The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production

Based on the characteristics of ready-mixed concrete enterprises, this paper puts forward that knowledge management (KM) is an effective way to contribute to enterprise production and operation. The knowledge content and relevant models of concrete enterprises are proposed, including advanced enterprise management, decision support for production operation, production and operation cost, and marketing-customer relationship. Afterwards knowledge contents are divided into static, strategic and reasoning knowledge. Besides knowledge unified expression is put forward accordingly. In addition, the KM system for process ready-mixed concrete enterprises management is established to facilitate effective production processing. As part of exploratory study, artificial neural network coupled with genetic algorithm (ANN-GA) as knowledge mining technology is applied in KM system to predict the 28-day compressive strength in concrete enterprises. The results shows that compared to back-propagation artificial neural network, the convergence rate of ANN-GA algorithm has been significantly improved and almost all the relative errors of predicted compressive strength of concrete C30 are within 3 %. It not only confirms the validity of the models, but also proves that ANN-GA algorithm is an effective knowledge mining method applied in concrete industry.     

Modelling of continuous steel–concrete composite beams: computational aspects

Steel–concrete composite members are an interesting option for structural designers, but the reliability of design procedures both in the case of gravity and seismic loads is in continuous development. The issue is very complex, since behaviour of continuous composite beams results from local phenomena of interaction such as partial shear connection and bond.Furthermore, composite beams in buildings generally are not characterised by a full continuity due to the beam to column connections; thus the analysis and the detailing of such parts have a key role in the development of suitable design procedures.In the present paper, some computational aspects related to the modelling of composite flexural members are discussed with reference to continuous and semi-continuous structural systems widely used in practice.     

Controllability and observability of an artificial advection–diffusion problem

In this paper we study the controllability of an artificial advection?Cdiffusion system through the boundary. Suitable Carleman estimates give us the observability of the adjoint system in the one dimensional case. We also study some basic properties of our problem such as backward uniqueness and we get an intuitive result on the control cost for vanishing viscosity.     

Soil–structure interaction: Parameters identification using particle swarm optimization

This paper deals with a numerical NDT method to identify mechanical parameters by inverse analysis using the particle swarm optimization algorithm. This method is using a finite element model of the structure to study in order to minimize the error between field data and data predicted by the FE model. The method is applied in the case of a laboratory experiment modelling a soil–structure interaction problem. Results stress the impact of the sensitivity of the solution to the accuracy of field measurements by simulating both different levels of measurement noise on data and using several placements of sensors.     

Measuring an artificial intelligence system’s performance on a Verbal IQ test for young children*

Abstract

We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 artificial intelligence (AI) system. The test questions (e.g. “Why do we shake hands?”) were translated into ConceptNet 4 inputs using a combination of the simple natural language processing tools that come with ConceptNet together with short Python programs that we wrote. The question answering used a version of ConceptNet based on spectral methods. The ConceptNet system scored a WPPSI-III VIQ that is average for a four-year-old child, but below average for 5–7 year olds. Large variations among subtests indicate potential areas of improvement. In particular, results were strongest for the Vocabulary and Similarities subtests, intermediate for the Information subtest and lowest for the Comprehension and Word Reasoning subtests. Comprehension is the subtest most strongly associated with common sense. The large variations among subtests and ordinary common sense strongly suggest that the WPPSI-III VIQ results do not show that “ConceptNet has the verbal abilities of a four-year-old”. Rather, children’s IQ tests offer one objective metric for the evaluation and comparison of AI systems. Also, this work continues previous research on psychometric AI.     

Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction

In human–robot interaction scenarios, an intelligent robot should be able to synthesize an appropriate behavior adapted to human profile (i.e., personality). Recent research studies discussed the effect of personality traits on human verbal and nonverbal behaviors. The dynamic characteristics of the generated gestures and postures during the nonverbal communication can differ according to personality traits, which similarly can influence the verbal content of human speech. This research tries to map human verbal behavior to a corresponding verbal and nonverbal combined robot behavior based on the extraversion–introversion personality dimension. We explore the human–robot personality matching aspect and the similarity attraction principle, in addition to the different effects of the adapted combined robot behavior expressed through speech and gestures, and the adapted speech-only robot behavior, on interaction. Experiments with the humanoid NAO robot are reported.     

