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 多传感器信息融合是一个新的研究领域，它应用到故障诊断领域还是近几年的事。文章主要论述了多传感器信息融合技术的基本概念、工作原理及常用方法，同时还介绍了其在故障诊断中的应用实例。     

一种机械故障监测的信息融合

在叶片振动故障监测过程中，单传感器的间断相位法只能测得叶片振幅值信息，本文应用两个传感器的信息融合技术，通过多源信息的综合、分析和推理，得出了叶片振动频率信息，提高了传感器系统的有效性，消除了单个传感器的局限性。     

高压大流量地下水隧道开挖超前帷幕注浆参数研究

锦屏水电站锦屏山隧道开挖中面临高压大流量地下水、且富水地段长(约占隧道全长的40%)的难题。为安全、可靠地进行隧道施工,以注浆材料的适应性、超前帷幕注浆的参数及工艺为研究对象,对浆液做了大量配合比试验,分析其流动性、物理力学性能和凝结时间。研究结果表明:用纯水泥进行单液浆帷幕注浆,其可注性好,可大大地提高注浆加固效果;纯水泥+双液浆对局部小的渗漏水具有很好的封堵效果。对于低于2 MPa的压力水,注浆压力取2~3倍的静水压力;对于高于5 MPa的压力水,注浆压力取净水压力加2~3 MPa。对于在2~5 MPa之间的压力水,注浆压力可灵活选取,但不宜超过7 MPa,注浆应采用孔内循环灌注的方式。地下水发生在断层破碎带时,超前帷幕注浆宜采用全断面帷幕;若出水段以溶蚀裂隙、断层、管道为主宜采用有针对性的局部超前帷幕注浆。该技术成功解决了锦屏山隧道高压大流量地下水封堵的难题,对富水地层地下隧洞施工具有重要参考价值。     

对低压接地故障保护几种不同方式的探讨

依据《低压配电设计规范》所认定的3种不同接地故障保护方式对其工作原理及条件进行分析，并对其所应采取的各类接地措施作了较为详尽的说明与分析，以便在实际工作中更好的确保配电线路在发生接地故障时，保护元件及时动作，防止人身电击伤亡、电气火灾和线路损害，减少工程造价。     

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。     

基于Flash的关键变量容错存储技术研究

嵌入式系统中往往需要保存少量复位或掉电后依赖的关键变量,利用处理器片内Flash存储参数已经成为常规选择,但与常规Flash存储器件相比,片内Flash擦除次数更为有限,难以满足系统生命周期要求。研究了如何延长微处理器片内Flash使用寿命以及关键系统变量容错存储技术,针对典型嵌入式系统应用,分析Flash擦写管理机制,基于写磨损均衡算法进行改良,进一步提升系统容错能力。该技术在基于STM32单片机硬件平台进行了测试验证。     

雅砻江楞古水电站场址区宋玉断层活动性研究

楞古水电站位于区域构造复杂的雅砻江中游, 场址周边断裂构造较发育, 场址区内发育有较大的宋玉断层, 研 究其活动性对枢纽建筑布置和工程抗震设防措施均具有重要意义。在遥感解译的基础上, 通过野外验证和追踪调 查, 结合断层构造岩石英形貌扫描、 电子自旋共振(ESR) 测龄和近代地震震中分析, 确定了宋玉断裂的活动年代, 得 出了宋玉断层不属于工程活动断层的结论。     

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.     

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.