Dynamic Knowledge Base Research Articles

Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph

To improve the level of modern hospital management and establish a scientific and effective medical equipment maintenance guarantee system, this paper proposes the construction of a dynamic interactive fault repair knowledge base system for magnetic resonance imaging (MRI) equipment based on knowledge graph technology. This innovative application of intelligent management for large medical equipment involves collecting and organizing actual maintenance data. The paper constructs the medical equipment maintenance knowledge base, with MRI equipment as a case study, and initially implements functions such as fault information retrieval, technical guidance, and training and learning for MRI equipment. These functions aim to provide an objective decision-making basis for the maintenance and management of large medical equipment, enhance the efficiency of fault repair and maintenance, and elevate the construction level of intelligent hospitals.

A Demonstration of TENDS: Time Series Management System Based on Model Selection

The growth in sensor technologies, IoT devices, and information systems has opened up new opportunities for managing time series data across various domains. Despite significant progress, existing time series management systems face two crucial limitations: insufficient functionality and inadequate adaptability. This highlights the need for more comprehensive systems that not only improve data quality and analysis but also effectively manage the variety and volume of time series data. This paper presents TENDS, a time series management system based on model selection. TENDS uniquely combines advanced functionalities for imputation, prediction, and anomaly detection, offering a comprehensive analysis of time series data. It features (i) an effective model selection mechanism to adapt to various data types and to improve efficiency; (ii) fourteen state-of-the-art prediction methods and three state-of-the-art imputation methods; and (iii) a dynamic expert knowledge base for anomaly detection, evolving continuously with new data to ensure accuracy. TENDS boasts a comprehensive suite of visualization tools. With its configurable offline and online interfaces, TENDS (i) provides extensive flexibility in model selection and parameter adjustment, (ii) facilitates easy visualization of training results, and (iii) supports real-time documentation and statistical analysis of time series.

Strategic innovation system and firm innovation performance: empirical analysis based on dynamic capabilities and knowledge base view

ABSTRACT The accelerating pace of consumer and market demand poses a substantial challenge for firms aiming to develop knowledge-intensive strategies to address demand gaps. These strategies are designed to establish and maintain competitive advantage by innovatively leveraging their competencies and capabilities. This study employs the dynamic capabilities and knowledge capabilities views to investigate the impact of strategic innovation systems (SIS) on firm innovation performance (FIP). To achieve this objective, the study analyzes the integration of knowledge into the innovation process to assess the influence of strategic systems on innovation performance. The research hypotheses were evaluated using a quantitative research design involving 111 SMEs in China. The findings reveal a negative impact of both SIS and distinct competencies (DC) on firm innovation performance. Furthermore, SIS demonstrated a positive influence on product innovation. Additionally, the mediation analysis showed an indirect effect of SIS on firm innovation performance through product innovation. Moreover, DC and externalities displayed mediating effects. These findings contribute to the literature on strategic systems and innovation performance by providing practical recommendations for managers seeking to enhance innovation performance.

Expert System For Diagnosing Alzheimer's Using Certainty Factor Method

This expert system for diagnosing Alzheimer's disease is a computer-based stem system used as a tool to diagnose Alzheimer's disease based on a dynamic knowledge base. This knowledge base contains knowledge obtained from various sources including from the experience of experts who are proficient in their fields and also books related to diagnosis of Alzheimer's disease and then collected into a database system needed for making conclusions. In this system, the certainty factor method will be used using a forward chaining inference engine. The purpose of making this root stem p can be useful for many people and can know clearly about Alzheimer's disease from its symptoms and solutions, which will be displayed in the form of a website using PHP programming with MySQL database.

Knowledge-based OpenBIM data exchange for building design

Building design is highly complex as it involves numerous professionals and their interactions, hence with diverse tools used and multi-resources and different structured data and information required to be processed. Despite the existing efforts to develop multi-objective decision making tools to support complex design, most of the research face difficulties to provide holistic, dynamic and collaborative knowledge base due to the complexity of the information interoperability issues across different parties and throughout life cycle. This paper developed an automatic data exchange framework that combines only the necessary data from BIM models using semantic web technology to eliminate inefficiencies in data exchange and improve decision-making early in the design stage. The proposed data acquisition method can produce a dynamic knowledge base to connect both static and dynamic information. A multi-objective knowledge base was developed to assist engineers associated with sustainability and cost in comparing different design options based on the existing BIM data. The proposed ontology was developed using a machine-readable format, allowing the ability to add more concepts to it in the future and work with other automated tools. The validated framework could reduce human involvement and errors while providing more efficient ways to leverage diverse information sources together to support holistic decision-making for building design.

