应用虚拟仪器技术实现系统模型识别与构建

讨论了系统识别的几个难点,并提出了虚拟仪器的解决方案。该方案通过通用软件接口,可进行各种测试功能的扩充,并允许用户使用其它硬件构建自己的功能模块。通过一个具体的试验,验证了集成测试环境的性能的可行性。

复杂流域水资源变化模拟的自适应系统识别单元模型

针对复杂流域水资源变化模拟这一难点问题,基于单元模型思想,依据水量平衡原理,采用水文系统识别方法,提出了自适应系统识别单元模型(ASIU),并在塔里木河流域进行应用检验。该模型方法集单元模型方法、水量平衡模型方法、水文系统识别方法之优点,能实时“自适应”模拟复杂流域水资源系统变化。通过应用实例分析,可以看出该模型方法具有显著的“自适应”、“适用于复杂流域”的优点,可为复杂流域水量调度和水资源管理提供基础模型和数据,具有重要的理论及应用意义。

大数据技术在本科高校贫困生认定中的应用策略研究

引领高等教育管理新视角的大数据技术,以其强大的技术雄厚建设,肩负起促进应用性本科学府贫困生确定工作的改进任务。这个研究,就是利用大数据技术,打造一套全面性的贫困生辨识模型,一种教育、家庭经济、日常习惯等多元素因素的评估模型。在实际施行时,模型能精准地标定出面临贫困问题的学子们,予以他们针对其情况的经济援助。相较于传统的贫困生指定方式,大数据技术所标注出的贫困生更为精确准确,判决更加公平公正,由此减低了人为主观因素对结果的偏误。同时,借助大数据技术的实时动态监控功能,可以追踪贫困生的学习、生活等状态的变化,及时发现那些之前未受关注的贫困生,防止他们因贫困而沉默,甚至走向辍学。本研究结果为应用型本科高校优化贫困生认定体系,提高其公平性和有效性,推动精准扶贫工作提供了可借鉴的实践路径和理论依据。

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

Discriminative Latent Model Based Chinese Multiword Expression Extraction

Discriminative Latent Model(DLM) is proposed for Multiword Expressions(MWEs) extraction in Chinese text to improve the performance of Machine Translation(MT) system such as Template Based MT(TBMT).For MT systems to become of further practical use,they need to be enhanced with MWEs processing capability.As our study towards this goal,we propose DLM,which is developed for sequence labeling task including hidden structures,to extract MWEs for MT systems.DLM combines the advantages of existing discriminative models,which can learn hidden structures in sequence labeling task.In our evaluations,DLM achieves precisions ranging up to 90.73% for some type of MWEs,which is higher than state-of-the-art discriminative models.Such results demonstrate that it is feasible to automatically identify many Chinese MWEs using our DLM tool.With MWEs processing model,BLEU score of MT system has also been increased by up to 0.3 in close test.

An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.

Design of increasing controllability of braking vehicle

The operating principle of an antilock braking system （ABS） is it compares current value of angular acceleration with the threshold value. The advantage of such system is that enough it has only the angular velocity sensors. The disadvantage is successive overshoot, i. e. successive transition from wheels locking mode to wheels rolling mode. So braking mechanism can’ t realize the maximum possible torque in the current road conditions. The idea of increasing the braking effectiveness is the intensity of rising pressure depends on the road conditions. The problem is the torque produced by braking mechanism, current road conditions and the value of traction coefficient is unknown For evaluation of these parameters built and training three neural networks. A simulator of random road condition＇s variation was built to test adequacy of the control unites operation in close to real conditions.

Time domain system identification of unknown initial conditions

System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One solution to avoid this noise problem is to skip the noisy data and then use the initial conditions as active parameters, to be found by using the system identification process. This paper describes the development of the equations for setting up the initial conditions as active parameters. The simulated data and response data from actual shear buildings were used to prove the accuracy of both the algorithm and the computer program, which include the initial conditions as active parameters. The numerical and experimental model analysis showed that the value of mass, stiffness and frequency were very reasonable and that the computed acceleration and measured acceleration matched very well.

Hybrid tracking model and GSLM based neural network for crowd behavior recognition

Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.

Functional Ontology for Conceptual Design Oriented Model Representation and Reuse

Functionality represents a blueprint of a product and plays a crucial role in problem-solving such as design.This article discusses the model representation from the angle of functional ontology by function deployment.We construct a framework of functional ontology which decomposes the function and contains a library of vocabulary to comprehensively represent the conceptual design model.The ontology enables the automatic identification system to search in the functional space.Furthermore,the functional ontology can form a systematic representation for the model so that it can be reused in the conceptual design and can be applied in the domain of knowledge fusion in our further work.

IDENTIFICATION ERROR BOUNDS AND ASYMPTOTIC DISTRIBUTIONS FOR SYSTEMS WITH STRUCTURAL UNCERTAINTIES

This work is concerned with identification of systems that are subject to not only measurement noises, but also structural uncertainties such as unmodeled dynamics, sensor nonlinear mismatch, and observation bins. Identification errors are analyzed for their dependence on these structural uncertainties. Asymptotic distributions of scaled sequences of estimation errors are derived.