Framework Of Intelligent System Research Articles

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Smart Public Art: Leveraging IoT and Digital Technology in Ganzhou’s Public Spaces

Public areas can have Internet of Things (IoT)-enabled sensing devices placed to keep track of possible security risks such as suspicious activity or misplaced possessions. In public areas, devices have the ability to gather information, but, in some cases, people must explicitly check out if they wish their data to be taken. This research presents an analysis of the IoT environment on Ganzhou’s public transportation, suggests a new IoT-based intelligent public transportation system framework, and shows how the communication network’s components are being applied. In this study, we detail the deployment of IoT sensors through the collection of data. We proposed a novel Salp Swarm weighted recurrent neural network (SS- WRNN) for prediction in Ganzhou’s public transport flow. The data were extracted using linear discriminate analysis (LDA) from high-dimensional sensor data into a lower-dimensional space. For resolving the dynamic bus scheduling and controlling issues, decision support algorithms are suggested. It can help decision-makers decrease passenger trip times, boost scheduling effectiveness, and raise the rate at which transportation resources are utilized.

An Intelligent Framework for English Teaching through Deep Learning and Reinforcement Learning with Interactive Mobile Technology

As globalization deepens, the significance of English teaching in the educational landscape has become more prominent. Traditional teaching methods are increasingly inadequate for providing personalized and efficient learning experiences. This gap is being addressed by the rapid advancements in artificial intelligence, especially through deep and reinforcement learning. These technologies provide a framework for intelligent English teaching systems by mimicking human learning processes to customize personalized learning experiences, optimize learning paths, and enhance efficiency. However, challenges remain in fine-tuning teaching strategies to meet the varying needs of individual learners and dynamically adapting to their evolving interests in the short term. This study introduces a novel framework for an intelligent English teaching system that leverages the potential of interactive mobile technology alongside a deep Q-network (DQN) algorithm to dynamically adjust English teaching strategies. This approach enables real-time personalization of teaching strategies to create optimal learning paths for individual learners. Moreover, it incorporates a model based on neural collaborative filtering to capture and adapt to learners’ short-term dynamic interests, thereby recommending relevant learning content in real-time. This framework enhances learning efficiency and personalizes content delivery, demonstrating considerable theoretical and practical value for the future of educational technology.

A privacy-preserving location data collection framework for intelligent systems in edge computing

With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.

Integrating multisensory information fusion and interaction technologies in smart healthcare systems

<p>The advent of intelligent medical systems has heralded a new era in healthcare, promising enhanced diagnostic accuracy, treatment efficacy, and personalized patient care. Central to these advancements is the application of multisensory information fusion and interaction technology, which integrates diverse data types—from imaging to auditory signals and electronic health records—to facilitate comprehensive patient assessments. This study examines the efficacy of such multisensory integration within an intelligent medical system framework, focusing on its impact on diagnostic accuracy and treatment effectiveness. A hypothetical dataset encompassing various sensory inputs for a cohort of patients was analyzed, revealing a significant improvement in diagnostic precision (average accuracy of 92.3%) and treatment outcomes, with a majority of interventions rated as highly effective. These findings underscore the potential of multisensory data fusion in revolutionizing medical diagnostics and treatment planning. Despite the promising results, limitations such as sample size and data quality were acknowledged, pointing towards the necessity for further research. This study not only corroborates the value of multisensory information fusion in enhancing healthcare delivery but also highlights the pathway for future advancements in intelligent medical systems. The article’s novelty lies in its approach to integrating multisensory data with AI technologies, leading to a more nuanced understanding of patient health. This method transcends traditional diagnostic techniques, allowing for a multifaceted analysis of medical conditions. It emphasizes the potential of this technology to detect diseases earlier and more accurately, tailor treatments to individual patient needs, and improve overall healthcare efficiency.</p>

Enhancing the driving experience of smart city users based on content delivery framework for intelligent transportation systems

Integration of several communication technologies that facilitate user access contributes to the rapid development of the smart city notion. Intelligent transportation systems (ITS) are introduced as part of smart city development to provide drivers with enhanced communication and information-sharing capabilities. The article introduces a novel ITS content delivery framework (CDF) that addresses communication outage issues. CDF-ITS uses End-to-end decision-making system modelling to examine factors such as communication, content distribution, and vehicle features. A suitable communication slot for vehicular users is determined by processing these characteristics based on outage time and variables. By allocating time-aware communication slots according to the classification of the propagation factor, outage problems may be reduced. End-to-end decision-making is used for classification and vehicle attribute balance, allowing immediate responses to user requests. The experimental outcomes show that the latency of 0.297 s, outage time of 0.0837, distributed messages of 276, and computing complexity of 11.87 are used to assess the proposed framework's efficiency across vehicle density and velocities.

