Knowledge-based Methodology Research Articles

Fingerprint Sequencing: An Authentication Mechanism that Integrates Fingerprints and a Knowledge-based Methodology to Promote Security and Usability

Fingerprint Sequencing: An Authentication Mechanism that Integrates Fingerprints and a Knowledge-based Methodology to Promote Security and Usability

APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems.

Mapping socio-environmental pressures to assess Portuguese soil vulnerability

Soil degradation has concerning impacts on ecosystem functions and biodiversity, decreasing essential processes such as food production or sustaining water quality. We studied eight soil threats to assess degradation processes: hydrogeological risks, soil sealing, salinization, desertification, contamination, compaction, erosion and decline in soil organic matter. A knowledge-based methodology was used measure the importance of 15 anthropogenic and environmental variables, with top weights of water availability (0.11), rate of land use change (0.09) and agricultural practices (0.09). Variables were combined into one additive model per threat to assess its distribution in Portugal. Merged soil threat vulnerability maps provided a spatial representation of the distribution of aggregated soil vulnerability, to find a 58% of moderate vulnerable soils where multiple threats coexist and are key for future management changes, a 32% of low vulnerable soils with reduced levels of soil threats, where temperature and precipitation are the main drivers of soil degradation and a 10% of highly vulnerable soils, which encompasses populated urban areas and rural civil parishes, where contamination and high rates of land turnover are the main drivers. Our maps can be used to make informed decisions on soil management and establish strategic policies for a sustainable use of land.

Knowledge acquisition and representation for intelligent operation support in offshore fields

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems’ instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support.

KNOWLEDGE-BASED EVALUATION OF PART ORIENTATION DESIRABILITY IN POWDER BED FUSION ADDITIVE MANUFACTURING

AbstractIn powder bed fusion (PBF) additive manufacturing, the definition of part orientation is one of the most important steps as it affects the quality, the cost and the build time of products. Different works already attempted to propose methodologies for the assessment of optimal build orientation based on criteria such as the minimization of support volume. Elicitation works with industry experts have shown that they use much more varied rules to determine the orientation of parts. For instance, they do not treat the different surfaces of the part the same way (e.g., experts state that “priority surfaces of the part must be oriented close to vertical”). Today, the available tools do not allow integrating these kind of specifications. This paper discusses a knowledge-based methodology for the evaluation of part candidate orientations in PBF. Desirability function approach is used to translate companies’ expertise in the form action rules into mathematical functions that are tested on geometries to provide metrics for assisting the decision-making. A case study is presented to illustrate the use of this desirability function approach on complex part orientation problem.

Topological reverse mirroring: a new efficient knowledge-based methodology of reachability analysis for Petri nets

‘Topological reverse mirroring’ (TRM) is a new knowledge-based methodology for more efficiently analysing reachability to derive the closed-form formulas (CFF) of the number of Control-Related states for PNs. According to variant k-th order systems, TRM is constructed by proving that both Gen-Right(k, gen) and Gen_Left(k, k−gen) are the topological reverse nets of Gen-Left(k, gen) such that there is a reversible one-to-one mapping (Mirroring) between these two systems. Thus Gen-Right(k, gen) and Gen-Left(k, k−gen) have same CFF which can be immediately derived by replacing the parameter ‘gen’ by ‘k−gen’ in validated CFF for Gen-Left(k, gen). The shown applications of TRM include the reachability analysing for systems with non-sharing sub-net; Deficient tokens; multi-non-sharing/multi-tokens resources; and multi-processes. This paper reports the core methodology to accelerate furthering emerging system-control applications for more complicated PNs based on the real-time information derived by their CFF.

Determination of Suitable Resource Discovery Tool and Methodology for High-Volume Internet of Things (IoT)

This paper discusses several issues supporting a knowledge-based methodology for discovery of high-volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using Distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context-based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches.

Knowledge-Based Sentiment Analysis and Visualization on Social Networks

A knowledge-based methodology is proposed for sentiment analysis on social networks. The work was focused on semantic processing taking into account the content handling the public user’s opinions as excerpts of knowledge. Our approach implements knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process. The results obtained were compared with data retrieved from Twitter and users’ reviews in Amazon. We measured the efficiency of our contribution with precision, recall, and the F-measure, comparing it with the traditional method of looking up concepts in dictionaries which usually assign averages. Moreover, an analysis was carried out to find the best performance for the classification by using polarity, sentiment, and a polarity–sentiment hybrid. A study is presented for arguing the advantage of using a disambiguation process in knowledge processing. A visualization system presents the social graphs to display the sentiment information of each comment as well as the social structure and communications in the network.

Text Semantic Annotation: A Distributed Methodology Based on Community Coherence

Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.

