Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?

Sales prediction is an essential part of stock planning for the wholesales and retail business. It is a complex task because of the large number of factors affecting the demand. Designing an intelligent predictor that would beat a simple moving average baseline across a number of products appears to be a non-trivial task. We present an intelligent two level sales prediction approach that switches the predictors depending on the properties of the historical sales. First, we learn how to categorize the sales time series into ‘predictable’ and ‘random’ based on structural, shape and relational features related to the products and the environment using meta learning approach. We introduce a set of novel meta features to capture behavior, shape and relational properties of the sales time series. Next, for the products identified as ‘predictable’ we apply an intelligent base predictor, while for ‘random’ we use a moving average. Using the real data from a food wholesales company we show how the prediction accuracy can be improved using this strategy, as compared to the baseline predictor as well as an ensemble of predictors. In our study we also show that by applying an intelligent predictor for the most ‘predictable’ products we can control the risk of performing worse than the baseline.     

Toward an information-integrated control system—the control-theoretic view of intelligent control technology

Recent computing technology is providing alternative strategies for the functionality and implementation of controllers for practical complex systems. An important issue will be how to integrate all information about control problems. In this paper, we present the control-theoretic view of intelligent control technology, and discuss a paradigm for an information-integrated control system. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.     

esign of power controller in CDMA system with power and SIR error minimization

In this paper, an uplink power control problem is considered for code division multiple access （CDMA） systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio （SIR）. A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a Kalman filter problem.     

Design of power controller in CDMA system with power and SIR error minimization

In this paper, an uplink power control problem is considered for code division multiple access (CDMA) systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio (SIR). A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a Kalman filter problem.     

DNA algorithms for fractal construction—an application of the SInsDelP system

The SInsDelP system with single contextual insertion and (u, v)-contextual deletion was introduced by Murugan and Easwarakumar. In this paper we discuss an application of the SInsDelP system for the construction of fractal images. The fractal images considered are of self-similar types, such as Koch Snowflake, Anti-Koch Snowflake, Fractal Tree and Sierpinski Triangle. This is performed in two stages. A DNA sequence for the fractal image is generated first by a sequence of SInsDelP operations, and then the respective image is produced through the specialized recognition algorithm.     

Model referenced monitoring and diagnosis — application to the manufacturing system

This paper describes “model referenced monitoring and diagnosis” a systematic method of monitoring and diagnosis. In this method, a model is used either to generate standard values for the monitoring parameters, or to derive a systematic diagnostic algorithm. As examples of the model referenced monitoring in manufacturing systems, the cutting torque prediction system and tool breakage detection system are described. In the former example, the cutting torque values at each point in time, which can be used as reference values in monitoring the abnormal cutting condition, are evaluated based on a solid modelling system. In the latter, an autoregressive (AR) model is adaptively fitted to the cutting torque signal in order to detect any sudden change in the cutting state due to tool breakage. Two examples are also described for the case of model referenced diagnosis; the diagnosis of sequentially controlled machines using the state graph model, and diagnosis by means of a failure causality model. The former method is applicable to machines controlled by sequence control. Based on the state graph of the machine and the controller, the diagnostic programme can be generated in combination with the control programme. The failure causality model represents the propagation of the effects of failures in the machine. All possible combinations of failure causes are obtained by solving the simultaneous Boolean equations derived from the model.     

Validation of computational prediction of horizontal gene transfer events—XenoCluster

In this paper, we assess the biological accuracy and relevance of XenoCluster results compared to a reported set of known xenologs in yeast (Hall et al. Eukaryot. Cell 4(6):1102–1115, 2005). We were able to assign a high-priority rank to all reported yeast xenologs for which we had sufficient genomic sequence data.     

An active intelligent decision support system — Architecture and simulation

Development and implementation of decision support systems to support intelligent decision making is an area of research that has gained in importance in recent years. Due to the increased complexity of decision making, active involvement of the user and the computer in an intelligent way is necessary in the decision process. This paper presents issues in the design of an active intelligent decision support system (IDSS), develops an architectural model based on cooperative distributed problem solving, and performs a simulation of the system using object oriented programming for an example application in airfleet control.     

Method and system for adaptive network security using intelligent packet analysis

<美国专利>6,499,107Gleichauf, et a1.December 24,2002<发明人>Gleichauf;Robert E.(San Antonio,TX);TealDaniel M.(San Antonio,TX);Wiley;Kevin L(Elgin,TX)<代理人>Cisco Technology,Inc.(San Jose,CA)<公开号>223071<公开日>December 29,1998<美国分类>713/20]<国际分类>G06F 01l/30<检索号>713/200,201 709/223,224,229,225,100,102,103,104,226 705/8,9<摘要>A method and system for adaptlye net-work security using intelligent packet analysisare provided.The method comprises monitoringnetwork data traff…     

Designer®—Intelligent expert system for intelligent manufacturing systems design

This survey on expert systems activities and trends in Yugoslavia offers some results already obtained in the domain of manufacturing science and technology. In the scope of a long-term research project “Intelligent Manufacturing Systems (IMS)—Theory and Application” a Designer® Intelligent Expert System for mafacturing engineering has been proposed and partially developed. Designer® IES is based on new developed knowledge automata theory enhanced with cellular automata concept. Induction learning by analogy and Quasimorphism knowledge mapping from real world to model world is used to generate a reasoning structure. The Intelligent Expert System is divided into three main subsystems, with a very large knowledge base:

• •Product designer
• •Process Designer, and
• •Production Planning and Control Designer.

