Using face images in the diagnosis of Obstructive Sleep Apnea （OSA） in children can reduce the burden of doctors and improve the accuracy of diagnosis. Firstly， the current methods and their limitations of OSA in children clinical diagnosis were briefly described. Then， on the basis of studying the existing methods， combining with the methods of computer-aided face diagnosis of other diseases， the computer-aided face diagnosis methods of OSA in children were divided into three types： traditional computer-aided face diagnosis methods， transfer learning based diagnosis methods， and 3D face data based diagnosis methods. The key steps of the three types of methods were summarized， and the methods used in these key steps were compared， which provides different entry points for the future research of computer-aided face diagnosis for OSA in children. Finally， the opportunities and challenges in the future research of OSA in children diagnosis were analyzed.

As many factors affect the thermal comfort of semi-enclosed buildings such as high-speed railway stations in a complex way and there exists contradiction between thermal comfort and energy consumption, a comprehensive prediction method for thermal comfort and energy consumption of high-speed railway station based on machine learning was proposed. Firstly, with sensor data capturing and Energy Plus platform, the indoor and outdoor status, the control units like multi-evaporator air conditioners and heat exchangers as well as the thermal energy transmission environment of high-speed railway station were modeled. Secondly, eight factors influencing the thermal comfort of high-speed railway station, such as the operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, the operating number of heat exchangers, passenger density, outdoor temperature, indoor temperature, indoor humidity, and indoor carbon dioxide concentration, were proposed, 424 model operating conditions and 3 714 240 instances were designed. Finally, in order to effectively predict indoor thermal comfort and energy consumption of high-speed railway station, six machine learning methods, which are deep neural network, support vector regression, decision tree regression, linear regression, ridge regression and Bayesian ridge regression, were designed. Experimental results show that decision tree regression has the best prediction performance in a short time with average mean squared error of 0.002 2. The obtained research results can directly provide actively predicted environmental parameters and realize real-time decision-making for the temperature control strategy in the next stage.

Variable Precision Rough Set (VPRS) can effectively process the noise data, but its portability is not good. Aiming at this problem, an improved vulnerability threat assessment model was proposed by introducing the threshold parameter α. First of all, an assessment decision table was created according to characteristic properties of vulnerability. Then, k-means algorithm was used to discretize the continuous attributes. Next, by adjusting the value of β and α, the attributes were reducted and the probabilistic decision rules were concluded. Finally, the test data was matched with the rule base and the vulnerability assessment results were obtained. The simulation results show that the accuracy of the proposed method is 19.66 percentage points higher than that of VPRS method, and the transplantability is enhanced.

Motivated by the poor performance of existing domestic pedigree systems on data sharing, scalability and editing efficiency, an online pedigree editing system was proposed based on Browser/Server (B/S) architecture and graph database. First, the proposed system took advantage of B/S architecture to support online collaborative entering, so as to promote data entering efficiency. Second, the system used database to store pedigrees for better management and retrieval, and promoted the data sharing. Third, the system greatly improved the efficiency of data processing, because it was managed by graph database and pedigrees are graphs in nature. Finally, the system is empirically proven to be effective through systematical experiments with real pedigree data, LIU's pedigree data, which contained over 200000 people. Specifically, the proposed system based on graph database Neo4j is 50% better than that based on relation database PostgreSQL on storage space; and the query responding time of the system based on Neo4j is respectively 20%, 80%, 16% and 15% of that based on PostgreSQL for descendant query, ancestor query, relative query and descendant gender query. According to the experimental results, a conclusion can be achieved that the system can be used to process massive pedigree data efficiently and support online collaborative entering.

To improve the efficiency of recognition while determining the emotional tendencies of goods evaluation accurately, this paper proposed a text classification approach based on Matrix Projection (MP) and Normalized Vector (NLV) to realize sentiment analysis for goods evaluation. Firstly, this approach extracted feature words of goods evaluation by utilizing matrix projection, and then computed the average Feature Frequency (FF) of feature words in each category, and obtained normalized vector through normalized processing to feature frequency of each category by using Normalized Function (NLF). Finally, it predicted the sentiment tendency by comparing similarity between feature vector of goods evaluation and normalized vector of each category. Compared with the k-Nearest Neighbor (kNN), Naive Bayesian (NB) and Support Vector Machine (SVM) algorithm, the experimental results show that the proposed approach has higher prediction accuracy and speed of classification. Especially compared with the kNN the approach has obvious advantages, its macro average F1 value is more than 12% higher than the kNN and classification time is reduced by 11/12〖BP(〗reduce to或reduce by〖BP)〗. Compared with the SVM its speed is greatly improved.

The basic ray-based method is time consuming and only uses the information of triangle facets. An extended ray-based method was proposed based on the principle of non-intersecting pencil of planes. The key points of this method were as follows: firstly, a group of rays was scattered evenly from the center of the 3D model to intersect with triangle facets, and the non-intersecting pencil of planes determined by the rays was used to get the intersection points; secondly, the retrieval model was established to improve the 3D model retrieval effectiveness, according to the distances from the center to those intersection points and the vertices of the 3D model. Applying this method on ten categories of 3D models in PSB (Princeton Shape Benchmark), the results show that this approach not only reduces the processing time, but improves the retrieval accuracy.

Ontology is normative, explicit and reusable when defining the domain concept, so it can be combined with topic maps to organize information resource for semantic navigation. An information retrieval model based on topic maps and ontology was proposed and defined formally. Firstly it specified a domain of tourism document. Secondly it defined the ontology and topic maps of tourism document in order to normalize query that user directly input in natural language, and identified the users real meaning of search. Thus, it can expand user' semantic search. Therefore analyzed the effect of the ontology was analyzed, and a valuable function of semantic navigation and sorting the retrieval result correlated with user's query was shown. Finally，the experimental result shows that the topic mapbased ontology information retrieval model can perform better than the traditional model.