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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Application of ANN and ANFIS for detection of brain tumors in MRIs by using DWT and GLCM texture analysis
摘要： In this work we combine different methodologies in order to develop algorithms for Computer-Aided Diagnosis (CAD) for brain tumors from the axial plane (T2 MRI). All methods utilize texture analysis by extracting features from raw data, without post-processing, based on different techniques, such as Gray Level Co-Occurrence Matrix (GLCM), or Discrete Wavelet Transform (DWT) and different classification methods, based on ANN or ANFIS. All of our proposed methodologies are developed, validated and verified on various sub data including 65% non-healthy MRIS. The total used database consists of 202 MRIs from non-healthy patients and 18 from healthy, segmented visually by an experienced neurosurgeon. Combining different subsets of features, our best results are by using 4 GLCM features for a 4 input and two hidden layers ANN, giving sensitivity 100%, specificity 77.8% accuracy 94.3%. It is proved that the input data to train such a CAD are considered to be unbiased if the ratio between healthy/un-healthy tissue MRIs is about 35%/65%, respectively.
关键词： MRI tumor CAD diagnosis,DWT,ANFIS,GLCM,ANN
更新于2025-09-23 15:23:52
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Self Optimization Beam-Forming Null Control Based SINR Improvement
摘要： In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3 dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.
关键词： self optimization beamforming null control,adaptive neural fuzzy inference system (ANFIS),SINR improvement,digital beam-forming (DBF)
更新于2025-09-23 15:22:29
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Investigation on Solar PV generation and design of switched reluctance motor for Smart Agriculture actuation system
摘要： This paper presents standalone solar photovoltaic (PV) powered fed actuation system employing a switched reluctance motor (SRM) particularly used in remote and rural areas. The converter efficiency is achieved by changing ON and OFF state of solar PV drive. An electronic commutation drives SRM drive with achieved by position hall sensor and encoder. The modified boost converter is proposed in single stage to conversion of PV fed power and inverter with reduced switching losses. Further proposed system is designed to reduce cost of system using simple design and control. This paper also proposes the speed control strategy of SRM motor with an artificial intelligent based Adaptive neuro fuzzy inference (ANFIS) system to achieve desired motor velocity as stated in reference velocity in farm lands. The system proposed is subjected to analysis the performance of drive and controller in both load and no-load conditions. Initially, a simulation model is modeled in MATLAB-SIMULINK with corresponding environments. The experimental setup for proposed system is developed using FPGA based SPEEDGOAT real time target machine. The simulation and hardware results suggest feasibility of proposed system in real time.
关键词： SRM drive,SPEED GOAT,MATLAB-SIMULINK,actuation,ANFIS,PV,Modified boost converter
更新于2025-09-23 15:22:29
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Evaluation of electrical efficiency of photovoltaic thermal solar collector
摘要： In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
关键词： hybrid machine learning model,Renewable energy,photovoltaic-thermal (PV/T),least square support vector machine (LSSVM),adaptive neuro-fuzzy inference system (ANFIS),neural networks (NNs)
更新于2025-09-23 15:21:01
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[IEEE 2019 International Conference on Sustainable Information Engineering and Technology (SIET) - Lombok, Indonesia (2019.9.28-2019.9.30)] 2019 International Conference on Sustainable Information Engineering and Technology (SIET) - ANFIS Design Based on Prediction Models for The Photovoltaic System
摘要： Photovoltaic system has uncertain output in generating electrical energy, as it is intensely influenced by different weather condition. This modeling system applies an Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to gain data of power prediction, voltage, current, and temperature. The mathematical representation of the photovoltaic using Matlab/Simulink setting has been developed and presented by using the photovoltaic basic solar irradiation effect and temperature changes. This model is divided into two systems run by ANFIS; ANFIS 1 and ANFIS 2. The design of ANFIS is expected to update its parameter to determine errors between output and target. MAPE (Mean Absolute Percentage Error) value for ANFIS 1 test of open circuit output voltage was 0.0104. This MAPE score is found to be excellent predictive data with less than 10% MAPE value. For the ANFIS 2 test, the AC output voltage was 0.026%, output current of 1.3035%, and 0.0046% of frequency. Based on the MAPE scores, very suitable data prediction has been produced with less than 10% MAPE value. Briefly, this study reveals that the ANFIS technique yields load prediction results that can improve the accuracy and rapidness of prediction as well as very minimum errors.
