检索结果-南通市图书馆

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy and AI 2024年第3期17卷 342-356页

作者： Philipp Pelger Johannes Steinleitner Alexander Sauer Fraunhofer Institute for manufacturing engineering and automation IPA Stuttgart 70569Germany Institute for Energy Efficiency in Production(EEP) University of StuttgartStuttgart 70569Germany

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

关键词： Non-intrusive load monitoring Energy transparency Energy consumption evaluation Industrial manufacturing Artificial neural networks

Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques–A Review

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy and AI 2024年第2期16卷 483-496页

作者： Lukas Baur Konstantin Ditschuneit Maximilian Schambach Can Kaymakci Thomas Wollmann Alexander Sauer Fraunhofer Institute for manufacturing engineering and automation IPA Nobelstraße 12Stuttgart70569Germany Institute for Energy Efficiency in Production(EEP) University of StuttgartNobelstraße 12Stuttgart70569Germany Merantix Momentum GmbH Max-Urich-Straße 3Berlin13355Germany

Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption *** to the increasing automation of these processes,meaningful and transparent forecasts become more and more ***,at the same time,the complexity of the used machine learning models and architectures *** there is an increasing interest in interpretable and explainable load forecasting methods,this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine *** on extensive literature research covering eight publication portals,recurring modeling approaches,trends,and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load *** results on interpretability show an increase in the use of probabilistic models,methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable *** explainable approaches are Feature Importance and Attention *** discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in *** to other applications of explainable and interpretable methods such as clustering,there are currently relatively few research results,but with an increasing trend.

关键词： Electric load forecasting Explainability InterpretabilityStructured review

Printability disparities in heterogeneous material combinations via laser directed energy deposition:a comparative study

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Extreme manufacturing 2024年第2期6卷 389-405页

作者： Jinsheng Ning Lida Zhu Shuhao Wang Zhichao Yang Peihua Xu Pengsheng Xue Hao Lu Miao Yu Yunhang Zhao Jiachen Li Susmita Bose Amit Bandyopadhyay School of Mechanical engineering and automation Northeastern UniversityShenyang 110819People’s Republic of China School of Machinery and automation Wuhan University of Science and TechnologyWuhan 430081People’s Republic of China School of Mechanical engineering Xi’an University of Science and TechnologyXi’an 710054People’s Republic of China Beijing Institute of Space Launch Technology Beijing 100076People’s Republic of China W.M.Keck Biomedical Materials Research Lab School of Mechanical and Materials EngineeringWashington State UniversityPullmanWA 99164United States of America

Additive manufacturing provides achievability for the fabrication of bimetallic and multi-material structures;however,the material compatibility and bondability directly affect the parts’formability and final *** is essential to understand the underlying printability of different material combinations based on an adapted ***,the printability disparities of two common and attractive material combinations(nickel-and iron-based alloys)are evaluated at the macro and micro levels via laser directed energy deposition(DED).The deposition processes were captured using in situ high-speed imaging,and the dissimilarities in melt pool features and track morphology were quantitatively investigated within specific process ***,the microstructure diversity of the tracks and blocks processed with varied material pairs was comparatively elaborated and,complemented with the informative multi-physics modeling,the presented non-uniformity in mechanical properties(microhardness)among the heterogeneous material pairs was *** differences in melt flow induced by the unlike thermophysical properties of the material pairs and the resulting element intermixing and localized re-alloying during solidification dominate the presented dissimilarity in printability among the material *** work provides an in-depth understanding of the phenomenological differences in the deposition of dissimilar materials and aims to guide more reliable DED forming of bimetallic parts.

关键词： directed energy deposition printability microstructure microhardness bimetallic parts

Bidirectional position attention lightweight network for massive MIMO CSI feedback

在线全文

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2024年

作者： Li Jun Wang Yukai Zhang Zhichen He Bo Zheng Wenjing Lin Fei School of Information Science and engineering， Shandong University School of Information and automation， Qilu University of Technology (Shandong Academy of Sciences)

In frequency-division duplex massive multiple-input multiple-output (MIMO) systems， a bidirectional positional attention network (BPANet) is proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information (CSI) feedback methods. Specifically， a bidirectional position attention module (BPAM) is designed in the proposed BPANet to improve the network performance. The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information， thereby enhancing the feature representation of the CSI matrix. Furthermore， channel attention is decomposed into two one-dimensional feature encoding processes， which effectively reduce computational costs. Simulation results demonstrate that， compared with the existing representative method CLNet， BPANet reduces computational complexity by an average of 19.4% and improves accuracy by an average of 7.1%. Additionally， it performs better in terms of running time delay and cosine similarity.

