Direct transmission between devices is now possible due to the emergence of device-to-device D2D technology. The D2D communication underlay cellular network intends to take advantage of device proximity and hence, boo...
Direct transmission between devices is now possible due to the emergence of device-to-device D2D technology. The D2D communication underlay cellular network intends to take advantage of device proximity and hence, boost consumer bitrates, as well as offer the ability to improve system capacity and energy efficiency while decreasing end-to-end latency. However, D2D devices that underlay cellular networks are vulnerable to co-channel interference, which negatively impacting network performance. Another factor that could also affect network performance and reduce the quality of service is inter-carrier interference in the orthogonal division multiple access (OFDMA) system. Most research has concentrated on co-channel interference only. This study aims to optimize network performance in the presence of both co-channel interference and OFDMA inter-carrier interference (ICI)-based multicast D2D underlay cellular networks. In comparison to different optimization techniques, the simulation results show that the proposed genetic algorithm (GA) achieves a cumulative distribution function of 0.5 at a capacity of 70 Mbps, compared to 5.5 and 6.5 for the greedy search algorithm (GSA) and binary power control (BPC), respectively. It shows the capability of successfully maintaining high network performance.
With the continuous development of economy and society, people have higher and higher requirements for quality of life. It also brings serious environmental problems, such as global warming and ozone layer destruction...
With the continuous development of economy and society, people have higher and higher requirements for quality of life. It also brings serious environmental problems, such as global warming and ozone layer destruction. To solve these problems, countries all over the world are actively exploring new technologies to achieve sustainable development. Among them, distributed power generation has attracted much attention as a new type of clean energy utilization. However, because of the intermittent, fluctuating and non-dispatchable characteristics of distributed power sources, their grid connection will bring certain impact on the safe and stable operation of the power system. Therefore, how to effectively improve the access capacity and power quality of distributed power sources has become an urgent problem to be solved. In this paper, we use the improved NSGA-II algorithm to solve the problem by establishing a mathematical model to improve the performance of the composite energy system.
the countries of the world are using various communication services in all areas of society on based satellite networks. The international Telecommunication Union has allocated two kind of satellite position in space ...
the countries of the world are using various communication services in all areas of society on based satellite networks. The international Telecommunication Union has allocated two kind of satellite position in space segment to Mongolia that are 113.6E longitude for Fixed Satellite Service-(FSS) and 74E longitude for Broadband Satellite Service-(BSS).Mongolia is planning to launch its national satellite in 2030. Therefore, there are studying many kind of science research including estimate satellite geometry parameters, rain attenuation, cloud attenuation and satellite network architecture based on national satellite that will be launched to space *** paper is described by detail that is the mathematical modelling of satellite geometry parameters particularly antenna look angles of the ground control *** this paper, aim of research work is determined the geometrical parameters of the national satellite 113.E and 74E to be launched in Mongolia by using a mathematical model to make a comparative analysis and to show some result.
To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recogni...
To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recognition. This paper proposes a bidirectional convolutional long short term memory-attention network for marine formation target intention recognition. The network takes the multi-source trajectory data of the marine ship formation target as the input, extracts and uses the target change rule and time domain characteristics of the Marine formation data, and trains the model to have the ability to independently learn the weight of information in different time periods. The simulation results show that the method has good performance and can meet the needs of practical application.
In order to improve the power quality of the power transmission system, engineers must measure the phase angle of the transmission line to evaluate the power quality. Therefore, this article measures the phase angle b...
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System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range...
System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.
In view of the traditional ant colony algorithm in the mobile robot path planning in slow convergence speed, easy to trap into local optimum and weak dynamic programming problem, an improved ant colony algorithm fusio...
In view of the traditional ant colony algorithm in the mobile robot path planning in slow convergence speed, easy to trap into local optimum and weak dynamic programming problem, an improved ant colony algorithm fusion dynamic window approach (DWA) path planning method was proposed. Firstly, the heuristic function and pheromone update method were optimized to improve the path search ability. Secondly, the adaptive evaporation factor update strategy was introduced to dynamically adjust it, accelerate the convergence speed and search rate of the algorithm, and realize the global path planning of the mobile robot. Then, the improved ant colony algorithm was integrated with DWA to enhance the local dynamic obstacle avoidance ability of the robot. Finally, the redundant point deletion strategy and the secondary polyline optimization were used to effectively reduce the path turning point and improve the smoothness. The simulation results show that the improved fusion algorithm has improved convergence speed, path length and path smoothness, and can effectively avoid static and dynamic obstacles.
In this study, a Spherical Magnetic Robot (SMR) was designed to remove Magnetic Nanoparticles (MNPs) particles from blood vessels by micro and nano robots, and was studied by dynamic simulation model. SMR is based on ...
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ISBN:
(数字)9798331529505
ISBN:
(纸本)9798331529512
In this study, a Spherical Magnetic Robot (SMR) was designed to remove Magnetic Nanoparticles (MNPs) particles from blood vessels by micro and nano robots, and was studied by dynamic simulation model. SMR is based on a miniaturized design that utilizes magnet drive and integrates a high permeability alloy material to achieve effective adsorption and transport of MNPs particles. In this study, multiphysics problems for vascular modeling, SMR design, motion control, and MNPs particle capture were integrated, and a simulation model was constructed using the COMSOL Multiphysics software. The results show that SMR shows excellent navigation accuracy and adsorption performance in the process of removing MNPs, which verifies the key role of high-precision magnetic field control in SMR motion and MNPs adsorption process.
Industrial control systems are at the core of critical national infrastructures such as petroleum, chemical, natural gas, power and metallurgy. However, with the integration of industrial control systems with the Inte...
Industrial control systems are at the core of critical national infrastructures such as petroleum, chemical, natural gas, power and metallurgy. However, with the integration of industrial control systems with the Internet, the Internet of Things and other network fields, industrial control systems have been penetrated by various security threats. At present, the existing anomaly detection methods have many limitations and cannot effectively identify various attacks. Therefore, in this paper, we propose an effective anomaly detection model for industrial control systems that combines Gaining-sharing knowledge based algorithm (GSK) and the LSTM network. Specifically, we first use the GSK algorithm for feature selection to eliminate redundant features, improve algorithm accuracy and reduce running time, and then use an LSTM classifier to classify different categories of attacks. Secondly, we used Taguchi method to customize the optimal solution for the GSK algorithm applied to the feature selection problem, which improves the efficiency and robustness of the algorithm. Furthermore, we experimentally validate the model using a real gas pipeline dataset. The experimental results show that the proposed TBGSK-LSTM model outperforms other traditional methods in terms of accuracy, precision, recall, F-score, average fitness function value and average number of selected features.
This paper deeply understands the functional modules of the VERICUT software system and the NX software modeling technology based on the computer virtual reality technology of CNC machine tool engineering. Then this p...
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This paper deeply understands the functional modules of the VERICUT software system and the NX software modeling technology based on the computer virtual reality technology of CNC machine tool engineering. Then this paper uses three-dimensional software to build the measured size model of machine tool engineering, and builds the virtual simulation environment of the machine tool in the numerical control virtual simulation processing software VERICUT. Then this paper obtains the parameters of the virtual material model of the joint based on the multi-objective optimization method, and builds an accurate virtual material model of the joint. In this way, an accurate dynamic model of the CNC machine tool is established. The system realizes the information exchange between the machine tool simulation controller and the model, and supports the virtual debugging function of the model. The simulation results show that the method is feasible and can effectively improve the virtual-real interaction ability, data visualization degree and state monitoring efficiency of machine tools.
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