Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student’s behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student’s knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.     

基于马尔可夫链的自适应性神经网络训练算法

提出一种基于马尔可夫链的自适应性神经网络训练方法,对传统的S型激励函数进行了改进,建立了自适应性的神经网络分类器。在假设样本中噪声服从于正态分布的情况下建立最大似然估计,通过后验概率建立马尔科夫链对样本进行训练,提高了神经网络训练速度。在轴承故障诊断中的测试结果表明,该算法可以迅速稳定地训练出神经网络,有效提高诊断的分类结果。     

Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.     

炼石水泥厂2300t/d熟料生产线的达产达标

福建水泥股份有限公司炼石水泥厂2300t／d生产线(即#4窑)由南京水泥工业设计研究院开发设计，2000年11月动工，建设周期为10个月，2001年9月即实现3d达标考核该线大量采用国产化设备，烧成系统、窑头喷煤管、煤磨系统等应用南京院专有专利技术2002年共生产熟料71．54万t，28d强度平均达57.3MPa，全年运转率达83．6％，全面实现了达产达标。     

基于非负矩阵分解的水量分配方案后评价

提出了水量分配方案后评价指标体系,建立了基于非负矩阵分解的水量分配方案后评价模型,并以1983年国务院批复的引滦水量分配方案为研究对象进行了水量分配方案诊断式后评价。该方法利用非负矩阵分解算法将指标矩阵最大程度分解为基向量和权向量,根据权向量元素的数值对水量分配方案进行评级,根据基向量元素的数值判断评价指标的实施状况。通过对引滦水量分配方案质量、实施过程、实施效果和可持续性的调查分析,得出方案综合后评价结果为优,但在来用水可持续性、对水环境影响及水质目标保证率等方面需加以改进。评价结果表明,基于非负矩阵分解原理的后评价方法评价效果较好,可为水量分配方案等项目的后评价提供科学依据。     

流化床二组分混合物的适宜分离气速

在等密度与非等密度二组分混合物分离体系中，考察操作气速对分离度的影响，提出物料的静床高对高效分离气速没有影响，而操作气速取其混合物最小流化速度的１．２倍左右时，二组分混合物产生高效分离，并且此操作气速随着二组分混合物分离状态的判定值（等密度混合物指Ｒｍｆ值，非等密度混合物主要指Ｒｐ值）的增加而拓宽。     

川中丘陵区地表干湿长程相关性及影响因素研究（研究生论坛）

为探究川中丘陵区干湿动态动力学机制,提供旱涝预测的理论依据。根据川中丘陵区8个气象站点1958~2013年逐日气象资料,采用Penman—Monteith 模型计算潜在蒸散量,构建出湿润指数。在此基础上,基于去趋势波动分析方法（DFA）,对逐月、分季节、极端干湿湿润指数进行长程相关性分析,并探讨了影响湿润指数长程相关性的气象因子。结果表明：除南充、遂宁外,其余地区逐月湿润指数变化长程相关,并非完全随机;分季节与极端干湿湿润指数均具有长程相关性;对于不同季节而言,长程相关性以夏季最强,秋、冬两季次之,春季最弱,对于极端气候而言,极端湿润长程相关性强于极端干旱;通过对湿润指数与气象因子长程相关性一致性分析,降水标度指数与湿润指数标度指数呈显著正相关。     

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

Background

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

Method

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

Results

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92 ± 0.54 mm, and the compensation accuracy is 74.3 ± 5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value = .003) although these landmarks had larger initial displacements.

Conclusions

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.     

Rheological properties and compatibility of NR/EPDM and NR/brominated EPDM blends

Ethylene–propylene–diene terpolymer (EPDM) was modified by bromination reaction. Blending the resulting brominated EPDM with natural rubber (STR5L) and blending the unmodified EPDM with STR5L at various compositions were carried out. The rheological properties of the blends were investigated using a capillary extrusion. Shear flow curves of the pure rubbers and their blends illustrated the pseudoplastic property as shear thinning behavior with a power law index n < 1. True shear viscosity of all blends showed the negative deviation in relation to their additive values. Rheological behavior and two T_g's found from the DSC thermograms at all blend compositions indicated blend incompatibility for both sets of blends. The incompatibility of the vulcanized blends was also found by measuring the spin–spin relaxation time T₂ by pulsed NMR. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 837–847, 2003     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.