An integrated framework for automatic green building evaluation:A case study of China

With the burgeoning emphasis on sustainable construction practices in China,the demand for green building assessment has significantly escalated.The overall evaluation process comprises two key components:The acquisition of evaluation data and the evaluation of green scores,both of which entail considerable time and effort.Previous research predominantly concentrated on automating the latter process,often neglecting the exploration of automating the former in accordance with the Chinese green building assessment system.Furthermore,there is a pressing requirement for more streamlined management of structured standard knowledge to facilitate broader dissemination.In response to these challenges,this paper presents a conceptual framework that integrates building information modeling,ontology,and web map services to augment the efficiency of the overall evaluation process and the management of standard knowledge.More specifically,in accordance with the Assessment Standard for Green Building(GB/T 50378-2019)in China,this study innovatively employs visual programming software,Dynamo in Autodesk Revit,and the application programming interface of web map services to expedite the acquisition of essential architectural data and geographic information for green building assessment.Subsequently,ontology technology is harnessed to visualize the management of standard knowledge related to green building assessment and to enable the derivation of green scores through logical reasoning.Ultimately,a residential building is employed as a case study to validate the theoretical and technical feasibility of the developed automated evaluation conceptual framework for green buildings.The research findings hold valuable utility in providing a self-assessment method for applicants in the field.

The Objective Assessment of Event-Related Potentials:An Influence of Chronic Pain on ERP Parameters

The article presents an original method for the automatic assessment of the quality of event-related potentials(ERPs),based on the calculation of the coefficientε,which describes the compliance of recorded ERPs with some statistically significant parameters.This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines.The frequency of migraine attacks was correlated with the spatial distribution of the coefficientsε,calculated for EEG channels.More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region.Patients with infrequent migraines exhibited maximum quality in the frontal areas.The automatic analysis of spatial maps of the coefficientεdemonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.

Toward automatic comparison of visualization techniques:Application to graph visualization

Many end-user evaluations of data visualization techniques have been run during the last decades.Their results are cornerstones to build efficient visualization systems.However,designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility.We believe that modern and efficient computer vision techniques,such as deep convolutional neural networks(CNNs),may help visualization researchers to build and/or adjust their evaluation hypothesis.The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task.Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model.As current machine learning models are not able to strictly reflect human capabilities,including their imperfections,such results should be interpreted with caution.However,we think that using machine learning-based preevaluation,as a pre-process of standard user evaluations,should help researchers to perform a more exhaustive study of their design space.Thus,it should improve their final user evaluation by providing it better test cases.In this paper,we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be.That study compares two mainstream graph visualization techniques:node-link(NL)and adjacency-matrix(AM)diagrams.Using two well-known deep convolutional neural networks,we partially reproduced user evaluations from Ghoniem et al.and from Okoe et al..These experiments showed that some user evaluation results can be reproduced automatically.

Experimental Study of Discriminative Adaptive Training and MLLR for Automatic Pronunciation Evaluation

A stronger canonical model was developed to improve the performance of automatic pronunciation evaluations. Three different strategies were investigated with speaker adaptive training to normalize variations among speakers, minimum phone error training to identify easily confused phones and maximum likelihood linear regression （MLLR） adaptation to compensate for accent variations between native and non-native speakers. The three schemes were combined to improve the correlation coefficient between machine scores and human scores from 0.651 to 0.679 on the sentence level and from 0.788 to 0.822 on the speaker level.

Improvement of Machine Translation Evaluation by Simple Linguistically Motivated Features

Adopting the regression SVM framework, this paper proposes a linguistically motivated feature engineering strategy to develop an MT evaluation metric with a better correlation with human assessments. In contrast to current practices of ＂greedy＂ combination of all available features, six features are suggested according to the human intuition for translation quality. Then the contribution of linguistic features is examined and analyzed via a hill-climbing strategy. Experiments indicate that, compared to either the SVM-ranking model or the previous attempts on exhaustive linguistic features, the regression SVM model with six linguistic information based features generalizes across different datasets better, and augmenting these linguistic features with proper non-linguistic metrics can achieve additional improvements.