Crop science: A foundation for advancing predictive agriculture

Crop science: A foundation for advancing predictive agriculture

Knowledge based recursive non-linear partial least squares (RNPLS)

Soft sensors driven by data are very common in industrial plants to perform indirect measurements of difficult to measure critical variables by using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the colinearity of the predictor variables, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed in many applications to model the linear relations between process variables, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set up by carrying out the PLS regression over the augmented input matrix, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the system’s nature, and takes into account the available knowledge about the process, which is provided by expert knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to modify and adapt the model according to the process changes. This RNPLS algorithm has been tested using two case studies according to the available knowledge, a real industrial evaporation station of the sugar industry, where the expert knowledge about the process permits the formulation of the relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the process is obtained by a software tool. The results show that the methodology involving knowledge regarding the process is able to adjust the process changes, providing highly accurate predictions.

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

Smart Adaptive Big Data Analysis with Advanced Deep Learning

Abstract Increasing volumes of data, referred as big data, require massive scale and complex computing. Artificial intelligence, deep learning, internet of things and cloud computing are proposed for heterogeneous datasets in hierarchical analytics to manage with the volume, variety, velocity and value of the big data. These solutions are not sufficient in technical systems where measurements, waveform signals, spectral data, images and sparse performance indicators require specific methods for the feature extraction before interactions can be properly analysed. In practical applications, the data analysis, knowledge-based methodologies and optimization need to be combined. The solutions require compact calculation units which can be adaptively modified. The artificial intelligence is extended with various methodologies of computational intelligence. The advanced deep learning approach proposed in this paper uses generalized norms in feature generation, nonlinear scaling in developing compact indicators and linear interactions in model-based systems. The intelligent temporal analysis is available for all indices, including for stress, condition and quality indicators. The service and automation solutions combine these data-driven solutions with the domain expertise by using fuzzy logic for case-based systems. The applications are developed gradually in connections, conversion, cyber, cognition and configuration layers. The advanced methodology is based on the integration of features, scaling functions and interaction models specified by parameters. All the sub-systems and different combinations of them can be recursively updated and optimized with evolutionary computing. The systems adapt to the changing operating conditions and provide situation awareness for the risk analysis. The approach supports different levels of the smart adaptive systems.

FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Electrocatalytic degradation and minimization of specific energy consumption of synthetic azo dye from wastewater by anodic oxidation process with an emphasis on enhancing economic efficiency and reaction mechanism

This work focused on the knowledge-based methodology for the development of an electrochemical system, enabling simultaneous optimization of various operating parameters such as current density (j), initial dye concentration (Co), NaCl concentration (CN) for the mineralization of Reactive Violet 2 (RV-2) and Acid Brown 14 (AB-14) dye on the efficiency of removal, energy consumption (EC), Chemical Oxygen Demands (COD), apparent rate constants (kapp) and Electrical Energy per Order (EEO) all of which have been examined. The relationship between kapp and EEO is also discussed. The degradation efficiency and kapp always rising at higher j and lower Co and CN while EC, EEO, and operating cost increased at higher j, Co and CN. On the other hand, The COD increased with decrease j, Co and higher CN. Due to the strong formation of hydroxyl radicals from water discharge, the graphite electrode possesses a strong power of electro-generation rate and competitive wasting reactions of organic compounds. The results demonstrated that the relatively high dye removal, COD and low specific energy consumption are obtained simultaneously only if the various parameters are regulated to a plausible value j of 79Am−2, Co of 100mg/L and CN of 1g/L within 60min of electrolysis. The color removal efficiency is much faster for RV-2 compared to AB-14 due to the contribution of azo bond in the dye molecule. Also, the EC and kapp are higher for RV-2 than AB-14 while is lower in terms of EEO and COD. A comprehensive reaction sequence of RV-2 and AB-14 mineralization involving all oxidation products was proposed. Formation and evolution of aromatic and aliphatic (short-chain carboxylic acids) intermediates during the treatment and a mineralization pathway is proposed. The estimated cost of operation for degradation at optimum conditions is calculated as 1.54 and 1.29 USD m−3/g dye for complete degradation RV-2 and AB-14, respectively.

PSS Pattern Concept for Knowledge Representation in Design Process of Industrial Product-service Systems

To save time and cost in development process of new customized product-service systems, engineers need methodological guidelines and generic knowledge that help constructing specific solutions for new customers’ requirements and business opportunities. Despite the specific character of every PSS due to several customization issues, many characteristics are shared between PSS from the same product and/or service family. This paper proposes a knowledge-based methodology to support the PSS design process, extending the concepts of pattern and instance as main knowledge fragments. The main idea is to encapsulate in the pattern a conceptual definition of a collection of potential verified solutions, able to achieve a product-service with certain performance value with regard to a set of working conditions. These solutions are then filtered and refined by means of the PSS instance when answering one specific PSS demand.

