scholarly journals An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings

Energy ◽  
2020 ◽  
Vol 190 ◽  
pp. 116370 ◽  
Author(s):  
Dac-Khuong Bui ◽  
Tuan Ngoc Nguyen ◽  
Tuan Duc Ngo ◽  
H. Nguyen-Xuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Expert System ◽  
Firefly Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Orbit Shape Automatic Recognition Based on Artificial Neural Network

2005 ◽  
Author(s):  
Dongmei Du ◽  
Qing He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Recognition Performance ◽  
Shape Recognition ◽  
Vibration Monitoring ◽  
Moment Invariants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

2018 ◽  
pp. 760-784
Author(s):  
Ahad Zare Ravasan ◽  
Saeed Rouhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Good Prediction ◽  
Industry Type ◽  
Artificial Neural ◽  
Expected Benefits ◽  
Artificial Neural Network Ann

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

Artificial Neural Network to Assist Psychiatric Diagnosis

1996 ◽  
Vol 169 (1) ◽  
pp. 64-67 ◽  
Author(s):  
Yizhuang Zou ◽  
Yucun Shen ◽  
Liang Shu ◽  
Yufeng Wang ◽  
Feng Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Psychiatric Diagnosis ◽  
Back Propagation ◽  
Composite International Diagnostic Interview ◽  
Diagnostic Interview ◽  
Bp Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

BackgroundArtificial Neural Network (ANN), as a potential powerful classifier, was explored to assist psychiatric diagnosis of the Composite International Diagnostic Interview (CIDI).MethodBoth Back-Propagation (BP) and Kohonen networks were developed to fit psychiatric diagnosis and programmed (using 60 cases) to classify neurosis, schizophrenia and normal people. The programmed networks were cross-tested using another 222 cases. All subjects were randomly selected from two mental hospitals in Beijing.ResultsCompared to ICD-10 diagnosis by psychiatrists, the overall kappa of BP network was 0.94 and that of Kohonen was 0.88 (both P < 0.01). In classifying patients who were difficult to diagnose, the kappa of BP was 0.69 (P < 0.01). ANN-assisted CIDI was compared with expert system assisted CIDI (kappa=0.72–0.76); ANN was more powerful than a traditional expert system.ConclusionANN might be used to improve psychiatric diagnosis.

scholarly journals A Prototype Model to Detect Elephants near the Railway Tracks

2020 ◽  
Vol 63 (1-4) ◽  
pp. 7-9
Author(s):  
Rakesh Kumar Mandal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Prototype Model ◽  
Ann Model ◽  
Railway Tracks ◽  
Research Areas ◽  
Exact Position ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) technologies are becoming very popular. It has been used almost in all the research areas. Approach in this paper is to develop an automated expert system to drive away Elephants found near the railway tracks to stop the casualties of these animals on the railway tracks. In this paper, a prototype model has been designed using Geophone Sensors which recognizes the vibrations of the Elephants roaming near the railway tracks. These vibrations are sent to the nearby servers with the help of Arduino. The server runs software based on the ANN model developed here. It detects the exact position of the Elephants present near the railway tracks and raises an alarm to drive them away.

scholarly journals Artificial neural network based technique for energy management prediction

2020 ◽  
Vol 17 (1) ◽  
pp. 94
Author(s):  
N. Ab. Wahab ◽  
Z. Mat Yasin ◽  
N. A. Salim ◽  
N. F. A. Aziz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Power Consumption ◽  
Energy Management ◽  
Electrical Machine ◽  
Electricity Cost ◽  
Artificial Neural ◽  
Low Efficiency ◽  
Artificial Neural Network Ann

<p>The energy management of electrical machine is significant to ensure efficient power consumption. Mismanagement of energy consumption could give impact on low efficiency of energy consumption that leads to power wastage.  This paper presents analysis of power consumption and electricity costing of the electrical machineries and equipment in High Voltage (HV) and Electrical Machine (EM) Laboratories at Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia. The electrical data are collected using Fluke Meter 1750. Based on the analysis, it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively. For prediction of energy consumption of the two laboratories, Artificial Neural Network (ANN) algorithm is applied as computational tool using feedforward network type. The results show that the ANN is successfully modelled to predict the energy consumption.</p>

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
Sign in / Sign up

Export Citation Format

Share Document