Backpropagaton in Hl7 in Medical Informatics to Analysis Speed of Sending Data

In this paper, analysis the speed of sending message in Healthcare standard 7 with the use of back propagation in neural network. Various algorithms are define in backpropagtion in neural network we can use trainlm algorithm for sending message purpose. This algorithm appears to be fastest method for training moderate sized feedforward neural network. It has a very efficient matlab implementation. The need of trainlm algorithm are used for analysis, increase the speed of sending message faster and accurately and more efficiently. The proposed work is used in healthcare medical data. With the use of backpropagation in health care standard seven (HL7) sending message between two systems. To increase the speed of the healthcare sending data we can use Train LM algorithm. Train LM algorithm is more fastest algorithm it can be increase efficiency and improve accuracy of the system and also provide real time application. To increase speed of sending message these algorithm used. With the use of this algorithm it can be decreasing time of sending message to the other system

Neural Network Based PI Controller Parameter Calculation on a Boiler Drum Level System

AbstractThe controller parameters influence the performance of the closed loop system. So we have to develop a tuning method for obtaining the optimum values of the controller parameters with respect to a particular process. Controller tuning is very much process dependent and any improper selection of the controller settings may lead to instability and affect performance of the closed loop system. Closed loop tuning methods like Ziegler-Nichols method depends on estimation of ultimate gain and ultimate time period. When trying different gains on an unknown process the amplitude of undampened oscillations can become unsafe or on the conversely for low initial gain settings the test can take a long time to reach sustained oscillation condition. This paper proposes a neural network based scheme to estimate ultimate gain and optimum proportional and integral value of PI controller within affordable time limit and safe input range when the parameters change

Modular dynamic RBF neural network for face recognition

Over the years, we have seen an increase in the use of RBF neural networks for the task of face recognition. However, the use of second order algorithms as the learning algorithm for all the adjustable parameters in such networks are rare due to the high computational complexity of the calculation of the Jacobian and Hessian matrix. Hence, in this paper, we propose a modular structural training architecture to adapt the Levenberg-Marquardt based RBF neural network for the application of face recognition. In addition to the proposal of the modular structural training architecture, we have also investigated the use of different front-end processors to reduce the dimension size of the feature vectors prior to its application to the LM-based RBF neural network. The investigative study was done on three standard face databases; ORL, Yale and AR databases

Modeling and Design of Digital Electronic Systems

The paper is concerned with the modern methodologies for holistic modeling of electronic systems enabling system-on-chip design. The method deals with the functional modeling of complete electronic systems using the behavioral features of Hardware Description Languages or high level languages then targeting programmable devices - mainly Field Programmable Gate Arrays (FPGAs) - for the rapid prototyping of digital electronic controllers. This approach offers major advantages such as: a unique modeling and evaluation environment for complete power systems, the same environment is used for the rapid prototyping of the digital controller, fast design development, short time to market, a CAD platform independent model, reusability of the model/design, generation of valuable IP, high level hardware/software partitioning of the design is enabled, Concurrent Engineering basic rules (unique EDA environment and common design database) are fulfilled. The recent evolution of such design methodologies is marked through references to case studies of electronic system modeling,simulation, controller design and implementation. Pointers for future trends / evolution of electronic design strategies and tools are given

Machine Learning Approach for Degradation Path Prediction Using Different Models and Architectures of Artificial Neural Networks

Degradation and failure prediction has become more and more crucial for maintenance planning and scheduling, the decision-making process, and many other areas of manufacturing systems. This paper presents an approach where different artificial neural network models were developed to predict the degradation path of a machine component using different architectures, including fully connected networks (FCN) and arbitrarily connected networks (ACN). These models were trained using the Neuron-by-Neuron (NBN) training algorithm with forward-backward computations, where NBN is an improved form of the Levenberg-Marquardt (LM) algorithm, combined with FCN and ACN architectures, which can be trained efficiently, it can give more accurate predictions with a fewer number of neurons used. The developed models were evaluated using the statistical performance measure of the sum of squared error (SSE). The results show that the used networks are successfully able to predict the degradation path; the 8-neurons model of FCN architecture and the 3-neurons model of ACN architecture with tanh (mbib) hidden layers activation function and linear function (mlin) of the outputs have the lowest prediction error (SSE) among all the developed models. The use of such architectures combined with NBN training algorithm can easily model manufacturing systems with complex component structures that provide a vast amount of data

SOM neural network design – a new Simulink library based approach targeting FPGA implementation

The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural networks with on-chip learning algorithm. The method aims to build up a specific neural network using generic blocks designed in the MathWorks Simulink environment. The main characteristics of this original solution are: on-chip learning algorithm implementation, high reconfiguration capability and operation under real time constraints. An extended analysis has been carried out on the hardware resources used to implement the whole SOM network, as well as each individual component block

Artificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Quality

Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical‐chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support Sys‐ tem (DSS) designed to predict the evolution of the quality of peaches, namely the storage time re‐ quired before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and val‐ idation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple predic‐ tion of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and con‐ sumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI‐based solutions for smart cities.This study is within the activities of project PrunusPós—Otimização de processos de ar‐ mazenamento, conservação em frio, embalamento ativo e/ou inteligente, e rastreabilidade da qual‐ idade alimentar no póscolheita de produtos frutícolas (Optimization of processes of storage, cold conservation, active and/or intelligent packaging, and traceability of food quality in the postharvest of fruit products), Operation n.º PDR2020‐101‐031695 (Partner), Consortium n.º 87, Initiative n.º 175 promoted by PDR2020 and co‐financed by FEADER under the Portugal 2020 initiative.info:eu-repo/semantics/publishedVersio

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented