Fault diagnosis in reconfigurable VLSI and WSI processor arrays

A systematic efficient fault diagnosis method for reconfigurable VLSI/WSI array architectures is presented. The basic idea is to utilize the output data path independence among a subset of processing elements (PEs) based on the topology of the array under test. The divide and conquer technique is applied to reduce the complexity of test application and enhance the controllability and observability of a processor array. The array under test is divided into nonoverlapping diagnosis blocks. Those PEs in the same diagnosis block can be diagnosed concurrently. The problem of finding diagnosis blocks is shown equivalent to a generalizedEight Queens problem. Three types of PEs and one type of switches, which are designed to be easily testable and reconfigurable, are used to show how to apply this approach. The main contribution of this paper is an efficient switch and link testing procedure, and a novel PE fault diagnosis approach which can speed up the testing by at leastO(V^1/2) for the processor arrays considered in this paper, where V is the number of PEs. The significance of our approach is the ability to detect as well as to locate multiple PE, switch, and link faults with little or no hardware overhead.     

Sorption of solvents in plastics and solvent desorption and retention

The experiments reported here have studied the following systems within the temperature range 25–50°C: sorption and desorption of solvents on LDPE, OPP, PS, rigid PVC, OPA, PETP and regenerated cellulose with methanol, ethanol, n-propanol, and ethyl acetate. With reference to these, the theory presented indicates the most important aspects related to the tests, refers to identification data on similar problems and enumerates the possibilities of interaction. The application of equation 1 and the reported measuring method allows the determination of diffusion coefficients within a vapour-saturated atmosphere system. As the gravimetric method also allows the determination of solubilities (α_L, the solubility coefficient), the permeation behaviour in the Fick range is calculable from The equation used by the author can be related to other equations from the technical literature, such as for the range Q_(t)Q∞>0.4, or where for the range <0.4 The agreement can be considered as good. The D values provide a calculation base within the scope of the outlined problem definition. The taste limits for solvents in water reveal which solvents could, as residual solvents, become critical. These values—connected with sorption/desorption identification values, evaporation numbers etc. —indicate where there is a risk and those cases where sufficient drying, or even the use of more adequate solvents is essential.     

基于改进结构保持数据降维方法的故障诊断研究

传统基于核主成分分析(Kernel principal component analysis, KPCA)的数据降维方法在提取有效特征信息时只考虑全局结构保持而未考虑样本间的局部近邻结构保持问题, 本文提出一种改进全局结构保持算法的特征提取与降维方法.改进的特征提取与降维方法将流形学习中核局部保持投影(Kernel locality preserving projection, KLPP)的思想融入核主成分分析的目标函数中, 使样本投影后的特征空间不仅保持原始样本空间的整体结构, 还保持样本空间相似的局部近邻结构, 包含更丰富的特征信息.上述方法通过同时进行的正交化处理可避免局部子空间结构发生失真, 并能够直观显示出低维结果, 将低维数据输入最近邻分类器, 以识别率和聚类分析结果作为衡量指标, 同时将所提方法应用于故障诊断中.使用AVL Boost软件模拟的柴油机故障数据和田纳西(Tennessee Eastman, TE)化工数据仿真, 验证了所提方法的有效性.     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

基于社交圈层和注意力机制的信息热度预测

社交网络现已成为现实世界中信息传播与扩散的主要媒介,对其中的热点信息进行建模和预测有着广泛的应用场景和商业价值,比如进行信息传播挖掘、广告推荐和用户行为分析等.目前的相关研究主要利用特征和时间序列进行建模,但是并没有考虑到社交网络中用户的社交圈层对于信息传播的作用.本文提出了一种基于社交圈层和注意力机制的热度预测模型SCAP(Social Circle and Attention based Popularity Prediction),首先对社交圈层进行定义,通过自动编码器提取用户历史文本序列的特征,对不同用户的社交圈层进行聚类划分,得到社交圈层特征.进而对于一条新发布的文本信息,通过长短期记忆网络与嵌入层提取其文本特征、用户特征和时序特征,并基于注意力机制,捕获到不同社交圈层对于该文本信息的影响程度,得到社交圈层注意力特征.最后将文本特征、用户特征、时序特征和社交圈层注意力特征进行特征融合,并通过两个全连接层进行建模学习,对社交信息的热度进行预测.在推特、微博和豆瓣等四个数据集上的实验结果表明,SCAP模型的预测表现相比于多个对比模型总体呈优,在不同数据集上均方误差(MSE)分别降低了0.017,0.022,0.021和0.031,F1分数分别提升0.034,0.021,0.034和0.025,能够较为准确地预测社交信息的热度.本文同时探究了不同实验参数对于模型的影响效果,如用户历史文本序列的数量、社交圈层的数量和时间序列的长度,最后验证了模型输入的各个特征和注意力机制的引入对于模型预测性能提升的有效性,在推特数据集中,引入社交圈层和注意力机制,模型的MSE指标分别降低了0.065和0.019.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