All these segments were developed in pilot versions of expert systems for specific groups of activities inside each of these three domains.     

Hierarchical system identification of states and parameters in interconnected power systems†

This paper presents an application of hierarchical identification procedures of the previous paper to the identification of interconnected power system states and parameters from input—output observed data. A three-area interconnected power system model is used to demonstrate the decomposition of the original system based on its particular characteristics and the implementation of hierarchical algorithms for system identification. The adaptivity of these procedures to structural changes are also illustrated. Numerical results are obtained by conducting a digital simulation of the three-area system and using the hierarchical identification and coordination algorithms to estimate the states and unknown system parameters. Computational aspects of the hierarchical system identification solutions are discussed.     

Adaptation of an automotive dialogue system to users’ expertise and evaluation of the system

Spoken dialogue systems (SDSs) can be used to operate devices, e.g. in the automotive environment. People using these systems usually have different levels of experience. However, most systems do not take this into account. In this paper, we present a method to build a dialogue system in an automotive environment that automatically adapts to the user’s experience with the system. We implemented the adaptation in a prototype and carried out exhaustive tests. Our usability tests show that adaptation increases both user performance and user satisfaction. We describe the tests that were performed, and the methods used to assess the test results. One of these methods is a modification of PARADISE, a framework for evaluating the performance of SDSs [Walker MA, Litman DJ, Kamm CA, Abella A (Comput Speech Lang 12(3):317–347, 1998)]. We discuss its drawbacks for the evaluation of SDSs like ours, the modifications we have carried out, and the test results.

Magix—an ICAE system for Problem-Based Learning

Intelligent Computer Aided Education (ICAE) systems are software, aimed at assisting in the teaching and learning of students. Magix is a prototype ICAE system for use in Problem-Based Learning (PBL). In this system the principles of constructivism, user-driven interaction, knowledge-based systems and metacognition are integrated. The fact that the use of a standard algorithm in problem-based learning problem-solving cannot be enforced, greatly complicates the development of the student model. Magix features a student model that consists of a cognitive component supplemented by a metacognitive component. The aim is to externalize the student model in order to facilitate reflection and encourage critical, creative and corrective self-monitoring. The system consists of two main subsystems, Magix-Ed the hypertext-based student component and Magix-An the expert system that gives an analysis of the problem-solving attempt performed in Magix-Ed. Magix-Ed presents the problem to be solved in a virtual world. The world consists of objects that visually represent the problem and tools that could be used to solve the problem. Magix-An analyses a student's problem-solving attempts. An evaluation report is produced that aims to encourage certain thinking dispositions, while enriching the student's repertoire of strategies through autoregulation.     

FireStation — an integrated software system for the numerical simulation of fire spread on complex topography

A software system aimed at the simulation of fire spread over complex topography is presented. The software implements a semi-empirical model for fire rate of spread, which takes as input local terrain slope, parameters describing fuel properties as well as the wind speed and direction. Fire shape is described with recourse to an ellipse-type model. Two different models are implemented for the simulation of the wind field. Both these models predict wind velocity and direction based on local observation taken at meteorological stations. The whole system was developed under a graphical interface, aiming at a better ease of use and output readability so as to facilitate its application under operational conditions. This work describes the mathematical models employed, provides an overview of the graphical interface and presents the results of some simulations tested against experimental data.     

Simulation of metal–polymer coating system for service life prediction

A degradation model of a metal-protective coating system is given. Prediction calculations of protective coating systems service life using the experimental data of non-adhesion area are executed. Comparative analysis of the coating service life values calculated and those under operating conditions has shown the adequacy of the offered prediction parities.     

Actuator fault diagnosis in mechanical systems — Fault power estimation approach

A systematic method is introduced to deal with actuator faults in a class of nonlinear mechanical control systems. The presence of the fault is detected by monitoring the fault induced modifications in the control system’s energy balance. From the energy deviation the residual generator estimates the power of the fault. For fault isolation and identification the set of fault power pattern signals is defined. Using signal processing methods the elements of this set are compared to the residual signal to determine the characteristics and the magnitude of the fault. The method does not require assumptions on the system’s model and it can handle parameter uncertainties. The applicability of the proposed method is shown through mechatronic examples.