论郑板桥的精勤读书与他的读有特识

郑板桥是"扬州八怪"之一,也是历史上一个著名的清官。在读书问题上他强调自刻苦、自愤激、自竖立,特别是他强调读书要精选精读,要寻找贯穿串,读有特识,自竖旗帜等重要的读书观,对今人仍有重要的参考和借鉴作用,值得我们作深入的研究。

模型水轮机初生空化的特征谱提取识别方法

目前水轮机模型初生空化识别方法仍采用人工识别法,这种方法获得结果的周期较长、主观性强、准确度及效率都较低。针对于此,对水轮机模型初生空化识别方法进行了研究与优化创新,提出了一种基于炮声谱与特殊脉动谱特征提取的水轮机空化智能识别方法,即多态泡音智能识别方法 PSVFR。该方法依据自主开发的水轮机空化噪声多态算法MTCSPC,对数据进行处理,通过采集初生空化音态特征向量,建立矩阵模型,与样本数据库中的定性矩阵进行特征比对、计算、判断,以帮助机器完成对模型水轮机空化噪声的学习和识别。与现有技术相比,该方法能够提高机器对水轮机初生空化现象的识别准确度和识别效率,识别效率可达80%。

Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

刘勰“正纬”的文论及其批评方法论意蕴

刘勰《文心雕龙.正纬》中不仅提出“正纬”之说,而且提出“酌乎纬”之论,辩证得出纬书“无益经典而有助文章”的结论,一方面在经纬之辨中进一步确立了“宗经”的指导思想,另一方面则以“文”视“纬”,以其“特识”的文学视角发掘可资文学借鉴和利用的神话、传说、故事资源和绚烂的辞采以及丰富多彩的表现手法,其“正”与“酌”的思维方式和研究方法具有文论及其批评方法论的意蕴。

Support vector machines for emotion recognition in Chinese speech

Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.

Feature fusing in face recognition

With the aim of extracting the features of face images in face recognition, a new method of face recognition by fusing global features and local features is presented. The global features are extracted using principal component analysis （PCA）. Active appearance model （AAM） locates 58 facial fiducial points, from which 17 points are characterized as local features using the Gabor wavelet transform （GWT）. Normalized global match degree （local match degree） can be obtained by global features （local features） of the probe image and each gallery image. After the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the gallery image corresponding to the largest fused match degree. The method is evaluated by the recognition rates over two face image databases （AR and SJTU-IPPR）. The experimental results show that the method outperforms PCA and elastic bunch graph matching （EBGM）. Moreover, it is effective and robust to expression, illumination and pose variation in some degree.

MACHINING-FEATURE BASED 3-AXIS AUTOMATIC NC PROGRAMMING FOR HIGH-SPEED MILLING

Machining-features of the workplace are described by using of the object-oriented （O-O） technology. Geometrical machining-features are recognized in the given cut region by using the maximum membership priciple about the fuzzy set. Depending on the IF-THEN rule and the fuzzy matching method, the rough information of the machining-process for high-speed milling （HSM） is extracted based on the database of machining-process for HSM. The optimization model of machining-process scheme is established to obtain shorter cut time, lower cost or higher surface quality. It is helpful to form successful cases for HSM. NC programming for HSM is realized according to optimized machining-process data from HSM cases selected by the optimization model and the extracted information of machining-process.

Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

WEB-BASED MANUFACTURING PROCESS DEVELOPER

Product and manufacturing process developments are knowledge intensive. For rapid product developments in today′s competitive global marketplace, we need tools to facilitate the effective utilization of critical design and manufacturing knowledge obtained during the previous product developments. The Internet technology has very rapidly evolved over past few years. The web is being increasingly used to support various activities of the pro duct development process. Java is a programming language that is highly tuned for the web environment. This paper is concerned with providing the solution of web based manufacturing process development. The architecture of web based application and the implementation of web based manufacturing process developer are discussed.

Comparative Study on VQ-Based Efficient Mandarin Speech Recognition Method

A VQ based efficient speech recognition method is introduced, and the key parameters of this method are comparatively studied. This method is especially designed for mandarin speaker dependent small size word set recognition. It has less complexity, less resource consumption but higher ARR (accurate recognition rate) compared with traditional HMM or NN approach. A large scale test on the task of 11 mandarin digits recognition shows that the WER(word error rate) can reach 3 86%. This method is suitable for being embedded in PDA (personal digital assistant), mobile phone and so on to perform voice controlling like digits dialing, name dialing, calculating, voice commanding, etc.

New Algorithm for Image Target Recognition Based on Fractal Feature Fusion

By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.

Principal Component Feature for ANN-Based Speech Recognition

Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects.

