Model Of Intelligent System Research Articles

User interfaces ontology in the cybernetic model of intelligent systems

A model of an intelligent system is proposed for implementing the control and professional use of a knowledge collections related to subject area of software interfaces with external users. The basis of formalization is an abstract model of an intelligent system. It is based on coordinated and balanced invariants of the knowledge representation formalism model. Invariants set includes classes of morphisms for abstract knowledge processing modelling with morphisms' domains, invariants of the multidimensional architecture of intelligent systems components, which includes intercomponent knowledge flows and processes of knowledge synthesis within components, as well as invariants of control agents for abstract knowledge flows and processes within intelligent systems. The intelligent system knowledge base is presented as subject area ontology by non-ordered series of simple knowledge. Fragments of such ontology are distributed between components of intelligent systems three-dimensional architecture. The basis of such structure based on the dimensions of abstraction, structuring and the level of knowledge addressed to and processed within intelligent system's separate components. The ontologies reflect the ideas about the structures of memory and the processes of thinking used to model the schemes of the professional activity of a specialist. The basis for the formalization of such concepts is the fundamental principles of philosophy, linguistics, cognitive psychology, mathematics, and system engineering. This allows deploying a comprehensive system of classes of information structures and processes for complex knowledge synthesizing that support achievement of various cognitive goals implemented by specialists within professional tasks implementation processes. The goals system includes extraction, analysis and application of knowledge about user interfaces. The variety of such goals is modeled by a high-level implementation pattern system. They are composed of basic types of goals and are implemented using knowledge processing cognitive operations. Knowledge structures in the format of semantic hierarchies are used as a unified representation of knowledge. Cognitive goals are realized by combinations of operations on structured knowledge, adapted to special classes of structures.

Emergency response model for intelligent logistics system based on resilience theory

The concept of intelligent logistics has become mature. However, most of the researches on risk management and emergency response for intelligent logistics system only focus on some specific systems, and ignore the discussions on the overall system. Thus, in order to establish emergency response model for intelligent logistics system, the accident management process of intelligent logistics system was analyzed, the accident management model of the system based on the resilience theory was designed, and finally a model of emergency response for intelligent logistics system from the perspective of dynamic accident management was developed. Results show that this model reflects the dynamic process of intelligent logistics system subjected to random accidental impacts, the in-depth learning of the intelligent logistics system for incident handling and the enhancement of the system’s risk resistance through feedback data on incident handling are reflected in the model. Conclusion obtained from this study provides a theoretical reference for the optimizing of researches on mechanism of emergency response.

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.

Modeling and Functional Realization of Intelligent Swimsuit System Based on Knowledge Reuse

Modeling and Functional Realization of Intelligent Swimsuit System Based on Knowledge Reuse

Fault Tolerant Service Composition Model Design of Intelligent Logistics System Based on Internet of Things Technology

The rise of Internet technology makes the intelligent logistics system develop further. Relying on advanced technology, intelligent logistics system can grasp the order trend in real time, scientifically allocate the system capacity, improve the supervision of distribution personnel, effectively improve the management level, logistics efficiency and customer satisfaction. The intelligent logistics system of Internet of things can effectively improve the efficiency of logistics, but in the case of unstable environment, there may be a series of service problems in the service end. It is necessary to study the logistics service combination of the system service end to avoid the internal management confusion or customer complaints caused by the problems. This paper discusses the design of fault tolerant service combination model of intelligent logistics system based on Internet of things technology.

Research on Knowledge Tree Growth Model for Intelligent English Teaching System based on Hypertext Structure

Research on Knowledge Tree Growth Model for Intelligent English Teaching System based on Hypertext Structure

Big Data Curriculum Design and Teaching Practice Based on Smart Learning Environment

This article proposes a smart learning system model based on big data. The system model is based on a big data architecture, combined with an adaptive general learning model, and provides information assistance and technical support for learners and teachers to learn through education big data and learning analysis technology. And based on the overall design framework, a learning early warning system model based on multi-modal data and an intervention mechanism process model are proposed. Experimental research shows that the intelligent learning system model can solve teaching resource management and the construction and application of various online courses. Help schools improve teaching quality, improve teaching efficiency, simplify teaching management, and promote teaching reform.

An Empirical Verification of the Proposed Distributor Marketing Intelligence System (DMiS) Model

An Empirical Verification of the Proposed Distributor Marketing Intelligence System (DMiS) Model

Knowledge-Based Automation for Smart Manufacturing Systems

Smart manufacturing is targeted as the next generation of manufacturing by many national and international strategic development. The increasingly rich production data, the integration and extensive application of information technology, and the intelligent data processing and system modeling methods have collectively enabled smart manufacturing. Building upon them, manufacturing system modeling, knowledge acquisition, design, and real-time control are the key components [item 1) in the Appendix], [item 2) in the Appendix]. It is still one of the really huge challenges to gather data, transform them into information, and derive knowledge out of this information, especially given the requirement of knowledge that can be trusted as manufacturing systems may harm humans and the environment if they come to the wrong conclusion. Despite the learning and derivation of knowledge, it could be modeled beforehand and taken, for example, as an environmental model for online decisions like in deliberative agent-based systems. Nevertheless, for decisions during operation, real-time requirements, dependability, and security issues are to be guaranteed. Finally, for acceptance and trust, humans need to “understand” the reasons behind automated decisions; therefore, explainability or at least a white box description is an issue of such knowledge-based systems.

