Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

Multi-level intelligence empowering lithium-ion batteries

With the significant and widespread application of lithium-ion batteries,there is a growing demand for improved performances of lithium-ion batteries.The intricate degradation throughout the whole lifecycle profoundly impacts the safety,durability,and reliability of lithium-ion batteries.To ensure the long-term,safe,and efficient operation of lithium-ion batteries in various fields,there is a pressing need for enhanced battery intelligence that can withstand extreme events.This work reviews the current status of intelligent battery technology from three perspectives:intelligent response,intelligent sensing,and intelligent management.The intelligent response of battery materials forms the foundation for battery stability,the intelligent sensing of multi-dimensional signals is essential for battery management,and the intelligent management ensures the long-term stable operation of lithium-ion batteries.The critical challenges encountered in the development of intelligent battery technology from each perspective are thoroughly analyzed,and potential solutions are proposed,aiming to facilitate the rapid development of intelligent battery technologies.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Analysis of the Creation of Demonstration Digital Archives: Taking the Construction of Smart Archives in Dongcheng District, Beijing as an Example

The construction of demonstration digital archives is a key project for the innovative development of the archival industry vigorously promoted by the National Archives Administration. Taking the construction of the Smart Archives in Dongcheng District, Beijing as an example, this paper deeply explores the work objectives, functional requirements, and technical means of creating a demonstration digital archive. One is scientific planning, which is building the basic framework of digital archives. Proposed the infrastructure of “three major supports, five major platforms, and three major guarantees”. The second is to highlight key points and improve the basic functions of digital archives. Including hardware construction, software configuration, digitization of library archives, electronic archive management, sharing and utilization of digital archive information, and formulation of management systems for digital archives. The third is to strive for practical results and deepen the construction of smart libraries. This includes vigorously promoting the construction of smart archive centers, establishing a comprehensive management platform for smart libraries, and introducing new-generation information technology to achieve the integration of smart libraries in order to explore some mature experiences and methods that can be referenced for the creation of demonstration digital archives.

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

The Intelligent Management System Of Campus Express In Tianjin

Courier services develop fast as e-commerce advances but challenged by the problem of ＂the last kilometer＂. The research analyzed problems of courier ser- vices in universities or colleges with a case study of universities in Tianjin and pro- posed suggestions on intelligent courier services.

Deep learning-based intelligent management for sewage treatment plants

It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

Intelligent Management Based on Chemical Reagent Quality Analysis

Access database technology and Visual Basic programming language are used to develop a set of chemical reagents quality analysis and inspection of intelligent management application software, and its stability was investigated. The results show that the sotfware is used to manage intelligently the quality analysis of chemical reagents test each linkage, to upload and save the analysis of test data and results in the network in time for users, to build a network of information sharing platform for enterprises and quality management departments, and its quality of chemical reagents test results is used to manage by the computer with practical application value. The software is simple, convenient, fast, friendly interface, stable operation, accurate and reliable results, with a high value to promote the application.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

Food Additives-Ascorbic Acid Quality Assay Test Intelligent Management

In this paper, based on the analysis and test methods of national standards （GB 14754-2010） and chemical analysis and test items carried out by chemical enterprises, a set of automatic processing of quality analysis test data of ascorbic acid products was developed by using access database technology and Visual Basic programming language system, and its stability was investigated. The results show that the software can manage intelligently all aspects of the quality analysis and test of ascorbic acid products, uploading timely the data and results of the analysis and inspection to the network and saving it for users, enterprises and quality management, which set up a network of information sharing platform to ensure the authenticity and reliability of measurement results, improving greatly the speed of data processing, saving valuable time, reducing production costs with good economic efficiency and social benefit. It has practical value for ascorbic acid quality analysis test data processing automatically the results of the implementation of intelligent management.

Intelligent Fault-tolerant Management of Electromechanical Equipment

In this paper, a method of intelligent fault tolerant management on electromechanical equipment is presented. It is based on condition monitoring of equipment and realized by condition prediction and condition control. An example is introduced and analyzed in this paper.

Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Intelligent Information Management in Aquaponics to Increase Mutual Benefits

Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.

Research on Restoration and Intelligent Management of the Global Village

A sharp rebound in global energy emissions in 2018 is disappointing as the carbon-dioxide data monitored by mon ppm,hawaii reached 415.09 on may 3,the highest level in at least 800,000 years.We are well known to emit 0.272 kg of carbon dust,0.997 kg of carbon dioxide(C02),0.03 kg of sulfur dioxide(S02),0.015 kg of nitrogen oxides(NOX)and huge amounts of heat to the earth for each electricity we use a 1 degree thermal power plant.Therefore,the full use of renewable energy instead of fossil energy,not only to achieve reduction.The effective measures to open the era of boiler and automobile cold emission are also the trend of the development of national environmental protection and energy strategy.

Research on Innovation of Engineering Project Management Mode and Intelligent Management

In order to realize the standardization and normalization of environmental protection project management.This paper uses the literature method to study the current status of the existing project management mode,and points out the problems:lack of a sound project management system,insufficient information application of project management,and unclear decentralization in project management;on this basis,this paper proposes optimization strategies from the aspects of perfecting the engineering project management system,clarifying the allocation of project management authority,and strengthening the construction of engineering project management information.And put forward the future development trend of project engineering management,on the basis of two mainstream management modes of BOT(Build-Operate-Transfer)and EPC(Engineering Procurement Construction),build a standardized project management platform to achieve intelligent management.Realize intelligent management of engineering project management to achieve standardization and standardization,greatly improving the efficiency of project management.

Intelligent Server Management Framework over Extensible Messaging and Presence Protocol

With the increasing importance of cloud services worldwide, the cloud infrastructure and platform management has become critical for cloud service providers. In this paper, a novel architecture of intelligent server management framework is proposed. In this framework, the communication layer is based on the Extensible Messaging and Presence Protocol (XMPP), which was developed for instant messaging and has been proven to be highly mature and suitable for mobile and large scalable deployment due to its extensibility and efficiency. The proposed architecture can simplify server management and increase flexibility and scalability when managing hundreds of thousands of servers in the cloud era.

基于BIM+技术的城市地下综合管廊智能运维管理研究

城市地下综合管廊是保障城市正常运转的“生命线”,关系着广大民众的日常生活和社会的稳定,如何使其平稳、高效运行已成为社会各界广为重视的问题。综合管廊运维管理涉及的主客体众多,以人工监管方式为主的传统运维方式效率低、准确率不足、安全性难以保障,因此,本文基于BIM+技术视角,为城市地下综合管廊的智能运维管理提供一种新思路,以期实现综合管廊管理的标准化、规范化,提高管理效率、降低管理成本。本文包括引言与结语共分为五个部分。首先,介绍本文的研究背景、意义,以及研究思路。其次,阐述综合管廊传统运维管理模式的现状及存在问题。然后,提出综合管廊运维管理的基本目标,并基于BIM+技术,尝试构建城市地下综合管廊的智能运维管理系统。最后,对文章进行总结并提出展望。

Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

Reliability Management for Information System

An integrated intelligent management is presented to help organizations manage many heterogeneous resources in their information system. A general architecture of management for information system reliability is proposed, and the architecture from two aspects, process model and hierarchical model, described. Data mining techniques are used in data analysis. A data analysis system applicable to real-time data analysis is developed by improved data mining on the critical processes. The framework of the integrated management for information system reliability based on real-time data mining is illustrated, and the development of integrated and intelligent management of information system discussed.

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.