou County in Xianyang, and west of Yaozhou District in Tongchuan. | 曾莉 李晶 | 2019 | Journal of Geographical Sciences2019，29，6： | 1 |
| 12 | AABN: Anonymity Assessment Model Based on Bayesian Network With Application to Blockchain显示文摘Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity of blockchain has caused widespread concern.In this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian Network.Firstly,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment schemes.Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail.Finally,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed. | Tianbo Lu Ru Yan Min Lei Zhimin Lin | 2019 | China Communications2019，16，6： | 1 |
| 13 | 多级Bayesian Network的影像纹理分类方法显示文摘在影像分类的实际应用中,所提取的特征(或波段)间往往存在较大的相关性。为了把Naive Bayes Clas- sifiers(NBC)模型更好地应用于分类中,本文在研究NBC模型的基础上,从特征空间划分的角度,将它进一步推广为多级Bayesian Network。实验结果分析表明:由于多级Bayesian Network模型综合考虑了特征之间的条件依赖关系,它在分类精度方面一般高于原始的NBC和最大似然法。然而,对于不同的n值,其分类结果也有所不同。 | 虞欣 郑肇葆 叶志伟 李林宜 | 2008 | 遥感学报2008，12，3： | 0 |
| 14 | 基于Bayesian Network的学习资源库推荐系统构建与实现显示文摘针对学习资源使用者的特点和当前网络学习模型的不足,提出运用贝叶斯网络建立一种个性化学习者模型。基于用户决策方案指导资源库的建设,提出了一种新的学习资源推荐算法,使学习资源的呈现符合学习者认知发展水平和个性特征,改善资源库的组织结构,实现智能化、个性化的学习资源库推荐系统。实践证明,对于本系统所推荐的学习资源,学习者非常满意。 | 肖建琼 罗兴贤 | 2009 | 软件导刊2009，8，4： | 0 |
| 15 | 基于贝叶斯网的抗肺结核诊疗数据分析显示文摘将贝叶斯网络方法应用于临床抗肺结核诊疗数据分析,挖掘筛选出有价值的信息,建立一个能够为以后的治疗提供有价值的参考作用的医院医疗方案以及模型。 | 陆维嘉 | 2010 | 计算机与数字工程2010，38，12： | 0 |
| 16 | Study on Early Warning Signals of Report on Adverse Reactions of Spontaneous Reporting System of Runzao Zhiyang Capsule显示文摘Spontaneous reporting system(SRS) is an important way to monitor the adverse drug reaction(ADR) and discover the ADR signal for marketed drugs. It can detect adverse reaction signals timely and effectively, and prevent the occurrence of drug damage. Runzao Zhiyang Capsule is mainly composed of Radix Polygoni Multiflor, Radix Polygoni Multiflori Preparata, Radix Rehmanniae Recens, Radix Sophorae Flavescentis, Folium Mori and Urtica dentata Hand.-Mazz. It has the functions of nourishing blood, nourishing yin, expelling wind to relieve itching and moistening the intestines to relieve constipation. It is mainly used for skin itching, acne, constipation and other diseases caused by blood deficiency and wind dryness. The large, national SRS database of ADRs needs effective evaluation methods. We reported on the use of Bayesian confidence propagation neural network method(BCPNN) and reporting rate ratio method(PRR) with propensity score to control confounding variables. The tendency score method was used to control the hybrid bias produced by SRS data analysis. After the calculation of PRR and BCPNN, the score of 'diarrhea', 'rash' and 'gastric dysfunction' showed that there was an early warning before and after matching. To sum up, it indicated that diarrhea, rash and gastric dysfunction were early warning signs. | 王领弟 谢雁鸣 王连心 张长 | 2018 | World Journal of Integrated Traditional and Western Medicine2018，4，4： | 0 |
| 17 | 信念传播算法在分类模型中的应用显示文摘本文主要讨论信念传播算法(BP算法)在Bayesian network模型的理论表示以及在分类中的实际应用,与传统的ICM-MRF模型相比,减少了分类时间,提高了分类质量。 | 刘洁晶 张建光 | 2012 | 福建电脑2012，28，10： | 0 |
| 18 | A selective view of stochastic inference and mod-eling problems in nanoscale biophysics显示文摘Advances in nanotechnology enable scientists for the first time to study biological pro-cesses on a nanoscale molecule-by-molecule basis.They also raise challenges and opportunities for statisticians and applied probabilists.To exemplify the stochastic inference and modeling problems in the field,this paper discusses a few selected cases,ranging from likelihood inference,Bayesian data augmentation,and semi-and non-parametric inference of nanometric biochemical systems to the uti-lization of stochastic integro-differential equations and stochastic networks to model single-molecule biophysical processes.We discuss the statistical and probabilistic issues as well as the biophysical motivation and physical meaning behind the problems,emphasizing the analysis and modeling of real experimental data. | KOU S.C. | 2009 | Science China Mathematics2009，52，6： | 0 |
| 19 | Studies on the Application of Bayesian Network in the Cause Analysis of Ship Collision显示文摘As an important means of logistics, shipping plays an important role in international trade. However, the growth of traffic density has increased the ship traffic accidents and collision takes up a greater portion. This paper renders the analysis of ship collision cases in recent years and summarizes the causes. By using the basic theory of Bayesian network, the paper sets up the collision cause model. The model can be used in the cause analysis in the probability of ship collision and prediction of accident probability in practice and thus effectively reduce the ship collision accidents. | Wang Yong | 2019 | Journal of Shipping and Ocean Engineering2019，9，1： | 0 |
| 20 | Representation and Decomposition of Complex Decision-Making Tasks in AOBDIDSS显示文摘Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligence decision support system (AOBDIDSS) structure model,applies generalized decision function (GDF) to the decomposition of decision task specifications, and determines decomposition criteria and properties of decision task specifications based on GDF. Because the task decomposition based on GDF is equivalent to the decomposition of Bayesian network, we present the representation and decomposition methods of decision tasks and properties based on Bayesian network. On these bases, the decision task decomposition problems can be entailed basically to construct a multi-sectioned Bayesian network and sub-Bayesian networks related to decision task specifications. The method is used to analyze the representation and decomposition of decision tasks in medical diagnosis. The results show that the model and method is not only feasible, but also effective and novel. | YANG Shanlin HU Xiaojian FANG Fang | 2005 | Tsinghua Science and Technology2005，10，z1： | 0 |
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