| Project/Area Number |
20K11832
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 60080:Database-related
|
|---|
| Research Institution | Tokai University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2020-04-01 – 2023-03-31
|
| Project Status |
Completed (Fiscal Year 2023)
|
| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2021: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2020: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 時系列データマイニング / Time series motifs / Symbolic Representation / DTW Motifs / Predicative Maintenance / データマイニング / IoT / 時系列データ |
|---|
| Outline of Research at the Start |
本研究では，新たなモチーフ解析手法として，機器の劣化や人の健康悪化などを検出するために，モチーフが少しずつ変化していく傾向を発見する方法を確立する。また，機器は，(a)位置決め，(b)吹付，(c)洗浄 などの一連のモチーフが一つの工程を作る場合が多く，モチーフの出現文脈を考慮した分析を可能にするため，モチーフで構成される文法的パターンを発見する問題を研究する。また，この問題の部分問題として，モチーフ発見の未解決問題である「モチーフの長さを決定する問題」を解決する。
|
| Outline of Final Research Achievements |
With the proliferation of IoT and big data, there is an increasing need for sensor data analysis, ranging from predictive maintenance in factories and public facilities to human behavior analysis and health management. However, traditional frequency analysis and time series analysis assume stationarity, making them often unsuitable for sensor data. To address this, we propose a novel time series decomposition method called Spikelet, which uses "the range of fluctuation of time series values" as a hyperparameter instead of "the time width of the time series." Additionally, we developed "grammatical motifs" to discover the structure of sensor data and "temporal evolution of motifs" to detect changes such as degradation or failure. This work has been accepted at top international conferences, including ACM-KDD and IEEE-ICDM.
|
| Academic Significance and Societal Importance of the Research Achievements |
四次産業革命やSociety 5.0を実現するために， IoT(Internet of Things)普及に伴って飛躍的に増加するセンサーデータを分析する基盤技術を確立する
|
