糖尿病患者专用鞋内无线多传感器测量系统

介绍一种专为糖尿病患者而开发的鞋内无线多传感器测量系统.该系统共包括鞋内感测装置、便携式无线警报器及监察台3个部份.通过实时感测病患者脚部不同位置的压力、温度及湿度, 以便当被测参数超出预设的安全指标时发出提示铃声.此外, 所感测的参数可传送到监察台作全面分析.     

Modelling for an expert system and a parameter validation method

A model–based engineering diagnostic method is typically based on the evaluation of the residuals generated from a comparison of important variable values from a simulated system and the corresponding measured values from the system's performance. Consequently, a model should describe the dynamic behaviour of the system as accurately as possible using suitably selected parameter values. This implies the need for validation of the performance of the model by comparison with the measurements of the actual system. This process is especially important when the detection of faults is performed in real–time conditions. In this paper, the modelling process for hydraulic systems as well as a new parameter validation method that has been developed using the DASYLab data acquisition and control software for the estimation of the uncertain parameter values of the model is presented. This model validation process led to the establishment of a model–based expert system that is able to diagnose real–time faults working in parallel with actual dynamic industrial automated processes.     

A computational method of forecasting based on fuzzy time series

In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.     

The main features of BAHUS — biometeorological system for messages on the occurrence of diseases in fruits and vines

Recently, there has been an increasing demand for providing a high level of production of wholesome plant food but without losing its quality from the consumer's point of view. In this regard, it is required to provide reliable information about the occurrence of plant diseases so as to ensure their efficient control. The reliability of this information increases substantially if both the meteorological and the biological quantities, measured or modelled, are properly integrated in a prognostic system. By the joint efforts of the authors' institutions the biometeorological system BAHUS for messages on the occurrence of the most important diseases in fruits and vines has been developed. This system has been developed in Microsoft FoxPro 2.6(X) following standards of analysis of a large amount of data. It consists of two modules. The first provides input for prediction in the form of the measured or modelled meteorological and biological data, while the second, on the basis of available input data, selects the corresponding method for messages on the occurrence of disease. Depending on the method selected, the meteorological data can be assimilated either from weather stations, atmospheric models or software packages LAPS (land–air parameterization schemes providing 10 min prognostic values) and KARLOS (providing their climatological values) integrated in the system as a whole. The BAHUS has been designed as an open system giving a wide range of possibilities for increasing its level of sophistication.     

Soft factors and their impact on time to market

Time to market is considered to be one of the most important quality attributes for an organization to retain its competitive edge. Soft factors are at least as important as technical issues in improving and controlling the time to market. Soft factors are used as a collective term for factors that are difficult to quantify exactly, i.e. non-technical aspects. Based on the identified importance of the soft factors, a method, incorporating the soft factors, has been developed to increase the predictability of time to market. The method consists of three main parts: model development, model usage and model maintenance. The proposed method is general, while the actual model is primarily useful for the organization for which data have been collected. The objective is two-fold: first to prevent a general method of incorporating soft factors into the prediction of the time to market, and secondly to identify a set of critical soft factors for the organization in which data have been collected. The model is based on data collection from 12 large software projects. Time to market, effort (manhours) and a grade for ten soft factors have been collected. The model is derived from a set of eight projects and it is evaluated using the remaining four projects. The method is shown to be superior to the prediction made without taking the soft factors into account. The need to maintain the model is stressed as the model will evolve with time through new experiences gained. The proposed method can be applied in practice to help software engineers and managers to plan and control time to market and soft factors in software projects.     

Robust estimation of walking robots velocity and tilt using proprioceptive sensors data fusion

Availability of the instantaneous velocity of a legged robot is usually required for its efficient control. However, estimation of velocity only on the basis of robot kinematics has a significant drawback: the robot is not in touch with the ground all the time, or its feet may twist. In this paper we introduce a method for velocity and tilt estimation in a walking robot. This method combines a kinematic model of the supporting leg and readouts from an inertial sensor. It can be used in any terrain, regardless of the robot’s body design or the control strategy applied, and it is robust in regard to foot twist. It is also immune to limited foot slide and temporary lack of foot contact.     

Dynamic state and parameter estimation applied to neuromorphic systems

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.     

Predictive human operator model to be utilized as a controller using linear,neuro-fuzzy and fuzzy-ARX modeling techniques

Modeling human operator's behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator's model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as they controlled a nonlinear system. Appropriate reference signals and scenarios were designed according to the system identification and human operator modeling theory, to collect data from subjects. Different modeling schemes, namely ARX models as linear approach, and adaptive-network-based fuzzy inference system (ANFIS) as intelligent modeling approach have been evaluated. A hybrid modeling method, fuzzy-ARX (F-ARX) model, has been developed and its performance was found to be better in terms of predicting human operator's control actions as well as replacing the operator as a stand-alone controller. It has been concluded that F-ARX models can be a good alternative for modeling the human operator.     

