基于GIS的村镇建设用地节地效果评价系统

针对目前村镇建设用地集约节约利用效果评价方法空缺及信息化低等问题,依据村镇建设用地节约集约利用效果评价模型,在地理信息系统组件技术和数据库技术支持下,采用可视化编程语言MicrosoftC#.net和GIS控件技术开发了村镇建设用地节约集约利用效果评价系统。以太仓市陆渡镇为开发案例,探讨了系统分析、设计和功能实现等主要技术问题。并利用该系统对太仓市陆渡镇村镇建设用地集约节约利用效果评价进行了评价,评价结果表明,该系统能够满足当前村镇建设用地集约节约利用效果评价工作的需要,为快速准确地实施节地效果评价提供技术支持。     

振频法索力测量系统中数据采样参数匹配研究

利用振频法进行索力测量,数据采样参数设置不当会大大降低基频识别精度进而降低索力测量精度。在分析采样参数即采样率和采样数对基频识别精度影响的基础上,根据奈奎斯特定律,兼顾测量实时性和有限的数据存储空间,提出最优化数据采样参数匹配方法,以提高测量精度。对马桑溪长江大桥斜拉索索力进行现场测试,结果表明:按照最优的参数匹配方法进行合理的参数设置,索力测量精度至少提高2倍,具有较好的工程应用价值。     

消化道生理参数检测胶囊系统的研究进展

胃肠道内的生理参数是评价胃肠道动力性疾病的重要参数.传统的插管方法会给病人带来严重的不适感,且会影响到病人的正常生活.为了克服传统插管方法的缺点,出现了一种新型的监测生理参数的胶囊系统,它可以根据生理参数的差异来诊断胃肠道疾病.针对生理参数检测胶囊系统对国际研究成果和国内研究现状进行了综述.     

Introducing dynamic diversity into a discrete particle swarm optimization

Particle swarm optimization (PSO) is an evolutionary metaheuristic inspired by the flocking behaviour of birds, which has successfully been used to solve several kinds of problems, although there are few studies aimed at solving discrete optimization problems. One disadvantage of PSO is the risk of a premature search convergence. To prevent this, we propose to introduce diversity into a discrete PSO by adding a random velocity. The degree of the introduced diversity is not static (i.e. preset before running PSO) but instead changes dynamically according to the heterogeneity of the population (i.e. if the search has converged or not). We solve the response time variability problem (RTVP) to test these two new ideas. The RTVP is an NP-hard combinatorial scheduling problem that has recently appeared in the literature. It occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. The most efficient algorithm for solving non-small instances of the RTVP published to date is a classical PSO algorithm, referred to by the authors as PSO-M1F. In this paper, we propose 10 discrete PSO algorithms for solving the RTVP: one based on the ideas described above (PSO  -_c3dyn$c_3\text{<italic>dyn</italic>}$) and nine based on strategies proposed in the literature and adapted for solving a discrete optimization problem such as the RTVP. We compare all 11 PSO algorithms and the computational experiment shows that, on average, the best results obtained are due to our proposal of dynamic control mechanism for introducing diversity.     

The scope of artificial neural network metamodels for precision casting process planning

Precision investment casting process planning has been tackled in the past according to experience. Recently, casting simulation software is being increasingly used to predict product quality by implementing ‘what-if’ scenarios. Input parameters include relatively simple factors such as mould temperature, melting temperature, casting material. They also include factors whose influence is more complex to quantify, such number and location of feeding points, diameter and length of inflow channels, angle of channel with respect to the main sprue axis. Simulation results cannot help the engineer for workpieces other than the one simulated. In this paper a series of feedforward artificial neural network (ANN) models is presented aiming at such generalisation. To achieve this, a large number of software simulation runs were conducted for a number of different small parts, with varying runner geometry and casting conditions. The parameters characterising part geometry have been chosen to be surface area and volume-to-area ratio. The different ANN models predictive capabilities are reflected to the respective training and generalisation errors. A user-friendly interface has been conducted for model execution in a complete application, whose main virtue is expandability.     

Work fatigue and physiological symptoms in different occupations of high-elevation construction workers

The objective of this study is to investigate whether work fatigue and physiological symptoms that high-elevation construction workers experience would be affected by the occupations. Questionnaires of demographic data and subjective fatigue symptoms as well as some physiological measurements were carried out, pre- and post-shift, on scaffolders, steel fixers, formworkers, electrician-plumbers, concreters and miscellaneous workers at a high-rise building construction site. This study found that some subjective fatigue symptoms coincide with the life style of some workers and that the extent of fatigue symptoms and physiological strains varies among different occupations of construction workers. Scaffolders, steel fixers and formworkers are categorized as physically demanding fatigue type of workers, while concreters, electrician-plumbers and miscellaneous workers as general type. The prevalence and occurrence of subjective fatigue symptoms indicate high-elevation workers have more complaints of “projection of physical impairment” than “drowsiness and dullness” and “difficulty in concentration”. Some unexpected changes (i.e., post-shift measurements are greater than pre-shift ones) in some strength tests in scaffolders and concreters were consistent with the observations of how they exercised their bodies during work shift. Considerable variation of average heart rate among occupations was found, with scaffolders the highest and concreters the lowest. This study concludes that questionnaires of subjective fatigue symptoms and some physiological measurements can be used as indicators to predict the extent of strains or hazards which construction workers encounter. In terms of management program of safety and health, more attention should be paid to those physically demanding workers, such as scaffolders, workers with lower sense of safety and health, such as miscellaneous workers, and workers with older age, such as concreters.     

Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks

The major drawbacks of backpropagation algorithm are local minima and slow convergence. This paper presents an efficient technique ANMBP for training single hidden layer neural network to improve convergence speed and to escape from local minima. The algorithm is based on modified backpropagation algorithm in neighborhood based neural network by replacing fixed learning parameters with adaptive learning parameters. The developed learning algorithm is applied to several problems. In all the problems, the proposed algorithm outperform well.     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

An integrative approach to requirements analysis: How task models support requirements reuse in a user-centric design framework

Many software systems fail to address their intended purpose because of a lack of user involvement and requirements deficiencies. This paper discusses the elaboration of a requirements-analysis process that integrates a critical-parameter-based approach to task modeling within a user-centric design framework. On one hand, adapting task models to capture requirements bridges the gap between scenarios and critical parameters which benefits design from the standpoint of user involvement and accurate requirements. On the other hand, using task models as a reusable component leverages requirements reuse which benefits design by increasing quality while simultaneously reducing development costs and time-to-market. First, we present the establishment of both a user-centric and reuse-centric requirements process along with its implementation within an integrated design tool suite. Secondly, we report the design, procedures, and findings of two user studies aimed at assessing the feasibility for novice designers to conduct the process as well as evaluating the resulting benefits upon requirements-analysis deliverables, requirements quality, and requirements reuse.