Hasil untuk "Cybernetics"

Zhe Ban, Carlos Pfeiffer

Abstract Data reconciliation techniques have been the subject of many classic studies in the data conditioning process. By reconciling the measurements, accurate estimation of the system output and unmeasured variables is provided. However, accurately determining measurement noise and parameter uncertainty in real time remains a significant challenge. How to simultaneously estimate parameters in the system has been attracting considerable interest. So far, very little attention has been paid to time-varying parameter estimation in oil production systems. In particular, estimation of parameter dynamics and the corresponding uncertainty without prior knowledge remains challenging. This work extends a previous study on dynamic parameter estimation by considering scenarios where parameters change both gradually and abruptly. To address these dynamics, nonlinear filtering methods are employed and compared. A comparative analysis was conducted using both quantitative metrics and visualization plots to evaluate the performance of various approaches. Under the same abrupt parameter change scenario, nonlinear filter-based methods demonstrated superior performance in parameter estimation, achieving a root mean square error of $$6.56 \times 10^{-11}$$ , compared to $$7.84 \times 10^{-11}$$ for the MCMC-based method-even without the use of prior information. Additionally, nonlinear filters showed a significant advantage in simultaneous state estimation, with a root mean square error of $$1.94 \times 10^{4}$$ , markedly lower than the $$1.47 \times 10^{6}$$ observed with the MCMC-based approach. The effectiveness of nonlinear filtering methods was further validated in scenarios involving gradual parameter changes, again without relying on prior knowledge. This work provides an important opportunity to advance the understanding of dynamic parameter estimation in the gas and oil industry, and the improved model can possibly be applied to real-time optimization and model-based control. Graphical abstract

Wei Li, Jianhang Shang, Guoliang Liu et al.

ABSTRACT This paper introduces a pioneering dynamic system optimisation for multiagent (DySOMA) framework, revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems. The core of DySOMA is an advanced auction‐based algorithm coupled with a novel task preemption ranking mechanism, seamlessly integrated with an ontology knowledge graph that dynamically updates. This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes. Compared to other advanced algorithms, the DySOMA algorithm shows significant performance improvements, with its RLB 26.8% higher than that of the best‐performing Consensus‐Based Parallel Auction and Execution (CBPAE) algorithm at 10 robots and 29.7% higher at 20 robots, demonstrating its superior capability in balancing task loads and optimising task completion times in larger, more complex environments. DySOMA sets a new benchmark for intelligent robot task scheduling, promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.

M.Y. Kurbakov, V.V. Sulimova, O.S. Seredin et al.

An important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the material properties. We have previously proposed an approach to formalize the order features based on the identification of lines by nanoparticles in the SEM image. This paper proposes a novel approach to line allocation that is based on the concept of constructing a minimum spanning forest. Additionally, it introduces a set of novel ordering functions that are derived from this approach. The experimental study demonstrates that the combination of these new and previously extracted features improves the recognition quality of SEM images with ordered and disordered nanoparticles arrangements. This approach allows us to gain a better understanding of the nanoparticles arrangement and their effect on the material properties.

Lorenzo Maria Lucarelli Tonini

L’articolo intende analizzare il complesso tema della tutela dei soggetti minori di età nell’ambito delle attività che essi svolgono online. In particolare, partendo dal presupposto per cui la presenza dei minori in rete è una realtà consolidata, verrà affrontato il tema della verifica dell’età del minore, c.d. “age verification”, alla luce del contesto normativo di riferimento, al fine di individuare un possibile bilanciamento tra la necessità di garantire l’accesso ai c.d. “servizi della società dell’informazione” e la necessità di apprestare una “tutela rafforzata” in virtù dei potenziali rischi presenti nella Rete. Si procederà, nello specifico, ad analizzare i principali provvedimenti emanati dall’Autorità garante per la protezione dei dati personali e dall’Autorità per le garanzie nelle comunicazioni, al fine di verificare l’efficacia dei mezzi disponibili in tema di “age verification”.

Сальников С.Г.

Представлены результаты анализа доступа домашних хозяйств в сельской местности в региональном разрезе. Анализируемый период составляет 2016-2022 гг. Результаты представлены в виде таблиц с данными. Всего задействовано 79 регионов, кроме городов федерального значения и автономных округов Архангельской и Тюменской областей.Также представлены результаты анализа динамики изменений этого показателя за указанный период. Как для анализа доступа, так и для анализа его динамики представлены как общее положение дел, так и лидирующие/отстающие регионы в данной сфере. Исследованы также перспективы развития доступа к Интернету домашних хозяйств сельской местности на ближайшие годы. Указаны регионы как с хорошими перспективами для развития цифровизации сельской местности, так и регионы, где перспективы такого развития очень низки.

Nurul Fathanah Mustamin, Firman Aziz, Firmansyah Firmansyah et al.

Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.

Maximilien Charlier, Remous-Aris Koutsiamanis, Bruno Quoitin

In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.

Xueming Yan, Haiwei Xue, Shengyi Jiang et al.

With the rapid development of social networks, more and more people express their emotions and opinions via online videos. However, most of the current research on multimodal sentiment analysis cannot do well with effective emotional fusion in multimodal data. To deal with the problem, we propose a multi-tensor fusion network with cross-modal modeling for multimodal sentiment analysis. In this study, the multimodal feature extraction with cross-modal modeling is utilized to obtain the relationship of emotional information between multiple modalities. Moreover, the multi-tensor fusion network is used to model the interaction of multiple pairs of bimodal and realize the emotional prediction of multimodal features. The proposed approach performs well in regression and different dimensions of classification experiments on the two public datasets CMU-MOSI and CMU-MOSEI.