Intelligent Tutoring System Using Bayesian Network for Vocational High Schools in Indonesia

The lack of personal tutorials during school hours caused the learning approach at Vocational High Schools to be less optimal, and the student's competence is not achieved maximumly. Some computer-based self-learning systems have been developed as solutions to these problems. Unfortunately, the system's weakness is that learning does not pay attention to the diversity of students' abilities. Based on those, this research proposes an Intelligent Tutoring System (ITS) model using Bayesian Network at Vocational High Schools (SMK) to determine the level of students' abilities and teach skills competency materials based on each student's ability level. This is quantitative research with quasi-experimental using one group pretest-posttest design. The research participants were 69 students of the Computer and Network Engineering expertise program at SMK Negeri 4 Gowa and SMK Negeri 1 Gowa, South Sulawesi Province, Indonesia. The results showed significant differences in students' learning outcomes after the use of the proposed ITS; in other words, the proposed ITS was effective in increasing the skill competency of Vocational High School Students. The evaluation results showed that the created Bayesian Network model had a high level of accuracy, reaching 84%.

An approach to assessing the technical condition of elements and nodes of transport systems using neural network modeling methods and digital twin technology

The development of intelligent methods and the development of tools for solving research problems of modeling and diagnosing the state of technical systems are relevant areas related to the introduction of digital technologies. Such problems include the problems of preparing correct data arrays for diagnosing and predicting the state of elements and nodes of transport systems. The purpose of the paper is to develop methods for preparing and analyzing data for modeling digital twins on the example of studying the temperature regime of the functioning of a railway car's axle box node, as well as choosing the architecture of neural networks for solving problems of assessing the technical condition of the axle box nodes. The features of the digital twin technology are characterized and the directions of its use for modeling and diagnostics of railway transport systems are considered. As part of the development of an approach to solving the problem of diagnosing the condition of the axle box units of railway cars, analytical dependences and generalized heat transfer equations related to changes in temperature regimes are considered. Examples of the presentation of such data on temperature series, which contain values for deviations from the normal functioning of the elements of the axle box node, are given. The choice of the neural network architecture adapted for solving the problems of estimating and predicting the temperature values of the axle box unit of the car was made. Options for preparing test data for a neural network model have been developed. The results can be used in the problems of creating algorithmic and software for preparing correct arrays of input data for technical diagnostics, in the problems of synthesis and analysis of models of intelligent systems, in various machine learning problems. The considered approach to modeling is aimed at developing methods for assessing and predicting the state of transport infrastructure elements and can be used in the development of intelligent transport systems and for improving the technology of digital twins.

Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network

Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.

Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence

PurposeThe triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation. MethodsThe study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014–2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients’ prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria. ResultsLearning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions. ConclusionData mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.

Simulation and prediction of discharge coefficient of combined weir–gate structure

Combined weir–gate structures are important structures for controlling the water level, measuring discharge and avoiding sediment deposition behind a weir. They can be used, for example, in irrigation channels and waste water systems. Using three-dimensional numerical software (Flow-3D) and experimental data from the literature, four combined systems of a triangular weir and a rectangular gate with different geometries were simulated. Dimensional analyses were then performed to determine the dimensionless parameters that affect the discharge coefficient of such structures. Four different intelligent system models were used to estimate the discharge coefficient and their results were compared. It was found that Flow-3D is highly capable of simulating the flow over a combined structure. Goodness of fit criteria also showed that the numerical solver estimated the water head and discharge coefficient very well. The results of all the models showed that the discharge coefficient had an inverse relation with the dimensionless parameters h/b, h/d and h/y (h being the water head over the weir, b the gate width, d the gate height and y the distance between the bottom of the weir and above the gate); the discharge coefficients were 0·3–0·9. Accuracy analyses of six intelligent system models showed that the multi-layer perceptron model was the superior model, followed by the M5P model, the regression equation, the radial basis function and the generalised regression neural network model.

A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19.

The control of spreading of COVID-19 in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a spherical intelligent fuzzy decision model for control and diagnosis of COVID-19. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of COVID19. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the spherical fuzzy information. In the proposed work, we will develop a newly and generalized technique for COVID19 based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under spherical fuzzy environment. Finally, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results.

