Intelligent Identification System Research Articles

AUTOMATIC IDENTIFICATION SYSTEM FOR RAW POULTRY PORTIONS

ABSTRACTIn poultry processing plants, the sorting of raw poultry portions into separate containers prior to packaging is performed mostly by human laborers. This sorting method has two problems: labor cost and health hazard due to possible contamination between raw meat and humans. Therefore, an automated sorting system is required to avoid these problems. In this article, we present an automatic portion identification system that uses image processing and a neural network to identify six different chicken portions that are usually preferred by consumers. The proposed rotational invariant system can identify the cut portions regardless of their orientation with an overall correct identification rate of 97.57%. Potentially, the output of the proposed identification system can be used to move robotic arms to physically separate the identified chicken portions into separate containers.PRACTICAL APPLICATIONSThis work contributes to the development of an automated sorting system for practical use in poultry processing plants. The presented intelligent identification system is the main part of such an automated system. We train the system in this work to recognize the main chicken portions; however, other portions and other poultry birds can also be identified upon including their portion images in the training phase. This work differs from existing systems, in that it relies totally on the shape and coarse texture of a portion, using images as input data, and discards information like weight or size of the portion. Another advantage is the elimination of the need for using many birds during the development of the system; in fact, training the system can be achieved using only one bird and its portions.

A NOVEL SOURCE MPEG-2 VIDEO IDENTIFICATION ALGORITHM

With the availability of powerful multimedia editing software, all types of personalized image and video resources are available in networks. Multimedia forensics technology has become a new topic in the field of information security. In this paper, a new source video system identification algorithm is proposed based on the features in the video stream; it takes full advantage of the different characteristics in the rate control module and the motion prediction module, which are two open parts in the MPEG-2 video compression standard, and combines a support vector machine classifier to build an intelligent computing system for video source identification. The experiments show this proposed algorithm can effectively identify video streams that come from a number of video coding systems.

Intelligent system for identification and replacement of faulty sensor measurements in Thermal Power Plants (IPPAMAS: Part 1)

This paper describes a procedure of identifying sensor faults and reconstructing the erroneous measurements. Knowledge discovery in databases is successfully applied for deriving models that estimate the value of one variable based on correlated others. The estimated values can then be used instead of the recorded ones of a measuring instrument with false reading. The aim is to reassure the correctness of data entered to an optimization software application that was developed for the Thermal Power Plants of Western Macedonia, Greece. The architecture of the application follows the Multi-Agent System approach in order to cope with its complexity and distributed nature. The application was tested on a case study and proved to be efficient.

Computer-Based Identification of Breast Cancer Using Digitized Mammograms

High-quality mammography is the most effective technology presently available for breast cancer screening. Efforts to improve mammography focus on refining the technology and improving how it is administered and X-ray films are interpreted. Computer-based intelligent system for identification of the breast cancer can be very useful in diagnosis and its management. This paper presents a comparative approach for classification of three kinds of mammogram namely normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers namely: the feedforward architecture neural network classifier, and Gaussian mixture model (GMM) for comparison.. Our protocol uses, 360 subjects consisting of normal, benign and cancer breast conditions. We demonstrate a sensitivity and specificity of more than 90% for these classifiers.

Comparative Performance of Intelligent Algorithms for System Identification and Control

This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed based on optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is men implemented, tested and its performance assessed for GAs and ANFIS based algorithms. Finally, a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments.

Estimating Model Uncertainty using Confidence Interval Networks: Applications to Robust Control

Robust control techniques require a dynamic model of the plant and bounds on model uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. In the case of H∞ control synthesis, a conservative weighting function for model uncertainty is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawback of this conservative, “hard computing” approach is reduced performance. This paper demonstrates a novel “soft computing” approach to estimate bounds of model uncertainty resulting from parameter variations, unmodeled dynamics, and nondeterministic processes in dynamic plants. This approach uses confidence interval networks (CINs), radial basis function networks trained using asymmetric bilinear error cost functions, to estimate confidence intervals associated with nominal models for robust control synthesis. This research couples the “hard computing” features of H∞ control with the “soft computing” characteristics of intelligent system identification, and realizes the combined advantages of both. Simulations and experimental demonstrations conducted on an active magnetic bearing test rig confirm these capabilities.

Remote intelligent identification system of structural damage

The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies.

Improvement of robotic cell intelligence through a dedicated recognition system

The need for an intelligent recognition system for object identification, coupled with its position and orientation, is critical for the development of robotic and manufacturing cell intelligence. At present, the availability of systems for this purpose has proved to be insufficient for the required applications. This paper proposes such a dedicated recognition system by means of an object dedicated dormant chip. The technical details are discussed in a comprehensive format. Experimental results are presented. The integration of the proposed system info a remotely controlled intelligently enhanced robotic cell, is explained.