关键词： MAPE,prediction,photovoltaic,ANFIS,design
更新于2025-09-23 15:21:01
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Comparative Analysis of Intelligent Controller Based MPPT for Photovoltaic System with Super Lift Boost Converter
摘要： In recent years, the electrical energy demand increases gradually and the power generation does not meet the demand due to lack of fossil fuel and environmental issues. The only solution is to use renewable energy sources for generating electricity and meet the consumers demand. In this paper, photovoltaic power system analyses their performance under various weather conditions. The objective of this paper is comparing the different intelligent controllers such as Fuzzy, ANFIS and Hybrid Fuzzy & Firefly Algorithm (HFFA) for Maximum Power Point Tracking (MPPT) of 100 Watts PV system using a Super Lift Boost Converter (SLBC). The proposed intelligent controller is designed and simulated in MATLAB environment under various weather conditions. The simulation results have been analyzed and the performance of the proposed model evaluated with changing irradiation conditions. Finally, the performance of Hybrid Fuzzy and firefly based MPPT has been suggested as the optimum controller for the photovoltaic system.
关键词： photovoltaic,ANFIS,Fuzzy Logic,MPPT,MATLAB,HFFA,super-lift boost converter
更新于2025-09-23 15:21:01
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ICM based ANFIS MPPT controller for grid connected photovoltaic system
摘要： In this paper, grid-connected photovoltaic (PV) system is presented. PV system consists of a photovoltaic module, a boost converter, and voltage source inverter. ANFIS based ICM (Incremental Conductance Method) MPPT (Maximum Power Point Tracking) controller is utilized to produce gate signal for DC-DC boost converter. This controller is used for optimizing the total performance of the Photovoltaic system in turn the errors were reduced in Voltage Source Inverter (VSI). The grid-connected PV system performance is evaluated and harmonics occurred in the system are decreased. The proposed methodology is implemented in MATLAB/Simulink.
关键词： Photovoltaic (PV),Boost Converter,Incremental Conductance Method (ICM),ANFIS Controller,Voltage Source Inverter (VSI)
更新于2025-09-23 15:19:57
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ANFIS Based Voltage Determination for Photovoltaic Systems According to the Specific Cell Parameters, and a Simulation for the Non-Isolated High Gain DCa??DC Boost Converter Control Regard to Voltage Fluctuations
摘要： In this study, a solar panel is modeled and parametric simulation studies are performed with Ansys-Electronics 2019 R2 software according to atmospheric and temperature coefficient parameters that affect the efficiency of electrical energy produced in photovoltaic (PV) systems. Based on parametric simulation studies, 482 data of the voltage value of the electrical energy produced were obtained. Then a certain part of this data was used for training in Adaptive Network Fuzzy Inference System (ANFIS) algorithms and the system was tested with the estimated values produced. Thus, in the generation of solar and electrical energy, the duty ratio controller performance designed with an uninsulated DC–DC converter circuit proposed to generate a 400 V constant value DC bus voltage at the output due to voltage changes at the input is reported.
关键词： Non-Isolated High Gain converter,photovoltaic (PV) systems,parametric simulation,Adaptive Network Fuzzy Inference System (ANFIS)
更新于2025-09-23 15:19:57
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An Analytical Approach to study Annealing Induced Interdiffusion of In and Ga for Truncated Pyramidal InAs/GaAs Quantum Dots
摘要： The brain–computer interface (BCI) identi?es brain patterns to translate thoughts into action. The identi?cation relies on the performance of the classi?er. In this paper, identi?cation and monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an ef?cient adaptive neuro-fuzzy classi?er (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classi?cation accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and ?re the fuzzy rule. The performance of the proposed classi?er, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classi?er not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classi?er is validated through Friedman test. Classi?cation result is used to control switching of light emitting diode, turning thoughts into action.
关键词： linguistic hedges,Neuro-fuzzy classi?er,ANFIS,SSCG,SCG,Jaya,BCI
更新于2025-09-19 17:13:59
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Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques
摘要： Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.
关键词： Laser drilling,ANFIS,Genetic programming,Stainless steel,Artificial intelligence,Ti6Al4V,Surface cracks
更新于2025-09-12 10:27:22