RESEARCH OF MODELS, METHODS AND MEANS OF ASSESSMENT OF TECHNICAL CONDITION OF CONSTRUCTION OBJECTS IN THE CONDITIONS OF LOADS AND INFLUENCES OF TRANSPORT MAGISTRALES

In the statistic, the tasks of assessing the technical camp of the construction industry, which are located in the inflow zone of high-speed transport lines. A complex of reasons for the destruction of a technical mill of non-loadable structures is been discerned, and it is possible to operate close to the main lines of the railroad rack transport. A model of a special intelligence system and assessment of a technical camp is been given. At the same time, the main respect is to come up with the problem of designing models and methods of assessing the technical standard of constructions, which has found in the zone of awakening activity. To provide information and analytical support for construction and technical expertise, it has proposed to use BIM-technology and artificial neural-fuzzy network Takagi-Sugeno-Kang. To model the motion of components of building structures, it has proposed to use the method of direct integration of motion, which allows conducting numerical experiments using the software package LIRA-CAD. It has proposed to apply to the input layer of the model the parameters of destruction of butt joints of load-bearing and horizontal structures of strongly branched cracks in each level in height, which indicate a significant effect of vibrodynamic loads from railways. The results of numerical simulation of the load has proposed to be presenting on the third nonlinear layer of the artificial neural network Takagi-Sugeno-Kang. It has shown that the application of this model makes it possible to automate the process of assessing the technical condition of construction and technical examinations. The expediency of teaching an artificial fuzzy neural network on the results of the analysis of field observations and numerical experiments using the software package LIRA CAD has substantiated. The practical application of the results of the study has expected in the implementation of a system of support for construction and technical expertise to substantiate proposals for the location and conditions of construction of a new facility in a dense urban development near highways. Further work will focus on presenting input information in a form acceptable for processing by an artificial neural network TSK.

Low power consumption and reliability of wireless communication network in intelligent parking system

With the rapid increase in the number of vehicles in our country, parking and parking management are becoming more and more difficult. Use information technology to improve the use of parking spaces and improve the efficiency of parking management, it is an effective way to alleviate the parking problem. In 2015, the research on "Internet + parking" accelerated rapidly in China. And various intelligent parking system models are introduced. Firstly, the parking conditions at home and abroad are studied, and the existing parking systems are analyzed. It is found that the current parking system can hardly be used in public parking areas such as residential areas, roadside and square. In order to solve the core part of intelligent parking system in public parking IoT Low energy consumption parking space control. Using new detection technology, parking lock control technology, wireless sensor network technology, solar charging technology, power management technology. Parking space detection module, parking lock control module and wireless sensor network module and power control management module with additional solar charging are designed. There are three problems specifically for intelligent parking in public parking lots: parking detection and automatic control, low-energy wireless signal transmission, efficient power management, effectively improve the performance of intelligent parking system and reduce energy consumption. This paper uses advanced wireless technology to construct and design intelligent parking system. Through the research on the development and management characteristics and application examples of intelligent transportation in developed countries such as Europe and America, based on China's basic national conditions and advanced wireless communication technology, radio frequency technology and computer technology research, establish an integrated system. Finally, the performance and power consumption of the intelligent parking system are tested, to ensure the feasibility of the system, this kind of system has certain application value in the scenario of resource sharing and intelligent management of public parking spaces.

Artificial Intelligence in the Digital Age

Artificial intelligence is not only a technological innovation, but also connection between physical and digital worlds, which completely will transform the way humans interact with machines. Cognitive intelligence already have a leading role in Europe’s digital future. Today the main challenge is how to implement it in a way, that is fully relevant to European values within a digital Europe. Authors in paper defines the concept of artificial intelligence and to indicate its historical development, proposed a classification of the types of artificial intelligence services, develop a generic architectural model of an artificial intelligence system with a self-study control method and develop a generic architectural model of an artificial intelligence system with a self-study control method.

Legal technology in contemporary USA and China

The US and China are two typical models that present legal tech trends that are common world over. In China, robust regional models of intelligent judicial systems have emerged alongside some common applications that include the same-type case referencing, automated sentencing decision, uniform standards of evidence, and judges’ data profiling systems. In the US, legal tech refers to artificial intelligence in domains such as innovative legal research, predictive litigation analysis, e-discovery, and contract review.The common elements in the development of legal tech in both countries are useful for other countries to understand. However, the legal tech in both countries has distinct characteristics, as seen in their different driving forces, target groups and purposes. The characteristics of legal tech are heavily related to each country's political background, legal system, and judicial structure. The different paths taken toward legal tech also remind us to reflect on the mistakes made and to explore some experiences pertaining to developing legal tech. For the strategic deployment, it is reasonable to apply cutting-edge technologies to the legal field until they are truly matured, and combine the top-level design with local pilot projects. For the target groups, litigants and vulnerable groups should not be neglected in legal tech service provision. For the purposes, machines should play an auxiliary role rather than replace judges altogether.

Multimedia English teaching analysis based on deep learning speech enhancement algorithm and robust expression positioning

In multimedia English teaching, learners face such an indifferent computer screen without emotion and feel the fun of interaction and emotional stimulation, which will cause resentment and affect the learner’s learning effect. In order to improve the efficiency of multimedia English teaching, aiming at the lack of emotion in multimedia English education, this study proposes an intelligent network teaching system model based on deep learning speech enhancement and facial expression recognition. Moreover, this study uses emotional calculation as the theoretical basis and uses facial expression recognition as the core technology to judge and understand the emotional state by capturing and recognizing the facial expressions of online learners. In addition, this study has carried out experimental tests on the effect of the identification method of this paper and verified that the method has good detection effect on the real smile micro-expressions through two sets of experiments and can provide theoretical reference for subsequent related research.