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Found 6423 publications

A Fuzzy Driven Framework for the Cognitive Modeling and Quantitative Analysis of Aggregated Avatars

Adam B. Csapo György Persa

Publication Name: IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics Automation Computer Science and Robotics Cinti Macro 2022 Proceedings

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 143-150

Description:

In this paper, our goal is to motivate the structured design and quantitative analysis of a class of avatars which we refer to as (fuzzy) aggregated avatars. Following a brief overview of the different avatar types based on the literature, we formulate a newly proposed definition of aggregated avatars, and also extend this concept with the more specialized class of fuzzy aggregated avatars. Further, we propose a framework to facilitate the design and experimental analysis of fuzzy aggregated avatars, and describe a use-case example in which user interaction patterns within a 3D virtual reality space are mapped onto dynamic avatar behaviors.

Open Access: Yes

DOI: 10.1109/CINTI-MACRo57952.2022.10029574

The Political Elite's Thematic Framing of China in Recent Central European Elections

László Csicsmann Jan Hornat Joanna Starzyk-Sulejewska

Publication Name: Issues and Studies

Publication Date: 2023-03-01

Volume: 59

Issue: 1

Page Range: Unknown

Description:

China is geographically distant from Central European states, but cultural, economic and political exchanges between the regions are increasing. As such, the perception of China in these societies is susceptible to frames and narratives that are either instrumentally or organically created by local political elites. This paper aims to scrutinize the narratives and frames employed in the Czech Republic, Hungary and Poland during their most recent election cycles. It identifies five basic types of frames employed in the selected countries: sovereignty, opportunity, the balance of trade, debt-traps and human rights. This paper then concludes by evaluating these thematic frames and summarizes the key similarities in public discourses concerning relations with China.

Open Access: Yes

DOI: 10.1142/S1013251123500029

The implication of business intelligence in risk management: a case study in agricultural insurance

Mohammad J. Tarokh Sayyed M. Haybatollahi Sayyed M. Haybatollahi Mehran Amini Farid Yousefinejad Sara Salimi

Publication Name: Journal of Data Information and Management

Publication Date: 2021-06-01

Volume: 3

Issue: 2

Page Range: 155-166

Description:

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

Open Access: Yes

DOI: 10.1007/s42488-021-00050-6

Structured learning for extraction of daily life log measured by smart phone sensors

János Botzheim Naoyuki Kubota

Publication Name: Smart Innovation Systems and Technologies

Publication Date: 2015-01-01

Volume: 30

Issue: Unknown

Page Range: 277-293

Description:

This chapter deals with producing information using structured learning to extract daily life log measured by smart phone sensors. Acceleration, angular velocity, and movement distance are measured by smart phone sensors and stored as the entries of the daily life log together with the activity information and timestamp.First, this chapter introduces the concept of Informationally Structured Space (ISS) and explains how smart phones can be used for collecting data for the daily life log. Then, structured learning is proposed for estimating the human activities based on the time series of the measured data. The method applies growing neural gas for performing clustering on the data, and spiking neural network for estimating the activity. A modified simple spike response model is applied to reduce the computational cost. The external input values of the spiking neurons depend on the growing neurons, and various metrics are investigated in the input calculation.Experiments are performed for verifying the effectiveness of the proposed method. Finally, the future direction on this research is discussed.

Open Access: Yes

DOI: 10.1007/978-3-319-13545-8_16

Data-driven terminal voltage prediction of li-ion batteries under dynamic loads

Adrienn Dineva Szabolcs Kocsis Szurke Istvan Vajda

Publication Name: 2020 21st International Symposium on Electrical Apparatus and Technologies Siela 2020 Proceedings

Publication Date: 2020-06-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Extensive investigation and prediction of the effects of dynamic battery loading is key to on-board Battery Management Systems (BMS) of Electric Vehicles (EVs) in order to ensure reliable operation and efficient energy management. In this paper, measurements of WLTP discharge tests at different temperatures are conducted on a Lithium Nickel Manganese Cobalt Oxide (LiNiMnCoO2) cell. Terminal voltage, discharge rate and temperatures at four points are taken into consideration. After, historical measurement data is used to build ensemble of boosted tree models and then predict cell voltage outcome sequence into the future. The efficiency of the performance is compared in case of various measurement sets. The results support the efficiency and applicability of direct multi-step-ahead forecasting strategy with standard Machine Learning techniques in battery SoC prediction.

