scholarly journals Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

2016 ◽  
Vol 2 (2) ◽  
pp. 79
Author(s):  
Achmad Arwan
Keyword(s):  
Neural Network ◽  
Manufacturing Industry ◽  
Complex Event Processing ◽  
Event Processing ◽  
The Neural Network ◽  
Event Log ◽  
Complex Events ◽  
Processing Event ◽  
Production Increase ◽  
Neural Network Input

AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman) yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats) that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results from training process), the system will issue a signal to increase production, otherwise system will keep monitor the events. Experiment result shows that the accuracy of this method is 77% for 39 series of event streams.Keywords: complex event processing, event, neural networks, process, production increase prediction.

System Security Monitoring Based on Complex Event Processing and Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 626-637
Author(s):  
Yi Fei Guo ◽  
Shi Si ◽  
Da Wei Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
System Security ◽  
Dynamic Structure ◽  
Complex Event Processing ◽  
Event Processing ◽  
Security Monitoring ◽  
Complex Events

Recently system security monitoring has meet several challenges. Therefore a system security monitoring approach based on complex event processing and dynamic structure-based neural networks is proposed in this paper. Firstly, complex event processing is used to handle real-time event streams and extract complex events from system security sensors. Secondly, the complex events from CEP would be used for further study by dynamic structure-based neural network. Finally the process of system security monitoring is showed and experiments would be applied to validate the feasibility, efficiency and precision of the approach.

Application of Neural Network in Bioactive Prediction of Inhibitor of Thymidylic Acid-Based Synthetase

2014 ◽  
Vol 937 ◽  
pp. 308-312
Author(s):  
Xi Hua Du ◽  
Xiao Hui Wang
Keyword(s):  
Neural Network ◽  
Biological Activity ◽  
Molecular Design ◽  
Structural Parameters ◽  
Regression Method ◽  
New Drugs ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Electrical State ◽  
Neural Network Input

Based on the molecular topology information and adjacency matrix, the 38 electrical state indices of molecules of inhibitor of thymidylic acid-based synthetase as five-membered heterocyclic pyrimidine derivatives were calculated to provide theoretical basis for molecular design of new drugs. By using variable regression method, the best subset of structural parameters ofE1,E2,E7,E16andE31were optimized. When the five structural parameters were used as the BP neural network input neurons and the neural network structure of 5:3:1 was used, an ideal prediction model of biological activity was obtained. Its total correlation coefficientrand average relative error were 0.972 and 2.13%, respectively. The result showed that the biological activity andE1,E2,E7,E16andE31have a good non-linear relationship with the biological activity, and the results predicted by neural networks was better than that by multiple regression method. The test proved that the model had good robust and predictive capabilities. Our research would provide theoretical guidance for the development of new drugs of inhibitor of thymidylic acid-based synthetase with efficient and low toxicity.

scholarly journals Complex Event Processing of Health Data in Real Time Predicting Heart Failure Risk

2019 ◽  
Vol 8 (3) ◽  
pp. 8623-8627
Keyword(s):  
Heart Failure ◽  
Health Data ◽  
Complex Event Processing ◽  
Health Conditions ◽  
Critical Conditions ◽  
Event Processing ◽  
Heart Attacks ◽  
Failure Risk ◽  
Complex Events ◽  
Prediction Techniques

In this article, we develop a scalable system that can perform heart failure prediction techniques based on complex event processing (CEP). The emergence of different health conditions can be seen as complex events and therefore this concept can be easily extended to other uses. The system uses MLP (Multilayer Perceptron) for the prediction of heart failure. First, perform preprocessing and after that collect the health parameter. The system monitors the patients of heart failure and predicts heart attacks. When critical conditions are occurs the system warns the patients. Experimental results show that MLP is more accurate than C 4.5, based on Precision-Recall and F1.

scholarly journals INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS

2018 ◽  
pp. 47-54
Author(s):  
Vitalii Lysenko ◽  
Oleksiy Opryshko ◽  
Dmytro Komarchuk ◽  
Natalia Pasichnyk ◽  
Natalya Zaets ◽  
...  
Keyword(s):  
Neural Network ◽  
Nitrogen Nutrition ◽  
Information Support ◽  
Statistical Characteristics ◽  
Intermediate Layers ◽  
The Neural Network ◽  
The Matrix ◽  
Data Volume ◽  
Linear Elements ◽  
Neural Network Input

The article addresses issues on application of unmanned aerial vehicles (UAV) to monitor nitrogen nutrition through the example of wheat plants. The optical spectral range can be used to monitor exploitation of the UAV. It is recommended to develop specialized spectral indices for such equipment. The article provides calibration curves for nitrogen nutrition monitoring. In the created neural networks, the linear model is represented as a network without intermediate layers, which in the output layer contains only linear elements, the weight corresponds to the elements of the matrix, and the thresholds are the components of the shear vector. During the operation, the neural network actually multiplies the vector of inputs into the matrix of scales, and then adds a vector of displacement to the resulting vector. Results of the research show how to create the specialized RPVI adapted to technological capabilities of UAVs. It has been experimentally proved that input parameters that describe the state of agricultural plantations are regularly distributed. The average statistical characteristics for additive color RGB model is advisable to be the neural network input instead of large sample data volume.

