Distributed Intelligence Systems Research Articles

An intelligent distribution system for green logistics operations in the blockchain environment

ABSTRACTTo ensure the safety of fresh food, the cold chain logistics is widely utilised, which can provide a low temperature environment. However, due to the high energy consumption, how to reduce carbon emission of cold chain logistics to develop sustainable distribution has become the research focus. In response, this study proposes an intelligent distribution system for cold chain logistics to achieve green operations. Specifically, the framework of the intelligent system based on blockchain technology is proposed to improve operational efficiency and enhance food traceability. Meanwhile, an electric vehicle routing optimisation model taking into account carbon emission is deployed in the intelligent system to plan the distribution routes. The suggested optimisation model seeks to reduce the total cost, including fixed, spoilage, refrigeration, punishment, queuing, charging and carbon emission costs. Furthermore, an ant colony algorithm is embedded in the intelligent system to help with distribution routes design. Finally, this study combines a real case to discuss the performance of the intelligent system, and the results show that the proposed system can significantly reduce distribution cost and carbon emission. Managers can use the suggested intelligent system to design distribution routes and monitor the distribution process.

Multi-UAV Coverage Path Planning: A Distributed Online Cooperation Method

Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) plays a significant role in intelligent distributed surveillance systems. However, due to poor cooperation, most existing CPP methods may cause strongly overlapped trajectories, missing areas, or even collisions in uncertain and complex environments, leading to long task completion time and low coverage efficiency. To this end, in this paper we propose a novel multi-UAV distributed online cooperation (MDOC) CPP method that aims to minimize task completion time. Moreover, this method allows UAVs to quickly respond to unknown obstacles and complex emergencies, such as UAV breakdown or communication interruption. To establish close cooperation between UAVs, we propose an efficient environmental information map (EI-map) fusion technique that enables them to obtain global exploration in real-time in a cooperative manner. Then we innovatively develop a distributed cooperative deep Q-learning (DCDQN) algorithm to obtain UAVs' coverage paths online that are determined by minimizing task time and avoiding overlaps, missing areas, and collisions. Specifically, attributing to the fused EI-map, we expand the state space of DCDQN to collect sufficient observations and design a novel cooperative learning pattern to efficiently plan the path for global optimization. Simulation results show that our method outperforms the state-of-the-art in task completion time and coverage efficiency, especially in uncertain and complex environments. In addition, we validate that our method can efficiently complete full coverage even in emergencies.

Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels

With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme.

Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

The trend toward designing an intelligent distribution system based on students’ individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system, which neglects the students’ personality traits, causes dormitory disputes, and affects the students’ quality of life and academic quality. This paper collects freshmen's data according to college students’ personal preferences, conducts a classification comparison, uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation, determines the description rules of students’ personal preferences and decision tree classification preferences, completes the conceptual design of the database of entity relations and data dictionaries, meets students’ personality classification requirements for the dormitory, and lays the foundation for the intelligent dormitory allocation system.

Retracted: Intelligent distribution system of university student dormitory based on data fusion optimisation algorithm

AbstractRetraction: [Tingting Zheng, Intelligent distribution system of university student dormitory based on data fusion optimisation algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12100)].The above article from IET Software, published online on 1 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Research on market trading strategy of multi-microgrid intelligent power distribution system based on Bi-level optimization

With the continuous promotion of the “dual carbon” idea, future power generation will rely heavily on renewable energy sources. As an effective utilization form of clean power sources, it is of positive significance to study the trading strategy of microgrids in the intelligent power distribution system under the influence of carbon quota. In this research, a bi-level optimization method is used to build a trading model of the distribution-side power market, with the upper-level planning aiming to reduce the cost of distribution system operators and the lower-level planning aiming to increase the revenue of microgrids. Secondly, the genetic algorithm and sequential quadratic planning algorithm are applied to the upper and bottom models to determine the optimal clearing strategy for the microgrid and the optimal scheduling scheme for the distribution system operator, respectively. Finally, a typical day is used as an example to analyze in detail the market trading strategy of a multi-microgrid intelligent distribution system under the influence of carbon quota, and the effectiveness of the method described in this paper is verified.

OPTIMIZATION RESOURCE ALLOCATION MODEL FOR PAEDIATRIC HEALTHCARE SYSTEMS DURING PATIENT REFERRAL

Patient referral is one of the strategies recommended for enhancing access to healthcare as stipulated by the Sustainable Development Goals Target 3 of the United Nations. However, the success of patient referral is directly dependent upon knowledge of the availability of requisite resources in the referral facility. Whereas several models based on heuristic search algorithms have been developed for healthcare system resource allocation, none seem to have been targeted at paediatric patients. This paper presents an intelligent distributed system resource allocation model based on Honey Bee Foraging theory for improving healthcare system provision to paediatric patients. The model was proved to be effective in enhancing healthcare service delivery due to informed decision making before a referral process was completed. More models should be designed which are capable of integrating patients and specialist data across county boundaries to facilitate wider referral options.

