Kumar A.,Sambalpur University |
Panda S.,Dhaneswar Rath Institute of Engineering and Management Studies |
Pani S.K.,OEC |
Panda A.,Texas A&M University
2014 IEEE 8th International Conference on Intelligent Systems and Control: Green Challenges and Smart Solutions, ISCO 2014 - Proceedings | Year: 2014
Fault-tolerant scheduling of real-time (RT) tasks in multiprocessor environment is essentially a NP-hard problem. This basically involves allocating a set of tasks to a set of processors so as to minimize the makespan and ensure tasks to meet their timing constraints. Many traditional heuristic approaches, such as earliest deadline first (EDF) and least laxity first (LLF) have been adopted to find optimal solution to this scheduling problem. However, conventional approach to achieve fault-tolerance (FT) in scheduling RT tasks based on traditional heuristic approach suffers from poor performance and results in inefficient processor utilization. Nature-inspired heuristic algorithms are gaining increased acceptance among researcher for solving real world NP-hard combinatorial optimization problems. This paper presents a comparative study of the novel primary-backup (PB) based fault-tolerant scheduling (PBFTS) technique for RT tasks in multiprocessor environment using two popular nature-inspired heuristic algorithms: the Ant Colony Optimization (ACO) and the Genetic Algorithm (GA). Exhaustive simulation reveals that the PBFTS algorithm based on GA and ACO both outperform the traditional PBFTS schemes in terms of performance, system utilization and efficiency. However, the comparative study also shows that the ACO based scheme surpasses the GA based scheme in terms of speed of execution whereas GA based scheme displays superior convergence with respect to ACO counterpart. © 2014 IEEE.
Sahoo S.,KIIT University |
Mohanty S.,OUAT |
Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014 | Year: 2014
This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: θ, low- β, and high- β. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before improvement of the input vector. © 2014 IEEE.
Buttgereit F.,Charité - Medical University of Berlin |
Mehta D.,Center for Arthritis and Osteoporosis |
Kirwan J.,University of Bristol |
Szechinski J.,Katedra i Klinika Reumatologii i Chorob Wewnetrznych AM |
And 7 more authors.
Annals of the Rheumatic Diseases | Year: 2013
Objective: To assess the efficacy and safety of low-dose prednisone chronotherapy using a new modified-release (MR) formulation for the treatment of rheumatoid arthritis (RA). Methods: In this 12-week, double-blind, placebo-controlled study, patients with active RA (n=350) were randomised 2:1 to receive MR prednisone 5 mg or placebo once daily in the evening in addition to their existing RA disease-modifying antirheumatic drug (DMARD) treatment. The primary end point was the percentage of patients achieving a 20% improvement in RA signs and symptoms according to American College of Rheumatology criteria (ie, an ACR20 response) at week 12. Changes in morning pain, duration of morning stiffness, 28-joint Disease Activity Score and health-related quality of life were also assessed. Results: MR prednisone plus DMARD treatment produced higher response rates for ACR20 (48% vs 29%, p<0.001) and ACR50 (22% vs 10%, p<0.006) and a greater median relative reduction from baseline in morning stiffness (55% vs 35%, p<0.002) at week 12 than placebo plus DMARD treatment. Significantly greater reductions in severity of RA (Disease Activity Score 28) (p<0.001) and fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue score) (p=0.003) as well as a greater improvement in physical function (36-item Short-Form Health Survey score) (p<0.001) were seen at week 12 for MR prednisone versus placebo. The incidence of adverse events was similar for MR prednisone (43%) and placebo (49%). Conclusion: Low-dose MR prednisone added to existing DMARD treatment produced rapid and relevant improvements in RA signs and symptoms.
