3/13/2023 0 Comments Simmons timetoolcsv file for each Month, and also split the logged Worktime into daily parts.Īfter that you may access a simple overview of your current Worktime by entering To turn the logging output into monthly enter In this case the Worktime would be logged for the Project 'TimeTool' with 'added Readme' as an Comment Timetool stop -p TimeTool -c 'added Readme' To use timetool from the Command Line simply enter time.csv)ĭIAGRAMTYPE ('bar' only, so far, only valid for -list) LOCATION (the location for the Logging-.csv-File) (Optional, if not present Setting is taken from. time.props)ĬOMMENT (Optional, if not present Setting is taken from. PROJECTNAME (Optional, if not present Setting is taken from. Lists the current Month, or the Month defined by -m and -y (requires corresponding '-monthly'-Files). csv defined by -l into separateMonths (also splits working hours into separate Days, if necessary) timetool.bat so you can simply type 'timetool' instead oof 'java -jar TimeTool.jar'. You may want to setup your Classpath to point to. '0.1') and extract it to your desired Directory. Windowsĭownload the timetool-x.zip-Archive (where x is the Version-Number e.g. This will install all Files to ~/TimeTool and setup a Shortcut in /usr/bin so you can simply type 'timetool' instead of 'java -jar TimeTool.jar'. '0.1') and extract it in some temp-Folder. time.props-File for all persistent Settings.ĭownload the timetool-x.zip-Archive (where x is the Version-Number e.g. He proposes a new insight of critical phenomenon to re-evaluate the long-term challenge in rock mechanics and develops a series of algorithms to make a full generalization of core principle of critical and cluster mechanics.This is a simple Tool for logging working hours. A very young research group of more than 30 members aiming at the original contribution of mechanic theory to solve the challenge of mining problems is organized by him. As a visiting scholar, he maintains a long-term cooperation with several key laboratories, such as State Key Laboratory of Coal Mine Disaster Dynamics and Control, and State Key Laboratory of Hydraulics and Mountain River Engineering. He is also a regular reviewer of more than 30 SCI/EI journals. He has been participated in a large number of Chinese national projects. His research interests include (1) mining-induced mechanics, (2) critical mechanics, (3) cluster mechanics and (4) intelligent rock mechanics. He is also the young member of editorial board of International Journal of Mining Science and Technology, and Journal of China University of Mining and Technology. He is an associate professor at CUMTB and associate editor-in-chief of International Journal of Coal Science and Technology. The proximity of measured angle and cluster-based angle supports the effectiveness of predication by the cube cluster approach.ĭongjie Xue obtained his PhD in Engineering Mechanics from China University of Mining and Technology (Beijing) (CUMTB), China, in 2013. The deviations prove the huge difficulty of describing the development of the fracture network uniquely dependent on a fracture angle. Finally, four classical models for calculating fracture angle are compared. Moreover, a comparison between dilatancy-based damage and porosity-based damage is made not to prove the best but provide an AE-based prediction of local damage evolution. More importantly, by the statistical analysis of cluster structure, the cluster model shows the potential of calculating fracture angle. Most fitting results support the linear correlation between two descriptors and volumetric strain, which verifies the sensitiveness of the cube cluster model to dilatancy. three-axis length sum and pore fraction) are introduced to correlate cluster model with dilatancy behavior. The evolutional cube clusters effectively present the geometric characteristics induced by the increasing dilatancy of fracture. Considering the interaction of local fractures, a cube cluster approach is established to describe the spatial correlation. The key to solving such a challenge is strongly dependent on the accurate modeling of the spatial correlation in fracture network, which could be indirectly re-constructed by the acoustic emission (AE) signal cloud. The invisibility of fracture network evolution in the rock under triaxial compression seriously restricts the correlation modeling between dilatancy behavior and fracture interconnectivity.
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