应急疏散语音告警信号设计参数的研究

Voice alarm plays an important role in emergency evacuation of public place, because it can provide information and instruct evacuation. This paper studied the optimization of acoustic and semantic parameters of voice alarms in emergency evacuation, so that alarm design can improve the evacuation performance. Both method of magnitude estimation and scale were implemented to investigate participants' perceived urgency of the alarms with different parameters. The results indicated that, participants evaluated the alarms with faster speech rate, with greater signal to noise ratio (SNR) and under louder noises more urgent. There was an interaction between noise level and content of voice alarm. Signals with speech rate below 4 characters / second were evaluated as non urgent at all. Intelligibility of the voice alarm was investigated by evaluating the key pointed recognition performance. The results showed that, speech rate’s effect was a marginal significance, and 7 characters / second has the highest intelligibility. It might because that the faster the signal spoken, the more attention was paid. Gender of speaker and SNR did not have a significant effect on the signals’ intelligibility. This paper also investigated impact of voice alarms' content on human behavior in emergency evacuation in a 3-D virtual reality environment. In condition of "telling the occupants what had happened and what to do", the number of participants who succeeded in evacuation was the largest. Further study, in which similar numbers of participants evacuate successfully in three conditions, indicated that the reaction time and evacuation time was the shortest in the aforesaid condition. Although one-way ANOVA shows that the difference was not significant, the results still provided some reference to the alarm design. In sum, parameters of voice alarm in emergency evacuation should be chosen to meet needs from both perceived urgency and intelligibility. Contents of the alarms should include "what had happened and what to do", and should vary according to noise levels in different public places.

基于模糊神经网络的人脸识别方法研究

人脸识别是模式识别研究领域的重要课题,具有广阔的应用前景。本文提出了基于模糊神 经网络的人脸识别方法。首先用最优鉴别分析方法提取人脸的最优鉴别矢量集,构成特征空间,然后在 特征空间中设计模糊神经网络分类器。在ORL人脸图象库上的实验结果表明了该方法的有效性。

五十万年来人类对黄土地区的适应性初步研究

After researching the coupling relationship among choosing raw material, stone technology, environmental change and Huaman evolution stage of archeological sites in different sediment in north China, the author thinks that: The human behavior is different in loessic region between glacial and interglacial ages. In Human evolution procession, Human erectus and early Human sapiens may co-exist in north China before L2, but after L2Human erectus disappear, and the stone technology of early Human sapiens become more progression. After comparing the age and environment, geology context, stone technology and using fire between them, we may make a preliminary conclusion that the environmental change during L2 maybe the outer reason and different capability of adaptation between Human erectus and early Human Sapiensis is the inner reason of Human erectus becoming disappear. The environmental change in last glacial climax and deglacial may result in new crowd and new culture entering into North China, which break the culture tradition which exist since early stage of palaeolithic. And play an important role from palaeolithic stage into neolithic stage. So unstable envirnmental change play an important role in Human evolution procession, and different scale environment change have different effect, large scale environmental change make small effect, but millenary scale even more short scale environmental change may bear more important role, some times it can transfer the evolution direction.

Aggregation behavior of amphiphilic D-pi-A molecules bearing recognition group

Aggregation behavior of two amphiphilic D-pi -A molecules bearing barbituric acid as both recogniton group and electron-drawing substituent, 5-(4-dodecyl oxybenzylidene)-(1H, 3H)-2,4,6-pyrimidine trione (PB12) and 5-(4-N,N-didodecyl aminobenzylidene)-(1H,3H)-2,4,6-pyrimidine trione (AB(12)) was studied by UV-visible, fluorescence, and surface voltaic spectroscopies (SPS). The experimental results indicate that PB12 tends to form J-aggregate and AB(12) tends to form H-aggregate under increasing concentration. An intramolecular twisted charge transfer (TICT) emission around 500 nm is observed when J-aggregate is formed between PB12 molecules, and an excimer emission around 600 nm is observed when H-aggregate is formed between AB(12) molecules.

Diet and feeding behavior of Rhinopithecus bieti at Xiaochangdu, Tibet: Adaptations to a marginal environment

The diet and feeding ecology of a wild subpopulation of black-and-white snub-nosed monkeys (Rhinopithecus bieti) were studied at Xiaochangdu in Honglaxueshan Nature Reserve, Tibet. This region is climatologically harsher than any other inhabited by non-human primates. Black-and-white snub-nosed monkeys fed on 48 parts of 25 plant species, at least three species of lichens and seven species of invertebrates. The number of food items exploited varied markedly among seasons, with dietary diversity being greatest in spring and summer. In winter, black-and-white snub-nosed monkeys had to subsist on fallback foods such as dried grass and bark. Ubiquitous lichens formed a major dietary constituent throughout the year, contributing about 75% of feeding records. Even though lichens act as a staple, our findings signify that the monkeys at Xiaochangdu prefer feeding on foliage, which is higher in protein content than the former. We provide evidence that black-and-white snub-nosed monkeys are able to cope with an array of food items other than lichens and hence can be regarded as feeding generalists. We discuss the results with reference to previous studies on other subpopulations living in habitats that are floristically more diverse and offer more plant food items than the marginal habitat at Xiaochangdu.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Mandarin speech emotion recognition based on high dimensional geometry theory

In this paper, a novel approach for mandarin speech emotion recognition, that is mandarin speech emotion recognition based on high dimensional geometry theory, is proposed. The human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. According to the characteristics of these emotional speech signals, the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. The new method called high dimensional geometry theory is applied for recognition. Compared with traditional GSVM model, the new method has some advantages. It is noted that this method has significant values for researches and applications henceforth.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

Image Ratio Features for Facial Expression Recognition Application

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e. g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Single-walled carbon nanotubes binding to human telomeric i-motif DNA: significant acceleration of S1 nuclease cleavage rate

Single-walled carbon nanotubes (SWNTs) binding to human telomeric i-motif DNA can significantly accelerate S1 nuclease cleavage rate by increasing the enzyme turnover number.

Immunosensors based on layer-by-layer self-assembled Au colloidal electrode for the electrochemical detection of antigen

Colloidal Au particles have been deposited on the gold electrode through layer-by-layer self-assembly using cysteamine as cross-linkers. Self-assembly of colloidal Au on the gold electrode resulted in ail easier attachment of antibody, larger electrode surface and ideal electrode behavior. The redox reactions of [Fe(CN)(6)]-/[Fe(CN)(6)](3-) on the gold surface were blocked due to antibody immobilization, which were investigated by cyclic voltammetry and impedance spectroscopy. The interaction of antigen with grafted antibody recognition layers was carried out by soaking the modified electrode into a phosphate buffer at pH 7.0 with various concentrations of antigen at 37degreesC for 30 min. Further, an amplification strategy to use biotin conjugated antibody was introduced for improving the sensitivity of impedance measurements. Thus, the sensor based oil this immobilization method exhibits a large linear dynamic range, from 5 - 400 mug/L for detection of Human IgG. The detection limit is about 0.5 mug/L.