Heat Shock Proteins in Association with Heat Tolerance in Grasses

The grass family Poaceae includes annual species cultivated as major grain crops and perennial species cultivated as forage or turf grasses. Heat stress is a primary factor limiting growth and productivity of cool-season grass species and is becoming a more significant problem in the context of global warming. Plants have developed various mechanisms in heat-stress adaptation, including changes in protein metabolism such as the induction of heat shock proteins (HSPs). This paper summarizes the structure and function of major HSPs, recent research progress on the association of HSPs with grass tolerance to heat stress, and incorporation of HSPs in heat-tolerant grass breeding

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Co-Design Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates (1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and (2) an Energy-Efficient, Multi-Level Computing Architecture Specifically Designed to Leverage the Multi-Resolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Physical Beam and Simulations of a Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency. © 2010 ACM

Research on artificial visual system for motion direction detection and motion speed perception based on HRC model

13301甲第5694号博士（工学）金沢大

Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Codesign Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates 1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and 2) an Energy-Efficient, Multilevel Computing Architecture Specifically Designed to Leverage the Multiresolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Simulated Truss Structure and a Real Full-Scale Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency