Innovation Networks Research Articles

RAN: Infrared and Visible Image Fusion Network Based on Residual Attention Decomposition

Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.

Study on the evolution of multi-level collaborative innovation networks in China’s cloud manufacturing industry

ABSTRACT CMfg is a new service-oriented manufacturing mode integrating new-generation information technologies, and its development promotes the digital transformation of China’s manufacturing industry. Exploring the evolution trend of CMfg's co-innovation networks is essential to the development of manufacturing digitalisation. The research is carried out from three levels: The evolutionary characteristics of the organisational co-innovation network in the CMfg industry are identified through SNA and THA; the evolution of technological co-innovation network is analysed by using FP-Growth and the textual analysis, to recognise the technology convergence trends; the evolution of the factors affecting regional collaborations are discovered by using QAP model to analyse the regional co-innovation network. Following conclusions are achieved: CMfg co-innovation is centred on state-owned enterprises in the power and telecommunication industries, with University–Industry collaboration as the main form; the current hot spots of CMfg technologies are the Resource Perception-and-Access, the Resource Virtualisation-and-Service; the regional collaboration in China's CMfg industry tends to be partnership-led and multi-polar. It suggests that: In the CMfg industry, more enterprises-supporting policy should be made, while the Virtualisation-and-Service technologies need more attention, and the partnerships among regions should be further enhanced.

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg−1 for Cd and 0.670 mg kg−1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg−1for Cd and 0.898 mg kg−1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.

A Deep Learning-Based Animation Video Image Data Anomaly Detection and Recognition Algorithm

Anomaly detection plays a crucial role in the field of machine learning, as it involves constructing detection models capable of identifying abnormal samples that deviate from expected patterns, using unlabeled or normal samples. In recent years, there has been a growing interest in integrating anomaly detection into image processing to tackle challenges related to target detection, particularly when dealing with limited sample availability. This paper introduces a novel fully connected network model enhanced with a memory augmentation mechanism. By harnessing the comprehensive feature capabilities of the fully connected network, this model effectively complements the representation capabilities of convolutional neural networks. Additionally, it incorporates a memory module to retain knowledge of normal patterns, thereby enhancing the performance of existing models for video anomaly detection. Furthermore, we present a video anomaly detection system designed to identify abnormal image data within surveillance videos, leveraging the innovative network architecture described above.

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Probing the remarkable vitrimeric performance of Poly(dithiourethanes): A comprehensive investigation into the dynamic behavior

Vitrimers and vitrimer-like systems are renowned for their exceptional mechanical and thermal characteristics, coupled with their inherent recyclability. In this study, a novel class of vitrimer-like systems is introduced, wherein a tetrathiol and diisothiocyanate are blended to create poly(dithiourethanes), catalyzed by a basic catalyst. An innovative covalent adaptable network (CAN) is synthesized, featuring a glass transition temperature (Tg) of 332 K as determined by calorimetry, with a tensile modulus of 2240 MPa. The material’s properties are investigated through solubility, stress-relaxation, and creep experiments, revealing relaxation times as short as 0.7 min at 363 K, indicative of its robust dynamic behavior. Dielectric spectroscopy is employed to analyze the various relaxation processes characteristic of such materials, particularly when the topology freezing transition (Tv) occurs below the Tg. This technique complements the findings of dynamic thermomechanical analysis (DMA), enhancing our understanding of the material’s behavior. Furthermore, the materials exhibit recyclability through a grinding and compression molding cycle without significant mechanical damage. Their dielectric constant and low loss factor, combined with the reversibility of their chemical structures, position them as promising candidates for energy-related applications.