Strategies to enhance the level of service and safety of rural roads: A case study.

Faced with the contradiction between the increasing traffic volume and the aging road infrastructures in the rural area, this paper aims to propose feasible strategies to enhance the level of service and safety, by a case study of the rural area in the north Jintan district. In order to figure out current issues related to rural roads, a carefully designed investigation has been conducted, and the results of the two-week investigation include roads' basic information, traffic signs and protective facilities, surrounding landscape, and etc. Based on the field driving tests, specific problems including signs category, signs installation and facility maintenance have been fully analyzed. Meanwhile, the problem of roadnet connectivity has also been pointed out through the theory of complex network, and results show that the average node clustering coefficient and shortest path length perform worse than the demonstration plot of other rural districts. For the sake of rural traffic safety and management efficiency, both quantified and qualified strategies have been put forward. The quantified strategies include the regular inspection indicators, the safety sight distance at T-type crossings, as well as the risk severity of sections and the crossings. The qualified strategies involve the management of trucks and roadworks, the setting of signalized intersections, and the timely updates of traffic signs and facilities. Finally, an intelligent management system framework has been established for rural road traffic, with highly interconnected modules of data acquisition, risk identification and information publishing.

Theoretical Framework and Research Proposal for Energy Utilization, Conservation, Production, and Intelligent Systems in Tropical Island Zero-Carbon Building

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan.

A Framework of Real-Time Intelligent Transportation System Based on Hybrid Fog-Cloud Computing

Recent years have witnessed a mounting increase in the loss of lives and properties resulted from low-efficient and unsafe traffic management. However, traditional Intelligent Transportation System (ITS) cannot meet the strict requirements of transportation management on the efficiency, especially in terms of the latency. The emergence of fog computing provides a feasible solution to the aforementioned contradiction. In this paper, a framework of Real-time Intelligent Transportation System based on Hybrid Fog-cloud Computing System (RITS-HF) is proposed. All the components of RITS-HF, including the user layer, the fog layer, and the cloud layer in RITS-HF are presented clearly. Subsequently, main functions of RITS-HF are described, which are expected to greatly improve traffic efficiency and strengthen safety. In the meantime, the detailed processes and functions of each layer under different traffic configurations are presented. Finally, the challenges faced by RITS-HF are pointed out and analyzed in detail. RITS-HF is expected to play a vital role in improving the driving safety, increasing traffic efficiency, along with reducing energy consumption to some extent.

Reconfigurable Manufacturing Systems through Intelligent Workpiece Handling and Artificial Intelligence

In recent years, businesses have strived to align their manufacturing goods with the unique needs and preferences of their customers. However, the mass customization paradigm introduces challenges mostly associated to the manipulation of small product batches and knowledge management. This paper proposes a framework for intelligent reconfigurable manufacturing systems, that leverages robotic automation supported by a collaborative dual-arm manipulator, cognitive mechatronic devices, and machine-learning algorithms. In more detail, pioneer end-effectors and machinery provide the capacity of grasping and handling a variety of products without time-consuming hardware modifications. The backbone of this manipulation scheme is a set of machine learning tools for: a) the planning and synchronization of handling agents, b) identification and localization of workpieces, c) the generation of optimal packaging configurations based on customer orders and identified workpieces, and d) the allocation of the respective handling operations to robots, operators, and machinery. The proposed framework is evaluated through its implementation on a use case deriving from the metal industry. The particularities of this use case, as expressed in product variety, workpiece complexity, customer orders and intralogistics, provide a fertile ground for the validation of the discussed technologies.

CNN and Binocular Vision-Based Target Detection and Ranging Framework of Intelligent Railway System

CNN and Binocular Vision-Based Target Detection and Ranging Framework of Intelligent Railway System

Application strategy of artificial intelligence technology in feed-based development of medicinal and edible homologous traditional Chinese medicine(TCM)resources in era of the "antibiotic ban"

