Intelligent System Architecture Research Articles

Research on the Realization Path of Railway Intelligent Construction Based on System Engineering

The implementation of railway intelligent construction is the need of national strategic development and the demand of society. Based on the idea of system engineering, this paper proposes a three-dimensional railway intelligent construction system architecture composed of a full life cycle, management level and technical support. Based on this architecture, a “three-step” implementation path is proposed. Then, it analyzes the technology support framework required in the architecture based on Building Information Modeling (BIM), incorporating Global Positioning System (GPS) and Geographic Information System (GIS), algorithmic prediction and machine learning technology, Internet of Things (IoT) and artificial intelligence technology, big data and cloud computing technology, and the application of railway intelligent construction system architecture is analyzed by taking a railway tunnel project in Zhejiang Province of China as an example. Finally, it discusses the problems that may be encountered in the implementation of railway intelligent construction and puts forward relevant suggestions. The results show that railway intelligent construction is an essential way. At present, China’s railway intelligent construction is still in the primary stage. The design organization should do a good job in the top-level design and accumulate sufficient data for the later stage. All parties in the middle stage of construction should do a good job in the induction and integration of information and accumulate sufficient experience. In this way, we can integrate into the advanced stage and give full play to the advantages of software and hardware integrated applications such as BIM, IoT, big data, cloud computing and intelligent devices so as to truly realize the intellectualization and modernization of railway construction.

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data.

In order to cope with the challenges that operators face after the impact of diversified social channels in the field of interactive services, we actively build a new generation of intelligent interactive systems based on artificial intelligence technology, a semantic understanding, and intention recognition, covering rich media content, omnichannel coverage, high-frequency knowledge updates, consistent service response, high-quality, and low-cost intelligent interactive solutions is proposed. The solution provides overall business modeling for scenario design and a scenario-based knowledge expression system, with the function of fragmented knowledge processing. With complete text and voice information, combined with pictures, text, audio, video, and other multimedia, we intelligently interact with users, allowing users to obtain required information and solve problems in a pleasant and relaxing interaction. Therefore, the research and exploration of the intelligent interactive system architecture based on artificial intelligence is a useful practice and strong support for operators to redefine the connotation and elements of “smart service” in the process of building “smart operation.” Through repeated tests, it can be seen that the language similarity has reached 0.75549, which is very close to 1.0000. It can be seen that the design of this platform has been successful.

The Development of an Intelligent System Architecture for Autonomous Care (ISAAC)

Unmanned systems and autonomous capabilities provide an avenue for improving enroute care in situations where medical evacuation assets are either not available or unable to reach the patient. To accomplish this, we conducted research to explore how intelligent algorithms can be used to supplement the current capabilities by improving the diagnosis, intervention, and monitoring of combat trauma care. Variability in the sensing technology available, environmental factors influencing the reliability of measurements, and lack of time and skills to synthesize this information can affect how data impacts treatment. The potential lack of knowledge and expertise of the on-the-site caregiver directly impacts whether important symptoms are seen, good medical decisions are made, and interventions are correctly executed. SoarTech has created the Intelligent System Architecture for Autonomous Care (ISAAC), a software system to support medical diagnosis, intervention, and monitoring. Our ISAAC Diagnosis algorithms provide support for physiological, medical imaging, and laboratory test results with an automated pipeline for processing raw data, identifying features, and mapping this information to symptoms. This information is reasoned over by an intelligent cognitive system that uses an expert understanding of symptom-condition mappings along with a logical inference reasoner to formulate hypotheses, recommend additional tests and construct diagnoses. Our ISAAC Intervention support provides knowledge and insight to instruct novice users to complete procedures from basic tunicate application to chest tube insertion. The combination of the diagnostic and intervention capabilities of ISAAC have to potential to provide life-saving support in austere combat environments.