一种时空注意力网络的交通预测模型

交通预测在智能交通中有着重要的意义和应用.由于交通数据的复杂性和高度的非线性,精确的交通预测的核心挑战在于如何对复杂的空间相关性和时间动态建立模型.在现实生活中,我们发现:1)区域间的空间依赖是动态的;2)时间依赖有日和周的模式,但由于有动态时间变化,它不具有严格周期性.为了解决这两个问题,我们提出了一个新的时空注意力网络(STAN),该模型的主要思想是区域间的动态相似性用一个门控机制学习,长期周期性时间转移现象由一个周期性注意力转移机制来学习,并考虑交通道路、天气状况等外部因素.通过与不同的方法在两个数据集上进行评估,实验结果表明,我们提出的模型有更好的准确性.     

一种新的考虑注意力机制的微博谣言检测模型

随着社交媒体的迅速发展,谣言通过社交媒体迅速传播,识别社交媒体网络上的谣言是社交网络研究中一个至关重要的问题.本文提出了一种新的考虑注意力机制的微博谣言检测模型,考虑到卷积神经网络(CNN)提取到的特征对输出结果影响力问题,在经典的文本卷积神经网络(Text CNN)上引入了注意力机制,通过CNN中的卷积层学习微博窗口的特征表示,再根据每个特征表示对输出结果的影响力不同通过注意力机制赋予不同的权重来进行谣言事件的检测.研究结果表明,本文提出的微博谣言检测模型准确率达到了96.8%,并且在召回率和F1值上也有提升,即本文提出的新的微博谣言检测模型具有更好的谣言识别能力.     

注意力ConvLSTM模型在RUL预测中的应用

预测性维护的应用能够极大地降低企业运维成本,而设备剩余使用寿命(Remaining Useful Life,RUL)预测是预测性维护的关键技术之一.针对传统RUL预测算法难以提取时序数据的潜藏特征以及特征权重分配不合理的问题,本文提出一种基于注意力机制(Attention Mechanism)的卷积长短时记忆(Convolution Long-Short Term Memory,ConvLSTM)预测模型,该模型充分利用LSTM网络处理和预测长期时间序列的优势,并引入注意力机制对产生显著影响的特征因子提高权重,极大地优化了模型的时空特征提取能力.为验证模型预测效果,本文以NASA提供的CMAPSS数据集为对象进行实验,以均方根误差(Root Mean Squared Error,RMSE)和数据集自定义的Score为评价指标,将预测结果与其他RUL预测算法作比对,证明了该模型具有更佳的预测准确性.     

融合关键对象识别与深层自注意力的Bi-LSTM情感分析模型

在线评论文本通常涉及多个评价对象,对象的表达方式有显式和隐式之分,针对不同对象的情感倾向可能不会完全一致.关键评价对象是评论中最受关注的对象,其相应的情感语义对整条评论的情感观点起主导作用.本文构建了融合关键对象识别与深层自注意力机制的Bi-LSTM模型,以提升短文本情感分类的效果.使用CNN处理文本,基于卷积层输出结果识别关键评价对象,并在此基础上完成深层自注意力的学习.将对象信息与文本信息进行融合,利用注意力机制强化的Bi-LSTM模型得到评论文本的情感分类结果.在酒店评论数据集上进行实验,与之前基于深度学习的模型相比,本文方法在精确率、召回率和F-score评价指标方面均有更好的表现.