Sistem Pakar Identifikasi Penyakit Kulit Melanoma dengan Metode Teorema Bayes

Melanoma is a skin cancer disease that occurs due to abnormal growth of melanocytes, which are cells that can produce pigment in the skin. Melanoma is one of the most vicious skin cancers and can easily spread to other organs in the human body, such as the lungs and brain. This expert system for diagnosing melanoma skin disease is a computer-based system that is used as a tool for diagnosing melanoma skin disease based on a dynamic knowledge base. The purpose of this research is to build an expert system that can identify Melanoma Skin Disease. An expert system built using the Bayes theorem method in a diagnostic process based on expert knowledge or experience. The results of this study present an expert system capable of diagnosing melanoma skin disease quickly, precisely and accurately. These results are presented in a system built in the form of a website using PHP programming with a MySQL database. Overall this expert system can be useful for patients or the general public to be able to know clearly about melanoma skin disease from the symptoms and can provide solutions in handling it.

Construction Method of Digital Twin System for Thin-Walled Workpiece Machining Error Control Based on Analysis of Machine Tool Dynamic Characteristics

In the intelligent optimization process of aerospace thin-walled parts, there are issues such as solidification of core knowledge base, high system coupling degree, and real-time evaluation and optimization feedback required for the knowledge base. These problems make it difficult to expand the functions of the digital twin system and meet the growing processing needs, ultimately hindering the application of digital twin technology. To address these issues, a digital twin system for controlling processing errors in thin-walled parts was built using a microservices architecture. In addition, a method for building a digital twin system at the processing unit level with the best coupling degree was proposed, mainly targeting the dynamic characteristics analysis knowledge base of thin-walled parts. Furthermore, to meet the requirements for backward compatibility of the processing unit level digital twin system, a comprehensive solution including the construction, operation, evaluation, optimization, and visualization of a knowledge base for the dynamic characteristics of the processing unit was proposed, providing guidance for the digital transformation and upgrading of CNC machine tools and the optimization of processing technology based on digital twin technology.

Professional Pledge in Social Work: Implications for Professional Identity and Value Integration

The social work profession lays a strong emphasis on internalizing and reinforcing core social work values and ethical standards in order to create a cadre of social workers who can withstand the criticalities of any crisis and commit themselves to firmly stand with those whose survival and well-being is fragile and “on the brink”. The performance of this role not only requires a dynamic and contextual knowledge base and evolving skills set, it mandates a deep rooted acquiescence with a fundamental set of values that shape the profession’s vision and mission, and establish the goals and priorities of the practitioners. The transition from an entry level neophyte to a competent social work professional entails a journey which is characterized by the assumption and bolstering of a professional persona that grounds the incumbent within the functional niche of social work. The present paper looks at the integration of a social work pledge as a viable means for social work incumbents to establish identification with social work values, don a professional identity, and assimilate with professional peers. It looks at the existing practice of pledge taking by highlighting the experience of a distinguished school of social work. It envisions the ‘pledge’ and the tradition of ‘taking the pledge’ evolving as a strong and more universal medium for creating an emotional connect of social work students with the value driven profession of social work. Keywords: Pledge, values, ethics, social work, profession

Understanding knowledge role transitions: A perspective of knowledge codification

Abstract Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility (whether new knowledge could be transitioned into formal knowledge) and transition pace (how long it would take). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multidimensional variables are essential to comprehensively understand knowledge role transition. More significantly, early funding support is more important for improving transition pace; journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; and weaker knowledge relatedness raises the transition possibility, whereas stronger knowledge relatedness improves the transition pace.

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

A dynamic knowledge base and its data compression with homomorphism

In reality there are always a large number of complex massive databases. The notion of homomorphism may be a mathematical tool for studying data compression in knowledge bases. This paper investigates a knowledge base in dynamic environments and its data compression with homomorphism, where “dynamic” refers to the fact that the involved information systems need to be updated with time due to the inflow of new information. First, the relationships among knowledge bases, information systems and relation information systems are illustrated. Next, the idea of non-incremental algorithm for data compression with homomorphism and the concept of dynamic knowledge base are introduced. Two incremental algorithms for data compression with homomorphism in dynamic knowledge bases are presented. Finally, an experimental analysis is employed to demonstrate the applications of the non-incremental algorithm and the incremental algorithms for data compression when calculating the knowledge reduction of dynamic knowledge bases.

