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MD-AVB: A Multi-Manifold Based Available Bandwidth Prediction Algorithm
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作者 Pei Zhang Changqing An +1 位作者 Zhanfeng Wang Fengyuan Ma 《清华大学学报自然科学版(英文版)》 EI CAS CSCD 2020年第1期140-148,共9页
The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of availabl... The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of available bandwidth prediction algorithms have been proposed to date, but none of the existing solutions are able to achieve a high level of accuracy. In this paper, a Multi-manifold based Available Bandwidth prediction algorithm(MD-AVB)is proposed, based on the observation that the available bandwidth space on the Internet is multi-manifold and asymmetrical. In the proposed algorithm, the available bandwidth space is divided into multiple lower-dimensional domains iteratively, and each domain is embedded separately to predict the available bandwidth. Experiments on HP S~3 datasets demonstrate that the proposed algorithm is more accurate than existing approaches. 展开更多
关键词 available BANDWIDTH SPACE performance prediction multi-manifold asymmetry
国际关系中的预测失败现象初探
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作者 王剑峰 《国际关系研究》 CSSCI 2019年第5期41-61,M0004,M0005,共23页
预测是国际关系理论的一项重要功能,亦是国际关系学科发展成熟的一个重要衡量标准。作为根植于社会科学领域的一门学科,国际关系预测失败是一种普遍的、正常的现象,其根源在于既定的预测逻辑机制遭到了外界因素的冲击。纵观整个国际关... 预测是国际关系理论的一项重要功能,亦是国际关系学科发展成熟的一个重要衡量标准。作为根植于社会科学领域的一门学科,国际关系预测失败是一种普遍的、正常的现象,其根源在于既定的预测逻辑机制遭到了外界因素的冲击。纵观整个国际关系预测的逻辑演进机制,预测在按照一般程序推进过程中,简化现实与“自我否定的预测”这两大干扰力量分别在预测的初始阶段和末期阶段改变了既有逻辑机制的运行轨迹。由此,内在逻辑机制的失灵最终阻碍了预测进程的正常发展,国际关系预测不可避免地走向了失败的境地。这就警示预测主体应该充分地认识到社会现实的复杂性问题,避免盲目地简化现实,同时,也要综合考量各种不确定和不稳定因素,在复杂的情势中作出相对准确的概率性预测。 展开更多
关键词 预测 国际关系预测 预测失败 简化现实 自我否定的预测
含误差预报校正的GM(1,1)卫星钟差预报新方法
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作者 于烨 张慧君 李孝辉 《测绘科学》 CSCD 北大核心 2019年第4期8-14,共7页
为了提高卫星钟差预报精度,该文提出用AR(p)模型对GM(1,1)建模过程中的模型残差进行建模预报,以此来提高GM(1,1)模型预报卫星钟差的精度。首先,剔除卫星钟差数据中的异常值,采用拉格朗日插值法将缺失的数据补齐;然后,用GM(1,1)模型对卫... 为了提高卫星钟差预报精度,该文提出用AR(p)模型对GM(1,1)建模过程中的模型残差进行建模预报,以此来提高GM(1,1)模型预报卫星钟差的精度。首先,剔除卫星钟差数据中的异常值,采用拉格朗日插值法将缺失的数据补齐;然后,用GM(1,1)模型对卫星钟差进行预报,对GM(1,1)的模型残差作平稳化处理后,采用AR(p)模型对处理后的残差序列进行预报;最后,将GM(1,1)和AR(p)模型的预报结果对应相加即得到钟差的最终预报值。此外,该文采用IGS公布的事后精密卫星钟差进行预报试验,并将该文结果与卫星钟差预报中常用的二次多项式和修正指数曲线法模型预报结果进行对比分析。结果表明,该方法可以对GPS卫星钟差进行高精度的中短期预报。 展开更多
关键词 卫星钟差 预报 GM(1 1)预报模型 AR(p)预报模型 误差校正
LSTM Based Reserve Prediction for Bank Outlets
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作者 Yu Liu Shuting Dong +1 位作者 Mingming Lu Jianxin Wang 《清华大学学报自然科学版(英文版)》 EI CAS CSCD 2019年第1期77-85,共9页
Reserve allocation is a significant problem faced by commercial banking businesses every day.To satisfy the cash requirement of customers and abate the vault cash pressure,commercial banks need to appropriately alloca... Reserve allocation is a significant problem faced by commercial banking businesses every day.To satisfy the cash requirement of customers and abate the vault cash pressure,commercial banks need to appropriately allocate reserves for each bank outlet.Excessive reserve would impact the revenue of bank outlets.Low reserves cannot guarantee the successful operation of bank outlets.Considering the reserve requirement is effected by the past cash balance,we deal the reserve allocation problem as a time series prediction problem,and the Long Short Time Memory (LSTM)network is adapted to solve it.In addition,the proposed LSTM prediction model regards date property,which can affect the cash balance,as a primary factor.The experiment results show that our method outperforms some existing traditional methods. 展开更多
关键词 RESERVE PREDICTION TIME series PREDICTION LONG SHORT TIME Memory (LSTM)network DATE property
Application of Combined Prediction Model in Predicting Total Water Consumption in Ningxia 预览
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作者 Yongliang WANG Jiongli CHEN Lian TANG 《亚洲农业研究:英文版》 2019年第3期45-47,共3页
The gray GM( 1,1) prediction model and Logistic equation gray prediction model were established separately,and then the combined prediction model was established. Taking the water consumption in Ningxia Hui Autonomous... The gray GM( 1,1) prediction model and Logistic equation gray prediction model were established separately,and then the combined prediction model was established. Taking the water consumption in Ningxia Hui Autonomous Region from 2006 to 2012 as modeling data,the total water consumption of the whole region of Ningxia in 2018-2020 was analyzed and predicted. The results show that the accuracy of the three prediction models meets the accuracy requirements,but the gray GM( 1,1) and combined prediction models better conform to the actual situation and have better applicability. 展开更多
关键词 GM (1 1) PREDICTION LOGISTIC equation GRAY PREDICT Combined PREDICTION Water CONSUMPTION
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Short-term Prediction of Ionospheric TEC Based on ARIMA Model 预览
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作者 Xiaohong ZHANG Xiaodong REN +1 位作者 Fengbo WU Qi LU 《测绘学报(英文版)》 2019年第1期9-16,共8页
In order to achieve high short-term prediction accuracy of ionospheric TEC,first,we transform a seasonal time series for ionospheric Total Electron Content (TEC) into a stationary time series by seasonal differences a... In order to achieve high short-term prediction accuracy of ionospheric TEC,first,we transform a seasonal time series for ionospheric Total Electron Content (TEC) into a stationary time series by seasonal differences and regular differences with a full consideration of the Multiplicative Seasonal model.Next,we use the Autoregressive Integrated Moving Average (ARIMA) model taken from time series analysis theory for modeling the stationary TEC values to predict the TEC series.Using TEC data from 2008 to 2012 provided by the Center for Orbit Determination in Europe (CODE) as sample data,we analyzed the precision of this method for prediction of ionospheric TEC values which vary from high to low latitudes during both quiet and active ionospheric periods.The effect of the TEC sample’s length on the predicted accuracy is analyzed,too.Results from numerical experiments show that during the ionospheric quiet period the average relative prediction accuracy for a six day time span reaches up to 83.3% with average prediction residual errors of about 0.18±1.9 TECu.During ionospheric active periods it changes to 86.6% with an average prediction residual error of about 0.69±2.6 TECu.For the quiet periods,above 90% of predicted residual is less than ±3 TECu while during active periods,it is only about 81%.The two periods show that that the higher the latitude,the higher the absolute precision,and the lower the predicted relative accuracy.In addition,the results show that prediction accuracy will improve with an increase of the TEC sample sequences length,but it will gradually reduce if the length exceeds the optimal length,about 30 days.On the other hand,with the same TEC sample,as the predicted days increase,the predictive accuracy decreases.Athough the predictive accuracy is not apparent at the beginning,it will be significantly reduced after 30 days. 展开更多
关键词 ARIMA IONOSPHERE PREDICTION TIME series analysis PREDICTION ACCURACY TEC
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Wind power prediction based on variational mode decomposition multi-frequency combinations 预览
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作者 Gang ZHANG Hongchi LIU +5 位作者 Jiangbin ZHANG Ye YAN Lei ZHANG Chen WU Xia HUA Yongqing WANG 《现代电力系统与清洁能源学报(英文)》 CSCD 2019年第2期281-288,共8页
Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power i... Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher. 展开更多
关键词 Wind power PREDICTION VARIATIONAL mode decomposition MULTI-FREQUENCY combination PREDICTION Back propagation neural network AUTOREGRESSIVE moving AVERAGE model Least square support vector machine
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Predictors for Predicting Temperature Optimum in Beta-Glucosidases 预览
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作者 Shaomin Yan Guang Wu 《生物医学工程(英文)》 2019年第8期414-426,共13页
This is the continuation of our studies on beta-glucosidase, which plays an important role in biological processes and recently strong interests focus on their potential role in biofeul production. In order to develop... This is the continuation of our studies on beta-glucosidase, which plays an important role in biological processes and recently strong interests focus on their potential role in biofeul production. In order to develop simple methods to predict the optimal working condition for beta-glucosidase, we used a 20-1 feedforward backpropagation neural network to screen possible predictors to predict the temperature optimum of beta-glucosidase from 25 amino-acid properties related to the primary structure of beta-glucosidases. The results show that the normalized polarizability index and amino-acid distribution probability can predict the temperature optimum of beta-glucosidase, which highlights a cost-effective way to predict various enzymatic parameters of beta-glucosidase. 展开更多