Shape optimisation by design of experiments and finite element methods—an application of steel wheels

The requirements made on industry, and particularly on development departments, are increasing constantly due to demands to reduce costs and development times and the introduction of new quality guidelines (Toutenburg and Gössl, Versuchsplanung in der industrie; moderne methoden und softwarelösungen. Proceedings des workshops versuchsplanung in der industrie der boehringer mannheim GmbH und SAS-Institute, Tutzing 30./31.10.1995. Prentice Hall Verlag, München, 1996). In particular, several loops are usually required within the development process during the development of new parts to obtain an optimal part shape. This process is extensively influenced by the experience and know-how of the developer or design engineer. A method that enables a specific and structured approach to part shape optimisation is presented in this paper. Design of experiments and the finite element method are interlinked in this method.     

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.     

Skill learning framework for human–robot interaction and manipulation tasks

In this article, a learning framework that enables robotic arms to replicate new skills from human demonstration is proposed. The learning framework makes use of online human motion data acquired using wearable devices as an interactive interface for providing the anticipated motion to the robot in an efficient and user-friendly way. This approach offers human tutors the ability to control all joints of the robotic manipulator in real-time and able to achieve complex manipulation. The robotic manipulator is controlled remotely with our low-cost wearable devices for easy calibration and continuous motion mapping. We believe that our approach might lead to improving the human-robot skill learning, adaptability, and sensitivity of the proposed human-robot interaction for flexible task execution and thereby giving room for skill transfer and repeatability without complex coding skills.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Diffusivity and atomic mobility for fcc Ni–Cu–Ti alloy: Measurements and an intelligent modeling

The diffusivities of fcc Ni-rich Ni–Cu–Ti alloy have been systematically investigated by means of experimental measurements and an intelligent modeling. Utilizing two bulk diffusion couples without intersection points in diffusion paths together with electron probe microanalysis technique, the interdiffusion coefficients along the whole diffusion path in fcc Ni–Cu–Ti alloy at 1273, 1373 and 1473 K are determined by a novel numerical inverse method. Three new diffusion couples together with two initial diffusion couples are used to obtain the diffusivities by Matano-Kirkaldy method. Two sets of atomic mobilities are established based on the interdiffusion coefficients obtained using numerical inverse method and Matano-Kirkaldy method, respectively, through an intelligent modeling in CALTPP (CALculation of ThermoPhysical Properties) program. The reliability of present two sets of atomic mobilities is verified by comprehensive comparisons between the calculated and experimental properties, including diffusivities, concentration profiles and diffusion paths. A preferable agreement is found in the comparisons where the diffusivities are calculated using numerical inverse method. The present investigation of diffusivities for fcc Ni–Cu–Ti alloy demonstrates that two diffusion couples combined with the numerical inverse method can establish a reliable atomic mobility for the targeted alloys.     

Public websites and human–computer interaction: an empirical study of measurement of website quality and user satisfaction

The focus of this paper is to investigate measurement of website quality and user satisfaction. More specifically, the paper reports on a study investigating whether users of high-quality public websites are more satisfied than those of low-quality websites. Adopting a human–computer interaction perspective, we have gathered data from the 2009 public website awards in Scandinavia. Our analysis of Norwegian and Danish websites reveals that the use of quality criteria is highly technical compared to the traditional usability testing focus on efficiency, effectiveness and satisfaction of the actual system use by representatives. A Pearson correlation analysis of user evaluation from 296 websites that participated in the Danish web award Bedst på Nettet (‘Top of the Web’) showed no significant positive correlation between website quality and user satisfaction. We put forward recommendations for further investigation: (1) inclusion of real users (citizens and businesses) in real-use setting in the evaluation process could help move forward the understanding of the relationship between website quality and end-user satisfaction; (2) the lack of correlation between website quality and user satisfaction could be a point of departure for critical discussions of future implementation of public information and services and (3) additional and in-depth research of the measurement of website quality in the public sector, user expectations and the impacts of website quality improvements on user satisfaction.