T91 - Justice Community Opioid Innovations Network Linkage Facilitation

T91 - Justice Community Opioid Innovations Network Linkage Facilitation

A deep learning-based approach for predicting coking duration in a commercial coke oven

ABSTRACT The prediction of coking duration is critical during operation of commercial coke oven batteries; however, it is still far from accurately predict since it has to rely on experience-based estimation. To address this problem, we proposed a machine learning-based approach, and presented a general practice and theoretical framework for predicting coking duration accurately and efficiently. Three types of neutral networks, including the multi-layer perceptron (MLP) model, the radial basis function (RBF) model, and the long short-term memory (LSTM) model, were tested and evaluated in terms of predicting accuracy, training and predicting time. On this basis, a novel two-stream joint MLP-RBF neural network was proposed. All the networks were trained and tested with onsite-collected datasets. In the modeling, the potential relationships between variables are deeply learnt automatically. The results show that the innovative joint network inherits advantages from both sides and exhibits the lowest prediction error, 2.79% (MAPE) or 4.26% (RMSPE), and is recommended as the most appropriate for practical application. This work not only presents an efficient approach for predicting coking duration, but also examines the extent to which the machine learning technology can be employed during the digital transformation of traditional industries.

Innovative Strategies for Bio-Waste Collection in Major Cities during the COVID-19 Pandemic: A Comprehensive Model for Sustainable Cities—The City of Athens Experience

This paper introduces an innovative model for the organization and management of municipal bio-waste collection networks in major cities, particularly relevant in the context of the COVID-19 pandemic. Embracing circular economy principles and sustainable city practices, the proposed model addresses the urgent need for sustainable urban bio-waste management systems. Delving into the dynamic urban landscape, with a focus on the city of Athens, the study highlights the necessity of a robust decision-making methodology, the implementation of resilient processes, and the evaluation of their efficacy, especially during challenging times. The model centers on the effective collection, transportation, and monitoring of bio-waste, with a strategic aim to moderate environmental impacts, limit greenhouse gas emissions, and advance sustainable development goals. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, this paper thoroughly examines critical components of an innovative bio-waste collection network, including infrastructure, technology, and human resources. By merging best practices from global urban centers and accounting for the unique characteristics of Athens, the model envisions a transition toward a circular economy. Notably, the proposed municipal bio-waste collection network at the source anticipates substantial contributions to achieving Sustainable Development Goals in major cities. The study concludes by showcasing the successful application of these methodologies in the Municipality of Athens, providing tangible evidence of their positive impact.

MONITORING ADMINSTRASI JARINGAN DENGAN MIKROTIK DAN TELEGRAM BOT PADA INTERNET SERVICE PROVIDER

Network management using MikroTik devices at Lajur Net still has shortcomings. Although internet access is available around the clock, the limited number of employees makes it difficult for managers to access computers to manage MikroTik. Administrators hope to manage the MikroTik network remotely through end devices. Currently, accessing MikroTik online incurs high operational costs for renting public IPs and web-based management application services. This study aims to utilize Telegram Bot for network management and monitoring that can be accessed remotely via the internet. The use of Telegram Bot for network management at Lajur Net can be successfully implemented. The implementation of Telegram Bot includes managing interfaces, hotspots, ICMP, and monitoring the resources of MikroTik RouterBoard devices in the Lajur Net network. Telegram Bot can be accessed remotely through the internet and works well on the Lajur Net network. The implementation of Telegram Bot includes interface administration, hotspots, ping, and monitoring the resources of MikroTik RouterBoard devices. The additional firewall feature allows network administrators to add and delete filter rules within the Lajur Net network security system. Moreover, Telegram Bot utilizes button features on Telegram messages to facilitate usage without having to type all commands. Thus, Telegram Bot offers an efficient and effective solution in MikroTik network management at Lajur Net, reducing the need for expensive public IPs and web-based management applications. This demonstrates that the use of chatbot technology can provide significant benefits in network administration, especially for internet service providers with limited resources. Through this research, it is hoped that the implementation of Telegram Bot can serve as a reference for developing more innovative and efficient network management in the future.

Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets

ABSTRACT This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01–2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.