Analysis of processing time between distributed geometric modeling systems

In recent years, the concept of distributed systems has been applied to industries to enable cooperative work and collect distributed information. A distributed geometrical modeling system has been developed to share the functions among the systems on the network with a peer-to-peer (P2P) structure. The systems that are linked on the network can use functions of other systems. Such a network can perform operations concurrently by using other systems, which saves time. The importance of constructing this kind of distributed CAD system is to transfer three-dimensional (3D) CAD model data and access the locations of providers. The developed system has a sharing of functions, which consist of client (requester) and server (provider) as a P2P system, and is constructed by using a CAD kernel and COM/DCOM technology. Simple operations have been performed and tested by the developed system, such as Boolean operation, obtaining properties, triangulation and tessellation. The developed system has been evaluated with stand-alone and client–server systems for simple operations based on two criteria. In the first evaluation, the processing timings have been compared for simple operations among the systems: the stand-alone system is faster than the other systems. In the second evaluation, the systems are overloaded and the processing timings have been compared: the developed system is faster than the other systems.     

Subdivision methods of converting STEP into VRML on Web

A typical product development team working in global manufacturing and marketing environment is often dispersed all over the world. Such a team may need a tool to aid effective communication and cooperation among the work groups. A 3 dimensional STEP geometry browsing system has been developed on the Internet and its associated Web. A major issue in implementing the system is the efficiency of computation and communication time of 3D visualization models. An adaptive subdivision method is developed to convert STEP data into a VRML visualization model. The method has been implemented and experimented, and the results show a substantial improvement in computational performance.     

Posture Stabilization Strategy for a Trotting Point-foot Quadruped Robot

This paper proposes a posture stabilization strategy for achieving the stable trot gait of a point-foot quadruped robot. Specifically, a stepping strategy (foot placement strategy) has been developed to achieve a stable trot gait. Because in the trot gait of a quadruped robot the diagonal legs can be considered to contact and leave the ground at the same time, the trot gait can be considered as a virtual biped gait. Based on the dynamic model of a virtual biped gait, the stepping point (or the foot placement) that achieves the stabilization of the robot is determined. Finally, the effectiveness of the proposed posture stabilization strategy is validated experimentally.     

Learning chaotic attractors by neural networks

An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored that tests the hypothesis that the reconstructed attractors of model-generated and measured data are the same. Training is stopped when the prediction error is low and the model passes this test. Two other features of the algorithm are (1) the way the state of the system, consisting of delays from the time series, has its dimension reduced by weighted principal component analysis data reduction, and (2) the user-adjustable prediction horizon obtained by "error propagation"-partially propagating prediction errors to the next time step. The algorithm is first applied to data from an experimental-driven chaotic pendulum, of which two of the three state variables are known. This is a comprehensive example that shows how well the Diks test can distinguish between slightly different attractors. Second, the algorithm is applied to the same problem, but now one of the two known state variables is ignored. Finally, we present a model for the laser data from the Santa Fe time-series competition (set A). It is the first model for these data that is not only useful for short-term predictions but also generates time series with similar chaotic characteristics as the measured data.     

Model reduction technique applied to a nuclear reactor turbine system

A method has been developed for determining a low-order model for a large multivariable system from its measured input-output data. It is based on the use of a matrix pseudo-inverse to estimate the parameters of the model which minimize the sum of the squares of the errors between the responses of the original system and the low-order model at the sampling instants. A recursive algorithm is proposed which makes it useful for on-line applications. The method is used to obtain low-order models for a nuclear reactor turbine system. The outputs of the low-order models and those of the large-scale model (consisting of 11 non-linear differential equations and 36 algebraic equations) are compared for the turbine system.     

工程数据核心管理系统EDBMS／2的设计与实现

工程数据的有效管理是集成化的CAD/CAM系统成功的关键。本文介绍了我们自行研制的一个面向工程应用的核心数据管理系统EDBMS/2,着重介绍了其数据模型、版本控制及其实现技术。文中给出的四种类型表表示结构具有灵活的变长数据处理、格式化与非格式化数据的集成管理、复杂对象的表示和处理等功能。在文中提出的DBV及DBV图机制具有版本管理(包括复合对象版本管理)、数据共享、数据管理的局部化等功能。目前,EDBMS/2已成功地应用于集成化的电子设备CAD系统EDCADS中,并正作为底层用于开发面向对象的工程数据库管理系统。     

基于VR和Kinect的定制鞋设计

针对传统制鞋业定制化程度低,无法适应足部多样性、舒适性,造成鞋业资源的浪费,无法满足当代顾客追求个性化的心里等问题,提出了一种基于VR和Kinect的定制鞋设计方法。对个体足部特征进行测量分析,利用三维建模以及VR技术,实现个体鞋的定制设计。首先,使用Kinect3D摄像机进行足部三维扫描,获取足部深度信息数据并转换成点云数据,利用kd-tree算法及贪婪投影三角化算法进行足部点云的去噪处理和点云重构,在Meshlab软件中测量数据并结合MAYA软件完成三维鞋的建模。最后,结合Unity引擎,设计虚拟交互界面,实现纹理贴图和更换,完成定制鞋系统。结果证明,该系统可以快速的进行人机交互,实现鞋的三维定制,满足顾客个性化的心理需求,会对鞋业的发展产生很大的推进作用。     

