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您的检索式：作者名="D.Sharmila"
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| 1 | Integrated supply chain model for deteriorating item with exponential demand and triangular fuzzy on holding costs显示文摘On this paper,we proposed a generalize,incorporated deliver chain model for providers and outlets where delay in bills is obtainable through the suppliers and the shops for consistent deteriorating items.First,we provide the mathematical formulations for the trouble beneath have a look at,and then endorse the solution process to derive the top-quality solution.right here shortages are not allowed.here we projected exponential call for for stores and the suppliers.An goal of this paper is to take a look at the stock modeling through fuzzy environment.right here we use triangular fuzzy range for purchasing the greatest answer.further an efficient algorithm is developed to decide most useful answer.Our technique is illustrated via a few numerical instance to showcase the utility and the overall performance of the proposed method. | D.Sharmila R.Uthayakumar | 2017 | Journal of Control and Decision2017，4，4： | 1 |
| 2 | A Novel Workload-Aware and Optimized Write Cycles in NVRAM显示文摘With the emergence of the Internet of things(IoT),embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare,home automation and mainly Industry 4.0.These Embedded IoT devices are mostly battery-driven.It has been analyzed that usage of Dynamic Random-Access Memory(DRAM)centered core memory is considered the most significant source of high energy utility in Embedded IoT devices.For achieving the low power consumption in these devices,Non-volatile memory(NVM)devices such as Parameter Random Access Memory(PRAM)and Spin-Transfer Torque Magnetic RandomAccess Memory(STT-RAM)are becoming popular among main memory alternatives in embedded IoT devices because of their features such as high thickness,byte addressability,high scalability and low power intake.Additionally,Non-volatile Random-Access Memory(NVRAM)is widely adopted to save the data in the embedded IoT devices.NVM,flash memories have a limited lifetime,so it is mandatory to adopt intelligent optimization in managing the NVRAM-based embedded devices using an intelligent controller while considering the endurance issue.To address this challenge,the paper proposes a powerful,lightweight machine learning-based workload-adaptive write schemes of the NVRAM,which can increase the lifetime and reduce the energy consumption of the processors.The proposed system consists of three phases like Workload Characterization,Intelligent Compression and Memory Allocators.These phases are used for distributing the write-cycles to NVRAM,following the energy-time consumption and number of data bytes.The extensive experimentations are carried out using the IoMT(Internet of Medical things)benchmark in which the different endurance factors such as application delay,energy and write-time factors were evaluated and compared with the different existing algorithms. | J.P.Shri Tharanyaa D.Sharmila R.Saravana Kumar | 2022 | Computers, Materials & Continua2022，，5： | 0 |
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