In the context of the "antibiotic ban" era, the feed conversion of medicinal and edible traditional Chinese medicine(TCM) resources is a research hotspot in the field of antibiotic alternatives development. How to develop feed products that are beneficial to agriculture and livestock while ensuring nutrient balance and precision using medicinal and edible TCM resources as raw materials has become a challenge. Artificial intelligence(AI) technology has unique advantages in feed production and improving the efficiency of intelligent breeding. If AI technology is applied to the feed development of medicinal and edible TCM resources, it is possible to realize feeding and antibiotic-replacement value while ensuring precise nutrition. In order to better apply AI technology in the field of feed development of medicinal and edible TCM resources, this article used CiteSpace software to carry out literature visualization analysis and found that AI technology had a good application in the field of feed formulation optimization in recent years. However, there is still a gap in the research on the intelligent utilization of medicinal and edible TCM resources. Nonetheless, it is feasible for AI technology to be applied to the feed conversion of medicinal and edible TCM resources. Therefore, this article proposed for the first time an intelligent formulation system framework for feed materials derived from medicinal and edible TCM resources to provide new ideas for research in the field of feed development of medicinal and edible TCM resources and the research on the development of antibiotic alternatives. At the same time, it can pave the way for a new green industry chain for contemporary animal husbandry and the TCM industry.

A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding

A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding

An Intelligent Scheduling System and Hybrid Optimization Algorithm for Ship Locks of the Three Gorges Hub on the Yangtze River

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world’s largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship entry timings, and ship placements within the lock chamber. By analyzing navigational rules, operational characteristics, and existing problems of TGH, this research introduces an intelligent scheduling system framework. With the incorporation of multiple objectives and constraints, a mixed-integer nonlinear programming(MINLP) model is formulated. Subsequently, a hybrid intelligent algorithm based on simulated annealing and heuristic search is constructed to achieve the optimization of the lock schedule. The algorithm efficacy is validated using the real-world data from TGH.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.

An ONTOLOGY BASED APPROACH FOR TRAFFIC DENSITY ESTIMATION IN INTELLIGENT TRANSPORTATION SYSTEMS

Intelligent transportation systems are a group of smart technologies which are intended to make road journey more efficient. The ability of vehicles to share data with all aspects of the road infrastructure via diverse applications is critical to the success of these systems. In this paper, an ontology has been integrated for every vehicle to give them the ability to perceive for themselves. A framework of intelligent transportation system which enables complexity is required for the success of this traffic information exchange and representation of knowledge using the protégé. Proposed and implemented methodology for traffic density estimation is being used to create an ontology in the ITS domain.

Simulation and Optimization of the Operation Efficiency of Heat Exchange Units in Intelligent Heating Systems Based on Entransy Dissipation Thermal Resistance

So as to investigate heat exchange efficiency of heat exchange units under different operating conditions, this paper builds a simulation analysis model for heat exchange units in the framework of intelligent district heating systems based on the Modelica unified modeling language and the MWorks platform. Analyze the real-time operating efficiency of heat exchange units using entransy dissipation thermal resistance as an evaluation indicator of heat exchange efficiency. This paper analyzes the trend of heat exchange efficiency changes of heat exchange units when the primary side flow rate, secondary side flow rate, primary side inlet temperature, secondary side inlet temperature, and secondary side bypass pipeline valve opening change. The simulation results verify that when the primary side and the secondary side flow match, the entransy dissipation thermal resistance is the smallest, and its heat transfer efficiency is the highest. Under the simulation condition where the convective heat transfer coefficient is constant, the entransy dissipation thermal resistance is independent of the inlet temperature changes on the primary and secondary sides. The minimum entransy dissipation thermal resistance could be achieved, which represents the optimal real-time heat exchange efficiency state, when the valve opening of the mixed water heat exchange unit is 45% under the specific flow conditions.

ID2S4FH: A Novel Framework of Intelligent Decision Support System for Fire Hazards

Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.

A secured IoT-based intelligent transport system (IoT-ITS) framework based on cognitive science.

As intelligent transport systems (ITS) is the advanced format of Vehicle Ad-hoc Network (VANET), it provides extended support in all the traffic management processes. Afterward, the salient features of the Internet of Things (IoT) are incorporated with ITS. IoT-ITS ensures automated mobility as connected and cooperative. However, the development of IoT-ITS is restricted by the risk factors related to data security and privacy. To overcome such issues, a secured IoT-ITS framework is proposed to use cognitive science. A detailed study is carried out regarding the risks related to the IoT-ITS environment. The main objectives of this security framework are to resolve the security demands of the Intelligent Transport Systems and resolve the associated risk factors without compromise. Cognitive science is utilized to design the security framework that differentiates the legitimate users of the transport system from malicious users. Data analysis is also performed in real-time with the help of cognitive science. Based on the experiment results, better results are attained.

An interpretability security framework for intelligent decision support systems based on saliency map

An interpretability security framework for intelligent decision support systems based on saliency map