Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.     

带权决策表的属性约简

属性约简是粗糙集理论的重要应用。考虑将决策表中的每行都作为一条决策规则时，若把表中出现相同决策规则的次数作为权，可得到带权决策表。提出了关于带权决策表的正域约简相应的辨识矩阵并给出了证明，从而得到了约简算法。相比于决策表中的正域约简时发现，通过将决策表转化为带权决策表后，再利用算法1进行约简时，其在一定程度上优于前者。提出了近似分类精度约简相应的辨识矩阵并给出了证明。对于2个算法，在选取的UCI数据集上进行了实验验证。通过实验进一步说明了所提出算法的可行性和有效性。     

Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

Abstract

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).     

An extended stochastic pseudo-spectral Galerkin finite element method (XS-PS-GFEM) for elliptic equations with hybrid uncertainties

Nouy and Clement introduced the stochastic extended finite element method to solve linear elasticity problem defined on random domain. The material properties and boundary conditions were assumed to be deterministic. In this work, we extend this framework to account for multiple independent input uncertainties, namely, material, geometry, and external force uncertainties. The stochastic field is represented using the polynomial chaos expansion. The challenge in numerical integration over multidimensional probabilistic space is addressed using the pseudo-spectral Galerkin method. Thereafter, a sensitivity analysis based on Sobol indices using the derived stochastic extended Finite Element Method solution is presented. The efficiency and accuracy of the proposed novel framework against conventional Monte Carlo methods is elucidated in detail for a few one and two dimensional problems.     

直线运动滚动导轨副混合润滑研究

综合考虑了直线运动导轨副接触几何、预紧力、真实表面粗糙度、曲率系数等因素,建立了直线运动导轨副混合润滑数值模型,研究了滑块移动速度、曲率半径系数、工作载荷、表面粗糙度对导轨接触副润滑特性的影响,得到结论:导轨副法向工作载荷、最大赫兹接触压力和赫兹接触半径随着外加总载荷的增大而增大,平均膜厚随着载荷的增大而减小;混合润滑模型可预测导轨副在大范围工况条件下完整的润滑状态;直线运动导轨大多工作在混合润滑状态下,随着滑块移动速度的增加,接触界面由边界润滑状态向混合润滑状态转变,润滑性能逐渐提高;适当增加曲率半径系数,有利于润滑油膜的形成与稳定。     

某核电机组水压试验泵汽轮发电机组启动试验不成功分析处理

对某核电机组水压试验泵汽轮发电机组启动试验不成功进行了分析与处理。由于限流器单向密封圈老化,导致2LLS003VV阀门开启时间与2LLS001VV阀门开启时间不匹配,从而导致小汽机瞬时进汽量过大,启动过程中跳机。根据原因分析制定处理措施,解决了小汽机启动试验不成功问题。     

水文水资源学家陈守煜先生学术研究的知识图谱分析

为探索水文水资源学家陈守煜先生学术研究历程及其演变规律,以其335篇文献作为数据基础,采用共现分析等文献计量可视化分析方法,通过文献发表的时间分布、学科和期刊分布、高被引文献分布和研究主题等方面的知识图谱分析,系统分析了陈先生的学术研究历程。结果显示:陈守煜先生学术研究生命周期历时达62年,在其60岁后出现了2个学术创新高峰期;其学术研究特色荟萃于其河渠非恒定流计算、模糊集理论的水文水资源应用、模糊水文水资源学基本理论、模糊聚类-识别-优选统一理论、可变模糊集理论和可变模糊集理论的水文水资源应用6个研究主题,其中主题1为后续的5个模糊水文水资源学创新主题奠定了数值计算的研究方法论,主题2为模糊集现有理论的应用探索,主题3至主题5为面向工程应用的模糊集理论不断深入创新,主题6为可变模糊集理论的应用探索,形成了"紧密结合应用-持续理论创新-创新理论再应用"这种理论与实践互馈的研究特色。他每一阶段的研究目标比较集中、明确,与其研究主题密切对应,显著提高了研究效率;他的学术研究影响广泛,合计被引8554次,缘于他的源头理论创新。陈先生的研究,理论与应用不断互馈,贵在厚积薄发、深入钻研、持续创新,其研究理念和方法论,对水利科学的学术创新研究具有重要的启示作用和深远影响。     

基于未确知集对联系数法的溃坝社会影响评价

针对溃坝社会影响因素的不确定性和复杂性易造成评价过程信息缺失的问题,采用未确知测度理论和层次分析法,将其不确定信息转换为具有相对确知信息的综合测度向量,结合集对分析可有效处理研究系统的确定性与不确定性关系的特点,引入五元集对联系数,建立了基于未知集对联系数法的溃坝社会影响评价模型,以未确知集对联系数所属区间的等级作为溃坝社会影响严重程度等级判定的依据,并通过工程实例验证了所建溃坝社会影响评价模型的合理性和有效性,为溃坝社会影响评价提供了一种新的方法和思路。     

A framework for improving the efficiency of operational testing through Bayesian adaptive design

When developing a product, it is important to consider product performance from a user perspective. This type of evaluation can be done through operational testing—assessing the ability of representative users to satisfactorily accomplish tasks with the product in operationally representative environments. This process can be expensive and time-consuming, but is critical to understanding whether the product can adequately do the job for which it was designed. We show how an existing design of experiments (DOEs) process for operational testing can be leveraged to construct a Bayesian adaptive design. This design, nested within the larger design created by the DOE process, allows interim analyses to stop testing early for success or futility. Representative simulations are presented to demonstrate how these interim analyses can be used in an operational test setting, and reductions in necessary test events are shown. The application of Bayesian-adaptive design methods will allow future operational testing to be conducted in less time and at less expense, on average, without compromising the ability of the existing process to verify the product meets the user's needs.     

Hydraulics of free overfall in steeply sloping rough rectangular channel: A general computational approach

All supercritical free overfalls were almost considered as frictionless flow or pseudo-uniform flow before, the effect of friction had not been paid due attention. Currently, a new mathematical model is proposed to analyze supercritical rectangular free overfall by Boussinesq-type energy equation accounting for frictional effect. A computational approach of loop iteration algorithm is utilized to simultaneously calculate the implicit water surface profile by Runge-Kutta method, end-depth-ratio, and discharge. Subsequently, the dependency of discharge on end depth and Nikuradse equivalent sand roughness for different slope is illustrated. Comparison of computed results with experimental observations reveals satisfactory agreement. Additionally, the present energy approach leads to more accurate results than the momentum method for high roughness. Finally, to aid application for engineers, an explicit solution of discharge for practical application in the commonly using range is also proposed. The most important purpose of this study is to present a general computational approach, which could be used for the rough supercritical free overfalls of any other cross-sections.