Enacting culturally sustaining immersion pedagogy through SFL and translanguaging design

AbstractFramed within the discussions of the knowledge base for language immersion teachers, this article presents a qualitative case study of a first‐year Grade 2 French immersion teacher's enactment of his systemic functional linguistic (SFL) knowledge to position his students in a culturally sustaining genre pedagogy. We examined interactive events and interview data to understand how Ahmed deployed his SFL knowledge base to position four African American students in classroom interactions as they deconstructed and reconstructed focal genres in this second‐grade French immersion classroom. Our findings revealed that Ahmed positioned his students in three ways: as an inclusive classroom community, as readers and writers, and as language analysts. Likewise, the students took up roles as genre analysts, active language users, and engaged community members. The findings also demonstrated that Ahmed selectively deployed his linguistic resources in varieties of French and English to connect with his students, create community with them, and achieve his instructional goals within a translanguaging space (e.g., Tian, 2021; Wei, 2011). These findings point to the potential of SFL and translanguaging pedagogy as a flexible and dynamic knowledge base for enacting culturally sustaining immersion education.

(Data-Driven) Development of dynamic scheduling in semiconductor manufacturing using a Q-learning approach

ABSTRACT In accordance with the operational characteristics of semiconductor wafer fabrication (FAB), multiple-dynamic-scheduling-rule (MDSR) selection mechanisms must be developed for dynamic scheduling in FAB. The main disadvantage of the classic MDSR selection approach is that it does not immediately reflect changes in production requirements in a dynamic FAB environment. A dynamic scheduling knowledge base (KB) is generally not static; therefore, if a major change occurs in the production system, a mechanism for modifying the KB must be established. Accordingly, we develop a dynamic scheduling scheme that uses a Q-learning-based MDSR selection mechanism to support shop floor control in FAB. This selection mechanism involves two phases: initial generation of an MDSR KB and revision of the MDSR KB. On the basis of various performance criteria, the proposed scheme exhibited superior system performance to a classic MDSR selection approach and an approach that applies fixed scheduling decisions.

A twin data and knowledge-driven intelligent process planning framework of aviation parts

ABSTRACT As the core link of intelligent manufacturing, the process planning of aviation parts still faces the challenges such as relying on manual experiences for process decision-making and lack of linkage between process design and manufacturing for process optimisation. Process knowledge could support scientific decision-making on process issues, while twin data, namely high-fidelity simulation data and feedback information of manufacturing site, could further verify the process plans and optimise process parameters, so as to continuously improve the quality of process plans. Consequently, this paper proposes a general framework for twin data and knowledge-driven intelligent process planning (TDKIPP) of aviation parts, and analyses four standard procedures that support the above-mentioned reference framework, namely mechanism-data fusion process digital twin model, dynamic process knowledge base, process decision-making and evaluation, machining quality prediction and process feedback optimisation. A thus constructed test bed of TDKIPP and its four application examples about the process planning of a micro turbojet engine integral impeller demonstrate the feasibility and effectiveness of the proposed approach.

Employee Humility as a Catalyst for Knowledge Sharing Behavior in Kenya’s Universities

Purpose – This paper is purposed to provide a deeper understanding to the current concept of humility and its role in higher education as far as knowledge dissemination is concerned. Since humility has become increasingly important in latest years, and is recognized as a critical asset for universities particularly due to the growing complexity of dynamic knowledge base environments and the further advancement of regulatory frameworks for competitive advantage. Humility in institutions of higher learning is an idea whose time has come. In light of anticipated challenges and changes that continue to unfold in the 21st century, scholars in public and private universities have suggested a greater need for organizational members to have the humility to acknowledge areas of ignorance and inexperience and to foster the learning and adaptation that will be required to succeed in an increasingly unpredictable workplace. Design/methodology/approach – Explanatory and confirmatory factor analysis have been conducted to explore and validate the factor of humility as a construct of emotional intelligence. The paper is based on an explanatory study that targeted a population of 6,423 academic staff employees in Kenya from 49 selected universities. A sample size of 378 employees was systematically selected and data collected using a structured questionnaire anchored on a five-point Likert scale. The instrument was evaluated for internal consistency and subjected to principal component analysis to explore extant dimensions. Findings: Though humility awareness by academic staff of universities is widely known, more insights need to be drawn to expound more from the benefit of knowledge sharing behavior’s regression results indicated that Humility has a positive and significant effect on knowledge sharing behavior (β = 0.30, p<0.05). Universities in Kenya should be encouraged to focus on humility in order to improve on knowledge sharing behaviours of academic staff since it was evident that whenever academic staff had greater humility in the university, they would inspire and influence universities competitive advantage through knowledge and emotional intelligence. It is treating all people regardless of who they are, with respect, gentleness, kindness, and forgiveness . A person who is humble shares knowledge without measure of superiority, arrogance, and haughtiness of a person towards other people and understands what drives their behaviour, as well as the effects that it has on others as the most common trademarks of intelligence that value the enhancement of knowledge sharing behavior in modern universities as revealed by the study. Originality/value – Humility awareness as a catalyst in universities in Kenya have relatively been downplayed by institutions of higher learning, University management and scholars especially in harnessing knowledge.

