鱼雷贮存可靠度预测模型

研究鱼雷存贮可靠性正确预测,就如何降低鱼雷贮存可靠度预测模型中参数估计的偏差进行了研究.可靠性与存贮环境和时间有影响,在定期检测修复条件下,引入两种预测模型,鉴于小样本情况下可靠度的极大似然估计可能存在较大偏差,容易引起模型参数估计的较大偏差,可引入半经验的Bayes方法修正公式对检测结果进行处理,通过最小二乘法来确定...     

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet, Huber Regressor, Lasso Lars, Passive Aggressive Regressor, K-Neighbors Regressor, Decision Tree Regressor, Extra Trees Regressor, Support Vector Regressions (SVR), AdaBoost Regressor, Random Forest Regressor, Bagging Regressor , AuoRegression, MovingAverage, Gradient Boosting Regressor, Autoregressive Moving Average (ARMA), Auto-Regressive Integrated Moving Averages (ARIMA), SimpleExpSmoothing, Exponential Smoothing, Holt-Winters, Simple Moving Average, Weighted Moving Average, Croston, and naive Bayes. Furthermore, our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods. A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance. Then, this best method is used for future forecasts, and consequently, we can collect the most accurate and dependable predictions.     

奶牛产奶量性状相关基因知识图谱的研究与构建

奶牛生产性状中产奶量一直是相关领域专家重点关注的性状之一,产奶量的提高对于经济和民生发展具有重要意义,因此产奶量的相关影响因素成为了研究提高奶产量和奶质量的焦点。针对目前缺少奶牛产奶量-基因知识图谱的问题,以PubMed生物医学文献库为相关来源,利用爬虫技术构建奶牛产奶量性状组学数据的相关文献数据集,通过知识抽取获得与奶牛产奶量相关的大约140个基因影响因素及其他影响因素。利用Neo4j图数据库的方式进行数据存储,构建与奶牛产奶量性状相关的知识图谱,最后形成奶牛产奶量性状数据知识库的可视化平台。     

Fuzzy Models for Link Prediction in Social Networks

Predicting missing links and links that may occur in the future in social networks is an attention grabbing topic amid the social network analysts. Owing to the relationship between human‐based system and social sciences in this field, granular computing can help us to model the systems more effectively. The present study aims to propose two new similarity indices, based on granular computing approach and fuzzy logic. It also presents a new hybrid model for creating synergy between various link prediction models. Results show that fuzzy system analysis, in comparison with the crisp approach, can make more effective predictions through better expression of network characteristics. The indices are tested on collaboration networks. It is found that the accuracy of predictions is significantly higher than the crisp approach. It can modify the models for computing the strength of the links and/or predicting the evolutions of the social networks.     

从预测模型中提取规则

知识挖掘（ＫＤＤ）应该不仅能够提供较精确的预测结果,而且提取的规则也应该是可以解释的。讨论了从预测模型中进行规则抽取的一般技术,并介绍了作者用神经网络方法抽取规则的算法。     

Fuzzy Prediction Models in Measurement

The paper investigates the feasibility of fuzzy models application in measurement procedures. It considers the problem of measurement information fusion from different sources, when one of the sources provides predictions regarding approximate values of the measured variables or their combinations. Typically, this information is given by an expert but may be mined from available data also. This information is formalized as fuzzy prediction models and is used in combination with the measurement results to improve the measurement accuracy. The properties of the modified estimates are studied in comparison with the conventional ones. The conditions when fuzzy models application can achieve a significant accuracy gain are derived, the gain value is evaluated, and the recommendations on fuzzy prediction model production and formalization in practical applications are given.      

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Very little existing research in corporate bankruptcy prediction discusses modeling where there are missing values. This paper investigates AdaBoost models for corporate bankruptcy prediction with missing data. Three AdaBoost models integrated with different imputation methods are tested on two data sets with very different sample sizes. The experimental results show that the AdaBoost algorithm combined with imputation methods has strong predictive accuracy in both data sets and it is a useful alternative for bankruptcy prediction with missing data.     

