Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

Technical Research on Characteristics and Network Planning of Railway 5G-R Network

The railway 5G-R network is ready for the engineering construction.In order to provide technical reserve for the preliminary engineering stage,it is necessary to study the characteristics of 5G-R network and network planning technology in advance.The 5G-R network,different from the operators'5G network,is mainly available for railway staff and covers the railway line.It is upgraded based on the previous generation of railway communication system-GSM-R technology.By comprehensively summarizing and reviewing the characteristics of 5G-R system architecture,network coverage,networking technology,business characteristics,equipment redundancy,network performance,etc.,we study the network planning technology with 5G-R characteristics based on the above characteristics,including frequency,coverage,capacity,parameters,indoor distribution,switching and other key technologies.On this basis,this paper puts forward 5G-R network planning suggestion,which can provide technical support for subsequent project deployment.

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.

Application and Realization of Hierarchical Network Planning Based on Web

Making use of Microsoft Visual Studio. NET platform, hierarchical network planning is realized in working procedure time-optimization of the construction by TBM, and hierarchical network graph of the construction by TBM is drawn based on browser. Then the theory of system realization is discussed, six components of system that can be reused are explained emphatically. The realization of hierarchical network panning in Internet provides available guarantee for controlling rate of progress in large-scale or middle-sized projects. Key words Web - network graph - hierarchical network planning CLC number TU721 Biography: WU Shi-jing (1963-), male, Professor, research direction: equipments management engineering, mechatronics, state monitoring and malfunction diagnostics of machine and electronics equipments.

Radio Network Planning and Optimization for 5G Telecommunication System Based on Physical Constraints

The paper mainly focuses on the network planning and optimization problem in the 5G telecommunication system based on the numerical investigation.There have been two portions of this work,such as network planning for efficient network models and optimization of power allocation in the 5G network.The radio network planning process has been completed based on a specific area.The data rate requirement can be solved by allowing the densification of the system by deploying small cells.The radio network planning scheme is the indispensable platform in arranging a wireless network that encounters convinced coverage method,capacity,and Quality of Service necessities.In this study,the eighty micro base stations and two-hundred mobile stations are deployed in the-15km×15km wide selected area in the Yangon downtown area.The optimization processes were also analyzed based on the source and destination nodes in the 5G network.The base stations’location is minimized and optimized in a selected geographical area with the linear programming technique and analyzed in this study.

Robust Charging Demand Prediction and Charging Network Planning for Heterogeneous Behavior of Electric Vehicles

This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.

Study on the correlation between the hierarchical urban system and high-speed railway network planning in China

This study examines the interrelatedness between the hierarchical structure of China＇s urban system and high-speed railway （HSR） network planning at the national level. As a multi-layered system, the Chinese HSR can be categorized into three sub-networks, namely, the national HSR trunk network, the national HSR extensional network, and the intercity HSR network. By examining the direct HSR network connection, HSR noda（ connection, and HSR operational frequency of 287 prefecture-level cities, this study demonstrates that the hierarchies of China＇s administrative, demographic, and economic urban systems strongly influence HSR network planning. The national HSR trunk network prioritizes the connection of top-level central cities, whereas the extensional network prioritizes cities at the （ower level of the urban system. Moreover, the national HSR system forms the backbone of the HSR network structure based on a national scale, whereas the intercity HSR system satisfies the travel needs within urban agglomerations based on the regional level.

Random mating mayfly algorithm for RFID network planning

In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was proposed.Firstly,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)problem.Secondly,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the boundary.Lastly,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and MA.The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP problems.The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms.When the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms.RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.

RFID network planning based on improved brain storm optimization algorithm

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

A New Network Planning Method Based on the Connection Number a+bi of SPA

The connection number a+bi is a new kind of number in the Set Pair Analysis (SPA) theory. This paper applies it to network planning technique and considers the expressing, processing and controlling methods of the synthetically uncertainties of the time limit caused by Fuzzy, random and indeterminate known factors by the connection number for a large\|scale project. The results illustrate that the new network planning and controlling method based on the connection number a+bi can provide more useful information such as primary critical paths, secondary critical paths and third critical paths etc. The new method is mroe flexible than the traditional method, and is more suitable to the practical application of project.

The Application of Grey Prediction toCommunication Network Planning

The first and foremost task of communication network planning is macro forecasting to theservice volume ,and the quality of planning ultimately depends upon the accuracy of forecasting. Theconventional methods usually as forecasting model are increase rate method, time sequence extensionmethod, regression correlation analysis method. However, nowadays in market economy drvelopingstage ,to meet the vigorous demand, communication servtice business, particularly the telecommunicationservice is drveloping rapidly such that forcasting by the conventional method fails achieve the goalfavourably. Thus we should emancipate our mind thinking deeply into the communication conoiationand the method of forecasting. To this end ,the author exploited the laiter ,introducing GM(1, 1) modelas forcastzng means. Comparint with the forecasting results from several other niodels the GM(1.1)has better reliability. Is has been put into practice in many urban telecomrnunication network planning. Allresults obtatned are desirable.

Digital Twin-Empowered Network Planning for Multi-Tier Computing

In this paper,we design a resource management scheme to support stateful applications,which will be prevalent in sixth generation(6G)networks.Different from stateless applications,stateful applications require context data while executing computing tasks from user terminals(UTs).Using a multi-tier computing paradigm with servers deployed at the core network,gateways,and base stations to support stateful applications,we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource reservation.The coupling among different resources and the impact of UT mobility create challenges in resource management.To address the challenges,we develop digital twin(DT)empowered network planning with two elements,i.e.,multi-resource reservation and resource reservation reconfiguration.First,DTs are designed for collecting UT status data,based on which UTs are grouped according to their mobility patterns.Second,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands.Last,a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost.Simulation results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.

Distribution Network Equipment Location and Capacity Planning Method Considering Energy Internet Attribute from the Perspective of Life Cycle Cost

For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost.Compared with the traditional energy network,the equipment capacity problem of energy interconnected distribution network which involves in electricity network,thermal energy network and natural gas network is comprehensively considered in this paper.On this basis,firstly,the operation architecture of energy interconnected distribution network is designed.Secondly,taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal,the equipment location and capacity optimization model of energy interconnected distribution network is established.Finally,an IEEE 33 bus energy mutual distribution grid system is taken for example analysis,and the improved chaotic particle swarmoptimization algorithm is used to solve it.The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network,and it can effectively improve the social and economic benefits of system operation.

Wireless Communication Networks' Planning in Underdeveloped Countries

The planning of wireless communication networks in underdeveloped countries is studied for landline telephone, fixed broadband wireless access and PLMN systems via investigation and discussion of the LDC networks＇ characteristic, technology selection, deployment consideration, network configuring, civil work evaluation and investment estimation. An example is embedded all through providing a general

SHARING AND PLANNING OF DISTRIBUTED EQUIPMENT FOR NETWORKED MANUFACTURING

Efficient utilization of the equipment distributed in different enterprises and optimal allocation of these resources is an important concern for networked manufacturing. The third party based equipment sharing approach is put forward to optimize the utilization of distributed equipment for networked manufacturing; Taking advantage of the shared equipment offered by equipment providers by means of lease agreement, the third party carries out production by establishing networked virtual factory. Operational mechanism of the third party based equipment sharing is discussed, and characteristics of this approach in achieving resource allocation are analyzed. Shared equipment planning is formulated as an optimization problem with the objective of maximizing profits for equipment coordinator, a mathematical model for shared equipment planning is developed. Finally a case study is discussed to show the effectiveness of the planning model.

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Learning and fatigue inspired method for optimized HTN planning

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.