SOM for classifying data sets with missing values: application to clinical data of bladder cancer patients

In this paper we investigate applying SOM (Self-Organizing Maps) for classification and rule extraction in data sets with missing values, in particular from real clinical data of bladder cancer patients. For this experiment, we used real data of bladder cancer patients provided by Kitasato University Hospital. When using input data with missing values for SOM, the missing value is either interpolated in the preprocessing stage, or the missing value is replaced with a specific value or property that marks it as a missing value. In either case, there is a possibility some rules can be extracted from data with missing values. On the other hand, these data can have a negative influence for the classification for data sets for which missing values should be neglected. In this research we propose a method where SOM is trained using an input vector in which the properties for the missing values are excluded. The influence of information on the missing values can be reduced by using the proposed method. Through computer simulation, we showed that the proposed method gave good results in classification and rule extraction from clinical data of bladder cancer patients. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

LAF: a new XML encoding and indexing strategy for keyword‐based XML search

As a large number of corpuses are represented, stored and published in XML format, how to find useful information from XML databases has become an increasingly important issue. Keyword search enables web users to easily access XML data without the need to learn a structured query language or to study complex data schemas. Most existing indexing strategies for XML keyword search are based upon Dewey encoding. In this paper, we proposed a new encoding method called Level Order and Father (LAF) for XML documents. With LAF encoding, we devised a new index structure, called two‐layer LAF inverted index, which can greatly decrease the space complexity compared with Dewey encoding‐based inverted index. Furthermore, with two‐layer LAF inverted index, we proposed a new keyword query algorithm called Algorithm based on Binary Search (ABS) that can quickly find all Smallest Lowest Common Ancestor. We experimentally evaluate two‐layer LAF inverted index and ABS algorithm on four real XML data sets selected from Wikipedia. The experimental results prove the advantages of our index method and querying algorithm. The space consumed by two‐layer LAF index is less than half of that consumed by Dewey inverted index. Moreover, ABS is about one to two orders of magnitude faster than the classic Stack algorithm. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.     

软PLC梯形图到语句表转换新策略的研究

提出了一种用于实现软PLC梯形图到语句表转换的新策略。首先将梯形图映射为AOV图,然后对每级的AOV图进行扫描以确定各顶点之间的不同层次的串并联关系,扫描的同时自底向上建立一棵逻辑树以保存扫描结果,最后通过遍历这棵树生成语句表。通过实例说明此策略能清晰地表达出梯形图各元件的逻辑关系,语句表转换过程准确快速。执行过程中还能对梯形图进行逻辑查错和规范检查。     

Brain programming as a new strategy to create visual routines for object tracking

Multimedia Tools and Applications - This work describes the use of brain programming for automating the video tracking design process. The challenge is that of creating visual programs that learn...     

Using backward elimination with a new model order reduction algorithm to select best double mixture model for document clustering

Probabilistic latent semantic analysis (PLSA) is a double structure mixture model which has got a wide application in text and web mining. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this approach, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes. This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA. This method starts with a model having a number of components more than the needed value, and then prunes the mixtures to reach their optimum size. During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward. To treat this problem, we introduce a new combined pruning method which selects the best options for removal, while keeping a low computational cost, at all. We conducted some experiments on two datasets from Reuters-21578 corpus. The obtained results show that this algorithm leads to an optimized number of latent variables and in turn achieves better clustering performance compared to the conventional model selection methods. It also shows superiority over the case in which a PLSA model with a fixed number of latent variables, equal to the real number of clusters, is exploited.     

Advances in micromilling techniques: a new apparatus for acquiring high-resolution oxygen and carbon stable isotope values and major/minor elemental ratios from accretionary carbonate

A computer-controlled micromilling apparatus that permits discrete sampling of accretionary biogenic carbonate specimens with micron-scale resolution has been developed for the purpose of acquiring high-resolution δ¹³C and δ¹⁸O values, and major/minor elemental chemistry. Secular variation in stable isotope ratios and major/minor elemental composition records inter-annual and intra-annual changes in the environmental parameters or animal behavior for extant and extinct species.A polished specimen is attached to a stage beneath a fixed micro-milling head, and viewed on a large-screen monitor via a color digital camera. Growth bands (analogous to tree rings) are generally a result of variable accretion rates in biogenic carbonates. These growth features are first digitized in real-time as a series of three-dimensional coordinates. To better characterize complex growth features, intermediate coordinates are interpolated using a cubic spline fit through the digital points. Intermediate sampling paths, which mimic less visible daily growth banding, are in turn calculated between digitized curves. Sampling path arrays serve to guide three high precision actuators, which position the sample stage relative to the fixed micromilling head. A fourth actuator provides vertical control of the digital color camera (compensating for vertical movement of the z-axis stage actuator) keeping the specimen image focused. This new micromilling device permits high-resolution sampling of complex internal structures via a user-friendly program interface.