Efficient Design of Scaled Rectangular (Saramki) Window

Background:: In recent years, there has been a high demand for executing digital signal processing and machine learning applications on energy-constrained devices. Squaring is a vital arithmetic operation used in such applications. Hence, improving the energy efficiency of squaring is crucial. Objective:: In this paper, a novel approximation method based on piecewise linear segmentation of the square function is proposed. Methods: Two-segment, four-segment and eight-segment accurate and energy-efficient 32-bit approximate designs for squaring were implemented using this method. The proposed 2-segment approximate squaring hardware showed 12.5% maximum relative error and delivered up to 55.6% energy saving when compared with state-of-the-art approximate multipliers used for squaring. Results: The proposed 4-segment hardware achieved a maximum relative error of 3.13% with up to 46.5% energy saving. Conclusion:: The proposed 8-segment design emerged as the most accurate squaring hardware with a maximum relative error of 0.78%. The comparison also revealed that the 8-segment design is the most efficient design in terms of error-area-delay-power product.

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Efficient design of a successful PROFIBUS project

IET Water Event 2007 ◽

10.1049/ic:20070564 ◽

2007 ◽

Author(s):

M. Cargill

Keyword(s):

Efficient Design

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Ultrasonic Torsion Welding of Aging Resistant Al/CFRP Joints - Concepts, Mechanical and Microstructural Properties

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.742.395 ◽

2017 ◽

Vol 742 ◽

pp. 395-400 ◽

Cited By ~ 1

Author(s):

Florian Staab ◽

Frank Balle ◽

Johannes Born

Keyword(s):

Mechanical Properties ◽

Process Parameters ◽

Materials Science ◽

Dissimilar Materials ◽

Material Design ◽

Ultrasonic Welding ◽

Fatigue Properties ◽

Aviation Industry ◽

Efficient Design ◽

Engineering Structures

Multi-material-design offers high potential for weight saving and optimization of engineering structures but inherits challenges as well, especially robust joining methods and long-term properties of hybrid structures. The application of joining techniques like ultrasonic welding allows a very efficient design of multi-material-components to enable further use of material specific advantages and are superior concerning mechanical properties.The Institute of Materials Science and Engineering of the University of Kaiserslautern (WKK) has a long-time experience on ultrasonic welding of dissimilar materials, for example different kinds of CFRP, light metals, steels or even glasses and ceramics. The mechanical properties are mostly optimized by using ideal process parameters, determined through statistical test planning methods.This gained knowledge is now to be transferred to application in aviation industry in cooperation with CTC GmbH and Airbus Operations GmbH. Therefore aircraft-related materials are joined by ultrasonic welding. The applied process parameters are recorded and analyzed in detail to be interlinked with the resulting mechanical properties of the hybrid joints. Aircraft derived multi-material demonstrators will be designed, manufactured and characterized with respect to their monotonic and fatigue properties as well as their resistance to aging.

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Thermal Efficiency and Economics of a Boil-Off Hydrogen Re-Liquefaction System Considering the Energy Efficiency Design Index for Liquid Hydrogen Carriers

Energies ◽

10.3390/en14154566 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4566

Author(s):

Minsoo Choi ◽

Wongwan Jung ◽

Sanghyuk Lee ◽

Taehwan Joung ◽

Daejun Chang

Keyword(s):

Flow Rate ◽

Life Cycle Cost ◽

Specific Energy Consumption ◽

Liquid Hydrogen ◽

Efficient Design ◽

Fuel Cell Stack ◽

Energy Efficiency Design Index ◽

The Cost ◽

Large Flow

This study analyzes the thermodynamic, economic, and regulatory aspects of boil-off hydrogen (BOH) in liquid hydrogen (LH2) carriers that can be re-liquefied using a proposed re-liquefaction system or used as fuel in a fuel cell stack. Five LH2 carriers sailing between two designated ports are considered in a case study. The specific energy consumption of the proposed re-liquefaction system varies from 8.22 to 10.80 kWh/kg as the re-liquefaction-to-generation fraction (R/G fraction) is varied. The economic evaluation results show that the cost of re-liquefaction decreases as the re-liquefied flow rate increases and converges to 1.5 $/kg at an adequately large flow rate. Three energy efficient design index (EEDI) candidates are proposed to determine feasible R/G fractions: an EEDI equivalent to that of LNG carriers, an EEDI that considers the energy density of LH2, and no EEDI restrictions. The first EEDI candidate is so strict that the majority of the BOH should be used as fuel. In the case of the second EEDI candidate, the permittable R/G fraction is between 25% and 33%. If the EEDI is not applied for LH2 carriers, as in the third candidate, the specific life-cycle cost decreases to 67% compared with the first EEDI regulation.

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An efficient design pattern algorithm for the environmental and hydrologic/hydraulic ubiquitous model developments

Proceedings of the 2009 International Conference on Hybrid Information Technology - ICHIT '09 ◽

10.1145/1644993.1645021 ◽

2009 ◽

Author(s):

Hyung Moo Kim ◽

Jae Soo Yoo

Keyword(s):

Design Pattern ◽

Efficient Design

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Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽

10.3390/en14154649 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4649

Author(s):

İsmail Hakkı ÇAVDAR ◽

Vahit FERYAD

Keyword(s):

Smart Grids ◽

Energy Demand ◽

Optimization Technique ◽

Cost Effective ◽

Optimization Method ◽

Long Term Memory ◽

Successful Implementation ◽

Efficient Design ◽

Energy Disaggregation ◽

Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

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