Economic efficiency of implementing robotic solutions and their impact on the ecology of the region

The relevance of creating a system of robotic plant protection is due to the need to preserve the planned yield and increase its competitiveness in the conditions of food security of the country and the region. Taking into account the decrease in the number of specialists and staff shortage in agriculture, related, among other things, to heavy physical work, the issue of transition to unmanned technologies in crop production becomes important. The paper describes the concept of an intelligent distributed system of robotic plant protection. The presented concept implies the creation of a multi-agent system consisting of autonomous mobile robots for various purposes: monitoring of crop condition, application of active substances (fertilisers and pesticides) and supplying the system with chemicals and energy. In addition, the paper describes the structure of autonomous mobile robots included in the plant protection system. The algorithm of the system operation is described, in particular, the question of full automation of the process of data collection and application of necessary chemicals to plantings is considered. The described system is based on the concept of “smart field”, when the main focus of agrotechnical operations from the whole field is switched to each individual plant. The use of a multi-agent system and open data exchange protocols will allow the proposed system to be easily scalable, and minor modifications to some of the autonomous robots included in the system will allow them to work with other crops. In addition, the article provides an economic assessment of the effectiveness of the introduction of such intelligent plant protection systems on the example of the introduction of an autonomous robot for monitoring and protection of maize crops.

Evaluation and Analysis of Intelligent Logistics Distribution Using the Expectation-Maximization Algorithm Calculation Model

The purpose of this article is to solve the problem that the accuracy of logistics distribution path planning is affected by the lack of data in the process of traditional logistics distribution planning and management. This exploration innovatively applies an effective data addition algorithm expectation-maximization (EM) algorithm to the intelligent logistics distribution system to improve logistics distribution’s overall efficiency and management quality. First, the concept of intelligent logistics and the composition and main functions of the intelligent logistics system are introduced. Then, the core idea of the EM algorithm and its applications in intelligent logistics are described. The logistics distribution of a chain company is taken as an example. Finally, the advantages and disadvantages of the intelligent logistics system based on the EM algorithm are compared with those of the traditional intelligent logistics systems based on variable neighborhood search (VNS), Tabu search (TS), and ant colony optimization (ACO). The performance test results show that the EM algorithm’s optimal solution times are 7 times. Its convergence speed is slightly lower than that of the ACO, but there is no obvious difference. The intelligent logistics distribution system based on the EM algorithm has faster order processing speed and higher efficiency in the actual case application. The average processing time of each order is 1.78 min, which is 0.237 min less than that of VNS and only 0.022 min more than that of ACO. It reveals that the intelligent logistics distribution system based on the EM algorithm is more efficient. The study provides a new idea for the efficient distribution of enterprise logistics.

Research on Optimization of Intelligent Logistics Agile Distribution Model in Supply Chain Networks

In the supply chain environment, agile distribution refined the division of operations, responded quickly to the customer needs of the mobile terminal, and promoted the construction of an intelligent logistics distribution system. In order to integrate the distribution resources of the distribution system of the logistics industry and maximize the overall benefits of distribution, this paper designs an intelligent logistics agile distribution model and optimizes the distribution strategy. Firstly, this paper puts forward a rationalization evaluation system based on the established logistics agile distribution model. Make full use of the fuzzy subsets of each level to quantify the fuzzy indexes to evaluate, and then comprehensively clarify the index attributes through the fuzzy transformation criteria to obtain the evaluation results. Secondly, aiming at the networked agile distribution organization form, the scientific location mathematical model of agile distribution center and the path mathematical model of the agile distribution are established. Finally, the uncertain influence of many objective conditions on distribution agility has been analyzed, and the dynamic optimization model of an agile distribution path with the concept of time window under random demand is constructed. Through the experimental analysis, the rationalization evaluation strategy and index of verification test results show that the model has significant optimization advantages, which not only improve the rationality and economic benefits of distribution, but also meet the time requirements of all users.

Intelligent Distribution System and Operation and Maintenance Management

Intelligent Distribution System and Operation and Maintenance Management

An approach for semantic interoperability in autonomic distributed intelligent systems

AbstractThe complexity and dynamism of distributed intelligent systems have motivated the utilization of autonomic computing in such systems. However, the interoperability of agents in order to attain self‐managing properties is itself a great challenge that requires more attention. Although a number of semantic approaches exist for central autonomic systems, there are not many attempts to develop mechanisms for facilitating semantic interoperability to attain a distributed feedback loop. In this paper, a semantic approach is presented to overcome this challenge. The main component of this approach is AutonoML language that provides the shared semantics for autonomic computing in distributed environments. Besides this language, the method of its application for enhancing the interoperability of the distributed MAPE‐K loop is presented. In order to evaluate this mechanism, three well‐known metrics from the ontological assessment area are utilized, and the results are compared with an existing autonomic ontology that shows greater relational and attributes richness of the proposed semantic structures. Moreover, in order to evaluate the applicability of the mechanism, a case from NASA‐ANTS project is studied, and the results show that the proposed mechanism is capable of facilitating the achievement of more stable self‐adaptive solutions in less time and by exchanging fewer messages.

