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S. Sindhu,N. Vijayalakshmi 한국전산응용수학회 2024 Journal of applied mathematics & informatics Vol.42 No.4
Accurately identifying brain tumors is crucial for medical imaging's precise diagnosis and treatment planning. This study presents a novel approach that uses cutting-edge image processing techniques to automatically segment brain tumors. with the use of the Pyramid Network algorithm. This technique accurately and robustly delineates tumor borders in MRI images. Our strategy incorporates special algorithms that efficiently address problems such as tumor heterogeneity and size and shape fluctuations. An assessment using the RESECT Dataset confirms the validity and reliability of the method and yields promising results in terms of accuracy and computing efficiency. This method has a great deal of promise to help physicians accurately identify tumors and assess the efficacy of treatments, which could lead to higher standards of care in the field of neuro-oncology.
Time Domain Based Digital Controller for Buck-Boost Converter
Vijayalakshmi, S,Sree Renga Raja, T 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.5
Design, Simulation and experimental analysis of closed loop time domain based Discrete PWM buck-boost converter are described. To improve the transient response and dynamic stability of the proposed converter, Discrete PID controller is the most preferable one. Discrete controller does not require any precise analytical model of the system to be controlled. The control system of the converter is designed using digital PWM technique. The proposed controller improves the dynamic performance of the buck-boost converter by achieving a robust output voltage against load disturbances, input voltage variations and changes in circuit components. The converter is designed through simulation using MATLAB / Simulink and performance parameters are also measured. The discrete controller is implemented, and design goal is achieved and the same is verified against theoretical calculation using LabVIEW.
A Novel Double Frequency SEPIC Converter with Improved Transient Characteristics and Efficiency
Vijayalakshmi S.,Marimuthu M.,Jayakumar N.,Vighneshwari B. Devi,Paranthagan B.,Rani Nisha C.,Shenbagalakshmi R. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
A novel double frequency SEPIC converter is discussed in this paper. Effi ciency, static and dynamic characteristics of the dc to dc power converters are the concerned factors in power electronics. In order to improve the above factors, switching frequency play a vital role in converters. Hence, include High switching frequency in the converter can improve the dynamic characteristics, but it reduces the effi ciency. In other words, put in low switching frequency in the converter may result in better effi ciency but produces poor dynamic characteristic. In this concern double-frequency SEPIC converter is proposed. This SEPIC dc-dc converter consists of two SEPIC cells: one functions at high switching frequency, and other functions at low switching frequency. The proposed SEPIC converter reveals enhanced steady state and transient characteristic than the other single frequency SEPIC converter and produces high effi ciency also. The results of simulation and hardware prove that the anticipated converter extremely progresses the effi ciency and displays more or less equal the same dynamics as the traditional high frequency SEPIC converter.
Investigations on the structural, optical and electronic properties of Nd doped ZnO thin films
Subramanian, M,Thakur, P,Gautam, S,Chae, K H,Tanemura, M,Hihara, T,Vijayalakshmi, S,Soga, T,Kim, S S,Asokan, K,Jayavel, R Institute of Physics [etc.] 2009 Journal of Physics. D, Applied Physics Vol.42 No.10
<P>We report the synthesis and characterization of Nd doped ZnO thin films grown on Si (1 0 0) substrates by the spray pyrolysis method. The surface morphology of these thin films was investigated by scanning electron microscopy and shows the presence of randomly distributed structures of nanorods. Grazing angle x-ray diffraction studies confirm that the doped Nd ions occupied Zn sites and these samples exhibited a wurtzite hexagonal-like crystal structure similar to that of the parent compound, ZnO. The micro-photoluminescence measurement shows a decrease in the near band edge position with Nd doping in the ZnO matrix due to the impurity levels. The near-edge x-ray absorption fine structure (NEXAFS) measurements at the O K edge clearly exhibit a pre-edge spectral feature which evolves with Nd doping, suggesting incorporation of more charge carriers in the ZnO system and the presence of strong hybridization between O 2p–Nd 5d orbitals. The Nd M<SUB>5</SUB> edge NEXAFS spectra reveal that the Nd ions are in the trivalent state.</P>
Multivariate Congestion Prediction using Stacked LSTM Autoencoder based Bidirectional LSTM Model
Vijayalakshmi B,Thanga Ramya S,Ramar K 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.1
In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.
Sameena Begum,Vijayalakshmi Arelli,Gangagni Rao Anupoju,Sridhar S,Suresh K. Bhargava,Nicky Eshtiaghi 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.90 No.-
Liquid–liquid extraction of volatile fatty acids (VFA) from landfill leachate (LL) as well as syntheticsolution (SS) whose concentration varied from 0.2 to 1 mol/L was investigated. The impact of extractantand feed VFA concentration on extraction efficiency (EE), distribution ratio (KD) and loading ratio (z) withthe help of statistical analysis and process optimization using response surface methodology ispresented. Physical extraction of VFA from SS using seven different diluents was performed to choose thebest diluent. Reactive extraction of VFA was investigated with 10–50% (v/v) trioctylamine (TOA) andtributylphosphate (TBP) as extractants in 1-Octanol. Reactive extraction of VFA results disclosedenhancement of EE due to the synergistic chemical interactions between extractant and diluent. Majorityof the acid extractant complexes formed were 1:1 with TBP while 1:1, 2:1 and 3:1 with TOA as verified byz > 0.5 The optimal extractant concentration for TOA and TBP was found to be 37.8% and 39.09% at a feedconcentration of 0.67 mol/L and 0.81 mol/L for SS respectively to achieve maximum EE of 91% while it was29.3% and 36.2% at 0.2 mol/L for LL to achieve EE of 52% and 57% correspondingly.
New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification
P. Murugeswari,S. Vijayalakshmi 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.4
In the last two decades, neural networks and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network (FNN) system framework infers the union of fuzzy logic and neural network system framework thoughts, which consolidates their advantages. The FNN system is applied in several scientific and engineering areas. Wherever there is uncertainty associated with the data, fuzzy logic places a vital rule. The fuzzy set can effectively represent and handle uncertain information. The main objective of the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either the neural network structures, activation functions, or learning algorithms. In computer vision and intelligent systems, convolutional neural networks (CNNs) have more popular architectures, and their performance is excellent in many applications. In this paper, fuzzy-based CNN image classification methods are analyzed, and an interval type-2 fuzzy-based CNN is proposed. The experimental results indicated that the performance of the proposed method was good.
M. Marimuthu,S. Vijayalakshmi,R. Shenbagalakshmi 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5
This paper presents a high gain non isolated Multilevel Cascaded Boost Converter (MCBC) for Electric vehicle applications. The proposed converter associates the basic cascaded Boost converter along with multilevel boost converter for boosting the voltages generated from diff erent sources like solar energy, fuel cell and Battery. The multilevel boost converter is intended to be utilized as a dc link in which the high gain boosted voltage is provided to the multilevel inverter. By expending a single driven semiconductor switch and an inductor, the proposed converter is capable of producing high voltage gain with continuous input current and much higher step up conversion ratio. It not only allow to operate at much higher frequencies but aids in operating with a minimum duty cycle without a transformer. The proposed converter with fi ve cascaded levels are simulated and is verifi ed experimentally. The results thus illustrated go in concurrence with each other.