This paper studied the residents＇ level of satisfaction in public housing units in Shenyang of China which aimed to identify how each housing unit feature correlates with the residents＇ overall satisfaction and impl...This paper studied the residents＇ level of satisfaction in public housing units in Shenyang of China which aimed to identify how each housing unit feature correlates with the residents＇ overall satisfaction and implication on policy and design. The discussion is based on a questionnaire survey conducted in 2011. This research concluded that more residents were satisfied with the housing unit features than those who were dissatisfied, while some remarkable differences could be observed if comparative analysis between two different public housing types in China were studied. The affordable housing residents are generally more satisfied with housing unit features than those living in low-rent housing. The most dissatisfying feature is the living room, followed by unit size and the floor plan. Additionally, the living room and the floor plans were features which were highly and positively correlated to the residents＇ overall satisfaction of housing unit features. Consequently, improving the living room size and the design of housing unit floor plans would clearly be beneficial to alleviating residents＇ dissatisfaction to the housing units.展开更多
In this paper,improvement on man-computer interactive classification of clouds based on hispeetral satellite imagery has been synthesized by using the maximum likelihood automatic clustering(MLAC)and the unit feature ...In this paper,improvement on man-computer interactive classification of clouds based on hispeetral satellite imagery has been synthesized by using the maximum likelihood automatic clustering(MLAC)and the unit feature space classification(UFSC)approaches.The improved classification not only shortens the time of sample-training in UFSC method,but also eliminates the inevitable shortcomings of the MLAC method.(e.g.,1.sample selecting and training is confined only to one cloud image:2.the result of clustering is pretty sensitive to the selection of initial cluster center:3.the actual classification basically can not satisfy the supposition of normal distribution required by MLAC method;4.errors in classification are difficult to be modified.) Moreover,it makes full use of the professionals'accumulated knowledge and experience of visual cloud classifications and the cloud report of ground observation,having ensured both the higher accuracy of classification and its wide application as well.展开更多
文摘This paper studied the residents＇ level of satisfaction in public housing units in Shenyang of China which aimed to identify how each housing unit feature correlates with the residents＇ overall satisfaction and implication on policy and design. The discussion is based on a questionnaire survey conducted in 2011. This research concluded that more residents were satisfied with the housing unit features than those who were dissatisfied, while some remarkable differences could be observed if comparative analysis between two different public housing types in China were studied. The affordable housing residents are generally more satisfied with housing unit features than those living in low-rent housing. The most dissatisfying feature is the living room, followed by unit size and the floor plan. Additionally, the living room and the floor plans were features which were highly and positively correlated to the residents＇ overall satisfaction of housing unit features. Consequently, improving the living room size and the design of housing unit floor plans would clearly be beneficial to alleviating residents＇ dissatisfaction to the housing units.
文摘In this paper,improvement on man-computer interactive classification of clouds based on hispeetral satellite imagery has been synthesized by using the maximum likelihood automatic clustering(MLAC)and the unit feature space classification(UFSC)approaches.The improved classification not only shortens the time of sample-training in UFSC method,but also eliminates the inevitable shortcomings of the MLAC method.(e.g.,1.sample selecting and training is confined only to one cloud image:2.the result of clustering is pretty sensitive to the selection of initial cluster center:3.the actual classification basically can not satisfy the supposition of normal distribution required by MLAC method;4.errors in classification are difficult to be modified.) Moreover,it makes full use of the professionals'accumulated knowledge and experience of visual cloud classifications and the cloud report of ground observation,having ensured both the higher accuracy of classification and its wide application as well.
