By using the fault tree analysis in reliability theory as the systematical analysis approach, the dust suppression mechanism in a spray system with wetting agent is shown in a logic tree and some graphical models. Fro...By using the fault tree analysis in reliability theory as the systematical analysis approach, the dust suppression mechanism in a spray system with wetting agent is shown in a logic tree and some graphical models. From these diagrams, all factors related to the spray system and their cause and effect relationship can be seen clearly. Based on the built logic tree, several mathematical models and new ideas for expressing the dust suppressing efficiency in the spray system are put forward. The significance of all factors related to the efficiency of suppressing dust is qualitatively described. Furthermore, the new concepts, such as, the effective reaction time between dust particle and droplet, the expansion phenomenon of laden dust droplet, the functions of volatile and the relative size distribution efficiency of wetting agent are presented. All this richenes the existing mechanism of dust abatement by spraying wetting agent. At last, several problems that need to be further investigated are also suggested in the paper.展开更多
A binary tree can be represented by a code reflecting the traversal of the corresponding regular binary tree in given monotonic order. A different coding scheme based on the branches of a regular binary tree with n-no...A binary tree can be represented by a code reflecting the traversal of the corresponding regular binary tree in given monotonic order. A different coding scheme based on the branches of a regular binary tree with n-nodes is proposed. It differs from the coding scheme generally used and makes no distinction between internal nodes and terminal nodes. A code of a regular binary tree with nnodes is formed by labeling the left branches by O’s and the right branches by l’s and then traversing these branches in pre-order. Root is always assumed to be on a left branch.展开更多
Advancements in airborne LiDAR analysis technology have made it possible to quantify forest resource volumes based on individual trees, and such technology may soon replace field surveys. Unlike individual tree detect...Advancements in airborne LiDAR analysis technology have made it possible to quantify forest resource volumes based on individual trees, and such technology may soon replace field surveys. Unlike individual tree detection or tree height measurements, diameter at breast height (DBH) is difficult to determine directly from measured data and is instead estimated indirectly using the correlation between crown size and DBH. Indicators that represent crown size include crown area, surface area, length, and length ratio, and were utilized with tree height as explanatory variables in ten combinations to determine a regression formula. DBH and tree height calculated from the regression formula were applied to an equation to calculate stem volumes of individual trees. Airborne LiDAR measurements were taken using ALS50-II and ALS60 (Leica) at a density of 4 points/m2. An evaluation of the relationship between the regression formulae and DBH estimates indicated that a combination of crown area, tree height, and crown ratio for Japanese cedar, and a combination of crown area and tree height for Japanese cypress, yielded the highest coefficients of determination. The average error and RMSE were 6.9% and 2.38 cm respectively for Japanese cedar, while the corresponding values for Japanese cypress were 8.35% and 2.51 cm. Once the relationship was extended to the stem volumes of individual trees, the average error was 14.4% and RMSE was 0.10 m3 for Japanese cedar. The corresponding values for Japanese cypress were 18.9% and 0.10 m3. These results demonstrate the potential use of airborne LiDAR as a substitute for field surveys.展开更多
The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing bui...The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system,which can be subdivided into three aspects:(i) building category;(i i) region category; and(iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating(DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics.To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system(i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.展开更多
Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Re...Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.展开更多
文摘By using the fault tree analysis in reliability theory as the systematical analysis approach, the dust suppression mechanism in a spray system with wetting agent is shown in a logic tree and some graphical models. From these diagrams, all factors related to the spray system and their cause and effect relationship can be seen clearly. Based on the built logic tree, several mathematical models and new ideas for expressing the dust suppressing efficiency in the spray system are put forward. The significance of all factors related to the efficiency of suppressing dust is qualitatively described. Furthermore, the new concepts, such as, the effective reaction time between dust particle and droplet, the expansion phenomenon of laden dust droplet, the functions of volatile and the relative size distribution efficiency of wetting agent are presented. All this richenes the existing mechanism of dust abatement by spraying wetting agent. At last, several problems that need to be further investigated are also suggested in the paper.
文摘A binary tree can be represented by a code reflecting the traversal of the corresponding regular binary tree in given monotonic order. A different coding scheme based on the branches of a regular binary tree with n-nodes is proposed. It differs from the coding scheme generally used and makes no distinction between internal nodes and terminal nodes. A code of a regular binary tree with nnodes is formed by labeling the left branches by O’s and the right branches by l’s and then traversing these branches in pre-order. Root is always assumed to be on a left branch.
文摘Advancements in airborne LiDAR analysis technology have made it possible to quantify forest resource volumes based on individual trees, and such technology may soon replace field surveys. Unlike individual tree detection or tree height measurements, diameter at breast height (DBH) is difficult to determine directly from measured data and is instead estimated indirectly using the correlation between crown size and DBH. Indicators that represent crown size include crown area, surface area, length, and length ratio, and were utilized with tree height as explanatory variables in ten combinations to determine a regression formula. DBH and tree height calculated from the regression formula were applied to an equation to calculate stem volumes of individual trees. Airborne LiDAR measurements were taken using ALS50-II and ALS60 (Leica) at a density of 4 points/m2. An evaluation of the relationship between the regression formulae and DBH estimates indicated that a combination of crown area, tree height, and crown ratio for Japanese cedar, and a combination of crown area and tree height for Japanese cypress, yielded the highest coefficients of determination. The average error and RMSE were 6.9% and 2.38 cm respectively for Japanese cedar, while the corresponding values for Japanese cypress were 8.35% and 2.51 cm. Once the relationship was extended to the stem volumes of individual trees, the average error was 14.4% and RMSE was 0.10 m3 for Japanese cedar. The corresponding values for Japanese cypress were 18.9% and 0.10 m3. These results demonstrate the potential use of airborne LiDAR as a substitute for field surveys.
文摘The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system,which can be subdivided into three aspects:(i) building category;(i i) region category; and(iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating(DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics.To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system(i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.
文摘Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.