关键词 BETA-GLUCOSIDASE ENZYME TEMPERATURE OPTIMUM PREDICTION
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A Novel Search Engine for Internet of Everything Based on Dynamic Prediction 预览
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作者 Hui Lu Shen Su +1 位作者 Zhihong Tian Chunsheng Zhu 《中国通信:英文版》 SCIE CSCD 2019年第3期42-52,共11页
In recent years,with the rapid development of sensing technology and deployment of various Internet of Everything devices,it becomes a crucial and practical challenge to enable real-time search queries for objects,dat... In recent years,with the rapid development of sensing technology and deployment of various Internet of Everything devices,it becomes a crucial and practical challenge to enable real-time search queries for objects,data,and services in the Internet of Everything.Moreover,such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues.By looking into the unique characteristics in the IoE application environment,such as high heterogeneity,high dynamics,and distributed,we develop a novel search engine model,and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment.We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab. 展开更多
关键词 IoE SEARCH ENGINE IoE security REAL-TIME SEARCH MODEL dynamic PREDICTION MODEL time series PREDICTION
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计及随机时滞与丢包的电力系统广域信号预测补偿方法 预览
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作者 陈中 唐浩然 +1 位作者 邢强 周涛 《电力系统保护与控制》 CSCD 北大核心 2019年第15期31-39,共9页
针对广域信号在通信系统的传输过程中具有时滞与丢包以及在实际场景下对量测信号采样过程中信号具有噪声等问题,建立了一种基于灰色Verhulst的预测补偿模型。将该模型与完整集成经验模态分解(Complete Ensemble Empirical Mode Decompos... 针对广域信号在通信系统的传输过程中具有时滞与丢包以及在实际场景下对量测信号采样过程中信号具有噪声等问题,建立了一种基于灰色Verhulst的预测补偿模型。将该模型与完整集成经验模态分解(Complete Ensemble Empirical Mode Decomposition with adaptive noise,CEEMDAN)算法相结合,提出了一种电力系统广域信号的预测补偿方法。该方法首先通过CEEMDAN对广域信号进行降噪,然后采用灰色Verhulst预测方法对多个广域信号分别进行预测,得出统一时标的控制器输入信号。最后在OPNET-Matlab仿真平台中搭建了计及通信系统影响的两区四机系统,并在3种不同的通信场景下对所提算法进行验证。测试结果表明该方法具有一定的抗噪性能并可实现对具有时滞与丢包的广域信号预测补偿,为广域阻尼控制器的有效应用提供了一种新途径。 展开更多
关键词 广域电力系统稳定器 预测补偿 时滞与丢包 灰色预测 完整集成经验模态分解
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DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy
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作者 Xiang Chen Dun Zhang +2 位作者 Zhan-Qi Cui Qing Gu Xiao-Lin Ju 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第5期1020-1038,共19页
In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results... In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results. Instead of SDP dataset sharing, SDP model sharing is a potential solution to alleviate this problem and can encourage researchers in the research community and practitioners in the industrial community to share more models. However, directly sharing models may result in privacy disclosure, such as model inversion attack. To the best of our knowledge, we are the first to apply differential privacy (DP) to privacy-preserving SDP model sharing and then propose a novel method DP-Share, since DP mechanisms can prevent this attack when the privacy budget is carefully selected. In particular, DP-Share first performs data preprocessing for the dataset, such as over-sampling for minority instances (i.e., defective modules) and conducting discretization for continuous features to optimize privacy budget allocation. Then, it uses a novel sampling strategy to create a set of training sets. Finally it constructs decision trees based on these training sets and these decision trees can form a random forest (i.e., model). The last phase of DP-Share uses Laplace and exponential mechanisms to satisfy the requirements of DP. In our empirical studies, we choose nine experimental subjects from real software projects. Then, we use AUC (area under ROC curve) as the performance measure and holdout as our model validation technique. After privacy and utility analysis, we find that DP-Share can achieve better performance than a baseline method DF-Enhance in most cases when using the same privacy budget. Moreover, we also provide guidelines to effectively use our proposed method. Our work attempts to fill the research gap in terms of differential privacy for SDP, which can encourage researchers and practitioners to share more SDP models and then effectively advance the state of the art of SDP. 展开更多
关键词 software DEFECT PREDICTION model SHARING differential PRIVACY cross project DEFECT PREDICTION empirical study