关键词：

Effect of varying spatial orientations on build time requirements for FDM process: A case study

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2017年第2期13卷 92-100页

作者： Sandeep Rathee Manu Srivastava Sachin Maheshwari Arshad Noor Siddiquee division of manufacturing processes and automation engineering Netaji Subhas Institute of Technology Department of Mechanical engineering Jamia Millia Islamia A Central University

In this research, effect of varying spatial orientations on the build time requirements for fused deposition modelling process is studied. Constructive solid geometry cylindrical primitive is taken as work piece and modeling is accomplished for it. Response surface methodology is used to design the experiments and obtain statistical models for build time requirements corresponding to different orientations of the given primitive in modeller build volume. Contour width, air gap, slice height, raster width, raster angle and angle of orientation are treated as process parameters. Percentage contribution of individual process parameter is found to change for build time corresponding to different spatial orientations. Also, the average of build time requirement changes with spatial orientation. This paper attempts to clearly discuss and describe the observations with an aim to develop a clear understanding of effect of spatial variations on the build time for Fused Deposition Modelling process. This work is an integral part of process layout optimization and these results can effectively aid designers specially while tackling nesting issues.

关键词： Fused deposition modeling Spatial orientation Process parameters Response Surface Methodology Build time

Effect of tool plunge depth on reinforcement particles distribution in surface composite fabrication via friction stir processing

在线全文

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2017年第2期13卷 86-91页

作者： Sandeep Rathee Sachin Maheshwari Arshad Noor Siddiquee Manu Srivastava division of manufacturing processes and automation engineering(MPAE) Netaji Subhas Institute of Technology Department of Mechanical engineering Jamia Millia Islamia A Central University

Aluminium matrix surface composites are gaining alluring role especially in aerospace, defence, and marine industries. Friction stir processing(FSP) is a promising novel solid state technique for surface composites fabrication. In this study, AA6061/SiC surface composites were fabricated and the effect of tool plunge depth on pattern of reinforcement particles dispersion in metal matrix was investigated. Six varying tool plunge depths were chosen at constant levels of shoulder diameter and tool tilt angle to observe the exclusive effect of plunge variation. Process parameters chosen for the experimentation are speed of rotation, travel speed and tool tilt angle which were taken as 1400 rpm, 40 mm/min, and 2.5 °respectively. Macro and the microstructural study were performed using stereo zoom and optical microscope respectively. Results reflected that lower plunge depth levels lead to insufficient heat generation and cavity formation towards the stir zone center. On the other hand, higher levels of plunge depth result in ejection of reinforcement particles and even sticking of material to tool shoulder. Thus, an optimal plunge depth is needed in developing defect free surface composites.

关键词： Metal matrix composites Friction stir processing Tool plunge depth Microstructural characterization

使用深度学习在CTA扫描卷下实现主动脉夹层分类和直径测量的双功能系统

在线全文

学校读者我要写书评

暂无评论

engineering 2024年第3期34卷 83-91页

作者： Zhihui Huang Rui Wang Hui Yu Yifan Xu Cheng Cheng Guangwei Wang Haosen Cao Xiang Wei Hai-Tao Zhang School of Artificial Intelligence and automation&the MOE engineering Research Center of Autonomous Intelligent Unmanned Systems&the State Key Laboratory of Intelligent manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China division of Cardiovascular Surgery Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Wuhan Children’s Hospital:Wuhan Women and Children Medical Care Center Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430010China

Acute aortic dissection is one of the most life-threatening cardiovascular diseases,with a high mortality *** prevalence ranges from 0.2%to 0.8%in humans,resulting in a significant number of deaths due to being missed in manual *** importantly,the aortic diameter—a critical indicator for surgical selection—significantly influences the outcomes of surgeries ***,it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic *** paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system(DDAsys)that enables both accurate classification of aortic dissection and precise diameter measurement of the *** this end,we created a dataset containing 61190 computed tomography angiography(CTA)images from 279 patients from the division of Cardiovascular Surgery at Tongji Hospital,Wuhan,*** dataset provides a slice-level summary of difficult-to-identify features,which helps to improve the accuracy of both recognition and *** system achieves a recognition F1 score of 0.984,an average classification F1 score of 0.935,and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error(RMSE).The high consistency(88.6%)between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt,precise,and consistent treatment strategy.

