| Project/Area Number |
07640255
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
解析学
|
|---|
| Research Institution | Science University of Tokyo |
|---|
Principal Investigator |
OHYA Masanori Science University of Tokyo, Faculty of Science and Technology, Professor, 理工学部, 教授 (90112896)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SUYARI Hiroki Science University of Tokyo, Faculty of Science and Technology, Assistant, 理工学部, 助手 (70246685)
WATANABE Noboru Science University of Tokyo, Faculty of Science and Technology, Lecture, 理工学部, 講師 (70191781)
戸川 美郎 東京理科大学, 理工学部, 助教授 (20112899)
下井田 宏雄 東京理科大学, 理工学部, 教授 (00112897)
|
|---|
| Project Period (FY) |
1995 – 1996
|
| Project Status |
Completed (Fiscal Year 1996)
|
| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1996: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1995: ¥900,000 (Direct Cost: ¥900,000)
|
|---|
| Keywords | Information Dynamics / Entropy / Complexity / State / Channel / Mutual Entropy / Fractal dimension of state / Lifting / 量子論的エントロピー / K-Sエントロピー / 光通信 / フラクタル次元 / 複雑量 / 量子チャネル |
|---|
| Research Abstract |
Information Dynamics was introduced by the representative of thei project, Masanori Ohya, in 1991 as a mathematical structure to unify various fields of mathematical physics. The Information Dynamics contains a mathematical structure to represent an information transmission through a channel and two kinds of complexities based on the initial information and its dynamics (channel). By using these mathematical structure and two complexities, we could apply the following topics and get each results : (1) Quantum Communication Theory (Optical Communication Theory) : Some information efficiencies such as mutual entropy, capacity, error probability and SNR can be rogorously formulated and applied to each communication processes such as attenuation processes and amplifier. (2) Applications of Fractal Dimension of States : Fractal Dimension of States could be applied to analysis of crators on the moon and shapes of rivers in Japan. (3) Information Genetics : Entropy evolution rate could be applied to analysis of genes and its structure of coding was investigated. (4) Quantum Computation : Quantum mutual entropy was computed for the Fredin model and its dynamics was discussed. Moreover, the quantum channel and mutual entropy was derived in quantum teleportation processes. (5) Improvement of Algorithms for Economic Models : Scarf algorithm was improved by introducing simulated annealing. (6) Applications of Mutual Entropy to Recognition Process : Mutual entropy can be applied to the discussiond for a recognition process of the reversible figure. (7) Applications of Complexties to Chaos Theory : The complexty was formulated by means of entropy and mutual entropy and it was applied to the analysis of Logostic mapping. (8) Applications of Mutual Entropy to Stochastic Algorithms : An information measure for stochastic algorithms was formulated and it was applied to the traveling saleman problems.
|