Implementation of an AI ready BACS system in Treviso school with DCV (Demand Control Ventilation)

The pandemic has highlighted the extreme importance of indoor air quality (IAQ). Especially in schools, so places that have always been critical for the spread of highly contagious viral diseases, IAQ is an increasingly necessary need. In the province of Treviso, a tailored system has been installed: there are both winter and summer air conditioning, plus AHU’s capable of guaranteeing the necessary air exchange to obtain a healthy level of the air inside the classrooms. The county also intends to replicate this experience in all schools of its territory and monitor the operation of these plants continuously. The important matter is not only to implement systems equipped with HRV (heat recovery ventilation), but also to equip them with automation and control systems capable of implementing an high automation standard according to EN 15232-1 and a high SRI or Smart Readiness Indicator defined by the EPBD III directive 844/2018. A high index ensures an implementation of a distributed intelligence system all over the plant, connected to the cloud and capable of extrapolating the physical variables, then processing them with ML (machine learning) and AI (artificial intelligence) techniques optimized for the HVAC algorithms. At the end, it is not enough to detect quantities such as EI (environmental index) but it is also necessary to process the data for the purposes of predictive maintenance and much more.

Fiducial Lower Confidence Limit of Reliability for a Power Distribution System

Reliability performance, especially the lower confidence limit of reliability, plays an important role in system risk and safety assessment. A good estimator of the lower confidence limit of system reliability can help engineers to make the right decisions. Based on the lifetime of the key component in a typical satellite intelligent power distribution system, the generalized fiducial method is adopted to estimate the lower confidence limit of the system reliability in this paper. First, the generalized pivotal quantity and the lower confidence limit of reliability for the key component are derived for the lifetimes of the exponential-type and Weibull-type components. Simulations show that the sample median is more appropriate than the sample mean when the lower confidence limit of reliability is estimated. Moreover, the lower confidence limit of reliability is obtained for the typical satellite intelligent power distribution system through the pseudo-lifetime data of the metallic oxide semiconductor field effect transistor. The lower confidence limit of reliability for this power distribution system at 15 years is 0.998, which meets the factory’s reliability requirement. Finally, through the comparison, a hot standby subsystem can be substituted with a cold standby subsystem to increase the lower confidence limit of the system reliability.

The multilayer distributed intelligence system model foremergency area scanning

Emergency situations have a huge impact on various important areas of human life. Every year there are many situations, the elimination of which requires a lot of financial and human resources. Therefore, the ability to reduce the impact of the consequences and increase the speed of their elimination is extremely important. In this article, a multi-level model of a system was proposed that provides support for performing operational tasks in emergency situations in open areas. The most important elements, areas of their responsibility, and interconnection were identified and described in architectural style. The idea of the work is to design asystem that should use Swarm intelligence under the hood to provide continuous support in emergency situations. The system consists of 4 main parts: Cloud, Swarm, Swarm operator, and Swarm Node. The Cloud (Swarm Wamb) is the main decision-maker that provides ETL data pipelines and operates under strategicallytasks. In accordance with the idea, Swarm womb should be a cloud service-like system with the ability to scale over the world. The Swarm is a combined set of multiple Swarm Nodes and only one Swarm Operator. The main task of the Swarm is to provide support in local operational tasks where SN is responsible for the execution and SOis for control. Rescue and search operation after any natural disaster is a target to show the system’s purpose. In practice, the cloud system (Swarm Wamb) receives requests to perform an operation, calculates resources effort first, anddelegates a task to the Swarm. When the swarm reaches the location, it starts executing. Operator with nodes tries to find survivors and collect as much important information as they can. Video, images, recognized objects are continuously sending to the Cloud for additional analysis in real-time. Any information in an emergency situation can help save more humans lives and reduce risks. In this article, the multilayer distributed intelligence system architecture for emergency area scanning was designed and described. The set of terminology was proposed as well. This architecture covers different levels of tactical and operational tasks.

Systems of distributed artificial intelligence for analysis of oil product transportation processes: evidence from Russia

Systems of distributed artificial intelligence for analysis of oil product transportation processes: evidence from Russia

Systems of distributed artificial intelligence for analysis of oil product transportation processes: evidence from Russia

Systems of distributed artificial intelligence for analysis of oil product transportation processes: evidence from Russia

Emerging Complexity in Distributed Intelligent Systems.

Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ БЕЗПЕКИ РОЗПІЗНАВАННЯ ОБ'ЄКТІВ У ВІДЕОПОТОЦ

The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

Fog computing context analytics

Developments in measurement science have enhanced the quality of measurement procedures, improving their efficiency and increasing their accuracy [1]. The implementation of intelligent and adaptive distributed systems using the Internet of Things (IoT) has recently seen a rapid increase. The synergy of these developments requires an extensive use of sensing systems that can measure, analyze and provide accurate results in real-time [2]. However, an increase in the number of sensors leads to challenges such as a necessity for complex processing strategies to handle multivariate data [3]. It also provides opportunities for analyzing multivariate, sensor-generated data to infer contextual knowledge that can be useful in decision making. Multivariate context information can offset the ambiguities that arise when single sensor data are used for inference and can uncover relationships previously unknown or difficult to confirm through conventional approaches [4].