University of Electro-Communications

About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.

Fibrous CdS/CdSe quantum dot co-sensitized solar cells based on ordered TiO2 nanotube arrays

Abstract: A new kind of fibrous quantum dot sensitized solar cell has been designed and fabricated by using CdS and CdSe co-sensitized TiO(2) nanotubes on Ti wire as the photoanode and highly active Cu(2)S as the counter electrode. By optimizing the CdSe deposition time and the length of the nanotube, a power conversion efficiency of 3.18% has been obtained under AM 1.5 illumination (100 mW cm(-2)). The potential application of this kind of solar cell has also been discussed in this paper.

Understanding Business Ecosystem Dynamics: A Data-Driven Approach

Abstract: Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships However, there is no well-established methodology to study the dynamics of this network Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft The article concludes with implications and future research opportunities

Effect of deformation temperature on microstructure evolution in aluminum alloy 2219 during hot ECAP

Abstract: The effect of deformation temperature on microstructure evolution during equal channel angular pressing (ECAP) was studied in a coarse-grained aluminum alloy 2219 in a wide temperature interval from 250 to 475 °C. The structural changes taking place during ECAP up to strains of 12 are classified into the following three stages irrespective of deformation temperatures: i.e. (1) an incubation period for formation of the embryos of deformation bands (DBs) at low strains; (2) development of large-scale DBs followed by grain fragmentation at moderate strains; (3) rapid development of new grain at high strains. Microstructure development in stages 1 and 2 is hardly influenced by temperature, while that in stage 3 is most significantly affected at higher temperature. An increase in the pressing temperature leads to decreasing the volume fraction of new grains and increasing the average grain size in stage 3. This can be attributed to relaxation of strain compatibility between grains due to frequent operation of dynamic recovery and grain boundary sliding at higher temperature. The mechanism of grain refinement is discussed in detail.

Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing

Abstract: The single linkage method is a fundamental agglomerative hierarchical clustering algorithm. This algorithm regards each point as a single cluster initially. In the agglomeration step, it connects a pair of clusters such that the distance between the nearest members is the shortest. This step is repeated until only one cluster remains. The single linkage method can efficiently detect clusters in arbitrary shapes. However, a drawback of this method is a large time complexity of O(n 2), where n represents the number of data points. This time complexity makes this method infeasible for large data. This paper proposes a fast approximation algorithm for the single linkage method. Our algorithm reduces the time complexity to O(nB) by rapidly finding the near clusters to be connected by Locality-Sensitive Hashing, a fast algorithm for the approximate nearest neighbor search. Here, B represents the maximum number of points going into a single hash entry and it practically diminishes to a small constant as compared to n for sufficiently large hash tables. Experimentally, we show that (1) the proposed algorithm obtains clustering results similar to those obtained by the single linkage method and (2) it runs faster for large data than the single linkage method.