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Optimizing pulsed current micro plasma arc welding parameters to maximize ultimate tensile strength of titanium (Ti-6Al-4V) alloy using dragon fly algorithm

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Materials Today: Proceedings 2020年 27卷 2086-2090页

作者： A. Sunny Kumar T.V. Hanumantha Rao V.V.S. Kesava Rao R.T. RamaKanth Department of IE GITAM University Visakhapatnam India Department of Mechanical Engineering Anil Neerukonda Institute of Technology & Sciences Visakhapatnam India Department of Mechanical Engineering Andhra University Visakhapatnam India Department of Mechanical Engineering Avanti Engineering College Narsipatnam India

This paper discusses the development of a mathematical model to optimize the input parameters while welding titanium alloys. In the aerospace industry, Ti-6Al-4V is mostly preferred for high-temperature applications especially in cold section of jet engines. Micro plasma arc welding is used to weld the joints. The parameters such as peak current, base current, pulse rate, and pulse width and output response like ultimate tensile strength are considered in the experimentation. The experimental design with four factors and five levels is selected. To analyze the influence of the process parameters Taguchi L 25 orthogonal matrix is successfully used. Results were evaluated by means of the analysis of variance (ANOVA) which indicated that the study is carried out at 95% confidence level. dragonfly algorithm is employed to optimize the developed mathematical model for maximum ultimate tensile strength and the same is compared with multi-response optimization technique via Grey relational analysis (GRA).

关键词： Titanium Micro plasma arc welding Grey relation analysis dragon fly algorithm Pulsed current

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Effective Microgrid Cost Reduction using dragon fly Optimization algorithm and Firefly algorithm

Effective Microgrid Cost Reduction using Dragon Fly Optimiza...

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5th International Conference on Computing, Communication and Security (ICCCS)

作者： Kumari, Kavitha K. S. Babu, R. Samuel Rajesh Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Instrumentat Engn Chennai 600119 Tamil Nadu India

ISBN: (纸本)9781728191805

Introducing distributed generation (DG) into distribution networks provide reliable power to the consumers. The dispatchable and non-dispatchable unit usually placed nearby distribution networks operates in medium/low voltage system. The dragonfly and firefly algorithm are used for micro grid optimal scheduling both in grid connected mode and island mode. Dispatchable and non-Dispatchable units in the micro grid are connected to the conventional grid to supply energy to the end user. The fire fly algorithm has a drawbacks to find the global minima and also they were not able to provide a proper balance between exploration and exploitation. The drawback of DA it does not give accurate optimal solution for multi-objective problem and it was not used for short term wind power prediction. To find the minimum operating cost of micro grid the protype is developed for dragonfly and firefly algorithm. Comparative performance analyses are done with the results of DA and firefly algorithm. Here the dragon fly algorithm outperforms firefly algorithm by evaluating with power demand and operating cost of micro grid

关键词： Distributed energy resource islanded operation dragon fly algorithm firefly algorithm

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MDEEPFIC: Food item classification with calorie calculation using modified dragonfly deep learning network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期45卷 3137-3148页

作者： Shermila, P. Josephin Ahilan, A. Malar, A. Jasmine Gnana Jothin, R. RMK Coll Engn & Technol Dept Artificial Intelligence & Data Sci Thiruvallur Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Commun Engn Tirunelveli Tamil Nadu India PSN Coll Engn & Technol Dept Elect Elect & Engn Tirunelveli Tamil Nadu India PSN Coll Engn & Technol Electron & Commun Engn Tirunelveli Tamil Nadu India

Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively.

关键词： Food bidirectional long short-term memory improved watershed recurrent neural network dragon fly algorithm

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Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends

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RENEWABLE ENERGY 2022年第0期198卷 549-567页

作者： Viswanathan, Vinoth Kannan Kaladgi, Abdul Razak Thomai, Pushparaj Agbulut, Umit Alwetaishi, Mamdooh Said, Zafar Shaik, Saboor Afzal, Asif Parisutham Inst Technol & Sci Thanjavur Tamil Nadu India PA Coll Engn Dept Mech Engn Mangaluru 574153 India Visvesvaraya Technol Univ Belagavi India Kings Coll Engn Dept Mech Engn Pudukkottai Tamil Nadu India Duzce Univ Fac Engn Dept Mech Engn TR-81620 Duzce Turkey Taif Univ Coll Engn Dept Civil Engn POB 11099 Taif 21944 Saudi Arabia Univ Sharjah Sustainable & Renewable Energy Engn Dept POB 27272 Sharjah 27272 U Arab Emirates Vellore Inst Technol Sch Mech Engn Vellore 632014 Tamil Nadu India Chandigarh Univ Univ Ctr Res & Dev Dept Comp Sci & Engn Mohali Punjab India Glocal Univ Sch Technol Dept Mech Engn Delhi Yamunotri MargSH-57 Mirzapur Pole 247121 Uttar Pradesh India