A formal functional representation methodology for conceptual design of material-flow processing devices

AbstractAlthough there has been considerable computer-aided conceptual design research, most of the proposed approaches are domain specific and can merely achieve conceptual design of energy flows-processing systems. Therefore, this research is devoted to the development of a general (i.e., domain-independent) and knowledge-based methodology that can search in a wide multidisciplinary solution space for suitable solution principles for desired material-flow processing functions without designers' biases toward familiar solution principles. It first proposes an ontology-based approach for representing desired material-flow processing functions in a formal and unambiguous manner. Then a rule-based approach is proposed to represent the functional knowledge of a known solution principle in a general and flexible manner. Thereafter, a simulation-based retrieval approach is developed, which can search for suitable solution principles for desired material-flow processing functions. The proposed approaches have been implemented as a computer-aided conceptual design system for test. The conceptual design of a coin-sorting device demonstrates that our functional representation methodology can make the proposed computer-aided conceptual design system to effectively and precisely retrieve suitable solution principles for a desired material-flow processing function.

Fully Electrified Regenerative Braking Control for Deep Energy Recovery and Maintaining Safety of Electric Vehicles

Recycling braking energy is significant to improve the total energy efficiency of electric vehicles (EVs). Moreover, braking safety must be maintained under complex conditions. As the actuator, the electric traction motor has much better features than the internal combustion engine, e.g., faster torque response and capability for energy feedback. From the perspective of energy efficiency and safety, highlighting regenerative braking is a meaningful but challenging problem. In this paper, a braking system using only electric motors/generators as the actuators is investigated, in which the energy may be potentially fed back to the onboard energy storage system (ESS) as much as possible. The energy that may be recovered to the ESS is analyzed. To maintain the stability and to improve the performance of the regenerative braking in unknown tire–road conditions, a knowledge-based methodology in a hierarchical control structure is proposed, where the maximum adhesion force and the motor reference torque (MRT) are determined online. The proposed methodology avoids the complex determination of the optimum slip ratio, while acquiring nearly the optimum antiskid braking performance. Simulation and experiment were carried out to validate the effectiveness.

Application of a multi-criteria decision model to select of design choices for WWTPs

Stricter regulations in the field of sanitation are driving new dimensions of analysis, in which socioeconomic criteria combined with associated environmental issues are in turn increasing the complexity of wastewater management. In this environment, the development of innovative wastewater treatment technologies provides decision-makers with many efficient alternatives to face these new challenges. This growing number of alternatives inevitably leads, however, to ever greater complexity in the design or upgrade of treatment facilities and demands the acquisition and integration of updated knowledge and well-co-ordinated expertise, encouraging a multi-disciplinary approach. In this paper, it was demonstrated that these requirements have been successfully met in a environmental decision support systems (EDSS). The EDSS was built according to a knowledge-based methodology, whose main objective is the identification and assessment of the most appropriate wastewater treatment technologies for the design of new facilities or the upgrading of obsolete plants. Because removal of nutrients is essential to this approach, this study explores the use of the EDSS to address the selection of biological treatment technologies for different scenarios characterized by wastewater composition (C/N ratio) and other relevant criteria such as environmental and economic factors, population size, discharge in sensitive areas, reuse, cost-benefit analysis, life-cycle analysis, and technical aspects (use of innovative technologies, space availability, reliability, and simplicity of operation).

Knowledge-based soil type classification using terrain segmentation

Soil information covering regional, continental, or even global scales is needed for modelling, prediction, or estimation of environmental risks, crop yield estimation, carbon stock estimation, or research on climate change. This study aims to evaluate the extent to which geographic object-based image analysis and expert-knowledge, using digital maps of climate, topography, vegetation, and geology as soil covariates (GEOBIA approach), might model and reproduce a conventional soil map at a scale 1:1000000 in the south-west of Romania. The environmental variables were segmented with a region-growing algorithm, the resulting objects being subsequently classified into soil types using expert-knowledge fuzzy classification rules. To assess the geographical support of classification for the modelling of a conventional soil map, we quantitatively evaluated a pixel-based soil map produced using the same expert-knowledge classification rules, as an alternative to an object-based approach. To evaluate the source of soil information, we quantitatively assessed the map of the World Reference Base soil groups produced by the data-driven global soil information system, SoilGrids, as an alternative to expert-knowledge rules. The digital soil maps were quantitatively compared with the conventional soil map. Evidence was provided that the similarity of soil types with the conventional soil map was higher when the modelling was conducted through GEOBIA approach (general similarity of 65% and fuzzy kappa index of 0.58) than the pixel-based approach and SoilGrids. Furthermore, the results showed that the SoilGrids map achieved higher similarity to conventional soil map than the pixel-based soil map. When tested in another area, without modification to the knowledge-based methodologies, the same conclusions could be drawn, although the two maps recorded lower similarity values. The overall reduction in similarity values is explained by a high variability of some soil types under different environmental conditions.