关键词： Aortic dissections Computed tomography angiography Classification Deep learning

Solvent-Free manufacturing of Lithium-Ion Battery Electrodes via Cold Plasma

在线全文

学校读者我要写书评

暂无评论

Energy & Environmental Materials 2024年第1期7卷 28-33页

作者： Zhiming Liang Tianyi Li Holden Chi Joseph Ziegelbauer Kai Sun Ming Wang Wei Zhang Tuo Liu Yang-Tse Cheng Zonghai Chen Xiaohong Gayden Chunmei Ban Paul M.Rady Department of Mechanical engineering University of Colorado BoulderBoulder CO 80309USA Advanced Photon Source Argonne National LaboratoryLemont IL 60439USA Intecells Inc. Southfield MI 48084USA Department of Materials Science and engineering Michigan Center for Materials CharacterizationUniversity of MichiganAnn Arbor MI 48109USA Department of Chemical and Materials engineering University of KentuckyLexington KY 40506USA Department of Chemistry University of KentuckyLexington KY 40506USA Chemical Sciences and engineering division Argonne National LaboratoryLemont IL 60439USA

Slurry casting has been used to fabricate lithium-ion battery electrodes for decades,which involves toxic and expensive organic solvents followed by high-cost vacuum drying and electrode *** work presents a new manufacturing method using a nonthermal plasma to create inter-particle binding without using any polymeric binding materials,enabling solvent-free manufacturing electrodes with any electrochemistry of *** cold-plasma-coating technique enables fabricating electrodes with thickness(>200 pm),high mass loading(>30 mg cm^(-2)),high peel strength,and the ability to print lithium-ion batteries in an arbitrary *** crosscutting,chemistry agnostic,platform technology would increase energy density,eliminate the use of solvents,vacuum drying,and calendering processes during production,and reduce manufacturing cost for current and future cell ***,lithium iron phosphate and lithium cobalt oxide were used as examples to demonstrate the efficacy of the cold-plasma-coating *** is found that the mechanical peel strength of cold-plasma-coating-manufactured lithium iron phosphate is over an order of magnitude higher than that of slurry-casted lithium iron phosphate *** cells assembled with a graphite anode and the cold-plasma-coating-lithium iron phosphate cathode offer highly reversible cycling performance with a capacity retention of 81.6%over 500 *** the highly conductive cathode material lithium cobalt oxide,an areal capacity of 4.2 mAh cm^(-2)at 0.2 C is *** anticipate that this new,highly scalable manufacturing technique will redefine global lithium-ion battery manufacturing providing significantly reduced plant footprints and material costs.

关键词： cold plasma deposition lithium-ion battery solvent-free manufacturing

Microstructures and fatigue properties of electron beam welds with beam oscillation for heavy section TC4-DT alloy

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2014年第4期27卷 1015-1021页

作者： Fu Pengfei Mao Zhiyong Zuo Congjin Wang Yajun Wang Chunming College of Materials Science and engineering Huazhong University of Science and Technology Science and Technology on Power Beam processes Laboratory Beijing Aeronautical Manufacturing Technology Research Institute Institute of Mechanical engineering and automation Beihang University

With the development of the manufacturing technology, electron beam welding（EBW） is capable of producing titanium alloy large parts in aero fields. To increase the applications and improve the properties, EBW with beam oscillation was investigated on TC4-DT alloy with50 mm thickness. We detected the welding samples by X-ray NDT, observed the microstructures of the welds, and tested the fatigue properties of the joints. The results showed that EBW with beam oscillation improved the weld morphology as well as welding quality, and the microstructure homogeneity of the welds and HAZ along the weld penetration were also improved. The fatigue properties of the joints with beam oscillation were more excellent than those of conventional EBW, even equal to those of the base metal under high stresses. The influences of the processing and the microstructure on the properties with beam oscillation were discussed.

关键词： Beam oscillation Electron beam welding(EBW) Fatigue Microstructure Titanium alloy