Architectures of analytics intelligent decision technologies systems (IDTS) for the COVID-19 pandemic

This article presents a selective literature review of Analytics Intelligent Decision Technologies Systems (Analytics IDTS) developed to support decision-making in business and public organizations, with a particular focus on the global COVID-19 pandemic. We select Analytics IDTS published in 2019–2020 and evaluate them with an Analytics IDTS Design and Evaluation Framework. We include the types of Analytics IDTS, their decisional services, architectural capabilities, and support for phases in the decision-making process. Results are shown for 33 articles in the general Analytics domain and 71 articles in the focused Public Health domain applied to COVID-19, including how these Analytics IDTS were architected and utilized for decision making. Research in descriptive and predictive models is evident in Public Health COVID-19 research reflecting the lak of knowledge about the disease, while predictive and prescriptive models are the primary focus of the general Analytics domain. IDTS in all disciplines rely on Algorithmic decision services and Heuristic Analysis services. Higher-level decisional Synthesis and Hybrid services such as design, explanations, discovery, and learning associated with human decision-making are missing in most types of decision support, indicating that research in Machine Learning and AI has many growth opportunities for future research.

Research on Design Method of Intelligent Operation and Maintenance System for Secondary Equipment in Intelligent Substation

Abstract Smart substations are an important part of establishing a strong smart grid. In view of the large variety of secondary equipment in the current power system, the rapid increase in quantity, and the complex working conditions of the current power supply company maintenance classes to inspect substations, a design method for the intelligent operation and maintenance system of secondary equipment in smart substations is proposed in this article. First, the intelligent operation and maintenance system architecture of the secondary equipment of the smart substation is established via Redis and ActiveMQ technologies. Then the system function module model is established. Finally, taking a 220kV substation in a certain area of southern China as an example, a new type of intelligent substation secondary equipment intelligent operation and maintenance system was installed and connected to the main line system, and the effectiveness of the proposed method was verified.

State Analysis of the Water Quality in Rivers in Consideration of Diffusion Phenomenon

The waters of rivers are not only used for consumption, industry and agriculture but have also found their way into the transport and energy generation sectors. Many disturbances introduced into the aquatic environment are of the natural variety, which are the result of “admixtures” contained in water, e.g., through contact with soil, and of man-made types, which are directly related to humanities destructive influences. In the presented examinations, the most important processes affecting the spread and transport of these pollutants are taken into account, i.e., advection and diffusion. The authors present observations on the influence of the diffusion phenomenon on river flow modelling processes. Such an approach allows for the separation of the dynamics of water flow and the dynamics of transport of the dissolved substance mass. Specifically, phenomena occurring in relation to spatial coordinates, time and variable parameter values in the proposed mathematical model were analysed. Ultimately, this research will contribute to the correct design and implementation of a complementary diffusion module as an extension to an intelligent water quality control and monitoring system. The Intelligent Analytical Computing Control System architecture under development already includes other modules such as the Intelligent Filtration and Prediction Module and, complemented by the Intelligent Diffusion Module, provides a complementary tool for monitoring river hydromorphology. Implementation of the above solution will help to improve water quality, thus preventing and eliminating the appearance of undesirable pollutants in rivers, and increase the standard of living in the current threatened environmental world.

A novel edge computing architecture for intelligent coal mining system

A novel edge computing architecture for intelligent coal mining system

A simple model of smart learning environment

ABSTRACT In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances.

A Real-time Detection Framework for Abnormal Devices in the Power Internet of Things

Power Internet of Things [1] is the application of Internet of Things in smart grid. By effectively integrating power system and communication infrastructure resources, the information level of power system and the utilization rate of existing resources are improved. Thus, important technical support is provided for power grid generation, transmission, transformation, distribution and electricity consumption. The Power Internet of Things contains a large number of terminal equipment, which involves the data interaction frequency of 100 million levels per second. Therefore, how to avoid the access of invalid data is an important topic in the construction process of power Internet of Things. In response to the detection requirements of abnormal devices, this paper, based on existing Intelligent IoT System architecture, designs a real-time detection framework that integrates a two-level control mechanism, which is universal, extensible, and supports multiple protocols, using the data interaction specifications formulated by the State Grid Corporation as the basic detection basis. As a result, the framework realizes the effective interception of abnormal devices in power Internet of Things.