Different meta-heuristic optimization algorithms have been used in a variety of fields due to their intelligent behavior and fast convergence. However, use of these algorithms in the engine behavior optimization is very-limited. The development of so-called hybrid optimization technique when these algorithms are combined with experimental design technique is an upcoming method in the field of renewable energy. Hence in this research, meta-heuristic optimization algorithms and experimental design methods were combined to optimize the engine behavior. Additionally, artificial neural networks (ANN) were employed to forecast the performance and emission behaviors of a CI engine running on a novel hybrid biodiesel blend of Cucurbita pepo. L (pumpkin) and Prosopis juliflora, mixed with a novel Elaeocarpus ganitrus (Rudraksha) additive. To assess the success of the ANN, four statistical benchmarks (R-2, and MSE) were used. Experiments were designed according to Design of Experiments (DOE) rules with performance and emission parameters as outputs. Response surface methodology (RSM) was employed to find the effect of interaction factors. Single objective and multi-objective optimization using highly efficient hybrid RSM-particle swarm optimization (RPSO) and dragon fly algorithm (RMODA) were employed to optimize the response of the obtained RSM equations. The outcomes demonstrated that RSM and ANN were excellent modelling techniques for these kinds of situations, with good accuracy. In addition, ANN's prediction performance (R-2 = 0.978 for BTE) was somewhat better than RSM's (R-2 = 0.960 for BTE). On the other hand, the PJB20 blend with 5 mL additive increased BTE by 52.8% and reduced BSFC by 34.9% at maximum load. The smoke opacity was lowered by 7.1% when compared to pure diesel, without any engine modifications. CO2 emission was seen to be shortened by 19.14%. Finally, it can be concluded that this novel biodiesel can be possibly utilized in CI engines with no modificati

关键词： Pumpkin Prosopis juliflora dragon fly algorithm Rudraksha Particle swarm optimization

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Day ahead dynamic available transfer capability evaluation incorporating probabilistic transmission capacity margins in presence of wind generators

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第1期31卷 p1-21页

作者： Karuppasamypandiyan, M. Jeyanthy, P. Aruna Devaraj, D. Selvi, V. Agnes Idhaya Kalasalingam Acad Res & Educ Elect & Elect Dept Krishnankoil Tamil Nadu India Kalasalingam Acad Res & Educ Sch Elect & Elect Technol Krishnankoil India

Assessment of day ahead Available Transfer Capability (ATC) is a most vital task in deregulated power system. The main purpose of the ATC determination is to ensure the secure power transaction between the interfaces. In recent times, with the high penetration of renewable energy sources into conventional grid, it is essential to study its impact on ATC. Due to stochastic nature of the wind speed, the wind power output in the power generation mix can bring uncertainties in the ATC calculation. So, it is important to predict the wind speed correctly to obtain accurate day ahead ATC. Artificial Neural Network (ANN) is used to predict the future wind speed based on time series data. Using historical wind speed data, the ANN is developed. The impact of different wind generators such as constant speed wind generator operated based on Squirrel cage induction generator and variable wind generator operated based on Doubly fed induction generator on Dynamic ATC (DATC) is also analyzed. The dynamic voltage stability namely Hopf bifurcation point is considered as a limit for DATC calculation. The two-reserve margins namely Transmission Reliability Margin (TRM) and Capacity Benefit Margin (CBM) play a major role in accurate DATC estimation. The TRM and CBM are calculated based on probabilistic approach and its impact on DATC also analyzed. A dragon fly algorithm (DFA) is applied to obtain the accurate dynamic voltage stability point for DATC evaluation. The proposed model and algorithm are tested and validated on New England 39 and South Indian 181 bus system.

