Performance Comparison of Fractional Controllers and Artificial Intelligence Techniques

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

Download Full-text

Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

Journal of Physics Conference Series ◽

10.1088/1742-6596/2070/1/012141 ◽

2021 ◽

Vol 2070 (1) ◽

pp. 012141

Author(s):

Pavan Sharma ◽

Hemant Amhia ◽

Sunil Datt Sharma

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Manufacturing Industry ◽

Rolling Bearing ◽

Performance Comparison ◽

Learning Methods ◽

Training Time ◽

Bearing Faults ◽

Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

Download Full-text

A Combination Model of Multiple Artificial Intelligence Techniques Based on Genetic Algorithms for Investment Decision Support Aid : An Application to KOSPI

The e-Business Studies ◽

10.15719/geba.10.1.200903.215 ◽

2009 ◽

Vol 10 (1) ◽

pp. 215-236 ◽

Cited By ~ 2

Author(s):

안현철 ◽

Hyoung-yong Lee

Keyword(s):

Artificial Intelligence ◽

Genetic Algorithms ◽

Decision Support ◽

Investment Decision ◽

Artificial Intelligence Techniques ◽

Combination Model

Download Full-text

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i5.669 ◽

2018 ◽

Vol 8 (5) ◽

pp. 80

Author(s):

Juveriya Afreen

Keyword(s):

Artificial Intelligence ◽

Cyber Security ◽

Data Security ◽

Human Reasoning ◽

Cyber Crime ◽

Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

Download Full-text

Interpretation of the impact parameter of acid-base status and electrolytes in blood gas analysis in the pulmonary system using artificial intelligence techniques

Computer Science and Systems Engineering ◽

10.2495/csse140691 ◽

2015 ◽

Author(s):

E. Beganović ◽

Z. Avdagić ◽

S. Omanović

Keyword(s):

Artificial Intelligence ◽

Impact Parameter ◽

Blood Gas Analysis ◽

Gas Analysis ◽

Blood Gas ◽

Acid Base ◽

Artificial Intelligence Techniques ◽

Pulmonary System ◽

Acid Base Status ◽

The Impact

Download Full-text

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v2i1.916 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Siti Nurhena ◽

Nelly Astuti Hasibuan ◽

Kurnia Ulfa

Keyword(s):

Artificial Intelligence ◽

Immune System ◽

Expert System ◽

Expert Systems ◽

Dengue Hemorrhagic Fever ◽

Hemorrhagic Fever ◽

Visual Basic ◽

Artificial Intelligence Techniques ◽

Individual Needs ◽

Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

Download Full-text