地区生产总值灰色关联预测模型群的研究

生产总值是受多种因素制约的处于动态变化的灰色系统。本文针对多种影响因素,运用灰色关联分析方法确定主因素变量,从而建立灰色关联预测模型GM（1,N）和GM（0,N）,并将预测结果与GM（1,1）模型进行协调综合,这样得到的数据更加合理,也大大提高了预测结果的可信度和应用价值。     

基于MARTE模型的系统可靠性预测

系统的可靠性是系统的重要非功能属性之一。传统的可靠性分析在系统开发结束后进行,可能会发现由于系统开发早期的架构设计不合理而导致的问题,这时再修改系统架构并重做后继开发步骤,将会浪费大量人力和物力。如果能在开发的早期阶段,在系统模型层面进行分析并预测,则可以尽早地发现系统可靠性方面的问题并将其修复。UML是一种通用的、标准化的建模语言,MARTE是UML在嵌入式实时系统领域的扩展。提出了基于MARTE模型的系统可靠性预测方法,该方法考虑的MARTE模型包括用例图、活动图、部署图。先将MARTE模型转换为马尔可夫决策过程网络模型,再利用概率模型检测工具PRISM进行分析,得到系统可靠性的预测结果。实例研究表明,所提方法不仅能够预测系统可靠性的最大值和最小值,还能通过调整各个资源的可靠性值,考察其对系统可靠性的影响,为设计人员的进一步工作提供参考。     

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.     

基于多ANN模型的复杂系统长时段预报

提出一种多ANN结构的极值聚类训练算法,并将这种方法应用于复杂系统长时段预报.采用这种方法,可以提高长时段预报精度、增强模型的可靠性.以这种模型为基础可以进一步建立基于多ANN模型的复杂系统预测控制.     

A Bayesian Multiple Models Combination Method for Time Series Prediction

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. Two examples of the method are given, based on real world data: (a) short term load forecasting for the Greek Public Power Corporation dispatching center of the island of Crete, and (b) prediction of sugar beet yield based on data collected from the Greek Sugar Industry. In both cases, the BCP outperforms conventional predictors.     

基于灰色Verhulst模型的短期风速预测研究

风速预测在保持风力发电系统稳定、风力发电功率预报、风电并网接入等方面都具有重要的应用.为了提高风速预测的精确性,提出了一种基于新陈代谢思想的灰色Verhulst模型的风速预测方法.该方法首先对灰色GM (1,1)模型和灰色Verhulst模型进行改进,其次引入了“新陈代谢”的概念,即在每一次风速预测的迭代过程中用风速真实序列的最新数据替代原有序列的最老数据,在不增加迭代维数的条件下,不断更新灰色Verhulst模型,将更新后的Verhulst模型进行优化,实现精确的风速预测.通过对实际风场风速数据的采集,运用该灰色Verhulst模型预测风速.实践仿真结果表明,与传统预测方法相比,此方法能有效的降低短期风速预测的误差,应用前景十分广阔.     

An Immune Algorithm for Protein Structure Prediction on Lattice Models

We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well-known PSP lattice models, the HP model in two-dimensional and three-dimensional square lattices', and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed IA is very competitive with the existing state-of-art algorithms for the PSP on lattice models     

Bayesian Network Models for Web Effort Prediction: A Comparative Study

OBJECTIVE – The objective of this paper is to compare, using a cross-company dataset, several Bayesian Network (BN) models for Web effort estimation. METHOD – Eight BNs were built; four automatically using Hugin and PowerSoft tools with two training sets, each with 130 Web projects from the Tukutuku database; four using a causal graph elicited by a domain expert, with parameters automatically fit using the same training sets used in the automated elicitation (hybrid models). Their accuracy was measured using two validation sets, each containing data on 65 projects, and point estimates. As a benchmark, the BN-based estimates were also compared to estimates obtained using Manual StepWise Regression (MSWR), Case-Based Reasoning (CBR), mean- and median-based effort models. RESULTS – MSWR presented significantly better predictions than any of the BN models built herein, and in addition was the only technique to provide significantly superior predictions to a Median-based effort model. CONCLUSIONS – This paper investigated data-driven and hybrid BN models using project data from the Tukutuku database. Our results suggest that the use of simpler models, such as the median effort, can outperform more complex models, such as BNs. In addition, MSWR seemed to be the only effective technique for Web effort estimation.     

Improved Language Models for Word Prediction and Completion with Application to Hebrew

Language models (LMs) are important components of many applications that work with natural language, such as word prediction and completion programs, automatic speech recognition, and machine translation. In this paper, we introduce various types of improvements for LMs dealing with word prediction and completion in Hebrew. Whereas previous systems for the Hebrew language apply known variants of existing LMs without any alteration, this study presents two types of improvements concerning the LMs: one is general and the other is special for the Hebrew language. These improvements enable all tested LMs to improve their keystroke saving abilities.     