孙中山的科学哲学——以《孙文学说》中的“生元”说为中心

一、开篇身处现代我们所耳熟能详的'科学'一词,在日本或者说在东亚扎根,其历史并不久远。英语'science'被翻译成'科学'这个汉语单词始于明治初期及中期交接的日本。①不久,这个日式汉语在以中国为首的汉语圈普及,更成为象征近代的词汇。孙中山也遇到了近代中国的科学这个问题。一般来说,科学被定义为'将人类探索事物构造和法则的理性的认知活动及其成果理论化、系统化的知识',②这也是孙中山科学观的根本。夫科学者,统系之学也,条理之学也。

Novel feature fusion method for speech emotion recognition based on multiple kernel learning

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78. 74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

Application of Computer Vision Technique to Maize Variety Identification

Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.

A Passive NCITS 256 UHF RFID Transponder

This paper presents a passive UHF radio frequency identification （RFID） transponder with 2k bit standard commercial EEPROM in compliance with the NCITS 256 protocol. The communication range is 1.5m for the read operation and 0.3m for the write operation with 4W effective isotropic radiated power （EIRP） at 915MHz. The integrated IC is implemented in SMIC 0.18μm EEPROM CMOS technology. The die size is 1mm × 1mm. The energy of the tag is harvested from RF electromagnetic waves transmitted by the reader with the help of a Schottky diode rectifier and achieves 25% power efficiency.

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

Action recognition using a hierarchy of feature groups

To improve the recognition performance of video human actions,an approach that models the video actions in a hierarchical way is proposed. This hierarchical model summarizes the action contents with different spatio-temporal domains according to the properties of human body movement.First,the temporal gradient combined with the constraint of coherent motion pattern is utilized to extract stable and dense motion features that are viewed as point features,then the mean-shift clustering algorithm with the adaptive scale kernel is used to label these features.After pooling the features with the same label to generate part-based representation,the visual word responses within one large scale volume are collected as video object representation.On the benchmark KTH（Kungliga Tekniska H？gskolan）and UCF （University of Central Florida）-sports action datasets,the experimental results show that the proposed method enhances the representative and discriminative power of action features, and improves recognition rates.Compared with other related literature,the proposed method obtains superior performance.

Identification and characterization of a novel isoform of hepatopoietin

AIM: To isolate a novel isoform of human HPO (HPO-205) from human fetal liver Marathon-ready cDNA and characterize its primary biological function. METHODS: 5'-RACE (rapid amplification of cDNA 5' ends) was used to isolate a novel isoform of hHPO in this paper. The constructed pcDNA(HPO-205), pcDNA(HPO) and pcDNA eukaryotic expression vectors were respectively transfected by lipofectamine method and the stimulation of DNA synthesis was observed by (3)H-TdR incorporation assay. Proteins extracted from different cells were analyzed by Western blot. RESULTS: A novel isoform of hHPO (HPO-205) encoding a 205 amino acid ORF corresponding to a translated production of 23 kDa was isolated and distinguished from the previous HPO that lacked the N-terminal 80 amino acids. The dose-dependent stimulation of DNA synthesis of HepG2 hepatoma cells by HPO-205 demonstrated its similar biological activity with HPO in vitro. The level of MAPK (Mitogen-activated protein kinase) phosphorylation by Western blot analysis revealed that HPO-205 might have the stronger activity of stimulating hepatic cell proliferation than that of HPO. CONCLUSION: A novel isoform of hHPO (HPO-205) was isolated from hepatic-derived cells. The comparison of HPO-205 and HPO will lead to a new insight into the structure and function of hHPO, and provide the new way of thinking to deeply elucidate the biological roles of HPO/ALR.

Monitoring and Recognition of Debris Flow Infrasonic Signals

Low frequency infrasonic waves are emitted during the formation and movement of debris flows, which are detectable in a radius of several kilometers, thereby to serve as the precondition for their remote monitoring.However, false message often arises from the simple mechanics of alarms under the ambient noise interference.To improve the accuracy of infrasound monitoring for early-warning against debris flows, it is necessary to analyze the monitor information to identify in them the infrasonic signals characteristic of debris flows.Therefore, a large amount of debris flow infrasound and ambient noises have been collected from different sources for analysis to sum up their frequency spectra, sound pressures, waveforms, time duration and other correlated characteristics so as to specify the key characteristic parameters for different sound sources in completing the development of the recognition system of debris flow infrasonic signals for identifying their possible existence in the monitor signals.The recognition performance of the system has been verified by simulating tests and long-term in-situ monitoring of debris flows in Jiangjia Gully,Dongchuan, China to be of high accuracy and applicability.The recognition system can provide the local government and residents with accurate precautionary information about debris flows in preparation for disaster mitigation and minimizing the loss of life and property.