PROGRESS IN THE MODELLING OF AIR FLOW PATTERNS IN TIMBER KILNS

Progress in modelling air flow patterns in timber kilns using Computational Fluid Dynamics is reviewed in this work. These simulations are intended to predict the distribution of the flow in the fillet spaces between boards in a hydraulic model of a timber kiln. Here, the flow regime between the boards is transitional between laminar and turbulent flow, with Reynolds numbers of the order of 5000. Running the simulation as a transient calculation has shown few problems with convergence issues, reaching a mass residual of 0.2% of the total inflow after 40-100 iterations per time step for time steps of 0.01 s. Grid sensitivity studies have shown that non-uniform grids are necessary because of the sudden changes in flow cross section, and the flow simulations are insensitive to grid refinement for non-uniform grids with more than 300,000 cells. The best agreement between the experimentally-measured flow distributions between fillet spaces and those predicted by the simulation have been achieved for (effective) bulk viscosities between the laminar viscosity for water and ten times that value. This change in viscosity is not very large (less than an order of magnitude), given that effective turbulent viscosities are typically several orders of magnitude greater than laminar ones. This result is consistent with the transitional flows here.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

Host odor and visual stimulus interaction during intratree host finding behavior ofRhagoletis pomonella flies

Responses ofRhagoletis pomonella (Walsh) (Diptera: Tephritidae) flies to host fruit visual stimuli (apples or models of apples) and chemical stimuli (synthetic apple volatile blend) were studied in semidwarf field-caged apple trees. Three different fruit or model densities (1, 4, or 16 fruit or models/ tree) and two odor release rates [ca. 0.7g/hr (close to the natural release rate of a ripe apple) and ca. 500g/hr (amount of odor released by commercially sold apple maggot traps)] were tested. Individually released flies were followed as they moved within a tree for a maximum of 20 min. We recorded three-dimensional search paths followed by foraging flies and computed such variables as total relative distance traveled before alighting on a fruit or model, track length between individual alightment sites, and directness of flight to fruits or models. Effect of odor on propensity to alight on fruit or models and host-searching behavior prior to alighting on fruit or on models varied according to fruit or model color and density. If the fruit visual stimulus was strong (e.g., red color), odor did not increase the probability of finding fruit or fruit models. As the visual stimulus became progressively weaker (red to green to clear), odor (irrespective of concentration) appeared to aid flies during the fruit-finding process. As density of fruit or models increased, the probability of flies finding a fruit or model also increased (e.g., 50% of flies found a red fruit model at 1 model/tree while 90% found a red model at 16 models/tree; 4% of flies found a clear model with odor at 1 model/tree while 35% found a clear model with odor at 16 models/tree). Findings reported elsewhere indicate thatR. pomonella flies are able to discover a point source of odor (an odor-bearing tree in a patch of trees) by flying upwind (in the tree patch) in response to intermittent exposure to odor. Findings here indicate that after arrival on a host tree (point source), flies discover individual apparent and abundant host fruit on the basis of vision. If fruit are less apparent or scarce, odor appears to interact with vision during the fruit-finding process.     

一种汽轮机组排汽焓和最优初压在线计算方法

通过对已往的汽轮机变工况计算方法的分析,提出了以汽轮机末级抽汽或次末级抽汽(过热蒸汽状态)为计算起点的汽轮机顺序变工况核算方法。根据初始假定的末级流量和现场实际的末级前热力状态和背压,用汽轮机变工况流型判别准则,判别级的流型,然后从末级前参数开始顺序进行一次级的变工况核算,得到新的排汽焓,最后算得机组热耗率和排汽焓。以此方法为变工况计算的基础,并利用系统分析方法,建立了在线确定单元机组最优运行初压的数学模型。为火电机组的优化运行提出了一种新思路。     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Test frame updating in CPM testing of Prolog programs

Category Partition Method (CPM) is a general approach to specification-based program testing, where test frame reduction and refinement are two important issues. Test frame reduction is necessary since too many test frames may be produced, and test frame refinement is important since during CPM testing new information about test frame generation may be achieved and considered incrementally. Besides the information provided by testers or users, implementation related knowledge offers alternative information for reducing and refining CPM test frames. This paper explores the idea by proposing a call patterns semantics based test frame updating method for Prolog programs, in which a call patterns analysis is used to collect information about the way in which procedures are used in a program. The updated test frames will be represented as constraints. The effect of our test frame updating is two-fold. On one hand, it removes “uncared” data from the original set of test frames; on the other hand, it refines the test frames to which we should pay more attention. The first effect makes the input domain on which a procedure must be tested a subset of the procedure’s input domain, and the latter makes testers stand more chance to find out the faults that are more likely to show their presence in the use of the program under consideration. Our test frame updating method preserves the effectiveness of CPM testing with respect to the detection of faults we care. The test case generation from the updated set of test frames is also discussed. In order to show the applicability of our method an approximation call patterns semantics is proposed, and the test frame updating on the semantics is illustrated by an example.

Continuity versus Discretization

The threefold interest in architecture, biology and mathematics motivated us to examine and justify new architectural forms. We discuss some notions of rhythm: Euclidean, morphogenetic and morphologic. Contemporary relationships between structure and form are based on the generation of shape by technological processes, thus the resulting objects are restricted to their material expression. Here a phenomenological organisation of form and its continuity with the landscape arise out of the mathematical and architectural creativity. The use of the computer is applied from outside to inside instead from inside to outside; this means that we are dealing with the organisational processes via continuous methods instead of evolutionary processes given by computer simulations, known as genetic algorithms, where the resulting configurations are reduced to a matter of routine. The role of design as an aesthetic component innovates the theoretical framework of structural engineering to establish the architectural environments.     

品味童装——看图案设计在童装中的应用

图案是所有设计专业的学生在学校学习时所必修的一门专业基础课。纵观中华五千年的文明,哪一朝哪一代都有自己独特的图案风格。那么,在今天的图案设计特别是儿童服装的图案设计与应用中,怎样为孩子营造一个童话的世界,展现孩子的童真与童趣?一名优秀的童装图案设计师,应该充分利用自然环境,社会环境,民族心理和传统文化结构所赋予的条件,来解决这个问题,从而构思出独特完美而富有表现力的童装图案设计作品。     

Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.