A novel optimization framework for integrated local energy system multi-objective dispatch problem based on dynamic knowledge base

Integrated local energy system (iLES) can take full advantage of local energy resources and diversify energy supplies. However, the complexity and flexibility of multiple-input multiple-output system structure makes it difficult for iLES to guarantee energy supply quality, which needs be solved by making reliable dispatching schemes. In this paper, the optimization model of typical iLES multi-objective dispatch problem (MDP) is built and the common problems are analyzed. Aiming at adapting to the changes of the iLES-MDPs model during the practical operation process, a dynamic knowledge base-based multi-objective optimization method is proposed, by which a knowledge base is established for iLES-MDPs taking account of the characteristic of system before optimization process. Besides, considering the frequent variations of system parameters, iLES structure, energy input and load demands, an updating approach is introduced to dynamically adjust the knowledge base. Thereafter, a multi-objective optimization framework is designed to find the feasible Pareto solutions for the iLES-MDPs, by utilizing the reference solutions in knowledge base during the evolutionary procedure. Finally, the proposed method is applied to a series of iLES-MDPs, which involve renewable energy sources, combined cooling heating and power system, energy storage devices and different operation modes. The simulation results verify that the method can well adapt to the challenges of all types of iLES-MDPs. Moreover, it can obtain feasible Pareto solutions efficiently as well as reduce the performance requirement of optimization algorithms.

Ontological synthesis of non-stationary dynamics models of on-board intelligence systems

An ontological synthesis of models for interpreting non-stationary dynamics in onboard intelligence systems operating in the emergency computing mode (Urgent Computing - UC) is considered. The ontological system of a dynamic knowledge base is formulated on the basis of theoretical models of the modern catastrophe theory (СT). The analysis and forecast of the evolutionary dynamics of a non-stationary object is implemented in a multiprocessor computing environment. Within the framework of the ontological system, an approach to the interpretation of non-stationary dynamics using fractal geometry and the theory of dynamical systems stability is formulated. The practical application of the developed ontology model is discussed in relation to the interpretation of the interaction of a marine dynamic object (MDO) with the external environment at a given time interval. The dynamic СT model determines the motion of the MDO system to the target attractor and in case of stability loss. Examples of the implementation of the ontological synthesis of non-stationary dynamics in safety systems for navigation and landing of ship-based aircraft are given.

A privacy-preserving group decision making expert system for medical diagnosis based on dynamic knowledge base

Most of the present researches on wise medical incorporate a static supporting data, while medical knowledge and rules are featured with a dynamic change, which affect calculation efficiency and accuracy of the result. This paper combined data mining technology with expert system framework, established a medical expert system with a dynamic knowledge base. In the innovative dynamic system, samples were together with their classification results would be added to the knowledge base. In addition, privacy protection was considered in the expert system by means of data removal and conversion of attribute value. At last, for a comprehensive and precise evaluation of both numeric and character attribute data, we proposed a new similarity measurement algorithm termed as E–JD, which combinate Euclidean Distance algorithm with Jaccard Distance algorithm. For more improvement of the data classifasion accuracy, based on KNN and the dynamic knowledge base, the innovative algorithm of D-SNN was proposed, with a modification in the selection and decision of the nearest neighbor objects. The experimental comparison between 1NN, KNN, KNN-means and D-SNN algorithm, shows that the classification performance of the proposed algorithm, D-SNN, exhibits an impressive improvement.

KBPearl

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in-time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end-to-end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state-of-the-art techniques, through extensive experiments on real-world datasets.

Research on Knowledge Representation and Automatic Recognition of Dynamic Words for Chinese Automatic Syntactic Analysis

There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence pattern structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.