In this paper, we introduce an end-To-end generative adversarial network (GAN) based on sparse learning for single image motion deblurring, which we called SL-CycleGAN. For the first time in image motion deblurring, w...
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To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltai...
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To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried *** has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the *** standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight *** proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub ***,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.
This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is impo...
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Fire is one of the most common hazards in US households. In 2006 alone, 2705 people were killed due to fire in building structures. 74% of the deaths result from fires in homes with no smoke alarms or no working smoke...
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Assembly and disassembly processes are similar from the viewpoint of automation. Assembly process is more or less a classical subject;disassembly process is more a quite new field. Flexible system are very important i...
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This paper presents the possibility of using statistical modeling to automate the process of dynamic pricing management with revenue control and adaptation to current legislation in this area. In addition, it is propo...
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Previous studies on facial expression analysis have been focused on recognizing basic expression categories. There is limited amount of work on the continuous expression intensity estimation, which is important for de...
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ISBN:
(纸本)9781467388511
Previous studies on facial expression analysis have been focused on recognizing basic expression categories. There is limited amount of work on the continuous expression intensity estimation, which is important for detecting and tracking emotion change. Part of the reason is the lack of labeled data with annotated expression intensity since expression intensity annotation requires expertise and is time consuming. In this work, we treat the expression intensity estimation as a regression problem. By taking advantage of the natural onset-apex-offset evolution pattern of facial expression, the proposed method can handle different amounts of annotations to perform frame-level expression intensity estimation. In fully supervised case, all the frames are provided with intensity annotations. In weakly supervised case, only the annotations of selected key frames are used. While in unsupervised case, expression intensity can be estimated without any annotations. An efficient optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed for solving the optimization problem associated with parameter learning. We demonstrate the effectiveness of proposed method by comparing it against both fully supervised and unsupervised approaches on benchmark facial expression datasets.
This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties...
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ISBN:
(纸本)9781538629185
This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional（2D） weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality（LMI） obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.
This paper presents a mathematical theory underlying a systematic method for constructing Prolog programs called stepwise enhancement. Stepwise enhancement dictates building a program starting with a skeleton program ...
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