TC4合金线性摩擦焊摩擦时间与变形量的关系

用自制的XMH-160型线性摩擦焊机，试验研究了TC4钛合金线性摩擦焊的摩擦时间对摩擦变形量的影响，结果表明，其变形量随摩擦时间的增加近似呈指数关系增大。分析了出现非线性摩擦变形量的原因，以及线性摩擦焊飞边形貌的一些基本特点。     

用模型元法建立变形力分布曲线

变形力分布曲线是用模型元法求解金属塑性成形问题的有力工具 ;而变形力分布曲线的建立又依据不同模型单元的力学模型。本文以开式模锻为例说明针对各式各样的体积成形过程建立变形力分布曲线的原理与方法 ;建立了开式模锻各个阶段的变形力分布曲线 ;综合考察了变形力分布曲线随变形过程的变化规律及特征 ;从而为变形力、变形功与节省材料的角度优化设计体积成形工艺及模具找到了有效途经 ;并以实验证明用模型元法建立变形力分布曲线的有效性与实用性。     

稀土纳米磁致冷复合材料

采用急冷快淬，高能球磨及粉末包套包覆轧制方法制备出Ｇｄ－Ｙ，Ｇｄ－Ｚｎ和Ｇｄ－Ｔｈ的纳米固体复合磁致冷材料。实验测试结果表明：与大块状材料相比，纳米固体的居里温度明显降低，比热显著增加：Ｇｄ－Ｔｈ和Ｇｄ－Ｚｎ合金的磁热熵效应有所降低，但Ｇｄ－Ｙ系的磁热熵效应则显著增加。对制备工艺条件及若干影响磁质变熵效应的因素进行了较详细的分析。     

一种基于DTW的新型故事时间序列相似性度量方法

现有时间序列相似性度量方法在进行股市序列相似性分析时,通常忽略成交量等其他重要因素对股价的影响,从而导致序列聚类、分类不精确。针对这一问题,本文提出了新的股市时间序列相似性度量方法。该方法在动态时间弯曲算法的基础上,通过引进时间衰竭因子,并结合成交量因素,给出了股市序列的最终度量公式。为了证明提出方法的可行性和有效性,本文实验部分通过选取家电等三个行业中的股票数据进行测试。实验结果表明,基于动态时间弯曲(Dynamic time warping,DTW)的新型股市时间序列相似性度量方法能够在保持股票序列形态特征的基础上,较好地解决股市技术分析中量价关系问题,从而更有效地应用于股市技术分析里关于模式发现等领域。     

Invariant representation of facial expressions for blended expression recognition on unknown subjects

Facial expressions analysis plays an important part in emotion detection. However, having an automatic and non-intrusive system to detect blended facial expression is still a challenging problem, especially when the subject is unknown to the system. Here, we propose a method that adapts to the morphology of the subject and that is based on a new invariant representation of facial expressions. In our system, one expression is defined by its relative position to 8 other expressions. As the mode of representation is relative, we show that the resulting expression space is person-independent. The 8 expressions are synthesized for each unknown subject from plausible distortions. Recognition tasks are performed in this space with a basic algorithm. The experiments have been performed on 22 different blended expressions and on either known or unknown subjects. The recognition results on known subjects demonstrate that the representation is robust to the type of data (shape and/or texture information) and to the dimensionality of the expression space. The recognition results on 22 expressions of unknown subjects show that a dimensionality of the expression space of 4 is enough to outperform traditional methods based on active appearance models and accurately describe an expression.     

基于自适应频率规整的鲁棒说话人辨认研究

该文提出了一种基于自适应频率规整的鉴别性特征提取算法。该方法通过对语音频谱的各个频带的鉴别性分析及其量化结果对各个频域进行自适应的频率规整,进行非均匀子带滤波设计提取鉴别性特征;同时在噪声环境下,在特征提取前端进行了预增强处理,解决了测试语音与训练语音失配的问题,保证了特征的正确提取。实验证明,该特征原理简单,稳定性好,对语音内容不存在依赖性,有良好的抗噪性能,并且结合预增强处理是有效的,能够进一步提高辨认系统的识别率和鲁棒性。     

基于FDA的居民用户空调用电行为分类分析方法

针对居民空调用电行为分类中存在事件型数据,导致分类分析耗时长、结果不准确等问题,提出一种基于函数型数据分析（FDA）模型的居民空调用电行为分类分析方法。该方法采用多重分形理论提取居民用电行为特征,使用函数型数据分析算法对居民空调用电行为进行聚类后获取居民空调用电行为类别,采用改进动态时间规整算法对居民空调用电行为实施分类处理,得到居民空调用电行为。根据非介入式设备采集到的实际居民用电行为信息检验该方法的有效性,实验结果表明:该方法可以较好地提取居民用电行为特征,且可有效提高用户空调用电行为分类精度以及缩短分类耗时,可充分描述居民空调开启情况以及消耗电量,具备较好的应用效果。     

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.     

A spatial warping method for freeform modeling based on a level-set method

Providing an intuitive and effective tool for freeform geometric modeling is important for product design. We introduce in this paper a level-set based spatial warping method for freeform modeling, allowing shape deformation to be initialed by rigid body transformations of volumetric tools. Intuitive user operations including imprinting, deformation and smoothing are developed to shield the user from the underlying geometric complexity. Unlike mesh-based spatial warping methods, the developed method represents a digital model by implicit distance field data and describes its change of geometry by the level-set method. This guarantees the generation of topologically correct triangular mesh models and circumvents the error-prone remeshing and mesh-repairing processes, thus preventing topological errors such as self-intersections. We present this method with algorithm details, numerical experiments and modeling examples.     

Weighted dynamic time warping for time series classification

Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, we propose a novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW. Our approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. The rationale underlying the proposed distance measure is demonstrated with some illustrative examples. A new weight function, called the modified logistic weight function (MLWF), is also proposed to systematically assign weights as a function of the phase difference between a reference point and a testing point. By applying different weights to adjacent points, the proposed algorithm can enhance the detection of similarity between two time series. We show that some popular distance measures such as DTW and Euclidean distance are special cases of our proposed WDTW measure. We extend the proposed idea to other variants of DTW such as derivative dynamic time warping (DDTW) and propose the weighted version of DDTW. We have compared the performances of our proposed procedures with other popular approaches using public data sets available through the UCR Time Series Data Mining Archive for both time series classification and clustering problems. The experimental results indicate that the proposed approaches can achieve improved accuracy for time series classification and clustering problems.