Intelligent identification system of flow regime of oil–gas–water multiphase flow

Instantaneous differential pressure signals of oil–gas–water multiphase flow in a horizontal pipe are measured with a piezo-resistance differential pressure transducer with fast response. The signals are denoised by using wavelet theory and then the characteristic vectors of various flow regimes are obtained from the denoised differential pressure signals with fractal theory. The characteristic vectors of known flow regimes are fed into a neural network for training and later on weight coefficients of neural network are obtained through training. Then, the characteristic vector of some kind of unknown flow regime of oil–gas–water multiphase flow is fed into the neural network and the neural network can automatically send out the information in respect to the classification of flow regime, thus the intelligent identification of flow regime of oil–gas–water multiphase flow is realized. Practice shows that this new method for identifying flow regimes of multiphase flow and the system constructed with the method has the merits of high accuracy, fast response and automatic identification without artificial intervention etc. It will have promising application prospect.

An intelligent multimedia system for identification of weed seedlings

An intelligent multimedia tool for identifying weeds was developed in order to facilitate weed identification in the seedling stage. The identification is based on a visual image approximation method and it is implemented using multimedia, hypertext, object oriented design and knowledge based technologies. The method of image approximation is based on a progressive approach to the actual seedling through a sequence of images with increasing definition. The implementation of this method has required the design of a visual identification key that includes the set of images with different abstraction levels and their logic relationship. Trials with small groups comprising of people with botanical knowledge and others without that knowledge showed that the method of image approximation makes it possible to reduce the botanical knowledge necessary for identification of weeds, as well as that, multimedia technology has a positive effect in the intuitiveness and the facility of use of the tool.

Fuzzy system as parameter estimator of nonlinear dynamic functions

In this paper we use adaptive fuzzy systems as intelligent identification systems for nonlinear time-varying plants. A new technique to design the fuzzy system that relies on the minimization of a loss function is presented. The design technique uses the centers of the fuzzy sets (labels) at the antecedent part of the rule base as the estimated parameters. This parametrization has the Linear In The Parameters (LITPs) characteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation method then performs as a nonlinear estimator. If several fuzzy sets are defined for the input variables at the antecedent part, the fuzzy system ("fuzzy estimator") then behaves as a collection of nonlinear estimators where different rules' regions have different parameters. The proposed scheme is potentially capable of estimating the parameters of highly nonlinear plants. Simulation examples, which use plants with highly nonlinear gain, show the power of the proposed estimation scheme in comparison to estimation using the linear model.

Fuzzy System as a Parameter Estimatior of Nonliner Dynamic Functions

In this paper Adaptive Fuzzy Systems are used as intelligent identification systems for nonlinear plants. A new technique to design the fuzzy system that relies on the minimisation of a loss function is presented. The design technique uses the centres of the fuzzy sets (labels) at the antecedent part of the Rule Base as the estimated parameters. This parametrisation has the Linear in The Parameters (LITPs) characteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation method then performs as a nonlinear estimator. If several fuzzy sets are defined for the input variables at the antecedent part, the fuzzy system (“fuzzy estimator”) then behaves as a collection of nonlinear estimators where different rule's regions have different parameters. The proposed scheme is potentially capable of estimating the parameters of highly nonlinear plants. Simulation examples, which use plants with highly nonlinear gain, show the power of the proposed estimation scheme in comparison to estimation using linear model.

Educating medical technologists to minimize errors in blood banking

TransfusionVolume 31, Issue 4 p. 290-292 Free Access Educating medical technologists to minimize errors in blood banking Karen R. Karni PHD, CLS(NCA), Karen R. Karni PHD, CLS(NCA) Department of Laboratory Medicine and Pathology University of Minnesota Minneapolis, MNSearch for more papers by this author Karen R. Karni PHD, CLS(NCA), Karen R. Karni PHD, CLS(NCA) Department of Laboratory Medicine and Pathology University of Minnesota Minneapolis, MNSearch for more papers by this author First published: May 1991 https://doi.org/10.1046/j.1537-2995.1991.31491213288.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References 1 Smith PJ, Miller TE, Fraser J., et al. An empirical evaluation of performance of antibody identification tasks. Transfusion 1991; 31: 313– 7. 2 Smith PJ, Miller TE, Fraser JM. An intelligent tutoring system for antibody identification. In: Proceedings of the 14th Annual Symposium on Computer Applications in Medical Care. Washington: American Medical Informatics Association, 1990: 1032– 3. 3 Pucel DJ, Jensrud Q., Damre S. The identification and stability of occupationally specific behaviors. St. Paul: Minnesota Research and Development Center for Vocational Education, 1990: 1. Volume31, Issue4May 1991Pages 290-292 ReferencesRelatedInformation

Intelligent knowledge-based system for dynamic system identification

The development and evaluation of an expert advisor for system identification (EASI) is described. The principles behind a suitable knowledge representation paradigm for a system which includes both intelligent tutoring and software package front-end concepts are outlined. The system has been constructed using an expert system ‘text-animation’ shell with linkage into Prolog-2 to provide enhancements. These extensions have included an online dictionary and bibliographic material. The identification domain aspects covered include experimental design, model structure determination, estimation algorithms, with applications to SISO and MIMO linear systems, and SISO nonlinear dynamics. Different levels of user-model are provided, and the advisor acts as a front-end to a number of in-house identification packages.