Artificial Neural Network Modeling to Predict the Non-Linearity in Reaction Conditions of Cholesterol Oxidase from <i>Streptomyces olivaceus</i><i>MTCC</i>6820 预览
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作者 Shraddha Sahu Shailendra Singh Shera Rathindra Mohan Banik 《生物科学与医学(英文)》 2019年第4期14-24,共11页
Cholesterol oxidase (COX) is widely used enzyme for total cholesterol estimation in human serum and for the fabrication of electro-chemical biosensors. COX is also used for the bioconversion of cholesterol;for the pro... Cholesterol oxidase (COX) is widely used enzyme for total cholesterol estimation in human serum and for the fabrication of electro-chemical biosensors. COX is also used for the bioconversion of cholesterol;for the production of precursors of steroidal drugs and hormones. Enzyme activity depends decisively on defined conditions with respect to pH, temperature, ionic strength of the buffer, substrate concentration, enzyme concentration, reaction time. Standardization of these parameters is desirable to attain optimum activity of the enzyme. The present work aims to build a neural network model using five input parameters (pH, cholesterol concentration, 4-aminoantipyrine concentration, crude COX volume and horseradish peroxidase) and one output i.e., COX activity (U/ml) as a response. A feed forward back propagation neural network with Levenberg-Marquardt training algorithm was used to train the network. The network performance was assessed in terms of regression (R2), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). A network topology of 5-10-1 was found to be optimum. The MSE, MAPE and R2 values of the neural model were 0.0075%, 0.12% and 0.9792% respectively. The maximum predicted activity of COX was 1.073 U/ml, which was close to the experimental value i.e., 1.1 U/ml at simulated optimum assay conditions. MSE and MAPE depicted the precision in the prediction efficiency of the developed ANN model. Higher R2 value showed a good correlation between the experimental and ANN predicted values. This proved the robustness of the ANN model to predict similar type of system (COX from other Streptomyces sp.) within the limits of the trained data set. The COX activity was enhanced by 1.71 folds after optimization of the reaction conditions. 展开更多
关键词 CHOLESTEROL OXIDASE Artificial Neural Network Optimization STREPTOMYCES OLIVACEUS Prediction
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Parallel Building: A Complex System Approach for Smart Building Energy Management 预览
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作者 Abdulaziz Almalaq Jun Hao +1 位作者 Jun Jason Zhang Fei-Yue Wang 《自动化学报:英文版》 CSCD 2019年第6期1452-1461,共10页
These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i... These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper. 展开更多
关键词 ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid
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Prediction of the Logistics Demand Based on an Innovative Mixed Model:an Empirical Case from Nanping City, China 预览
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作者 王波 魏乐琴 +3 位作者 陈金雄 蔡尚斌 张立中 边舫 《东华大学学报:英文版》 EI CAS 2019年第5期498-506,共9页
The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy develop... The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy development of urban logistics industry.It constructs a comprehensive index system composed of freight volume and other eight relevant economic indices to form the foundation for the model construction.Combining forecasting models of principal component regression and GM(1,1)together,it makes mathematical calculation to predict the logistics demand of Nanping City from the years 2018 to 2022.The research makes systematical analyses of the indices influencing the precise prediction of logistics demand from a new perspective,which offers an innovative and practical option for urban logistics prediction.In line with the prediction,it offers some suggestions for the improvement of demand prediction and some strategies for the better development of the logistics industry in Nanping City. 展开更多
关键词 regional logistics demand prediction principal component regression GM(1 1)prediction
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Predictions of El Ni&#241;o, La Ni&#241;a and Record Low Chicago Temperature by Sunspot Number 预览
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作者 Tai-Jin Kim 《自然科学期刊(英文)》 2019年第6期204-220,共17页