关键词： capacity benefit margin dragon fly algorithm dynamic available transfer capability transmission reliability margin voltage stability wind generators

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Eagle arithmetic optimization algorithm for renewable energy-based load frequency stabilization of power systems

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 40卷

作者： Tang, Ligang Kong, Tong Innab, Nisreen Hubei Polytech Univ Sch Elect & Elect Informat Engn Huangshi 435003 Peoples R China Daye Nonferrous Design & Res Inst Co Ltd Huangshi 435005 Peoples R China AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh Saudi Arabia

Power systems' efficient management and planning are crucial in renewable energy-based systems. As the global electricity demand continues to rise, there is a growing need for alternative energy sources such as solar, wind, and hydropower. Consequently, numerous research studies have focused on maintaining load balancing within the renewable energy system and improving the forecasting of renewable energy resources. This paper presents the Eagle Arithmetic Optimization algorithm (EAOA) as a novel approach to address these challenges. By utilizing a fuzzy-based dragonfly optimization algorithm (fuzzy-DFOA), the proposed method enhances the accuracy of load-balancing analysis in renewable energy resources. Through its innovative techniques, the EAOA demonstrates its potential to significantly improve the efficiency and effectiveness of managing renewable energy systems, paving the way for a more sustainable and reliable power grid. The accuracy rate of both wind and solar datasets is given. For the wind dataset, our proposed work got 92.63%, SVR got 75.89%, CNN got 87.54%, and QODA got 83.16%. For the solar dataset presented work of fuzzy-based DFOA got 92.59%, SVR got 69.16%, CNN got 86.25%, and QODA got 82.37%.

关键词： Renewable energy Energy consumption Fuzzy dragon fly algorithm Load Balancing Smart residence energy forecasting

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Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network

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International Journal of Intelligent Computing and Cybernetics 2020年第3期13卷 283-310页

作者： Ambaji S.Jadhav Pushpa B.Patil Sunil Biradar Department of Electrical and Electronics B.L.D.E.A’s V.P.Dr.P.G.Halakatti College of Engineering and Technology(Affiliated to Visvesvaraya Technological University)VijayapurIndia Department of Ophthalmology Shri B.M.Patil Medical College and Research Center(Deemed to be University)VijayapurIndia

Purpose-Diabetic retinopathy(DR)is a central root of blindness all over the *** DR is tough to diagnose in starting stages,and the detection procedure might be time-consuming even for qualified ***,intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various ***,a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark ***/methodology/approach-The proposed DR diagnostic procedure involves four main steps:(1)image pre-processing,(2)blood vessel segmentation,(3)feature extraction,and(4)***,the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization(CLAHE)and average *** the next step,the blood vessel segmentation is carried out using a segmentation process with optimized gray-level *** the blood vessels are extracted,feature extraction is done,using Local Binary Pattern(LBP),Texture Energy Measurement(TEM based on Laws of Texture Energy),and two entropy computations-Shanon’s entropy,and Kapur’s *** collected features are subjected to a classifier called Neural Network(NN)with an optimized training *** the gray-level thresholding and NN is enhanced by the Modified Levy Updated-dragonfly algorithm(MLU-DA),which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the ***,this classification error can correctly prove the efficiency of the proposed DR detection ***-The overall accuracy of the proposed MLU-DA was 16.6%superior to conventional classifiers,and the precision of the developed MLU-DA was 22%better than LM-NN,16.6%better than PSO-NN,GWO-NN,and ***,it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting ***

关键词： Diabetic retinopathy detection Gray-level thresholding Optimal trained neural network dragon fly algorithm Levy update Performance metrics

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Modified fractional order IMC design based drug scheduling for cancer treatment

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 109卷 121-137页

作者： Pachauri, Nikhil Yadav, Jyoti Rani, Asha Singh, Vijander Thapar Inst Engn & Technol Elect & Instrumentat Engn Patiala 147004 Punjab India NSIT Instrumentat & Control Engn Div Azad Hind Fauz Marg New Delhi 110078 India

This paper aims to improve chemotherapy drug(s) scheduling of the drug dose injected into a patient. The existing practice has been established on experiments performed during the drug development process. In general, chemotherapeutic drugs are highly toxic in nature and directly affect the probability of a patient's survival. Therefore, a modified fractional order internal model control (MFOIMC) scheme with a minimal number of design parameters is proposed for effective drug scheduling. The proposed control scheme is a combination of fractional calculus and IMC. The fractional order IMC (FOIMC) is modified by incorporating an extra control loop with a proportional transfer function. Further, IPD and IMC control techniques are also designed for comparative analysis. The dragon fly algorithm (DA) is employed to optimize the parameters of the controller. Results show that the proposed control scheme provides a continuous and precise chemotherapeutic drug dose to the patient on a daily basis. It is also shown that MFOIMC improves IAE by 34% and 32% in comparison to IMC and FOIMC, respectively, which proves its superiority over other controllers.

关键词： Chemotherapy FOIMC Modified FOIMC dragon fly algorithm

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