智能交通系统中标准化的作用

今天,道路运输在国民经济和社会发展中起着举足轻重的作用。随着交通需求的急剧增长,道路运输所带来的交通拥堵、交通事故和环境污染等负面效应也日益突出,越来越影响着社会经济发展和人们的生活。目前,在资源、环境矛盾越来越突出的情况下,如何破解路网通过能力满足不了交通量增长需要的难题,对世界各国来讲都是一个难题,仅仅依靠提供更多的道路设施来满足道路运输增长的需求是不现实的,这就需要依靠提供除设施之外的技术方法来满足这一需求。智能运输系统(IntelligentTransportationSystems)就是解决这一矛盾的途径之一,其重要性正被越…     

Prediction of load-carrying capacity of adhesive anchors by soft computing techniques

The paper presented herein aims at providing explicit formulations for predicting the load-carrying capacity of the single adhesive anchors post-installed into uncracked hardened concrete. For this purpose, the worldwide database for the adhesive anchors compiled by the ACI Committee 355 was obtained and reduced to generate the training and the testing data sets to construct the closed-form solutions by means of the soft computing techniques such as Neural Network (NN) and Genetic Programming (GEP). Therefore, the NN and GEP models were developed with a correlation coefficient of as high as 0.99 and 0.95, respectively. Moreover, the mean absolute percentages of errors for the proposed models were fairly reasonable considering the noisy nature of the database. The prediction performance of the developed models improved with increase in the embedment depth, anchor diameter, and the concrete strength. The ratio of predicted to observed values for the developed NN and GEP models were much smaller than that of the well known Concrete Capacity Design (CCD) method which generally overpredicted the experimental values.     

Back analysis of model parameters in geotechnical engineering by means of soft computing

In this paper, a parameter identification (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artificial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, finally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for large‐scale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identification of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined. Copyright © 2003 John Wiley & Sons, Ltd.     

Architectural approach to the role of optics in monoprocessor and multiprocessor machines

The relevance of introducing optical interconnects (OI's) in monoprocessors and multiprocessors is studied from an architectural point of view. We show that perhaps the major explanation for why optical technologies have nearly been unable to penetrate into computers is that OI's generally do not shorten the memory-access time, which is the most critical issue for today's stored-program machines. In monoprocessors the memory-access time is dominated by the electronic latency of the memory itself. Thus implementing OI's inside the memory hierarchy without changing the memory architecture cannot dramatically improve the global performance. In strongly coupled multiprocessors the node-bypass latency dominates. Therefore the higher the connectivity (possibly with optics), the shorter the path to another node, but the more expensive the network and the more complex the structure of electronic nodes. This relation leaves the choice of the best network open in terms of simplicity and latency reduction. The bottlenecks resulting from and the benefits of implementing OI's are discussed with respect to symmetric multiprocessors, rings, and distributed shared-memory supercomputers.     

The role of soft colloidal templates in controlling the size and shape of inorganic nanocrystals

In the past decade, colloidal solutions have been assumed to be very efficient templates for controlling particle size and shape. A large number of groups have used reverse micelles to control the size of spherical nanoparticles. This makes it possible to determine the various parameters involved in such processes, and demonstrates that nanoparticles can be considered to be efficient nanoreactors. However, some discrepancies arise. There are few reports concerning the control of particle shape, and it is still rather difficult to determine the key parameters, such as the adsorption of salts and other molecules, and the synthesis procedure. Here, we discuss these controls of the size and shape of inorganic nanomaterials.     

Hybrid intelligent water drops algorithm to unrelated parallel machines scheduling problem: a just-in-time approach

Minimising earliness and tardiness penalties as well as maximum completion time (makespan) simultaneously on unrelated parallel machines is tackled in this research. Jobs are sequence-dependent set-up times and due dates are distinct. Since the machines are unrelated, jobs processing time/cost on different machines may vary, i.e. each job could be processed at different processing times with regard to other machines. A mathematical model which minimises the mentioned objective is proposed which is solved optimally via lingo in small-sized cases. An intelligent water drop (IWD) algorithm, as a new swarm-based nature-inspired optimisation one, is also adopted to solve this multi-criteria problem. The IDW algorithm is inspired from natural rivers. A set of good paths among plenty of possible paths could be found via a natural river in its ways from the starting place (source) to the destination which results in eventually finding a very good path to their destination. A comprehensive computational and statistical analysis is conducted to analyse the algorithms’ performances. Experimental results reveal that the proposed hybrid IWD algorithm is a trustable and proficient one in finding very good solutions, since it is already proved that the IWD algorithm has the property of the convergence in value.     

An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles

With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial.     

Digital water: artificial intelligence and soft computing applications for drinking water quality assessment

Clean Technologies and Environmental Policy - Water quality deterioration in drinking water systems (i.e., system failure) causing serious outbreaks have frequently been happening around the world....     

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

The artificial neural network methodology presented in this paper was trained to predict the green shear strength of compacted samples made from iron powder. Iron powders of three different morphologies admixed with three types of lubricants in different amounts were considered. Green compacts were pressed uniaxially in a square floating die. The more or less cubic slugs were sheared to fracture perpendicularly and parallel to the direction of compaction. From the maximum loads at the start of decohesion the green shear strengths were calculated. Compaction parameters together with corresponding shear strength records were used as sets of data for the training process. The performance of the network was verified by putting aside one set of data and testing the network against it. Comparison of the predicted and experimental data confirmed the accuracy of the model.     

Sustainable design procedure: The role of composite materials to combine mechanical and environmental features for agricultural machines

The improvement of environmental performance of products is the main focus for many companies. Although, methodologies are fundamental in the design process in order to introduce environmental inputs in the development, it has not been a comfortable practice for companies. In this context, this study is concerned with the development of procedural guidelines for how ecodesign concepts may be introduced to the selection of material. The results point out the importance of the introduction of environmental parameters, since the beginning of the project. In regards to composite material, it is possible to note that vegetable fibers are good choice replacements for fiberglass.     

The allocation of non-identical machines among non-identical servers

The problem of allocating non-identical machines among non-identical servers is considered under steady-state conditions for the case of quasirandom input and exponential service times. Machines are assigned to operators with the objective of minimizing an expected cost model of the queueing system. Different classes of machines have different service rotes and, for a given class of machines, the service rates are different among servers. A pattern search procedure is used to solve the resulting optimization problem. Computational experience is presented, together with an analysis of the sensitivity of the model to errors in estimating the values of the parameters of the model.     

The flexural or rotational stiffness of testing machines

The flexural or rotational stiffness of testing machines is defined and its significance in testing is outlined. A method of estimating rotational stiffness of machines is described.     

Men and machines: The computational metaphor

In the 20th century the interpretation of the human mind and brain as a computer has replaced the 18th century metaphor of “man as a machine”. This paper traces the development of the computational metaphor with some attention to its 18th century roots, and then argues that its employment need not lead to the mechanization of thinking and the autonomy of technique. An awareness of the metaphoric and, therefore, hypothetical status of the computational metaphor will prevent technique from escaping intentional human control. This is a shortened version of a paper included in C. Mitcham and Alois Huning, eds., Philosophy and Technology II: Information Technology and Computers in Theory and Practice (Boston: D. Reidel, in press), and is included here with permission.