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Search results for: KNOWLEDGE-BASED MODELS
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Low-Cost and Highly-Accurate Behavioral Modeling of Antenna Structures by Means of Knowledge-Based Domain-Constrained Deep Learning Surrogates
PublicationThe awareness and practical benefits of behavioral modeling methods have been steadily growing in the antenna engineering community over the last decade or so. Undoubtedly, the most important advantage thereof is a possibility of a dramatic reduction of computational expenses associated with computer-aided design procedures, especially those relying on full-wave electromagnetic (EM) simulations. In particular, the employment of...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Prediction of protein structure using a knowledge-based off-lattice united-residue force field and global optimization methods
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Commitment-based human resource practices, job satisfaction and proactive knowledge-seeking behavior: The moderating role of organizational identification
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Efficient Surrogate Modeling and Design Optimization of Compact Integrated On-Chip Inductors Based on Multi-Fidelity EM Simulation Models
PublicationHigh-performance and small-size on-chip inductors play a critical role in contemporary radio-frequency integrated circuits. This work presents a reliable surrogate modeling technique combining low-fidelity EM simulation models, response surface approximations based on kriging interpolation, and space mapping technology. The reported method is useful for the development of broadband and highly accurate data-driven models of integrated...
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A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia, 2008 to 2015
PublicationLogit and discriminant analyses have been used for corporate bankruptcy prediction in several studies since the last century. In recent years there have been dozens of studies comparing the several models available, including the ones mentioned above and also probit, artificial neural networks, support vector machines, among others. For the first time for Colombia, this paper presents a comparative analysis of the effectiveness...
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Fouling mechanisms in anoxic-aerobic sequencing batch membrane bioreactor based on adapted Hermia models and main foulant characteristics
PublicationVarious derivatives of Hermia models (complete pore blocking, intermediate pore blocking, cake layer formation, and standard pore blocking) and different assessments of foulant characteristics have long been used to determine the membrane fouling mechanisms. Accordingly, this study aims to adapt Hermia models and their combination according to the operating conditions of an anoxic-aerobic sequencing batch membrane bioreactor (A/O-SBMBR)....
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Knowledge about coeliac disease: Results of survey conducted among persons screened using a self-administered transglutaminase-based test
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Switched-capacitor DC-DC converters in arbitrary switching mode - topologically derived resistive models based on incremental graph approach.
PublicationIn the preceding paper we reviewed some of modeling approaches aimed at systematic formulation and solution of switched capacitor DC-DC converters. In our review, special attention was paid to computationally efficient and mathematically elegant methods. In so doing we had tried to demonstrate the virtues of unified Incremental Graph (IG) approach. Incremental Graph is, in concept, a tool originally created for analysis and synthesis...
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Prediction of protein structure with the coarse-grained UNRES force field assisted by small X-ray scattering data and knowledge-based information
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Hydrochars as a bio-based adsorbent for heavy metals removal: A review of production processes, adsorption mechanisms, kinetic models, regeneration and reusability of hydrochar
PublicationThe spread of heavy metals throughout the ecosystem has extremely endangered human health, animals, plants, and natural resources. Hydrochar has emerged as a promising adsorbent for removing heavy metals from water and wastewater. Hydrochar, obtained from hydrothermal carbonization of biomass, owns unique physical and chemical properties that are highly potent in capturing heavy metals via surface complexation, electrostatic interactions,...
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Pharmacophore models based studies on the affinity and selectivity toward 5-HT1A with reference to α1-adrenergic receptors among arylpiperazine derivatives of phenytoin
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Quality of Institutions for Knowledge-based Economy within New Institutional Economics Framework. Multiple Criteria Decision Analysis for European Countries in the Years 2000–2013
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Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models
PublicationHigh-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate...
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Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations
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Optimization-Based Robustness Enhancement of Compact Microwave Component Designs with Response Feature Regression Surrogates
PublicationThe ability to evaluate the effects of fabrication tolerances and other types of uncertainties is a critical part of microwave design process. Improving the immunity of the device to parameter deviations is equally important, especially when the performance specifications are stringent and can barely be met even assuming a perfect manufacturing process. In the case of modern miniaturized microwave components of complex topologies,...
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Fast EM-Driven Parameter Tuning of Microwave Circuits with Sparse Sensitivity Updates via Principal Directions
PublicationNumerical optimization has become more important than ever in the design of microwave components and systems, primarily as a consequence of increasing performance demands and growing complexity of the circuits. As the parameter tuning is more and more often executed using full-wave electromagnetic (EM) models, the CPU cost of the overall process tends to be excessive even for local optimization. Some ways of alleviating these issues...
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Low-Cost and Precise Automated Re-Design of Antenna Structures Using Interleaved Geometry Scaling and Gradient-Based Optimization
PublicationDesign of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ensure the best achievable system performance. If the center frequency at the current design is distant from the target one, local tuning methods generally fail, whereas global algorithms...
