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Tone model integration based on discriminative weight training for Putonghua speech recognition

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Chinese Journal of Acoustics 2008年第3期27卷 193-202页

作者： huang hao zhu jie Department of Electronic Engineering Shanghai Jiaotong University Shanghai 200240

A discriminative framework of tone model integration in continuous speech recognition was proposed. The method uses model dependent weights to scale probabilities of the hidden Markov models based on spectral features and tone models based on tonal features. The weights are discriminatively trained by minimum phone error criterion. Update equation of the model weights based on extended Baum-Welch algorithm is derived. Various schemes of model weight combination are evaluated and a smoothing technique is introduced to make training robust to over fitting. The proposed method is ewluated on tonal syllable output and character output speech recognition tasks. The experimental results show the proposed method has obtained 9.5% and 4.7% relative error reduction than global weight on the two tasks due to a better interpolation of the given models. This proves the effectiveness of discriminative trained model weights for tone model integration.

关键词： continuous speech recognition model integration hidden Markov models smoothing technique error reduction

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基于语料库的朝鲜语命名实体结构特征研究

外语学刊 2025年第1期 9-18页

作者：黄政豪金光洙延边大学工学院延吉133002 延边大学外国语学院延吉133002

本文统计Klue⁃ner和Kochet⁃ner两个命名实体语料库中的新闻、评论和文化遗产文本数据包含的不同类别朝鲜语命名实体。根据统计结果分析朝鲜语命名实体的音节长度特征分布和格词尾结合率。分析结果表明,音节长度和格词尾的使用在命名实... 详细信息

本文统计Klue⁃ner和Kochet⁃ner两个命名实体语料库中的新闻、评论和文化遗产文本数据包含的不同类别朝鲜语命名实体。根据统计结果分析朝鲜语命名实体的音节长度特征分布和格词尾结合率。分析结果表明,音节长度和格词尾的使用在命名实体分类中具有一定的规律可循。本文的研究成果可用于命名实体分类工作,同时也可以为朝鲜语命名实体语料库构建提供分布结构建议。

关键词：朝鲜语格词尾命名实体识别特征提取名词分类

Self-reinforcement of Light, Temperature-Resistant Silica Nanofibrous Aerogels with Tunable Mechanical Properties

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Advanced Fiber Materials 2020年第6期2卷 338-347页

作者： Tao huang Yue zhu jie zhu hao Yu Qinghua Zhang Meifang zhu Key Laboratory of High Performance Fibers and Products Ministry of EducationDonghua UniversityShanghai 201620People’s Republic of China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620People’s Republic of China

Silica aerogels have attracted significant interest in thermal insulation applications because of their low thermal conductivity and great thermal stability,however,their fragility has limited their application in every-day ***,a self-reinforcing strategy to design silica nanofibrous aerogels(SNFAs)is proposed using electrospun SiO2 nanofibers as the matrix and a silica sol as a high-temperature *** this approach results in a strong and compatible interfacial interaction between the SiO_(2) fibers and the silica sol,which results in the SNFAs exhibiting high-temperature-resistant and tunable mechanical properties from elastic to ***,additional properties such as low density,high thermal insulation performance,and fire-resistance are still *** self-reinforcing method described herein may be extended to numerous other new ceramic aerogels that require robust mechanical properties and high-temperature resistance.

关键词： Nanofiber aerogel Silica Electrospinning Self-reinforcement

Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 126-137页

作者： Dongfang Li jie huang Rob Law Xin Xu Limin zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

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Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第10期9卷 1390-1397页

作者： Peng huang jie zhu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China

A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

关键词： Object-oriented software Fault-proneness Support vector machine Structured kernel

Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks

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International Journal of Automation and computing 2020年第1期17卷 44-54页

作者： Chang-hao zhu jie Zhang School of Engineering Merz Court Newcastle University

This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial *** of online measurement instruments becomes a problem in polymer quality *** effective solution is to use soft sensors to estimate the quality variables from process *** recent years,deep learning has achieved many successful applications in image classification and speech *** as one novel technique has strong generalization capability to model complex dynamic processes due to its deep *** can meet the demand of modelling accuracy when applied to actual *** to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training *** mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of *** of DBN structure is investigated in the *** modelling results achieved by DBN and feedforward neural networks are compared in this *** is shown that the DBN models give very accurate estimations of the polymer melt index.