Mohanty A.,Templecity Institute of Technology and Engineering |
Mohanty P.P.,OSME Keonjhar |
International Review on Modelling and Simulations | Year: 2013
The paper presents a comparison study of Transient Stability and Reactive Power Compensation Issues in a Wind Diesel Hybrid System with different FACTS Controllers. A Small signal model of the Hybrid System is taken with the use of Fuzzy Logic based PI Controller to compensate the Reactive power generated in an Isolated Wind Diesel hybrid system. Detailed analysis of the system is undertaken with varying loading conditions. Linearised small signal models of SVC, STATCOM and UPFC are taken to study the transient stability analysis of the proposed system with IEEE type 1 Excitation System. A Self tuned Fuzzy PI Controller is implemented to tune the parameters of KP and Ki of the Hybrid System which undergoes through Voltage Instability due to sudden change in load. Simulation result shows that the proposed controller attains steady state value with less time. © 2013 Praise Worthy Prize S.r.l. -All rights reserved.
Girija D.K.,University Shillog |
Shashidhara M.S.,OEC |
Giri M.,SITAMS Chittoor andhra Pradesh
Proceedings - 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013 | Year: 2013
Uterus fibroid diagnosing could be a advanced task which needs a lot of expertise and knowledge. Diagnosing of uterine fibroids is formed by bimanual pelic examination. Traditional method of predicting fibroid disease is doctor's examination or variety of medical tests like, MRI, Ultrasound scan, Biopsy etc., Nowadays, health care trade contains large quantity of health care knowledge that contains hidden data. This hidden information is beneficial for creating effective choices. Computer primarily based data together with advanced data mining techniques are used for acceptable results. Neural network is wide algorithm used for predicting fibroid disease diagnosing. During this analysis paper, a Fibroid Disease Prediction System (FDPS) is developed exploitation neural network. The FDPS system predicts the likelihood of patient obtaining a fibroid disease. For prediction, the system uses age, heavy bleeding, status of marriage or single, pelvic pain, etc., 10 medical parameters are used. From the result, it's been seen that the neural network predict fibroid disease with nearly 98% accuracy. © 2013 IEEE.
Panda S.,VSSUT |
Baliarsingh A.K.,OEC |
Mahapatra S.,KIIT University |
Swain S.C.,KIIT University
Mechanical Systems and Signal Processing | Year: 2015
This paper proposes to use gravitational search algorithm (GSA) to design a supplementary sub-synchronous damping controller for the static synchronous series compensator (SSSC) device to damp the torsional oscillations. The IEEE second benchmark model (SBM) which is a series compensated ac system is considered in this study for design and analysis purpose. The design problem is formulated as an optimization problem and GSA is employed to search for the optimal controller parameters. The dynamic performance of the system under study is evaluated at various levels of series compensation with different types of disturbances. Fast Fourier Transform (FFT) analysis and robustness analysis against operating point changes and system uncertainties are also investigated. For specific system studied in the paper, the superiority of the GSA optimization technique over genetic algorithm (GA) optimization technique is also shown by comparing the simulation results and various performance indexes. © 2015 Elsevier Ltd.
Chakravarty S.,OEC |
2015 IEEE Power, Communication and Information Technology Conference, PCITC 2015 - Proceedings | Year: 2015
Classification plays an important role in various fields such as science, engineering, medicine and business. This paper proposes a cuckoo search based hybrid model i.e. Functional Link Neural Fuzzy Network named as CSFLNFN for classification of multi-class datasets. Both FLANN, as an efficient computational technique and fuzzy logic, as a basis of much inference system are combined to take the advantages from both the techniques. These two techniques are supplementary to each other in a way that one is helping other to overcome their limitations. The proposed CSFLNFN model uses FLANN to the consequent part of the fuzzy rules. The parameters of the models are optimized by the evolutionary algorithm, Cuckoo Search (CS). The CSFLNFN model is evaluated with one medical dataset, dermatology and three other frequently used multi-class datasets, wine, glass and iris. Further, to get more classification accuracy, Principal Component Analysis (PCA) has been used to extract the features from the datasets. Performance of the model is measured by number of measures like confusion matrix, accuracy, sensitivity, specificity, F-score, gmean and area under the receiver operating characteristic (ROC) curve. In this study, a comparison has been made between results before and after features extraction and it is seen that the classification accuracy increases with extracted features from the datasets. However the results demonstrate the superiority of the CSFLNFN compare to other models including CSMLP, CSFLANN, Naïve Bayesian and K-Nearest Neighbor irrespective of the feature extraction. © 2015 IEEE.
