分组移动对象的区域偏离监视算法

以大量可分组移动对象的区域偏离监视问题为研究背景,扩展了基于区域覆盖的空间索引结构——虚拟网格四分树,提出了一种新的索引结构VGQ_G。基于VGQ_G,实现了区域偏离监视算法RDMA;给出了监视算法的性能对比实验。实验结果表明,在移动对象数量足够大的时候,设计的监视算法比朴素的监视算法在响应时间上提高了一个数量级。     

Random walk based rank aggregation to improving web search

In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of related queries to improve the retrieval quality. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. We specifically propose a generic rank aggregation framework which consists of three steps. First we build a so-called Win/Loss graph of Web pages according to a competition rule, and then apply the random walk mechanism on the Win/Loss graph. Last we sort these Web pages by their ranks using a PageRank-like rank mechanism. The proposed framework considers not only the number of wins that an item won in competitions, but also the quality of its competitor items in calculating the ranking of Web page items. Experimental results show that our search system can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also provide empirical evidences as to demonstrate how different rank aggregation methods affect the retrieval quality.     

Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models

We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.     

New pixel driving circuit using self‐discharging compensation method for high‐resolution OLED micro displays on a silicon backplane

A new 4T2C pixel circuit formed on a silicon substrate is proposed to realize a high‐resolution 7.8‐μm pixel pitch AMOLED microdisplay. In order to achieve high luminance uniformity, the pixel circuit compensates its Vth variation of the MOSFET for the driving transistor internally by using self‐discharging method. Also presented are 0.5‐in Quad‐VGA and 1.25‐in wide Quad‐XGA microdisplays with the proposed pixel circuit.     

Automatic transaction of signal via statistical modeling

The statistical information processing can be characterized by the likelihood function defined by giving an explicit form for an approximation to the true distribution. This mathematical representation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike^2,3) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler (K-L) information which measures the similarity between the predictive distribution of the model and the true distribution. Akaike information criterion (AIC) is an estimate of the K-L information and makes it possible to evaluate and compare the goodness of many models objectively. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this article, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. Genshiro, Kitagawa, Ph.D.: He is a Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently Deputy Director of the Institute of Statistical Mathematics and Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the Kyushu University in 1983. His primary research interests are in time series analysis, non-Gaussian nonlinear filtering, and statistical modeling. He has published over 50 research papers. He was awarded the 2nd Japan Statistical Society Prize in 1997. Tomoyuki Higuchi, Ph.D.: He is an Associate Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently an Associate Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the University of Tokyo in 1989. His research interests are in statistical modeling of space-time data, stochastic optimization techniques, and data mining. He has published over 30 research papers.     

Electrical conductivity and domain structure in ferroelastic Tl2SeO4

We have carried out the optical observation, electrical conductivity and ²⁰⁵Tl NMR measurements, and subsequently investigated the origin of the large conductivity above ferroelastic phase transition temperature T_c (=661 K) on the basis of the domain structure and the crystal structure. Electrical conductivity exhibits the discontinuous increase around T_c with increasing temperature and becomes approximately 5 × 10⁻³ S m⁻¹ above T_c. Moreover, from the ²⁰⁵Tl NMR measurements, it is found that mobile Tl ions exist above T_c. Furthermore, from the analysis of the domain structure based on the crystal structure in the low-temperature ferroelastic phase, it is also found that the anomalously large fluctuations of SeO₄ tetrahedrons exist above T_c. It is deduced from these results that the high electrical conductivity above T_c is caused by the mobile Tl ions closely related to the anomalously large fluctuations of SeO₄ tetrahedrons accompanied by the ferroelastic phase transition.     

Stochastic Asset Pricing with Seismic Hazard Risk

Since cash flow of asset in the long run is generally subject to economic fluctuation, and also to potential damage due to seismic hazard risk, asset pricing is more relevantly evaluated, provided that uncertainties of both cash flow and seismic risk are properly taken into account. This study will propose a discounted cash flow method to evaluate asset pricing, in which incomes gained from asset are modeled as a Markov process and seismic loss is modeled due to only one earthquake event during the service time. A numerical example is demonstrated for an 11 story steel reinforced commercial building considering the service time of 50 years. Various risk–asset price curves are numerically obtained in the form of probability of excess versus asset price with discount rate as a parameter for cases with and without earthquake loss taken into account, and for a case in which earthquake strengthening measure is implemented. These curves contribute information to decision makers in charge of risk and investment management.