CPS: Role, Characteristics, Architectures and Future Potentials

CPS entails the incorporation of four key characteristic technologies. CPS device technologies, CPS computational technologies, Cyber Physical System of Systems (CPSoS) and CPS networking and communication (CPS-ComNet) technologies. Multiple CPS architectures, of which the Industrial Internet Reference Architecture (IIRA), Reference Architecture Model for Industrie 4.0 (RAMI 4.0) and Intelligent Manufacturing System Architecture (IMSA) are the major referenced architectures and this demand to facilitate interoperability between such architectures for effective integration. This framework has evolved toward having smart CPS with intelligence and decision-making capabilities and highly networked and integrated components. CPSs have been categorized into two types namely Cyber Physical Production Systems (CPPS) for the development of products and Cyber Physical Service Systems (CPSS) for the servicing of products. These developments facilitate the adoption of the CPS in smart cities, smart farming systems, smart manufacturing, and smart supply chain systems, etc., while it gives CPS the importance as one of the core technologies making up Industry 4.0 era. Despite these advances, cybersecurity and privacy, real-time control of systems, interoperability of services, etc. are key challenges facing the development of CPS and they demand solutions overcoming such challenges. This paper, therefore, presents the role, evolution, characteristics, architectures, and applications of CPSs and their future potentials.

An intelligent knowledge-based chatbot for customer service

• An intelligent knowledge-based conversational agent system architecture for customer services. • A prototype system is built in a real-world setting. • Case analysis in a leading women’s intimate apparel manufacturing firm. • System prototype evaluation are satisfactory and support the contention that the system is effective. This study proposes an intelligent knowledge-based conversational agent system architecture to support customer services in e-commerce sales and marketing. A pilot implementation of a chatbot for customer services is reported in a leading women’s intimate apparel manufacturing firm. The proposed system incorporates various emerging technologies, including web crawling, natural language processing, knowledge bases, and artificial intelligence. In this study, a prototype system is built in a real-world setting. The results of the system prototype evaluation are satisfactory and support the contention that the system is effective. The study also discusses the challenges and lessons learned during system implementation and the theoretical and managerial implications of this study.

Fog-based Intelligent Transportation System for Traffic Light Optimization

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

Intelligent Control System Architecture for Phosphorus Production from Apatite-Nepheline Ore Waste

This paper proposes multilevel architecture for an intelligent control system for the complex chemical energy technological process of yellow phosphorus production from apatite-nepheline ore processing waste. The research revealed that, when controlling this process, one has to deal with large amounts of multiformat and polymodal information, and control goals differ at different levels not only in effectiveness criteria, but also in the structuredness of the level problems. On this basis, it is proposed that intelligent methods be used for the implementation of information processes and control goals at individual levels and the whole system. The artificial intelligence methods underlying the informational model of a control system offer solutions to problems of analyzing control processes at different hierarchy levels, namely the initial level of sensing devices, the levels of programmable logic controllers, dispatching of control and production processes, enterprise management and strategic planning. Besides, the intelligent control system architecture includes analytical and simulation models of processes occurring in the multistage procedure of ore waste processing by a plant consisting of a granulating machine, a conveyor-type multichambercalcining machine, and an ore thermal furnace. The architecture of information support for the control system comprises a knowledge-based inference block intended for implementing the self-refinement of neural network and simulation models. Fuzzy logic methods are proposed for constructing this block. The paper considers the deployment of control algorithms for a phosphorus production system using the Matlab software environment on the basis of a modern complex system development paradigm known as the model-oriented design concept.

Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning

Traffic congestion has several causes, including insufficient road capacity, unrestricted demand and improper scheduling of traffic signal phases. A great variety of efforts have been made to properly program such phases. Some of them are based on traditional transportation assumptions, and others are adaptive, allowing the system to learn the control law (signal program) from data obtained from different sources. Reinforcement Learning (RL) is a technique commonly used in previous research. However, properly determining the states and the reward is key to obtain good results and to have a real chance to implement it. This paper proposes and implements a traffic signal control system (TSCS), detailing its development stages: (a) Intelligent Transportation System (ITS) architecture design for the TSCS; (b) design and development of a system prototype, including an RL algorithm to minimize the vehicle queue at intersections, and detection and calculation of such queues by adapting a computer vision algorithm; and (c) design and development of system tests to validate operation of the algorithms and the system prototype. Results include the development of the tests for each module (vehicle queue measurement and RL algorithm) and real-time integration tests. Finally, the article presents a system simulation in the context of a medium-sized city in a developing country, showing that the proposed system allowed reduction of vehicle queues by 29%, of waiting time by 50%, and of lost time by 50%, when compared to fixed phase times in traffic signals.