Lyu G.,OEC |
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015
Adaptive Gaussian Chirplet Decomposition (AGCD) is a time-frequency signal decomposition algorithm with high resolution. The Gaussian chirplet basis adopted has variable time width, frequency center with linear chirp, which has both good time and frequency energy localization. But this basis is not orthogonal, and the computation in searching basises when decomposing a signal is very huge. AGCD can reduce computation by convert the optimization process to a traditional curve-fitting problem. But the performance of the AGCD is highly dependent on the initial selection. Traditional energy based initial selection fails in some cases when two or more basis has deep cross. The proposed maximum matching based initial selection is a fast and accurate basis searching algorithm, which choose the best correlated basis each time within several candidates. Simulation results show that the new algorithm is much more stable and accurate than the energy based one without increasing computation. © 2015 SPIE.
Hu W.L.,OEC |
Yang R.S.,OEC |
Applied Mechanics and Materials | Year: 2014
In order to solve the problem of electricity from the grid, designing a set of independentpower supply system based on micro-network technology, through the use of solar, wind, diesel,battery, etc. This paper introduces the structure and working principle of the system, focuses on thedesign of the control system to meet under no circumstances, intelligent control of the entire system tomake it stable operation, and optimization method proposed system configuration. © (2014) Trans Tech Publications, Switzerland.
News Article | December 6, 2016
OEConnection LLC (OEC), the parts ecommerce technology leader for original equipment manufacturers’ (OEM) distribution networks, announced today that Hyundai Motor America has added CollisionLink to its Hyundai Go Genuine Collision Conquest program. The CollisionLink parts ordering and fulfillment solution will allow Hyundai’s network of dealers to access competitive pricing on eligible OE parts and expand their market penetration in the U.S. By the end of the first quarter, 2017, Hyundai dealers will have access to the Hyundai Go Genuine Collision Conquest program via CollisionLink. “We are excited to bring Hyundai on board and to assist with the Hyundai Go Genuine Collision Conquest program,” said Bill Lopez, OEC Vice President & General Manager, Collision. “With our CollisionLink solution, Hyundai dealers can compete more effectively and increase their OE part sales while also improving order processing efficiency and customer satisfaction.” Hyundai represents the twenty-second automotive dealer network in North America to use CollisionLink to facilitate their parts marketing program. With the addition of Hyundai, 99% of all consumer vehicles on the road in the U.S. are now supported by CollisionLink, and OEM parts marketing programs managed through CollisionLink now cover 3 out of every 4 U.S. consumer vehicles on the road. “CollisionLink is the missing puzzle piece. It will give us more complete coverage in the market,” said Frank Ferrara, Executive Vice President, Customer Satisfaction, Hyundai Motor America. “It will allow our dealers to offer their customers more competitive pricing and sell more Hyundai OE parts.” OEConnection (OEC) is the leading parts ecommerce technology provider for OEM distribution networks, serving over 20 OEMs and 100,000 dealership and repair customers. Customers use OEC solutions millions of times each month to access real-time, dynamic pricing and to market, manage and move original equipment parts, facilitating an estimated $20 billion in annual replacement parts trade. The company is headquartered in the greater Cleveland area at 4205 Highlander Parkway, Richfield, Ohio, 44286. Additional information is available at http://www.oeconnection.com or by e-mailing Geo Money at Geo(dot)Money(at)oeconnection(dot)com.