Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term hea...
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Digital twins (DTs) are virtual representations of physical systems, replicating their behavior, dynamics, and interactions with the environment. Hosted on computational platforms, DTs have access to more extensive in...
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ISBN:
(数字)9798331513269
ISBN:
(纸本)9798331513276
Digital twins (DTs) are virtual representations of physical systems, replicating their behavior, dynamics, and interactions with the environment. Hosted on computational platforms, DTs have access to more extensive information and computational power than their physical counterparts (PTs), making them suitable for control applications. This paper proposes a novel DT-based control architecture that leverages causal learning to achieve autonomous control. In this framework, the DT employs causal inference to determine the desired system behavior and, subsequently, instructs the PT accordingly. By incorporating causal learning, a DT can gain self-training capabilities, enabling it to generate and analyze hypothetical scenarios based on cause-and-effect relationships. Simulation results show that DT-based control outperforms the traditional local control method by 49% in terms of the tracking performance measured through the mean squared error. Additionally, causal learning demonstrates 16% better tracking performance over statistical learning in a self-training scenario, as measured by the mean squared error.
This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s appl...
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ISBN:
(数字)9798350369441
ISBN:
(纸本)9798350369458
This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s application, challenges, and opportunities, offering insights into their role in enhancing pedagogy and learning outcomes. Specifically, our exploration uncovers the transformative potential of ER and IoT in fostering critical thinking, problem-solving skills, and digital literacy among students and reveals the pivotal role these technologies play in preparing learners for the demands of the digital age and Industry 4.0, while also highlighting the need for strategic implementation and teacher support. Our findings elucidate the potential of ER and IoT to innovate teaching strategies and curriculum design, serving as a guide for educational stakeholders, such as curriculum developers, educators, researchers, and policymakers, in leveraging these technologies to revolutionize instructional practices.
The two-dimensional(2-D)system has a wide range of applications in different fields,including satellite meteorological maps,process control,and digital ***,the research on the stability of 2-D systems is of great *** ...
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The two-dimensional(2-D)system has a wide range of applications in different fields,including satellite meteorological maps,process control,and digital ***,the research on the stability of 2-D systems is of great *** that multiple systems exist in switching and alternating work in the actual production process,but the system itself often has external perturbation and *** solve the above problems,this paper investigates the output feedback robust H_(∞)stabilization for a class of discrete-time 2-D switched systems,which the Roesser model with uncertainties ***,sufficient conditions for exponential stability are derived via the average dwell time method,when the system’s interference and external input are ***,in the case of introducing the external interference,the weighted robust H_(∞)disturbance attenuation performance of the underlying system is further *** output feedback controller is then proposed to guarantee that the resulting closed-loop system is exponentially stable and has a prescribed disturbance attenuation levelγ.All theorems mentioned in the article will also be given in the form of linear matrix inequalities(LMI).Finally,a numerical example is given,which takes two uncertain values respectively and solves the output feedback controller’s parameters by the theorem proposed in the *** to the required controller parameter values,the validity of the theorem proposed in the article is compared and verified by simulation.
This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system...
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This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the sample size and the nu...
This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the sample size and the number of regression coefficients both increase. This subject has two cases: one is the dependence of performance on the hyperparameters in prior distribution and the other is how the performance depends on the sample size and the number of nonzero components in regression coefficients. To address this subject, we employ methods of statistical mechanics in physics. An equivalent of thermodynamic potential is calculated, following some similarities between Bayes inference and statistical mechanics. It is known in physics that the potential provides physical quantities. In a similar way, the equivalent of potential provides us with the asymptotic evaluation of estimation performance instead of physical quantities. The estimation performance in the two cases appears to have sharply divided regions in which the estimation succeeds and fails. These results are compared with related studies.
Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limite...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.
Mobile Edge Computing (MEC), with advantages in high bandwidth and low latency, enables the development of numerous promising commercial services on edge servers near users. However, complex associations among users, ...
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Mobile Edge Computing (MEC), with advantages in high bandwidth and low latency, enables the development of numerous promising commercial services on edge servers near users. However, complex associations among users, servers and services require nontrivial collaboration to boost the performance of heterogeneous applications with diverse service requirements. In this paper, we study a jointing task offloading and service caching problem in MEC networks applied in commercial domains, with the aim of minimizing the delay and computational cost for all commercial services. To this end, we first formulate the above issue as a complex optimization problem, and decompose it into two sub-problems for reducing computational complexity while maintaining its accuracy. Then, we propose a data-driven Hybrid Soft Actor-Critic scheme, where the deep reinforcement learning-based part determines the near-optimal service caching decisions, and the convex optimization technology-based part calculates the optimal offloading decisions. Finally, simulation results show that our proposed scheme improves the performance of accuracy and convergence when dealing with high-dimensional action spaces, and it outperforms the baseline schemes. IEEE
The withdrawal of US troops from Afghanistan and the sub-sequent collapse of the Afghan government threatens the lives, security, and human right of many people. Twitter and other social media platforms took the lead ...
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This article discusses the integration of Brillouin optical time-domain analysis (BOTDA) and Phase-sensitive Optical Time Domain Reflectometry ( $\Phi-\text{OTDR}$ ) for distributed optical fiber sensing systems. Comb...
This article discusses the integration of Brillouin optical time-domain analysis (BOTDA) and Phase-sensitive Optical Time Domain Reflectometry ( $\Phi-\text{OTDR}$ ) for distributed optical fiber sensing systems. Combining these two systems and sharing typical instrumentation can reduce the system cost. On the same 18.7 km optical fiber, temperature, strain, and vibration are detected.
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