关键词： Polymer melt index soft sensor deep learning deep belief network(DBN) unsupervised training

Multi-instance learning for software quality estimation in object-oriented systems:a case study

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第2期11卷 130-138页

作者： Peng huang jie zhu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China

We investigate a problem of object-oriented (OO) software quality estimation from a multi-instance (MI) perspective. In detail,each set of classes that have an inheritance relation,named 'class hierarchy',is regarded as a bag,while each class in the set is regarded as an instance. The learning task in this study is to estimate the label of unseen bags,i.e.,the fault-proneness of untested class hierarchies. A fault-prone class hierarchy contains at least one fault-prone (negative) class,while a non-fault-prone (positive) one has no negative class. Based on the modification records (MRs) of the previous project releases and OO software metrics,the fault-proneness of an untested class hierarchy can be predicted. Several selected MI learning algorithms were evalu-ated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the ex-periments,the kernel method using a dedicated MI-kernel was better than the others in accurately and correctly predicting the fault-proneness of the class hierarchies. In addition,when compared to a supervised support vector machine (SVM) algorithm,the MI-kernel method still had a competitive performance with much less cost.

关键词： Object-oriented (OO) software Multi-instance (MI) learning Software quality estimation Kernel methods

Fast prediction for multi-parameters(concentration,temperature and humidity)of indoor environment towards the online control of HVAC system

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Building Simulation 2021年第3期14卷 649-665页

作者： hao-Cheng zhu Chen Ren Shi-jie Cao School of Architecture Southeast University2 SipailouNanjing27009China Global Centre for Clean Air Research(GCARE) Department of Civil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyUK Academy of Building Energy Efficiency School of Civil EngineeringGuangzhou UniversityGuangzhou 570006China

Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation,through the fast prediction methods by using low-dimensional linear ventilation model(LLVM)based artificial neural network(ANN)and low-dimensional linear temperature model(LLTM)based contribution ratio of indoor climate(CRI_((T))).To be continued for integrated control of multi-parameters,we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model(LLHM)and contribution ratio of indoor humidity(CRI_((H))),and thermal sensation index(TS)for *** was used to construct the prediction database for CO_(2),temperature and ***-dimensional linear models(LLM),including LLVM,LLTM and LLHM,were adopted to expand database for the sake of data storage ***,coupling with ANN,CRI_((T)) and CRI_((H)), the distributions of indoor CO_(2) concentration,temperature,and humidity were rapidly predicted on the basis of LLVM-based ANN,LLTM-based CRIm and LLHM-based CRM ***,according to the self-defined indices(i.e.,E_(V),E_(T),E_(H)),the optimal balancing between IEQ(indicated by CO_(2) concentration,PMV and TS)and energy consumption(indicated by ventilation rate,supplied air temperature and humidity)were synthetically *** total HVAC energy consumption could be reduced by 35%on the strength of current control *** work can further contribute to development of the intelligent online control for HVAC systems.

关键词： HVAC system indoor air pollution thermal comfort indoor humidity control

A real-time calibration method based on time-to-digital converter for accelerator timing system

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Nuclear Science and Techniques 2024年第9期35卷 127-140页

作者： Qi-hao Duan Liang Ge Yan-hao Jia jie-Yu zhu Wei Zhang Institute of modern physics Chinese Academy of SciencesLanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China

The high-intensity heavy-ion accelerator facility(HIAF)is a scientific research facility complex composed of multiple cas-cade accelerators of different types,which pose a scheduling problem for devices distributed over a certain range of 2 km,involving over a hundred *** white rabbit,a technology-enhancing Gigabit Ethernet,has shown the capability of scheduling distributed timing devices but still faces the challenge of obtaining real-time synchronization calibration param-eters with high *** study presents a calibration system based on a time-to-digital converter implemented on an ARM-based System-on-Chip(SoC).The system consists of four multi-sample delay lines,a bubble-proof encoder,an edge controller for managing data from different channels,and a highly effective calibration module that benefits from the SoC *** performance was evaluated with an average RMS precision of 5.51 ps by measuring the time intervals from 0 to 24,000 ps with 120,000 data for every *** design presented in this study refines the calibration precision of the HIAF timing *** eliminates the errors caused by manual calibration without efficiency loss and provides data support for fault *** can also be easily tailored or ported to other devices for specific applications and provides more space for developing timing systems for particle accelerators,such as white rabbits on HIAF.

关键词： HIAF White rabbit Calibration system Time-to-digital converter(TDC)