Cynomorium songaricum: UHPLC/ESI-LTQ-Orbitrap-MS analysis and mechanistic study on insulin sensitivity of a flavonoid-enriched fraction

BackgroundType 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels, posing a significant global health concern due to its increasing prevalence. Insulin resistance (IR) plays a major role in the development of T2DM and is often linked to factors such as obesity, physical inactivity, and a sedentary lifestyle. Recently, there has been growing interest in exploring the potential of natural products for improving insulin sensitivity and glucose metabolism. Among these, Cynomorium songaricum Rupr., an edible parasitic plant, has shown promising antidiabetic effects. However, research on its beneficial effects on IR is still nascent. Therefore, this study aims to investigate the application of a Cynomorium songaricum flavonoid-enriched fraction (CSF) in the treatment of IR in T2DM, along with elucidating the chemical and biochemical mechanisms involved. MethodFirst, UHPLC/ESI-LTQ-Orbitrap-MS was utilized to perform a chemical profiling of CSF. Subsequently, glycogen synthesis, gluconeogenesis and glucose consumption assays were conducted on HepG2 cells with a high glucose high insulin-induced IR model to illustrate the favorable impacts of CSF on IR. Then, an innovative network pharmacology analysis was executed to predict the potential chemical components and hub genes contributing to CSF's protective effect against IR. To further elucidate molecular interactions, molecular docking studies were performed, focusing on the binding interactions between active constituents of CSF and crucial targets. Additionally, an RNA-sequencing assay was employed to uncover the underlying biochemical signaling pathway responsible for CSF's beneficial effects. To validate these findings, western blot and qPCR assays were employed to verify the pathways related to IR and the potential signaling cascades leading to the amelioration of IR. ResultsThe UHPLC/ESI-LTQ-Orbitrap-MS analysis successfully identified a total of thirty-six flavonoids derived from CSF. Moreover, CSF was shown to significantly improve glycogen synthesis and glucose consumption as well as inhibit gluconeogenesis in HepG2 cells of IR. An innovative network pharmacology analysis unveiled key hub genes—AKT1 and PI3K—integral to metabolic syndrome-related signaling pathways, which contributed to the favorable impact of CSF against IR. Noteworthy active ingredients including quercetin, ellagic acid and naringenin were identified as potential contributors to these effects. The results of western blot and qPCR assays provided compelling evidence that CSF improved insulin sensitivity by modulating the PI3K-Akt signaling pathway. Subsequent RNA-sequencing analysis, in tandem with western blot assays, delved deeper into the potential mechanisms underlying CSF's advantageous effects against IR, potentially associated with the enhancement of endoplasmic reticulum (ER) proteostasis. ConclusionCSF exhibited a remarkable ability to enhance insulin sensitivity in the IR model of HepG2 cells. This was evident through enhancements in glycogen synthesis and glucose consumption, along with its inhibitory impact on gluconeogenesis. Furthermore, CSF demonstrated an improvement in the insulin-mediated PI3K-Akt signaling pathway. The potential active constituents were identified as quercetin, ellagic acid and naringenin. The underlying biochemical mechanisms responsible for CSF's beneficial effects against IR were closely linked to its capacity to mitigate ER stress, thereby offering a comprehensive understanding of its protective action.

Do global innovation networks influence the status of global value chains? Based on a patent cooperation network perspective

Under the impact of economic globalization, the internationalization of production and trade has given rise to global value chains (GVCs) and global innovation networks (GINs); however, few studies have explored the link between these two networks. This paper constructs a global innovation cooperation network based on patent cooperation data and then analyzes the impact of different network characteristics on improving global value chain status. Empirical research shows that the characteristics of GINs significantly affect GVC status; the higher the eigenvector centrality and network connectivity, the higher the country’s GVC status. Moreover, a higher degree of constraint is associated with a lower GVC status for the country concerned. The heterogeneity analysis further reveals apparent differences in the impact of GINs on different countries, manifesting as a catalytic effect in developed countries and as a disincentive in emerging economies; the effect is less apparent in developing countries. This paper provides theoretical support for the development of technological innovation activities and provides a reference for government departments to formulate policies on cross-border technological innovation cooperation.

Addressing out-of-hospital cardiac arrest with current technology advances: Breaking the deadlock with a mobile network.