An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners

An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners

Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems

In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of other target objects. Hence, there is still a need to do more research on solving the above problems to prevent collisions in inland waterways. In this paper, we introduce a new convolutional neural network classification algorithm capable of classifying five classes of ships, including cargo, military, carrier, cruise and tanker ships, in inland waterways. The game of deep learning ship dataset, which is a public dataset originating from Kaggle, has been used for all experiments. Initially, the five pretrained models (which are AlexNet, VGG, Inception V3 ResNet and GoogleNet) were used on the dataset in order to select the best model based on its performance. Resnet-152 achieved the best model with an accuracy of 90.56%, and AlexNet achieved a lower accuracy of 63.42%. Furthermore, Resnet-152 was improved by adding a classification block which contained two fully connected layers, followed by ReLu for learning new characteristics of our training dataset and a dropout layer to resolve the problem of a diminishing gradient. For generalization, our proposed method was also tested on the MARVEL dataset, which consists of more than 10,000 images and 26 categories of ships. Furthermore, the proposed algorithm was compared with existing algorithms and obtained high performance compared with the others, with an accuracy of 95.8%, precision of 95.83%, recall of 95.80%, specificity of 95.07% and F1 score of 95.81%.

An Empirical Study on Application of Machine Learning and Neural Network in English Learning

With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.

An UAV-assisted VANET architecture for intelligent transportation system in smart cities

Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been successfully used for intelligent transportation system and entertainment purposes for realization of smart cities. However, intermittent connectivity, high routing overhead, inflexible communication infrastructure, unscalable networks, and high packet collision are the key challenges that put hindrances on the wide applications of vehicular ad hoc network. The severity of these challenges become even more intensified when deployed in urban areas. To overcome these hurdles, integrating micro unmanned aerial vehicles with vehicular ad hoc network provides a viable solution. In this article, we proposed an unmanned aerial vehicle–assisted vehicular ad hoc network communication architecture in which unmanned aerial vehicles fly over the deployed area and provide communication services to underlying coverage area. Unmanned aerial vehicle–assisted vehicular ad hoc network avails the advantages of line-of-sight communication, load balancing, flexible, and cost effective deployment. The performance of the proposed model is evaluated against a case study of vehicle collision on highway. Results show that utilization of unmanned aerial vehicles ensures the guaranteed and timely delivery of emergency messages to nearby vehicles so that a safe action can be taken to avoid further damages.

Specific features of the use of artificial intelligence in the development of the architecture of intelligent fault-tolerant radar systems

The problem of architecture development of modern radar systems using artificial intelligence technology is considered. The main difference is the use of a neural network in the form of a set of heterogeneous neuromultimicroprocessor modules, which are rebuilt in the process of solving the problem systematically in real time by the means of the operating system. This architecture promotes the implementation of cognitive technologies that take into account the requirements for the purpose, the influence of external and internal factors. The concept of resource in general and abstract resource of reliability in particular and its role in designing a neuromultimicroprocessor with fault tolerance properties is introduced. The variation of the ratio of performance and reliability of a fault-tolerant neuromultimicroprocessor of real time with a shortage of reliability resources at the system level by means of the operating system is shown, dynamically changing the architectural appearance of the system with structural redundancy, using fault-tolerant technologies and dependable computing.

Intelligent Library Architecture Based on Edge Computing

The development of the Internet of Things promotes the integration of physical space and information space. In addition, more and more people, machines and things are connected to the information space. The centralized cloud computing model has been gradually hard to accommodate the resulting massive data treating requirements. However, the emerging edge computing provides a new technical means to solve this problem. By comparing and analyzing the basic framework and features of edge computing and cloud computing, taking the typical case of Intelligent Library as an example, this paper constructs an Intelligent library system architecture based on edge computing. It analyzes the functions of each level of the architecture. The architecture can reduce the minimum delay and network bandwidth pressure of Library intelligent service, thus saving the data processing cost of Intelligent Library.