Industry 4.0 and deep learning methods have been widely used for output projection in wafer fabrication. Although such applications appeared to be effective, they were difficult to understand and/or improve. Therefore...
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This paper studies a risk minimization problem with decision dependent data distribution. The problem pertains to the performative prediction setting in which a trained model can affect the outcome estimated by the mo...
Industrial applications that require steam for their end-use generally utilize steam boilers that are typically oversized,citing operations ***,gas turbine-based power plants corroborate a gas turbine system that may ...
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Industrial applications that require steam for their end-use generally utilize steam boilers that are typically oversized,citing operations ***,gas turbine-based power plants corroborate a gas turbine system that may eventually relieve the usable exhaust into the *** study explores the economic and technical feasibility of a topping cycle combined heat and power(CHP)*** does so by leveraging a partially loaded boiler or gas turbine by increasing its unused load to generate steam and heat for subsequent *** this end,a decision support tool(COGENTEC)was developed,which emulates a given facility’s boiler or gas-turbine system,and its operational parameters with the application of steam *** tool provides necessary insights into the most appropriate parameters that enable a CHP system to be technically and economically *** on input variables such as boiler-rated capacity,steam pressure,steam temperature,and existing boiler load,among others,COGENTEC designs a topping cycle CHP system to inform a user whether this system is feasible in their facility or *** applicable,the tool assists the user to realize the point of break-even(fuel cost incurred and cost savings)at the desired steam flow *** also conducts sensitivity analyses between energy usage,cost savings,and payback on the investment of the operating parameters to understand the relationship between relevant *** utilizing parameters from a pulp and paper manufacturing facility,the research determines that the fuel cost,electricity cost,and steam flow rate are the most important parameters for the feasibility of the system with a desirable payback on the investment.
Accurate and timely detection of lung cancer is crucial for effective treatment planning. This study introduces MobileNetV2-SGRU, a novel transfer learning-based predictor for lung cancer classification. It utilizes M...
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This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these *** study compares the performance of the convolutional neural network-...
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This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these *** study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain ***-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature *** indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through *** long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction *** study provides evidence of the potential profitability of predictive models in Bitcoin trading.
Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing...
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We consider multiclass make-to-stock production/inventory systems in which the manager makes three decisions, including pricing, outsourcing, and scheduling, to maximize the long-run average profit. For a sequence of ...
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Large Language Models (LLMs) have exhibited significant potentials across various tasks. However, how to leverage the power of LLMs in the mispronunciation detection and diagnosis (MDD) task is still under-explored. I...
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Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to des...
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In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory...
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In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the a
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