Out-of-hospital cardiac arrest (OHCA) is a global public health problem, with survival rates remaining low at around 10% or less despite widespread cardiopulmonary resuscitation (CPR) training and availability of automated external defibrillators (AEDs). This is partly due to the challenges of knowing when and where a sudden OHCA occurs and where the nearest AED is located. In response, countries around the world have begun to use network technology-based smartphone applications. These applications are activated by emergency medical service dispatchers and alert preregistered volunteer first responders (VFRs) to nearby OHCAs using Global Positioning System localization. Accumulating evidence, although mostly from observational studies, shows their effectiveness in increasing the rate of bystander CPR, defibrillation, and patient survival. Current guidelines recommend the use of these VFR alerting systems, and the results of ongoing randomized trials are awaited for further dissemination. This article also proposed the concept of a life-saving mobile network (LMN), which uses opportunistic network and wireless sensor network technologies to create a dynamic mesh network of potential victims, rescuers, and defibrillators. The LMN works by detecting a fatal arrhythmia with a wearable sensor device, localizing the victim and the nearest AED with nearby smartphones, and notifying VFRs through peer-to-peer communication. While there are challenges and limitations to implementing the LMN in society, this innovative network technology would reduce the tragedy of sudden cardiac death from OHCA.

AIS-NIDS: An intelligent and self-sustaining network intrusion detection system

The ever-evolving landscape of network security is continually molded by the dynamic evolution of attack vectors and the relentless emergence of new, highly sophisticated attacks. Attackers consistently employ increasingly advanced techniques, rendering their actions elusive and formidable. In response to this ever-growing threat, the demand for intelligent and autonomous security systems has reached paramount importance. In this paper, we introduce AIS-NIDS (An Intelligent and Self-Sustaining Network Intrusion Detection System), an innovative network intrusion detection system (NIDS) that delves into the realm of packet-level analysis. By doing so, AIS-NIDS is capable of identifying threats with intricate payload-level details, a level of granularity that traditional NIDS relying solely on flow-level data may overlook. The defining feature of AIS-NIDS is its dual functionality, driven by autonomous and intelligent learning. It not only autonomously distinguishes between benign and unknown attacks using machine learning models but also conducts incremental learning, adapting to new attack classes. In essence, AIS-NIDS bridges the gap between traditional NIDS and the next generation of intelligent systems, endowing the system with the capacity for independent decision-making and real-time adaptability in the face of evolving threats. Our extensive experiments stand as a testament to AIS-NIDS’ ability to efficiently manage and identify new attack classes, thus establishing it as a valuable asset in the reinforcement of network infrastructures. Through our experimentation, we have demonstrated the practical efficacy of the proposed approach by simulating a real-world scenario in which certain attack classes are unknown. AIS-NIDS not only effectively identified these unknown threats but also autonomously learned to recognize them as it encountered them, enhancing the system’s capabilities for future encounters with these threats.

1170 Bridging the Gap in Surgical Robotics Education

Abstract Aim Despite rapid evolution of surgical robotics systems, training opportunities are very limited especially for medical students and junior doctors. We organised a free-of-charge regional robotics workshop in collaboration with CMR Surgical with the goal of increasing exposure to surgical robotics technology. We also evaluated the delegates’ perception on current surgical robotics training and the workshop. Method CMR Surgical was invited to exhibit their Versius® robot and Virtual-Reality trainer for a one-day workshop at Glenfield Hospital, Leicester. The workshop was advertised across universities and hospital trusts in East Midlands. Registration form included pre-workshop questionnaire which assessed the motivation and barriers to attending surgical robotics courses, and their familiarity with robotics surgery. After 1-hour hands-on exercise supervised by consultants and CMR Surgical trainers, delegates were asked to complete post-workshop questionnaire to evaluate shift in familiarity, interest, and confidence in basic robotic skills. Results 75 delegates registered with 93% having never attended similar workshop before and the barriers were perceiving that robotics surgery skills are for advanced surgeons only (37%), cost (26%), no accessible courses (24%), time constraints (9%) and others (4%). The primary motivation to attend this workshop were learning basic robotic skills (49%), exploring innovative technology (44%) and networking (7%). 30 delegates were shortlisted for 1-hour hands-on experience into six groups consisting of 18 students and 12 junior doctors. Post-workshop feedback showed a positive shift in familiarity, interest, and confidence in basic robotic skills. Delegates also expressed increased interest in surgical specialties after this workshop. They also found VR station was enjoyable and believed it has potential in training future robotic surgeons. Conclusions These findings highlight the feasibility and importance of creating accessible training opportunities to bridge the gap in surgical robotics education.