An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools

Spatially distributed modelling is an important instrument for studying the hydrological cycle, both concerning its present state as well as possible future changes in climate and land use. Results of such simulations are particularly relevant for the fields of water resources, natural hazards and hydropower. The semi-distributed hydrological modelling system PREVAH (PREecipitation-Runoff-EVApotranspiration HRU Model) implements a conceptual process-oriented approach and has been developed especially to suit conditions in mountainous environments with their highly variable environmental and climatic conditions.This article presents an overview of the actual model core of PREVAH and introduces the various tools which have been developed for obtaining a comprehensive, user-friendly modelling system: DATAWIZARD for importing and managing hydrometeorological data, WINMET for pre-processing meteorological data, GRIDMATH for carrying out elementary raster data operations, FAOSOIL for processing FAO World Soil Map information, WINHRU for pre-processing spatial data and aggregating hydrological response units (HRU), WINPREVAH for operating the model, HYDROGRAPH for visualising hydrograph data and VIEWOPTIM for visualising the calibration procedure. The PREVAH components introduced here support a modelling task from pre-processing the data over the actual model calibration and validation to visualising and interpreting the results (post-processing). A brief overview of current PREVAH applications demonstrates the flexibility of the modelling system with examples that range from water balance modelling over flood estimation and flood forecasting to drought analysis in Switzerland, Austria, China, Russia and Sweden.     

Development of a Leg Part of a Humanoid Robot—Development of a Biped Walking Robot Adapting to the Humans' Normal Living Floor

The authors are engaged in studies of biped walking robots from thefollowing two viewpoints. One is a viewpoint as a human science. Theother is a viewpoint towards the development of humanoid robots.In this paper, the authors introduce an anthropomorphic dynamic bipedwalking robot adapting to the humans' living floor. The robot has tworemarkable systems: (1) a special foot system to obtain the positionrelative to the landing surface and the gradient of the surfaceduring its dynamic walking; (2) an adaptive walking control system toadapt to the path surfaces with unknown shapes by utilizing theinformation of the landing surface, obtained by the foot system. Twounits of the foot system WAF-3 were produced, a biped walking robotWL-12RVII that had the foot system and the adaptive walking controlsystem installed inside it was developed, and a walking experimentwith WL-12RVII was performed. As a result, dynamic biped walkingadapting to humans' floors with unknown shapes was realized. Themaximum walking speed was 1.28 s/step with a 0.3 m step length, andthe adaptable deviation range was from -16 to+16 mm/step in the vertical direction, and from-3 to +3° in the tilt angle.     

A Multibody Dynamic Model for Escalator Handrail Systems and its Application to Dynamic Characteristics

In this paper, a multibody dynamic model is developed which reflects the experimental results observed in the handrail system of an escalator. Certain model inputs such as non-linear rotational and longitudinal spring coefficients of the handrail and friction coefficient-slip ratio curve of the handrail drive are determined empirically from experimental measurements. The dynamic model effectively explains dynamic characteristics measured in the escalator with respect to various handrail tensions and retarding force. The model has been validated through comparison of experimental and simulated results such as slip ratio, power consumption and vibration of the handrail. The statistical design analysis, response surface method, is applied to determine the vital factors and their optimized levels to improve performance of the escalator. The developed model and its simulation results are used rigorously for design of the escalator handrail system.     

CAD/CAM技术在脚医学中的应用

将CAD／CAM技术与脚医学结合，研究开发脚支撑设计制造系统，包括脚底曲面数据的读取及处理，根据医疗规划完成三维脚支撑模型的构造，平衡及光滑处理，激光快速原型文件和铣削加工刀具轨迹的自动生成等。     

Windows software for cardiac electrophysiology studies and ablation monitoring

A system for cardiac electrophysiology (EP) studies consisting of a Windows software package, a standard 120 MHz Pentium PC with a high-performance video card and a data acquisition card has been developed during this study. The system is capable of real time data acquisition and storage of 24 channels with simultaneous display of 1-16 arbitrarily chosen channels at a sampling rate of 500 Hz. It can be used clinically in electrophysiology studies and during catheter radio-frequency ablation treatment for monitoring the ablation and its effects. The built-in ablation monitoring capability enables combined EP study and ablation treatment, thus helping to reduce exposure times and the total time needed per patient. For clinical use the software includes versatile tools for data analysis and reduction. Our system has been developed in association with Department of Cardiology of Tampere University Hospital and has been in regular clinical use there.