The continuous evolution of artificial intelligence and advanced algorithms capable of generating information from simplified input creates new opportunities for several scientific fields. Currently, the applicability...
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Logistics distribution center is an important part of logistics system, whether its location is reasonable will directly affect the operational efficiency and economic benefits of logistics system. Scientific and reas...
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ISBN:
(纸本)9781510674479
Logistics distribution center is an important part of logistics system, whether its location is reasonable will directly affect the operational efficiency and economic benefits of logistics system. Scientific and reasonable location distribution can effectively reduce transportation costs, save distribution time and improve logistics service quality. In this paper, taking the location of distribution center of R enterprise as an example, the combination of hierarchical analysis and gray correlation method is used to clarify the relative relationship between the elements and the target decision by the gray correlation method, while the relative importance between the elements is chosen to analyze by the hierarchical analysis method, and finally the comprehensive correlation degree is calculated. The fusion of these two methods is an advanced algorithm.
This study evaluates the compressive strength (C-S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed advanced ML approac...
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This study evaluates the compressive strength (C-S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed advanced ML approaches used for the analysis are Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and eXtreme Gradient Boosting (XGB). Furthermore, the level of accuracy for the employed advanced algorithms is also evaluated by the k-fold cross-validation technique. Statistical checks, i.e., root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE), are also applied to validate the performance of algorithms. Sensitivity analysis is also made to explore the influence of input parameters on the C-S of NS-FRC. Among all, the XGB technique is found most effective for an accurate C-S prediction of NS-FRC. In XGB model, the coefficient of determination (R2) is 0.95, which is comparatively more than that of SVM (0.90) and MLP (0.90). The MAE value of XGB algorithm is 3.3 MPa which is lower than that of SVM (4.8 MPa) and MLP (4.5 MPa). In addition, RMSE value is also less for XGB algorithm (3.8 MPa) as compared to that of SVM (5.5 MPa) and MLP (5.9 MPa). Furthermore, the employed XGB models exhibited highest R2 of 0.95 as compared to the models reported in the available literature. The sensitivity analysis revealed that the nano-silica influenced the C-S of NS-FRC by 7%. Moreover, discussion reveals that nano-silica in concrete can have several benefits, such as improved microstructure, enhanced strength, prolonged durability, reduced cement content, and less carbon emission.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
the last year, we have witnessed the popularization of generative artificial intelligence. Its output includes text, code, image, audio, speech, voice, music, and video. Therefore, it impacts education courses where s...
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ISBN:
(纸本)9798350394023;9798350394030
the last year, we have witnessed the popularization of generative artificial intelligence. Its output includes text, code, image, audio, speech, voice, music, and video. Therefore, it impacts education courses where students are required to elaborate on any of these artifacts. In particular, the generation of code affects informatics courses, where assignments usually ask students to develop and deliver programming code. The impact of generative artificial intelligence on informatics courses has been mainly studied for introductory programming courses. These studies have shown that generative artificial intelligence is able to produce highly sophisticated programs, but also that its results and rationale can be inaccurate. Moreover, the impact of generative artificial intelligence has not been studied for other informatics subjects. In this paper, we present our preliminary experience and proposals on three advanced software courses, namely video games, advanced algorithms and language processors. For the video games course, we present the opportunities of use of generative artificial intelligence and the results of a survey conducted with students on their use to obtain different media products. For the algorithms course, we present the result of a session driven by the instructor on different design techniques, showing the merits and demerits of the answers generated. For the language processors course, a proposal of use of generative artificial intelligence is presented, broken down into the parts of a typical language processor. The paper concludes with some suggestions for instructors.
New approaches to early detection and early treatment are needed because it is a most concern cause of increase in cases of Brain deaths. Goal of this work is to increase the accuracy and timeliness of stroke risk ass...
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This paper focuses on the advancements of the edge computing paradigms regarding the creation of new emerging architectures and algorithms for improving real-time computing. Looking at the results obtained, the propos...
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This study evaluates the performance of 15 machine learning models for predicting energy consumption (30-100 kWh/m2year) and occupant dissatisfaction (Percentage of Dissatisfied, PPD: 6-90%), key metrics for optimizin...
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This study evaluates the performance of 15 machine learning models for predicting energy consumption (30-100 kWh/m2year) and occupant dissatisfaction (Percentage of Dissatisfied, PPD: 6-90%), key metrics for optimizing building performance. Ten evaluation metrics, including Mean Absolute Error (MAE, average prediction error), Root Mean Squared Error (RMSE, penalizing large errors), and the coefficient of determination (R2, variance explained by the model), are used. XGBoost achieves the highest accuracy, with an energy MAE of 1.55 kWh/m2year and a PPD MAE of 3.14%, alongside R2 values of 0.99 and 0.97, respectively. While these metrics highlight XGBoost's superiority, its margin of improvement over LightGBM (energy MAE: 2.35 kWh/m2year, PPD MAE: 3.89%) is context-dependent, suggesting its application in high-precision scenarios. ANN excelled at PPD predictions, achieving the lowest MAE (1.55%) and Mean Absolute Percentage Error (MAPE: 4.97%), demonstrating its ability to model complex nonlinear relationships. This nonlinear modeling advantage contrasts with LightGBM's balance of speed and accuracy, making it suitable for computationally constrained tasks. In contrast, traditional models like linear regression and KNN exhibit high errors (e.g., energy MAE: 17.56 kWh/m2year, PPD MAE: 17.89%), underscoring their limitations with respect to capturing the complexities of building performance datasets. The results indicate that advanced methods like XGBoost and ANN are particularly effective owing to their ability to model intricate relationships and manage high-dimensional data. Future research should validate these findings with diverse real-world datasets, including those representing varying building types and climates. Hybrid models combining the interpretability of linear methods with the precision of ensemble or neural models should be explored. Additionally, integrating these machine learning techniques with digital
Many photoacoustic tomography (PAT) systems have been developed using conventional linear ultrasound probes with reduced angular spectra, which limits the resolution of tomographic reconstructions. These limitations a...
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ISBN:
(数字)9781665466578
ISBN:
(纸本)9781665466578
Many photoacoustic tomography (PAT) systems have been developed using conventional linear ultrasound probes with reduced angular spectra, which limits the resolution of tomographic reconstructions. These limitations are partially compensated by employing advanced reconstruction algorithms that take advantage of the intrinsic characteristics of the photoacoustic signals. The aim of this work is to theoretically, numerically and empirically characterise the lateral and axial resolution using two nearby point objects that are separated until they can be distinguished as two independent sources in the reconstructed image. It is also intended to study the effect of the main limitations of linear probes (geometry, frequency response and angular spectrum) on the spatial resolution. The results of this study shed light on the suitability of using each type of algorithm depending on the limiting conditions of the probe used and the imaging objective.
Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hen...
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Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment.
In this work,we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual *** robotic system can map its environment in real time and...
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In this work,we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual *** robotic system can map its environment in real time and locate objects to pick *** uses advanced algorithms to grasp objects in a way that suits human preference and employs path planning and obstacle avoidance to navigate back to the human *** robot adjusts its movements during handover by analyzing the human’s posture and movements through visual sensors,ensuring a smooth and collision-free *** of our system show that it can successfully hand over various objects to humans and adapt to changes in the human’s hand position,highlighting improvements in safety and versatility for robotic handovers.
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