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An agent-based approach to ANN training
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A novel version of simulated annealing based on linguistic patterns for solving facility layout problems
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On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction
PublicationNitrogen dioxide (NO2) is a prevalent air pollutant, particularly abundant in densely populated urban regions. Given its harmful impact on health and the environment, precise real-time monitoring of NO2 concentration is crucial, particularly for devising and executing risk mitigation strategies. However, achieving precise measurements of NO2 is challenging due to the need for expensive and cumbersome equipment. This has spurred...
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A fuzzy logic model for forecasting exchange rates
PublicationThis article is devoted to the issue of forecasting exchange rates. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology - fuzzy logic theory - and to evaluate its effectiveness in times of prosperity and during the financial crisis. The model is based on sets of rules written by the author in the form of IF-THEN, where expert knowledge is stored. This model is the result...
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On Decision-Making Strategies for Improved-Reliability Size Reduction of Microwave Passives: Intermittent Correction of Equality Constraints and Adaptive Handling of Inequality Constraints
PublicationDesign optimization of passive microwave components is an intricate process, especially if the primary objective is a reduction of the physical size of the structure. The latter has become an important design consideration for a growing number of modern applications (mobile communications, wearable/implantable devices, internet of things), where miniaturization is imperative due to a limited space allocated for the electronic circuitry....
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Rapid Antenna Optimization with Restricted Sensitivity Updates by Automated Dominant Direction Identification
PublicationMeticulous tuning of geometry parameters turns pivotal in improving performance of antenna systems. It is more and more often realized using formal optimization methods, which is demonstrably the most efficient way of handling multiple design variables, objectives, and constraints. Although in some cases a need for launching global search arises, a typical design scenario only requires local optimization, especially when a decent...
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Dimensionality-Reduced Antenna Modeling with Stochastically Established Constrained Domain
PublicationOver the recent years, surrogate modeling methods have become increasingly widespread in the design of contemporary antenna systems. On the one hand, it is associated with a growing awareness of numerical optimization, instrumental in achieving high-performance structures. On the other hand, considerable computational expenses incurred by massive full-wave electromagnetic (EM) analyses, routinely employed as a major design tool,...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublicationAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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A new multi-process collaborative architecture for time series classification
PublicationTime series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery...
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International Conference on Knowledge-based Intelligent Electronic Systems
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International Conference on Knowledge-Based and Intelligent Information and Engineering Systems
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International Conference on Legal Knowledge and Information Systems (International Conference on Legal Knowledge-based Systems)
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Information based integration for complex systems. W: Knowledge and infor-mation technology management in the 21st century organizations. Ed. A. Gu- nasekaran, O. Khalil, M.R. Syed. London: Idea**2002 s. 89-104 Informacyjna integracja systemów złożonych.
PublicationW rozdziale zaproponowano strukturę inteligentnego systemu wspomagania pro-cesu integracji dla złożonych systemów wytwarzania. System wspomagania opar-to na bazie wiedzy, w której wiedza modelowana jest regulami produkcji. Zbu-dowano również iteracyjny algorytm integracji. Samą ideę integracji opartona przepływach informacyjnych.
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Optimal detection observers based on eigenstructure assignment. W: FaultDiagnosis. Models, artificial intelligence, applications. Ed. J. Korbicz, J.M. Kościelny, Z. Kowalczuk, W. Cholewa. Berlin: Springer Verlag**2004 s. 219-259, 7 rys. bibliogr. 41 poz. Optymalne obseratory detekcyjne oparte na strukturze własnej.
PublicationPraca dotyczy analitycznych metod syntezy algorytmów detekcji uszkodzeń. De-finiując wektor resztowy jako ważony błąd uzyskanej oceny wyjścia danego o-biektu, poszukuje się takich obserwatorów stanu, dostarczających owych osza-cowań, dla których wektor resztowy jest w możlwie wysokim stopniu niezależnyod niemierzalnych zakłóceń oddziałujących na obiekt oraz od niemierzalnychszumów w torach pomiarowych. Rozważa się algorytmy...
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Implementation and performance evaluation of the agent-based algorithm for ANN training
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Intelligent integration for autonomous manufacturing systems
PublicationW artykule zaproponowano inteligentną platformę integracyjną dla autonomicznych systemów produkcyjnych. Autonomie zdefiniowano w pracy jako samowystarczalne zamkniecie informacyjne. Inteligencje natomiast zdefiniowana jako umiejętność podejmowania właściwych decyzji w zmiennym otoczenie. Platformę integracyjną oparto na technice systemów ekspertowych.
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Towards an experience based collective computational intelligence for manufacturing
PublicationKnowledge based support can play a vital role not only in the new fast emerging information and communication technology based industry, but also in traditional manufacturing. In this regard, several domain specific research endeavors have taken place in the past with limited success. Thus, there is a need to develop a flexible domain independent mechanism to capture, store, reuse, and share manufacturing knowledge. Consequently,...
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Applying Fuzzy Logic of Expert Knowledge for Accurate Predictive Algorithms of Customer Traffic Flows in Theme Parks
PublicationThis study analyzes two forecasting models based on the application of fuzzy logic and evaluates their effectiveness in predicting visitor expenditure and length of stay at a popular theme park. The forecasting models are based on a set of more than 600 decision rules constructed in the form of a complex series of IF-THEN statements. These algorithms store expert knowledge. A descriptive instrument that records the individual visitor's...
