《天工开物》科技插图的图学探析

以《天工开物》科技插图为研究对象,重点分析《天工开物》科技插图中的背景和布局、插图的表现形式、插图的绘图技术、插图的绘画风格等方面内容,总结出其图学特征为:构图布局与《天工开物》的哲学思想统一;绘图技术与中国古代经验科学的特点相吻合;白描式绘图形式充分体现了中国绘画风格。

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

CT image morphology features of pulmonary sclerosing hemangiomas

Objective: The aim of the study was to analyze the CT morphology features of pulmonary sclerosing hemangiomas (PSHs) and improve the diagnosis ability of this disease.Methods: The 18 cases of pulmonary sclerosing hemangioma (PSH) confirmed by operation and histopathology from August 2002 to May 2009 were collected,including 17 females and 2 males,aged from 19 to 60 years old,with an average age of 43 years.All the cases underwent plain CT scan,among them,16 cases received enhanced CT scan.Results: The 18 cases had isolated mass.Mean long-axis diameter of these lesions was (2.7 ± 1.3) cm (range,1.9–4.2 cm).Of all cases,5 cases (27.8%) were round in shape,9 cases (50%) were oval,4 cases (22.2%) were lobulated,and 14 cases (77.8%) were smooth margin.The air meniscus sign was in 2 cases (11.1%),and the halo sign in 3 cases (16.7%).Two cases (11.1%) contained small nodular calcification,the remaining 16 cases (70%) were homogeneous density,the CT density of the masses ranged from 24–47 HU,and the mean value was 35 HU.Sixteen cases received enhanced scan,the welt vessel sign was in 8 cases (44.4%),1 case showed less enhancement,5 cases showed marked homogeneous enhancement and 10 cases showed intense and patchy heterogeneous enhanced.The CT density of the enhancing masses ranged from 60–110 HU,the mean value was 35 HU,and the net enhancement value was 14–80 HU,the mean value was 55 HU.Conclusion: PSH should be considered in middle-aged female whose CT found that single round or oval pulmonary nodules,with smooth margin,or associated with the air meniscus sign,the halo sign,or the marked enhancement.

Chemical fingerprinting of Su-He-Xiang-Wan and attribution of major characteristic peaks for its quality control by GC-MS

A simple and facile gas chromatography-mass spectrometer （GC-MS） fingerprint of Su-He-Xiang-Wan （SHXW） was developed, the similarity analysis was conducted, and attribution of the major characteristic peaks was identified for SHXW quality control. GC-MS analysis was performed on a QP2010 instrument （Shimadzu, Japan） equipped with a capillary column of RTX-5MS. The column temperature was initiated at 50℃, held for 5 min, increased at the rate of 3 ℃/min to 120 ℃, held for 2 min, and then increased at the rate of 4 ℃/min to 220℃, held for 10 min. Helium carrier gas was used at a constant flow rate of 1.3 mL/min. Mass conditions were ionization voltage, 70 eV; injector temperature, 250℃; ion source temperature, 250 ℃; splitting ratio, 30：1; full scan mode in the 40-500 Da mass ranges with rate of 0.2 s per scan. Attribution of the major characteristic peaks was identified for SHXW by comparing the chemical standards, references of Chinese herbal medicines and the negative controls of prescription samples （NC） of SHXW. With the help of the temperature-programmed retention indices （PTRIs） used together with mass spectra and chemical standards, 25 major characteristic peaks have been identified. Nine volatile medicinal materials were identified in the prescription of SHXW by attributing to the 27 major characteristic peaks. The results demonstrate that the proposed method is a powerful approach to quality control of complex herbal medicines.

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

Interaction behavior recognition from multiple views

This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.

Body Fuzzy Pattern Recognition in Woman Basic Block Design

Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body not to record it. This paper reports the Algorithm of woman body fuzzy pattern recognition. It is organized in three sections:(i) extracting woman body feature; (ii) establishing membership functions of feature indexes;(iii) presenting an Algorithm for woman body fuzzy pattern recognition by example.

Two-level hierarchical feature learning for image classification

In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

Automatic malware classification and new malware detection using machine learning

The explosive growth ofmalware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware pro- grams. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules： data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import fimctions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor （SNN） clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the un- known malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.

（n, 2n）-Dominating Numbers of Undirected Toroidal Mesh C（3,3,...,3）

The(d,k)-dominating number is a new measure to characterize reliability of resource- sharing in fault tolerant networks.This paper obtains that the(n,2n)-dominating number of the n-dimensional undirected toroidal mesh C(3,3,…,3)is equal to 3(n≥3).