The El Nino Index, defined as 4 intensities (very strong, strong, moderate, weak) in Oceanic Ni&#241;o Index (ONI), was positively correlated with the average sunspot number at each intensity. The La Ni&#241;a... The El Nino Index, defined as 4 intensities (very strong, strong, moderate, weak) in Oceanic Ni&#241;o Index (ONI), was positively correlated with the average sunspot number at each intensity. The La Ni&#241;a Index, defined as 3 intensities (strong, moderate, weak) in ONI, was negatively correlated with the average sunspot number from 1954 to 2017. It appears that very strong El Ni&#241;o events occur frequently during the maximal sunspot number while strong La Ni&#241;a events more often occur during the minimal sunspot number. Since greenhouse-gas is continuously increased, it is therefore proposed that the maximal sunspot number is a major parameter for prediction of El Ni&#241;o while the minimal sunspot number applies in the same way for La Ni&#241;a. El Nino/La Nina events can be classified as four typical cases depending upon the submarine volcanic activities at seamounts in Antarctica and South America. The Sea Surface Temperature (SST) of the South and Central Americas are warmer than SST of East Australian Current (EAC), due to the strong volcanic eruptions in the Seamounts and the Ridges in South and Central Americas. This results in the Central Pacific Current (CPC) flowing from east to west due to the second law of thermodynamics for thermal flow from hot source to cold sink. In contrast the opposite direction is made if SST in EAC is warmer than SST in the Central/South American Seamounts and Ridges, due to the strong volcanic eruptions in the Antarctic Seamounts and Ridges. Chicago was selected as a case study for the relationship between extreme cold weather conditions and minimal sunspot number. Previous attempts at predicting weather patterns in Chicago have largely failed. The years of the record low temperatures in Chicago were significantly correlated with the years of the minimal sunspot number from 1873 to 2019. It is forecast that there may occur a weak La Ni&#241;a in 2019 and another record low temperature in Chicago in January of 2020 due to the phase of the minimal sunspot number in 2 展开更多
关键词 Prediction EL Ni?o LA Ni?a RECORD LOW Chicago Temperature Sunspot Number
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Predicting pH Optimum for Activity of Beta-Glucosidases 预览
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作者 Shaomin Yan Guang Wu 《生物医学工程(英文)》 2019年第7期354-367,共14页
The working conditions for enzymatic reaction are elegant, but not many optimal conditions are documented in literatures. For newly mutated and newly found enzymes, the optimal working conditions can only be extrapola... The working conditions for enzymatic reaction are elegant, but not many optimal conditions are documented in literatures. For newly mutated and newly found enzymes, the optimal working conditions can only be extrapolated from our previous experience. Therefore a question raised here is whether we can use the knowledge on enzyme structure to predict the optimal working conditions. Although working conditions for enzymes can be easily measured in experiments, the predictions of working conditions for enzymes are still important because they can minimize the experimental cost and time. In this study, we develop a 20-1 feedforward backpropagation neural network with information on amino acid sequence to predict the pH optimum for the activity of beta-glucosidase, because this enzyme has drawn much attention for its role in bio-fuel industries. Among 25 features of amino acids being screened, the results show that 11 features can be used as predictors in this model and the amino-acid distribution probability is the best in predicting the pH optimum for the activity of beta-glucosidases. Our study paves the way for predicting the optimal working conditions of enzymes based on the amino-acid features. 展开更多
关键词 BETA-GLUCOSIDASE ENZYME PH OPTIMUM PREDICTION
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Hierarchical mobility prediction in the future heterogeneous networks for the tradeoff between accuracy and complexity
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作者 Liu Zhenya Li Xi +1 位作者 Ji Hong Zhang Heli 《中国邮电高校学报:英文版》 EI CSCD 2019年第3期15-24,共10页
Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely co... Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely complicated due to the widespread deployment of different types of small-cell base stations(SBSs). For this complex network topology, traditional mobility prediction methods may cost unacceptable overhead to maintain high prediction accuracy. This problem is studied in this paper, and the hierarchical mobility prediction scheme(HMPS) is proposed for the future HetNets. By dividing the entire process into two prediction stages with different granularity, the tradeoff between prediction accuracy and computational complexity is investigated. Before performing prediction of user mobility, some frequently visited locations are identified from the user’s trajectory, and each location represents an important geographic area(IGA). In the coarse-grained prediction phase, the next most possible location to be visited is predicted at the level of the possible geographic areas by using a second-order Markov chain with fallback. Then, the fine-grained prediction of user position is performed based on hidden Markov model(HMM) from temporal and spacial dimensions. Simulation results demonstrate that, compared with the existing prediction methods, the proposed HMPS can achieve a good compromise between prediction accuracy and complexity. 展开更多
关键词 MOBILITY PREDICTION heterogeneous networks PREDICTION ACCURACY and COMPLEXITY MARKOV chain HMM
Improving Seasonal Climate Forecasts over Various Regions of Africa Using the Multimodel Superensemble Approach 预览
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作者 Joseph Nzau Mutemi 《大气和气候科学(英文)》 2019年第4期600-625,共26页
Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the a... Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the approach originally developed at Florida State University (FSU). The technique takes more advantage of the skill in the climate forecast data sets from atmosphere-ocean general circulation models running at many centres worldwide including the WMO global producing centers (GPCs). The module used in this work drew data sets from the Four versions of FSU coupled model system, seven models from the DEMETER project which is the forerun to the current European Ensembles Forecast System, the NCAR Model, and the Predictive Ocean Atmosphere Model for Australia (POAMA), all making a set of 13 individual models. An archive consisting of monthly simulations of precipitation was available over all the 5 regions of Africa, namely Eastern, Central, Northern, Southern, and Western Africa. The results showed that the SSE forecast for precipitation carries a higher skill compared to each of the member models and the ensemble mean. Relative to the ensemble mean (EM), the SSE provides an improvement of 18% in simulation of season cycle of precipitation climatology. In Eastern Africa, during December-February season, a north-south gradient of precipitation prevails between Tropical East Africa and the sector of the region towards Southern Africa. This regional scale climate pattern is a direct influence of the Intertropical Convergence Zone (ITZC) across the African continent during this time of the year. The SSE emerges with superior skill scores such as lowest root mean square error above the EM and the member models, for example in the prediction of spatial location and precipitation magnitudes that characterize the see-saw precipitation pattern in Eastern Africa. In all parts of Africa, and especially Eastern Africa where seasonal precipitation variability is a frequent cause huge human suffering due to droughts and 展开更多
关键词 AFRICA RAINFALL VARIABILITY Prediction Multimodel Superensemble Synthetic SKILL
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Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study 预览
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作者 Abdullah Alsaeedi Mohammad Zubair Khan 《软件工程与应用(英文)》 2019年第5期85-100,共16页
An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, whic... An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others. 展开更多
关键词 MACHINE Learning ENSEMBLES Prediction SOFTWARE Metrics SOFTWARE DEFECT
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Research on Railway Passenger Flow Prediction Method Based on GA Improved BP Neural Network 预览
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作者 Jian Zhang Weihao Guo 《电脑和通信(英文)》 2019年第7期283-292,共10页
This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its... This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability. 展开更多
关键词 RAILWAY PASSENGER Flow PREDICTION BP NEURAL Network GENETIC Algorithm
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