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Overview of Knowledge Management in Occupational Safety, Health, and Ergonomics
PublicationOccupational safety, health, and ergonomics (OSHE) are strategic pillars of contemporary organizations. In order to provide safer workplace, it is vital to manage the organizational knowledge, so that effective decision making along with absolute compliance to standards can be executed. The critical challenge for OSHE in modern industry is management of existing individual knowledge (experience), structure knowledge, and organizational...
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Fundamentals of Physics-Based Surrogate Modeling
PublicationChapter 1 was focused on data-driven (or approximation-based) modeling methods. The second major class of surrogates are physics-based models outlined in this chapter. Although they are not as popular, their importance is growing because of the challenges related to construction and handling of approximation surrogates for many real-world problems. The high cost of evaluating computational models, nonlinearity of system responses,...
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Cognitum Ontorion: Knowledge Representation and Reasoning System
Publication“If knowledge can create problems, it is not through ignorance that we can solve them.” (Isaac Asimov). Nevertheless, at any point of human activity, knowledge (besides practice) is a key factor in understanding and solving any given problem. Nowadays, computer systems have the ability to support their users in an efficient and reliable way. In this paper we present and describe the functionality of the Cognitum Ontorion system....
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Cognitum Ontorion: Knowledge Representation and Reasoning System
PublicationAt any point of human activity, knowledge and expertise are a key factors in understanding and solving any given problem. In present days, computer systems have the ability to support their users in an efficient and reliable way in gathering and processing knowledge. In this chapter we show how to use Cognitum Ontorion system in this areas. In first section, we identify emerging issues focused on how to represent and inference...
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Agent-based social network as a simulation of a market behaviour
PublicationRecent years and the outbreak of world's economic crisis in 2008 proved the crucial importance of reliable analysis of market dynamics. However, werarely apply models of proper detail level (the global prosperity forecast of 2007 can be seen as a grim proof). The behaviour of individuals and companies is far from being ideal and rational. Many claims that the economic paradigm of rational expectations (coming from J. Muth and R....
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Michał Tomasz Tomczak dr hab.
PeopleMichał T. Tomczak, PhD. DSc is an Associate Professor of Human Resources Management and Vice Dean for Cooperation and Development at Faculty of Management and Economics. He was a Visiting Scholar at University of North Texas, TX; Curtin University and University of Western Australia. Author and coauthor of more than 50 publications in the field of human resource management. Principal Investigator and co-investigator of several...
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Decisional-DNA Based Smart Production Performance Analysis Model
Publicationn order to allocate resources effectively according to the production plan and to reduce disturbances, a framework for smart production performance analysis is proposed in this article. Decisional DNA based knowledge models of engineering objects, processes and factory are developed within the proposed framework. These models are the virtual representation of manufacturing resources, and with help of Internet of Things, are capable...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublicationIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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Marek Szelągowski dr
PeopleMarek Szelągowski has participated in the creation and implementation of IT solutions in the fields of accounting, human resources management, production, IT infrastructure management, etc. As the CIO of the BUDIMEX Group in 2000–2008 he was responsible for the accommodation of informatization strategies to the changing needs of the business sector. He was managing and participating in analyses and optimizations of business processes...
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Multi-objective optimization of expensive electromagnetic simulation models
PublicationVast majority of practical engineering design problems require simultaneous handling of several criteria. For the sake of simplicity and through a priori preference articulation one can turn many design tasks into single-objective problems that can be handled using conventional numerical optimization routines. However, in some situations, acquiring comprehensive knowledge about the system at hand, in particular, about possible...
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Krzysztof Goczyła prof. dr hab. inż.
PeopleKrzysztof Goczyła, full professor of Gdańsk University of Technology, computer scientist, a specialist in software engineering, knowledge engineering and databases. He graduated from the Faculty of Electronics Technical University of Gdansk in 1976 with a degree in electronic engineering, specializing in automation. Since then he has been working at Gdańsk University of Technology. In 1982 he obtained a doctorate in computer science...
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Subjective tests for gathering knowledge for applying color grading to video clips automatically
PublicationThe analysis of film music concerning caused emotions may allow for a more accurate adaptation of the color of the film in the context of color grading. Therefore, this paper aims to gather knowledge on the correlation between the applied color palette to a video clip, music associated with a particular shot, and emotions evoked. For that purpose, subjective tests are prepared in which several video clips are presented with or...
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Investigating Analytics Dashboards’ Support for the Value-based Healthcare Delivery Model
PublicationImproving the value of care is one of the essential aspects of Value-Based Healthcare (VBHC) model today. VBHC is a new HC delivery model which is centered on patient health outcomes and improvements. There is anecdotal evidence that the use of decision aid tools like dashboards can play a significant role in the successful implementation of VBHC models. However, there has been little or no systematic studies and reviews to establish...