面向全球数值天气预报模式的加权等积并行数据划分算法

极区计算对全球数值预报模式设计的重要性主要体现在2个方面：模式动力框架中的极区处理和极区并行数据划分带来的并行负载不平衡问题.其中后者是全球数值预报模式大规模并行计算的性能瓶颈,对此提出一种新的基于加权等积的球面数据划分算法.该算法以球带数目和权函数为参数,将南北两极分别划分到单独的子区域,形成极点通区,使从极点到赤道方向每个纬度对应的子区域数目逐渐增多,灵活地实现球面网格的高质量划分.从理论上分析该算法的划分质量后,以基于球谐谱的浅水波模式PSTSWM为实验平台,验证了提出的划分算法具有很好的并行划分性能以及可扩展性.结合我国自主设计的GRAPES全球模式,展望了该算法的应用前景.     

Feature Fusion Models for Deep Autoencoders: Application to Traffic Flow Prediction

ABSTRACT

Due to reduction in dimensionality and extraction of the definitive features of input data, deep architectures have achieved significant success in various machine learning applications. Considering their successful applications in speech recognition and image classification, the main goal of this research is to investigate the performance of the sparse autoencoders utilized in regression analysis. To this end, deep sparse autoencoders with the standard method of training, cascaded, and partially cascaded architectures, fed with the fusion of low- and high-level features, are proposed and implemented. The regression task is to forecast the vehicular flow rate of a location on an arterial highway using different traffic variables of several locations ahead in the Twin Cities Metro area of Minneapolis. The results demonstrate that the partially cascaded model exhibits advancements in yielding more accurate results than the other two architectures fed with the features that correlate the most to the traffic flow rate.     

Affective Modelling: Profiling Geometrical Models with Human Emotional Responses

In this paper, a novel concept, Affective Modelling, is introduced to encapsulate the idea of creating 3D models based on the emotional responses that they may invoke. Research on perceptually‐related issues in Computer Graphics focuses mostly on the rendering aspect. Low‐level perceptual criteria taken from established Psychology theories or identified by purposefully‐designed experiments are utilised to reduce rendering effort or derive quality evaluation schemes. For modelling, similar ideas have been applied to optimise the level of geometrical details. High‐level cognitive responses such as emotions/feelings are less addressed in graphics literatures. This paper investigates the possibility of incorporating emotional/affective factors for 3D model creations. Using a glasses frame model as our test case, we demonstrate a methodological framework to build the links between human emotional responses and geometrical features. We design and carry out a factorial experiment to systematically analyse how certain shape factors individually and interactively influence the viewer's impression of the shape of glasses frames. The findings serve as a basis for establishing computational models that facilitate emotionally‐guided 3D modelling.     

Age‐Related Physiological Responses to Working in Deep Cold

Professional order‐picking in deep cold‐storage depots (i.e., storing, picking, and dispatching of frozen groceries) requires employees to remain working at temperatures of approximately ?24°C for a whole workday. Actually, a well‐regulated organization of working times and breaks has not been established, and no competent knowledge exists as to whether an additional age‐differentiated organization is necessary. To assess the physiological effects while working in deep cold, 30 male subjects (Ss) were classified into two age groups (20‐ to 35‐year‐olds and 40‐ to 65‐year‐olds). In whole workday tests, possible age‐dependent effects on the strain were measured to guarantee the preservation of the subjects' ability to work in the long run. For the objectification of the physiological strain, heart rate and skin surface temperature were registered continuously, and blood pressure and body core temperature were measured and recorded discretely during cold exposures of 80, 100, and 120 min, separated by 20‐min warming‐up breaks. Systematic differences of blood pressure could not be found. The heart rate values indicated a high physiological strain for both younger and older Ss, with work‐related increases above the resting level of 30 bpm and more. Due to increases over time, endurance level sometimes was exceeded. Age‐related differences in skin temperature could not be recorded, but the ability to generate heat deteriorated with advancing age, which is shown by more substantial decreases of body core temperature in the group of 40‐ to 65‐year‐olds taken at the tympanum. Regarding physiological strain brought about by maximum heart rate decreasing with age and declined heat generation, correspondingly adapted workday break regimes have to be provided for older employees to ensure their ability to work in the long run. © 2011 Wiley Periodicals, Inc.