scholarly journals Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN

2015 ◽  
Vol 9 (1) ◽  
pp. 922-926 ◽  
Author(s):  
Zhao Xuejun ◽  
Wang Mingfang ◽  
Wang Jie ◽  
Tong Chuangming ◽  
Yuan Xiujiu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Speed ◽  
Analog Circuit ◽  
Optimal Solution ◽  
Intelligent Algorithm ◽  
The Neural Network ◽  
Filter Unit ◽  
Band Sequence ◽  
Neural Network Input

This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well as the ability of fault tolerance to build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit for fault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize the initial weights, threshold, expansion factor and translation factor of WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of using analog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with a combination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.

scholarly journals Prediction of Corrosion Inhibitor Efficiency of Some Aromatic Hydrazides and Schiff Bases Compounds by Using Artificial Neural Network

2009 ◽  
Vol 2 (1) ◽  
pp. 108-113
Author(s):  
Hanan A. Al-Hazam
Keyword(s):  
Neural Network ◽  
Corrosion Inhibitor ◽  
Molecular Orbital ◽  
Schiff Bases ◽  
Experimental Tests ◽  
Molecular Volume ◽  
Inhibitor Efficiency ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Input

Artificial neural networks are used for evaluating the corrosion inhibitor efficiency of some aromatic hydrazides and Schiff bases compounds. The nodes of neural network input layer represent the quantum parameters, total negative charge (TNC) on molecule, energy of highest occupied molecular orbital (E Homo), energy of lowest unoccupied molecular orbital (E Lomo), dipole moment (μ), total energy (TE), molecular volume (V), dipolar-polarizability factor (Π) and inhibitor  concentration (C). The neural network output is the corrosion inhibitor efficiency (E) for the mentioned compounds. The training and testing of the developed network are based on a database of 31 published experimental tests obtained by weight loss. The neural network predictions for corrosion inhibitor efficiency are more reliable than prediction using other conventional theoretical methods such as AM1, PM3, Mindo, and Mindo-3. Key words: Neural network; Corrosion inhibitor efficiency. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reservedDOI: 10.3329/jsr.v2i1.2757                 J. Sci. Res. 2 (1), 108-113  (2010) 

Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network

2012 ◽  
Vol 614-615 ◽  
pp. 1303-1306 ◽  
Author(s):  
Hui Da Duan ◽  
Xin Yao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Power Transformers ◽  
The Neural Network ◽  
Gradient Descent Algorithm ◽  
Neural Network Input

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. Improved three-ratio is an effective method for transformer fault diagnosis used in recent years. This paper applies appropriate Artificial Neural Networks (ANN) to resolve the online fault diagnosis problems for oil-filled power transformer based on improved three-ratio. Because of the characteristic of improved three-ratio boundary is too absolute, a method using fuzzy math theory to deal with the data of the neural network input is also proposed. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure. In addition, to improve the convergence speed, an improved gradient descent algorithm is used in training RBFNN. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed method.

scholarly journals Complex Event Processing of Market Data through Data Modeling in Big Data

2019 ◽  
Vol 8 (5S3) ◽  
pp. 11-17
Keyword(s):  
Big Data ◽  
Stock Exchange ◽  
Mathematical Finance ◽  
Complex Event Processing ◽  
Data Driven ◽  
Event Processing ◽  
Data Sets ◽  
Market Data ◽  
Complex Events ◽  
The Right

Mathematical Finance utilizes advance refined mathematic models and advanced computer techniques to forecast the movement of worldwide markets. To possess an ability to react intelligently to the fast-paced changes in the business is a winning factor. Complex event processing with advanced toolchains plays a crucial role in the explosive growth and diversified forms of market data. To resolve such issues, we have developed a model based on Big Data that processes the intricate tasks to assess the market data. The model executes complex events in a data-driven mode in parallel computing on copious data sets, this model is known as StatCloud. To implement StatCloud, we have used datasets from the Bombay Stock Exchange to determine the performance. We execute the model with the help of Data analysis techniques and Data Modelling. The experiment results show that this model obtains high throughput and latency. It executes data dependent tasks through a data-driven strategy and implements a standard style approach for developing Mathematical Finance analysis models. This integrated model facilitates the work process of complex events in a financial organization to enhance the efficiency to implement the right strategies by the financial engineers.

scholarly journals Design and Implementation of SMS Based Anomalous Event Mitigation Process for Complex Event Processing Application

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Mohamad Hanif Md Saad
Keyword(s):  
Monitoring System ◽  
Lower Order ◽  
Complex Event Processing ◽  
Event Processing ◽  
Processing Application ◽  
Design And Implementation ◽  
Anomalous Event ◽  
Complex Events ◽  
Mitigation Action

<p class="TTPAbstract">This paper describes the design and implementation of SMS based event mitigation for Complex Event Processing (CEP) application. The CAISER<sup>TM</sup>'s CEP platform were used to develop event processing systems which detects and identifies complex events based on patterns of previous and current lower order events. CAISER<sup>TM</sup> then generates mitigation action for anomalous events and executes them via 3 types of SMS based notification. An implementation of the SMS based event mitigation in a CEP based Server Farm Monitoring system is also described in this paper. The performance of the event mitigation process using SMS is evaluated and described in this paper.</p>

Sign in / Sign up

Export Citation Format

Share Document