In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using...In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.展开更多
Due to increased tourist activity,many cities now have a large number of hotel buildings.It is necessary to establish measures to evaluate energy use intensity to effectively manage energy consumption in this sector.T...Due to increased tourist activity,many cities now have a large number of hotel buildings.It is necessary to establish measures to evaluate energy use intensity to effectively manage energy consumption in this sector.This study uses a combined strategy to establish an energy benchmark for hotel buildings in Vietnam.First,a survey and analysis of actual building stock data of 50 hotels in Danang,Vietnam,was conducted.The survey-based benchmark and its related data was then used to build a reference energy model to estimate an energy benchmark for other climatic regions in Vietnam by using the energy simulation method.The results reveal that the average energy use intensity for hotels in Danang was 87.4 kWh/m2.year or 8628.6 kWh/guestroom.year.However,this study proposes that because of the differing expectations of comfort standards,hotels of different grades should have separate benchmarks.This study also proposes an energy intensity-based rating scale,including 7 grades from the least energy intensive(grade A)to the most energy intensive(grade G),which can be used to manage,label,or encourage sustainable energy use in hotel buildings.The relationship between the energy use intensity and the occupancy rate of the hotels was reported,compared,and explained.It was found that occupancy rate has no significant impact on the energy use intensity.From the survey result,some predictive models were developed to estimate annual energy consumption of hotel buildings based on their grades.The simulated benchmarks for other regions were also achieved.The results demonstrate many potential applications in the management,design and construction,and renovation of this building type.展开更多
文摘In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.
基金supported by The University of Danang,University of Science and Technology,code number of the project:T2017-02-75.David Rockwood’s participation was made possible by a Core Fulbright U.S.Scholar award to Vietnam,sponsored by the U.S.Department of State’s Bureau of Educational and Cultural Affairs.
文摘Due to increased tourist activity,many cities now have a large number of hotel buildings.It is necessary to establish measures to evaluate energy use intensity to effectively manage energy consumption in this sector.This study uses a combined strategy to establish an energy benchmark for hotel buildings in Vietnam.First,a survey and analysis of actual building stock data of 50 hotels in Danang,Vietnam,was conducted.The survey-based benchmark and its related data was then used to build a reference energy model to estimate an energy benchmark for other climatic regions in Vietnam by using the energy simulation method.The results reveal that the average energy use intensity for hotels in Danang was 87.4 kWh/m2.year or 8628.6 kWh/guestroom.year.However,this study proposes that because of the differing expectations of comfort standards,hotels of different grades should have separate benchmarks.This study also proposes an energy intensity-based rating scale,including 7 grades from the least energy intensive(grade A)to the most energy intensive(grade G),which can be used to manage,label,or encourage sustainable energy use in hotel buildings.The relationship between the energy use intensity and the occupancy rate of the hotels was reported,compared,and explained.It was found that occupancy rate has no significant impact on the energy use intensity.From the survey result,some predictive models were developed to estimate annual energy consumption of hotel buildings based on their grades.The simulated benchmarks for other regions were also achieved.The results demonstrate many potential applications in the management,design and construction,and renovation of this building type.