Open Access: Yes

DOI: 10.1109/SIELA49118.2020.9167039

Probabilistic occupancy grid map building for Neobotix MP500 robot

Claudiu Radu Pozna Erno Horváth

Publication Name: Proceedings of the 2016 13th Workshop on Positioning Navigation and Communication Wpnc 2016

Publication Date: 2017-01-17

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

In vehicle and robot navigation low-level tasks such as path planning, obstacle avoidance and autonomous operation are extensively studied nowadays. Most of these task require map building. In this paper a map representation is discussed with the focus for the singular domain of our Neobotix MP500 mobile robot. Among others the state of the art map building techniques will be introduced such as topological map, line map, landmark-based map and of course in more detail the occupancy grid based map. The probabilistic representation of the occupancy grid will be examined as a map building problem for the given mobile robot.

Open Access: Yes

DOI: 10.1109/WPNC.2016.7822843

Examination and development of a radio frequency inductor

Miklós Kuczmann Zoltán Pólik

Publication Name: Przeglad Elektrotechniczny

Publication Date: 2008-12-01

Volume: 84

Issue: 12

Page Range: 230-233

Description:

Nowadays, inductors can be found in almost every electrical and electronic product. These key components are needed to store electrical energy, select frequencies, and protect againist overvoltage and overcurrent. In the case of the inductors, which usually work in the range of radio frequency, the one of the most important attributes is the quality factor. The aim of the project is to increase the Q-factor of an RF SMT inductor and to favour the industrial research and development about inductors by using the finite element method.

Open Access: Yes

DOI: DOI not available

Sustainability Indicators in Industrial Robotic Systems

Peter Galambos Adam B. Csapo Csaba Hajdu Károly Széll Zoltán Szilágyi

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 107

Issue: Unknown

Page Range: 79-84

Description:

Nowadays, the application of industrial robots in manufacturing is widespread to automatize tedious and repeatable precision-sensitive processes. Modern robotic cells compete with the human workforce and specialized industrial machines in general efficiency. Additionally, robotic cells can be typically reprogrammed for different tasks, thus providing flexible applications, especially compared to more traditional methods. Advanced autonomous and intelligent capabilities are widespread in modern robotic systems, supplying quantitative factors like higher availability, fault tolerance, precision, and system flexibility. Robotic systems typically work measurably more efficiently regarding resource usage, electrical power, and waste than other industrial production line systems. New intelligent and interconnected methods (e.g., cooperative robotics, big data analysis, cyber-physical systems) contribute further to the operation efficiency. Recently, sustainability has become a significant question in robotics: modern technical society faces problems such as decreasing availability of natural resources, workforce scarcity, and environmental challenges. This work focuses on industrial robotic systems as a primary pillar for sustainability efforts in numerous production sectors, such as metallurgy, assembly lines, and construction. This paper aims to review and analyze possible sustainability indicators of the industrial applications of robotics. Based on the analysis, the paper presents a manufacturing process model with industrial robots that considers sustainability indicators. This model highlights the relationship between industrial robotization and sustainability from a quantitative and qualitative perspective. The model indicates that energy efficiency and data interconnectivity play a crucial role in the sustainability of industrial processes. The aim of the model is to help identify indicators playing a role in the sustainability of industrial robot-based manufacturing lifecycle and maintenance.

Open Access: Yes

DOI: 10.3303/CET23107014

Hybrid generative–ensemble approach for predicting recycled aggregate concrete strength properties

Milica V. Vasić Paul O. Awoyera Olaolu George Fadugba Majid Movahedi Rad Lenganji Simwanda Udeme Udo Imoh Majid Movahedi Rad Milica V. Vasić Md Azree Othuman Mydin Andi Asiz

Publication Name: Scientific Reports

Publication Date: 2026-12-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

This study proposes a hybrid generative–ensemble framework to predict key mechanical properties of recycled aggregate concrete from mix proportions. An established database of 112 mixes was used to model compressive strength, split tensile strength, flexural strength, and elastic modulus. To mitigate data scarcity, a conditional variational autoencoder was trained on the training data only and used to generate additional physically plausible input samples, after which seven supervised learning algorithms were trained and compared using cross-validation. Gradient boosting and support vector regression achieved the most accurate and stable predictions across all targets, outperforming baseline linear models and commonly used empirical correlations. Feature-attribution analysis was used to identify the dominant drivers of each property, showing that binder-related variables primarily govern strength, while aggregate-related variables dominate stiffness. The results support practical, data-driven screening of recycled aggregate concrete mixes and provide interpretable guidance for sustainable mix design.

Open Access: Yes

DOI: 10.1038/s41598-026-42598-6