Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

基于多注意力长短时记忆的实体属性情感分析

属性情感分析是细粒度的情感分类任务。针对传统神经网络模型无法准确构建属性情感特征的问题，提出了一种融合多注意力和属性上下文的长短时记忆（LSTM-MATT-AC）神经网络模型。在双向长短时记忆（LSTM）的不同位置加入不同类型的注意力机制，充分利用多注意力机制的优势，让模型能够从不同的角度关注句子中特定属性的情感信息，弥补了单一注意力机制的不足；同时，融合双向LSTM独立编码的属性上下文语义信息，获取更深层次的情感特征，有效识别特定属性的情感极性；最后在SemEval2014 Task4和Twitter数据集上进行实验，验证了不同注意力机制和独立上下文处理方式对属性情感分析模型的有效性。实验结果表明，模型在Restaurant、Laptop和Twitter领域数据集上的准确率分别达到了80.6%、75.1%和71.1%，较之前基于神经网络的情感分析模型在准确率上有了进一步的提高。     

物联网中基于Multi-Agent的物间动态关系发现

物联网中存在大量异构关系的实体，其信息间的交互造成了物联网的内在矛盾。针对这一问题，提出将物联网中实体抽象化为对应的Agent，并以个体人为中心，利用本体的语义匹配及改进的物间动态关系计算方法，计算出针对个体人的物间动态关系；将原本异构的实体关系转化为Agent间基于动态关系紧密度排序的网络关系。经实验验证，该方法构建的关系网络可明显改善异构实体间交互的矛盾，而且相比原有类似方法，该方法解决问题的准确率和误差率均有改善，说明了该方法的准确性和可行性。     

Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore

Natural language search engines should be developed to provide a friendly environment for business-to-consumer e-commerce that reduce the fatigue customers experience and help them decide what to buy. To support product information retrieval and reuse, this paper presents a novel framework for a case-based reasoning system that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. Furthermore, the case retrieval agent integrates short-text semantic similarity (STSS) and recognizing textual entailment (RTE). The proposed approach was evaluated using competitive methods in the performance of STSS and RTE, and according to the results, the proposed approach outperforms most previously described approaches. Finally, the effectiveness of the proposed approach was investigated using a case study of an online bookstore, and according to the results of case study, the proposed approach outperforms a compared system using string similarity and an existing e-commerce system, Amazon.     

Formal modeling and verification of fractional order linear systems

This paper presents a formalization of a fractional order linear system in a higher-order logic (HOL) theorem proving system. Based on the formalization of the Grünwald–Letnikov (GL) definition, we formally specify and verify the linear and superposition properties of fractional order systems. The proof provides a rigor and solid underpinnings for verifying concrete fractional order linear control systems. Our implementation in HOL demonstrates the effectiveness of our approach in practical applications.     

提高辐射温度计检定结果准确度的方法研究

现行的辐射温度计检定规程,概念不够清晰,具体操作步骤描述过于简单,造成检定的实际测量条件有明显的差异,由此导致检定结果的差异。本文从技术角度出发,提出了一种可行的操作方法,力图提高辐射温度计检定结果的准确度。     

基于CFD的汽车水泵数值模拟与试验验证

以雷诺时均N-S方程为基本控制方程,采用标准k-ε湍流模型,利用计算流体动力学软件Pump Linx模拟了汽车水泵内部的三维湍流流场,研究了水泵叶轮平衡孔及口环间隙对多工况下外特性的影响,并以叶轮有口环间隙、平衡孔这一工况对复杂流场中各个位置上的压力分布、速度分布和叶轮汽蚀进行分析。通过与试验数据进行对比验证,试验结果与数值预测结论基本吻合。提出了减小汽车水泵各部件水力损失的合理性建议,研究结论有效地指导了汽车水泵的开发过程,对其他同类产品的研发也具有指导意义。     

Trial application of ASHRAE 1051-RP: calibration method for building energy simulation

ASHRAE research project 1051-RP generated a method to improve the process of calibrating whole building energy simulation models based on monthly utility data. The approach, using manual generation of simulation model variations, was applied to a 12,000 m² high-performance, dual energy, cold climate building. This led to 27 models that met the ASHRAE Guideline 14 monthly goodness-of-fit criteria for electricity, but had fit values for gas that were about 5–7 times the normalized mean bias error (NMBE) acceptance threshold. Five models met the criteria for natural gas and had acceptable coefficient of variation of the root-mean-square error for electricity, but NMBE was about 100% too high. Use of finer interval monitored data yielded a model with electrical NMBE about 60% above the acceptance threshold, and gas use about 200% above. Hourly analysis of the thermal energy demand on the plant showed wide discrepancies with the estimates on an hourly and half daily basis.     

基于SWT和Hilbert的密封磁阻马达式CRDM振动信号去噪

针对控制棒驱动机构滚轮振动信号采集过程受到噪声干扰的问题，提出了小波半软阈值（SWT）和Hilbert变换法相结合的去噪方法。该方法首先利用小波半软阈值算法在时频域对滚轮振动信号进行降噪处理，然后进行Hilbert变换求出其包络谱，分析寿命试验与缺陷验证试验中不同的滚轮振动信号。试验结果表明，该方法可有效消除噪声对振动信号的干扰，证明了小波半软阈值与Hilbert变换相结合的方法在驱动机构滚轮状态识别和故障诊断中的有效性，为驱动机构的状态判别提供了理论支持。