The unexpected discovery of intrinsic ferromagnetism in layered van der Waals materials has sparked interest in both their fundamental properties and their potential for novel applications. Recent studies suggest near...
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The unexpected discovery of intrinsic ferromagnetism in layered van der Waals materials has sparked interest in both their fundamental properties and their potential for novel applications. Recent studies suggest near room-temperature ferromagnetism in the pressurized van der Waals crystal CrGeTe3. We perform a comprehensive experimental and theoretical investigation of magnetism and electronic correlations in CrGeTe3, combining broad-frequency reflectivity measurements with density functional theory and dynamical mean-field theory calculations. Our experimental optical conductivity spectra trace the signatures of developing ferromagnetic order and of the insulator-to-metal transition (IMT) as a function of temperature and hydrostatic pressure. With increasing pressure, we observe the emergence of a midinfrared feature in the optical conductivity, indicating the development of strong orbital-selective correlations in the high-pressure ferromagnetic phase. We find a distinct relationship between the plasma frequency and Curie temperature of CrGeTe3, which strongly suggests that a double-exchange mechanism is responsible for the observed near room-temperature ferromagnetism. Our results clearly demonstrate the existence of a charge-transfer gap in the metallic phase, ruling out its previously conjectured collapse under pressure.
Following with the age of technology, many advanced continuous developing the multi-functional small sur-face boats, and applied to the real-time monitoring of the near-shore environment, salvage and search operations...
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The gaming industry has changed since advanced technologies and hardware specifications were able to create the possibility of gaming with connective network or known as online gaming. Some communities are accepting p...
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The gaming industry has changed since advanced technologies and hardware specifications were able to create the possibility of gaming with connective network or known as online gaming. Some communities are accepting positively, and others are contradicted. Thus, there is an urgency to know the insight from their sentiment. The present research extracts 10 thousand sentiments at social media with sentiment text analysis. The text is interpreted into six based emotions namely anger, disgust, fear, joy, sadness, and surprise. The mining analysis by lexicon valance aware dictionary and sentiment reasoner (VADER) projects the Joy emotions as the domination result with 94.74%. Positive joy expression on online gaming is revealed by extracting the sentiment analysis. In total, ten thousand data were collected from the corpus, which resulted in the happy sentiments that were experienced.
Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in prov...
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Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions...
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ISBN:
(数字)9798350307566
ISBN:
(纸本)9798350307573
Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions. While previous research has demonstrated NARX's effectiveness in estimating earthquake frequencies, the architecture still relies on several pre-determined parameters. These parameters include the number of hidden layers, number of neurons, and delay time. This study aims to investigate the effectiveness of Ensemble Deep Learning NARX neural networks in predicting time series of earthquakes. The proposed ensemble model integrates multiple NARX neural networks, each trained on a distinct subset of earthquake data. This approach aims to enhance prediction accuracy and bolster model robustness. The dataset consists of time series records detailing earthquake occurrence frequencies and magnitudes. The results show performance evaluation metrics in terms of Mean Square Error (MSE) values. For training frequency data, the MSE is 4.10e-26, and for testing, it is 6.05e-22. Regarding training magnitude data, the MSE is 2.86e-21 for training and 3.17e-19 for testing. This study makes a valuable contribution to the advancement of earthquake prediction techniques, underscoring the potential of Ensemble Deep Learning NARX neural networks for precise time series estimation in seismology.
As spectrum utilization becomes increasingly scarce due to the exponential growth of intelligent connected devices in the Internet of Things (IoT), developing efficient communication protocols with simultaneous improv...
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Multi-robot belief space planning (MR-BSP) is essential for reliable and safe autonomy. While planning, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the fu...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Multi-robot belief space planning (MR-BSP) is essential for reliable and safe autonomy. While planning, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the future for different candidate actions. Yet, existing MR-BSP works have a common assumption that the beliefs of different robots are consistent at planning time. Such an assumption is often highly unrealistic, as it requires prohibitively extensive and frequent communication capabilities. In practice, each robot may have a different belief about the state of the environment. Crucially, when the beliefs of different robots are inconsistent, state-of-the-art MR-BSP approaches could result in a lack of coordination between the robots, and in general, could yield dangerous, unsafe and suboptimal decisions. In this paper, we tackle this crucial gap. We develop a novel decentralized algorithm that is guaranteed to find a consistent joint action. For a given robot, our algorithm reasons for action preferences about 1) its local information, 2) what it perceives about the reasoning of the other robot, and 3) what it perceives about the reasoning of itself perceived by the other robot. This algorithm finds a consistent joint action whenever these steps yield the same best joint action obtained by reasoning about action preferences; otherwise, it self-triggers communication between the robots. Experimental results show efficacy of our algorithm in comparison with two baseline algorithms.
Direct current microgrids (DCMG) have garnered immense popularity due to the absence of frequency synchronization, reactive power compensation, and skin effect issues. However, challenges associated with the voltage r...
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Direct current microgrids (DCMG) have garnered immense popularity due to the absence of frequency synchronization, reactive power compensation, and skin effect issues. However, challenges associated with the voltage regulation of dc bus persist in DCMG. This paper introduces a DCMG consisting of wind, photovoltaic, hydrogen storage system, ultracapacitor, and battery. The primary objectives are to sustain the dc bus voltage and maintain the power balance. Hence, to achieve the desired objectives and for the reliable operation of DCMG, an integral sliding mode controller (ISMC) is proposed. To evaluate the performance of ISMC, DCMG is set up and simulated in Matlab/Simulink. Through comprehensive simulations, the effectiveness of the proposed ISMC is demonstrated. Furthermore, the efficacy of the implemented control approach is validated through the real-time experiments conducted with hardware in the loop.
Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges...
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ISBN:
(数字)9798350307566
ISBN:
(纸本)9798350307573
Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges in the surveillance system or ADAS field related to thermal cameras are occlusion and thermal crossover between objects with similar appearances during object detection or object tracking tasks, which can lead to misdetection, false positives, and lost tracking. In this paper, performance analysis of you-only-look-once (YOLO) combined with deep online real-time tracking (DeepSORT) on thermal video-based online multi-object tracking (MOT) in occlusion and thermal crossover scene is presented. YOLO, as one of state-of-the-art method for detection task, is used for detection system. Then, the detected object from YOLO is tracked using DeepSORT. The results demonstrate that the online MOT of sequential thermal images using YOLO-DeepSORT achieved a MOTA score of 44.2% and IDF1 of 45.3%. Thus, negative example was added in YOLO training process to reduce false detection, and it gives improvement with MOTA score of 63.8% and IDF1 score of 54.6%.
Improving the energy efficiency of existing power systems is part of energy conservation and carbon reduction efforts. Internal combustion engines (ICEs) are currently the most commonly used power source in transporta...
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