Information measures are examined with a focus on two distinct types: those related to Shannon entropy and those connected to Tsallis entropy. Important in reliability contexts, residual and past entropies are among the information measures being considered.
This paper investigates how logic-based switching adaptive control can be implemented. A comparative analysis of two cases will be performed. Concerning a specific kind of nonlinear system, the issue of finite-time stabilization is investigated in the initial case. A logic-based switching adaptive control methodology is formulated, drawing from the recently developed barrier power integrator technique. In comparison to the outcomes of prior research, finite-time stability is demonstrably possible within systems exhibiting both completely unknown nonlinearities and unknown control directions. Furthermore, the proposed controller boasts a remarkably straightforward architecture, eliminating the need for approximation techniques such as neural networks or fuzzy logic. A study of sampled-data control for a class of nonlinear systems is presented in the second instance. The newly proposed switching mechanism employs sampled data and logic. The considered nonlinear system, in contrast to preceding studies, exhibits an uncertain linear growth rate. Adaptive adjustment of control parameters and sampling time guarantees exponential stability in the closed-loop system. Robotic manipulator applications serve as a means of verifying the suggested outcomes.
By employing statistical information theory, the amount of stochastic uncertainty within a system can be determined. From the realm of communication theory, this theory emerged. The diverse array of fields has been enriched by the application of information theoretic methods. Information theoretic publications found in the Scopus database are the subject of this paper's bibliometric analysis. Data concerning 3701 documents was extracted specifically from the Scopus database. Among the software employed for analysis are Harzing's Publish or Perish and VOSviewer. This report displays results concerning publication growth, subject categorization, global contributions, inter-country collaborations, leading-edge publications, keyword interrelationships, and citation measurements. Publications have increased steadily, demonstrating a consistent pattern since the year 2003. The United States leads all other countries in terms of the number of publications, and it also accounts for more than half of the total citations from a global pool of 3701 publications. Among published works, computer science, engineering, and mathematics topics are prevalent. The United States, the United Kingdom, and China are the countries with the most extensive collaborations on a global scale. The emphasis on information theory is gradually transitioning from abstract mathematical models to practical applications in fields like machine learning and robotics. A study of information-theoretic publications' emerging trends and developments provides insight into current methodologies, allowing researchers to contextualize their future contributions in this research field.
To uphold oral hygiene, the prevention of caries is of utmost importance. The need for a fully automated procedure arises due to the need to reduce reliance on human labor and the inherent risk of human error. A fully automated approach for identifying and delineating tooth regions of interest from panoramic radiographs is presented in this paper for caries diagnosis. First, the patient's panoramic oral radiograph, which any dental clinic can provide, is separated into distinct segments representing individual teeth. Teeth features are extracted using pre-trained deep learning models, such as VGG, ResNet, or Xception, with the intention to provide insightful information. cardiac remodeling biomarkers To learn each extracted feature, one can use classification models such as random forests, k-nearest neighbor algorithms, or support vector machines. A majority-voting approach determines the final diagnosis, considering each classifier model's prediction as a separate, contributing opinion. Remarkably, the proposed approach yielded an accuracy rate of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, suggesting its suitability for widespread implementation across diverse settings. Reliability, a key feature of the proposed method, significantly surpasses existing methods, enabling more efficient dental diagnosis and reducing the need for cumbersome procedures.
For enhanced computing rates and device sustainability within the Internet of Things (IoT), Mobile Edge Computing (MEC) and Simultaneous Wireless Information and Power Transfer (SWIPT) are essential. However, the prevailing system models in the most relevant publications examined multi-terminal structures, omitting the consideration of multi-server setups. This paper accordingly targets the IoT framework with multiple terminals, servers, and relays, intending to optimize computational speed and cost through the utilization of deep reinforcement learning (DRL). First, the proposed scenario yields formulas for computing rate and cost. Furthermore, the implementation of a modified Actor-Critic (AC) algorithm and a convex optimization algorithm enables the derivation of an offloading scheme and time allocation plan which yield the maximum computing rate. The selection scheme that minimizes computing costs was found using the AC algorithm. The theoretical analysis is substantiated by the evidence presented in the simulation results. This paper's proposed algorithm effectively minimizes program execution delay while simultaneously achieving near-optimal computing rate and cost, all while fully exploiting SWIPT's energy harvesting capabilities for improved energy utilization.
Multiple single image datasets can be processed by image fusion technology, yielding more dependable and comprehensive data, thus supporting precise target identification and subsequent image analysis. Due to incomplete image decomposition, redundant infrared energy extraction, and insufficient visible image feature extraction in existing algorithms, a novel fusion algorithm for infrared and visible images is introduced, employing a three-scale decomposition and ResNet feature transfer approach. Differing from existing image decomposition methods, the three-scale decomposition method utilizes two decomposition stages to precisely subdivide the source image into layered components. In the subsequent step, a refined WLS strategy is developed to fuse the energy layer, incorporating the complete infrared energy data and fine visible-light detail. Another approach involves a ResNet feature transfer mechanism for fusing detail layers, facilitating the extraction of detail, including refined contour features. Finally, the structural strata are fused together via a weighted average calculation. Evaluation results from experiments reveal the superior performance of the proposed algorithm in visual effects and quantitative measures, when compared to the five alternative methods.
The burgeoning internet technology landscape has elevated the significance and innovative worth of the open-source product community (OSPC). For the dependable development of OSPC, with its open features, high robustness is a fundamental requirement. Evaluating the importance of nodes in robustness analysis often involves the use of degree and betweenness. However, the two indexes are deactivated so as to completely assess the impactful nodes within the community network's structure. Subsequently, users of great influence garner a multitude of followers. The susceptibility of network structures to the influence of irrational following patterns deserves exploration. Employing a sophisticated network modeling approach, we built a typical OSPC network, assessed its structural characteristics, and proposed an improved method to identify significant nodes by integrating network topology features. Subsequently, we proposed a model consisting of a range of relevant node-loss approaches to simulate how the OSPC network's robustness would change. The evaluation results strongly suggest that the suggested technique yields a more effective identification of significant nodes within the network's interconnectedness. Additionally, the network's overall durability will be severely impaired by node removal tactics that concentrate on crucial nodes, such as nodes representing structural holes and opinion leaders, profoundly affecting the network's robustness. 2-deoxyglucose The results confirmed that the indexes and model of robustness analysis were practical and effective as intended.
Global optimal solutions are achievable via Bayesian Network (BN) structure learning algorithms employing dynamic programming. However, when the sample does not encapsulate all aspects of the actual structure, notably when the sample size is small, the extracted structure will be inaccurate. This paper examines the planning approach and significance of dynamic programming, limiting its process using edge and path constraints, and introduces a dynamic programming-based BN structure learning algorithm incorporating double constraints, appropriate for small sample datasets. The dynamic programming planning process is constrained by dual constraints implemented by the algorithm, resulting in a reduced planning space. trichohepatoenteric syndrome Eventually, double constraints are employed to curtail the optimal parent node selection process, ensuring that the resulting optimal structure reflects established knowledge. In the final stage, the performance of the integrating prior-knowledge method and the non-integrating prior-knowledge method is evaluated through simulation. The simulation outcomes corroborate the effectiveness of the proposed technique, proving that the integration of prior knowledge greatly improves the efficiency and accuracy of Bayesian network structure learning.
The co-evolution of opinions and social dynamics, within an agent-based framework, is investigated, influenced by multiplicative noise, which we introduce. In this computational model, each agent is described by their social standing and a continuous opinion value.