Модель интеграции педагогического вуза в региональную инновационную сетевую инфраструктуру

Раскрыты значение и особенности, представлена общая характеристика педагогической интеграции, рассмотрен перечень показателей, свидетельствующих об интегрированности современного педагогического вуза в инновационную инфраструктуру региональной системы образования. Дано описание авторской модели интеграции педагогического вуза в региональную инновационную сетевую инфраструктуру, построенной с учётом специфики системы образования Краснодарского края. Существенное внимание уделено характеристике формализованного и инициативного форматов интеграции. При описании модели актуализированы основные субъекты её реализации, направления взаимодействия и практические мероприятия, обеспечивающие интеграционные процессы, раскрыты возможности использования ресурсов педагогического вуза и инновационной инфраструктуры региональной системы образования. Указаны ожидаемые эффекты для современной системы образования от реализации авторской модели. The meaning, features and general characteristics of pedagogical integration are revealed, a list of indicators is considered that indicate the integration of a modern pedagogical university into the innovative infrastructure of the regional education system. A description of the author's model for integrating a pedagogical university into a regional innovative network infrastructure, built taking into account the specifics of the education system of the Krasnodar Territory, is given. Significant attention is paid to the characteristics of formalized and initiative formats of integration. When describing the model, the main subjects of its implementation, areas of interaction and practical measures that ensure integration processes are updated and the possibilities of using the resources of a pedagogical university and the innovative infrastructure of the regional education system are revealed. The expected effects for the modern education system from the implementation of the author's model are indicated.

Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.

Full analysis and deep learning optimization of a sustainable and eco-friendly power plant to generate green hydrogen and electricity; A zero-carbon approach

Among alternative energy sources for fossil fuels, hydrogen has emerged as a promising candidate; however, its production methods, reliant on fossil fuels, constrain its widespread adoption as a sustainable energy source. To transcend this limitation, researchers have proposed the concept of green hydrogen production, leveraging renewable energy sources, as a viable solution. This study presents a comprehensive techno-economic analysis that seeks to evaluate the feasibility of establishing a multi-generation green power plant in Iran. Extensive climate data from diverse locations across Iran were collected to determine the most influential design parameters. A mathematical model was developed to simulate the entire system, and an innovative neural network algorithm was employed to optimize the objective functions, given the substantial computational time required for the optimization process. The results of the optimization revealed Qeshm island as the optimal location for the green power plant, considering its favorable annual average temperature, wind speed, and solar radiation intensity. Remarkably, the proposed green power plant boasts a robust power generation capacity of 10.1 MW, enabling the production of 250 tons of hydrogen per year, exhibiting a 10 % improvement in exergy efficiency, and a 6 % reduction in LCOE, in comparison to similar studies. Furthermore, it exhibits an impressive total exergy efficiency of 41.6 %, resulting in a substantial annual income of $3.9 million and achieving a commendable payback period of 1.8 years.

THE USE OF DIGITAL TECHNOLOGIES FOR INTELLIGENT TRAFFIC MANAGEMENT

The purpose of the study is to develop, with the help of artificial intelligence, an innovative digital microcontroller network traffic light object capable of adaptively responding to traffic congestion and changing the time phases of enabling the green signal in interaction with neighboring traffic light objects. The study uses a method of system analysis using computer simulation of existing analog and hybrid traffic light controllers. As a result of the study, an assessment of their effectiveness in the adaptive regulation of modern traffic flows in densely populated cities of the country is given. The results of simulation modeling in the AnyLogic PLE environment were used for calculations. The experimental data of complex entrance/exit road junctions of Almaty and Astana cities were taken as a basis, taking into account the time cyclograms of traffic flow characteristics of highways. An assessment was also given of the existing methods of regulating complex road transport hubs to streamline negative congestion in the center of Almaty and its expressways.