MTTSNet:Military time-sensitive targets stealth network via real-time mask generation

The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.

Integrating QoS Management in TLMM:Three Level Mobility Model for Next Generation Wireless IP-Based Networks

In this paper we have designed an implemented an integrated framework of QoS for Three Level Mobility Model(TLMM),which has been recently proved to be the optimal mobility management solution for next generation wireless IP-based networks.The QoS solution uses a combination of IntServ and DiffServ models incorporated in TLMM architecture.The paper also proposes an effi cient dynamic handover policy that takes care of false handover.Simulation and analytical results have shown that this infrastructure guarantees eff icient QoS handling and scalability among end users.To provide a comparative understanding of the QoS mechanism and signaling load of TLMM we have used TeleMIP(without QoS support) and MIP as alternative mobility management protocols.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

Multi-style Chord Music Generation Based on Artificial Neural Network

With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.

An Enhanced GAN for Image Generation

Generative adversarial networks(GANs)with gaming abilities have been widely applied in image generation.However,gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes.Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation.In this paper,we propose an enhanced GAN via improving a generator for image generation(EIGGAN).EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness of the generated images.Taking into relation the context account,parallel residual operations are fused into a generation network to extract more structural information from the different layers.Finally,a mixed loss function in a GAN is exploited to make a tradeoff between speed and accuracy to generate more realistic images.Experimental results show that the proposed method is superior to popular methods,i.e.,Wasserstein GAN with gradient penalty(WGAN-GP)in terms of many indexes,i.e.,Frechet Inception Distance,Learned Perceptual Image Patch Similarity,Multi-Scale Structural Similarity Index Measure,Kernel Inception Distance,Number of Statistically-Different Bins,Inception Score and some visual images for image generation.

Strategic Thinking on Next Generation Network

It is noted that the revolutionary development of technologies,fundamental change of traffic composition,trend of network convergence as well as market opening and competition have become the driving forces to develop Next Generation Networks (NGN).After introducing the concepts and characteristics of NGN,the paper details its 5 strategic development directions:evolution to softswitch-based next generation switching network, evolution to next generation mobile communication network represented by 3G,evolution to IPv6-based next generation Internet,evolution to diversified broadband access network,and evolution to next generation transport network based on optical networking.Finally,it briefs the strategic thinking on NGN of China Telecom,the largest fixed network carrier in the world.

Next Generation Network

Next Generation Network(NGN)is not a single architecture but a set of architectures with a common set of principles and hence varies by service provider history,target applications and assets.The paper introduces NGN functional requirements,NGN services and NGN architectural features.It also discusses why NGN is needed,when NGN is targeted,NGN trends and NGN deployment.It concludes that it is no longer a case whether NGN is needed but rather when and at what speed of the evolution.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Next Generation Network Service Provision Mode and Its Developing Trend

The provision mode of the telecommunication service has experienced an evolving process, and showing the developing trend from distributed to centralized, from integrated to separated, and from closed to open. To suit this trend, there will be three provision modes as Session Initiation Protocol (SIP) server, Open Service Access (OSA) application server and intelligent network(IN) in Next Generation Network (NGN), provides all kinds of services and applications to the subscribers. With the popularity of broadband access and Internet, the NGN will provide single telecommunication service and act as the important national infrastructure to offer various information services to the subscribers. The service provision mode will be more open, diversified, and individualized.

Telecom Regulation of Next Generation Network in China

The advent of the Next Generation Network (NGN), a new service-driven network, urges the telecom service operators to consider transforming from single-service providers to full-service providers. During the transformation, they should be concerned about the network user number and the network quality as well as the value added network information. The low threshold for service provision brings a new breed of service providers, which impacts upon the current regulation policy. To adapt to the development of the NGN, it is a necessity to improve the regulation policy in terms of service operators management, user management, Quality of Service (QoS) assurance, service monitoring, charging, and settlement. Meanwhile, regulatory authorities should establish a new body as quickly as possible to meet the trend of the NGN convergence. The new regulatory body would be responsible for regulating operators who will be awarded full-service licenses, and managing new service providers effectively to guarantee the user’s interests.

Cost Effective Scheme for OLT in Next Generation Passive Optical Access Network Based on Noise Free Optical Multi Carrier

We propos e a cos t-effective multi-carrier generation technique which minimizes the passive optical access network(PON) costs. In this study replacement of laser array with multi-carrier source at optical line terminal(OLT) side in PON is addressed. With 25-GHz frequency spacing, the generated optical multi-carriers exhibit good tone to noise ratio(TNR) i. e. above 20 d B, and least amplitude difference i. e. 1.5d B. At the OLT, multi-carriers signal based multiplexed differential phase shift keying(DPSK) data from all the channels each having 10 Gbps for downlink is transmitted through 25 km single mode fiber. While the transmitted information is retrieved at optical network unit(ONU), part of the downlink signal is re-modulated using intensity modulated(IM) on-off keying(OOK) for upstream transmission at 10-Gbps. Simulation results are in good agreement with the theoretical analysis, showing error free transmission in downlink and uplink with 10 Gbps symmetric data rate at each channel. The receivedpower, both for uplink and downlink transmission, is adequate for all channels at BER of 10-9 with minimum power penalties. Power budget is calculated for different splitting ratios showing excellent system margins for any unseen losses. The proposed setup provides a cost-effective way minimizing transmission losses, and providing greater system's margin in PON architecture.

Wavelet Neural Network Based Traffic Prediction for Next Generation Network

By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network （NGN） operator. To build a wavelet basis neural network （NN）, the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day＇s traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

Next Generation Mobile Video Networking

HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattle, USA, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research fi＇om 2003 to 2005, and from 2011-2015. He is current- ly the Associate Chair for Global Affairs and Inter- national Development in the EE Depamnent. Hehas written more than 330 journal papers, conference papers and book chapters in the areas of machine learning, muhimedia signal processing, and muhimedia system integration and networking, including an au- thored textbook on ＂Multimedia Networking： from Theory to Practice,＂ published by Cambridge University Press. Dr. HWANG has close work- ing relationship with the industry on muhimedia signal processing and nmltimedia networking.

Next Generation Optical Access Network Using CWDM Technology

We are developing a novel technology for the next generation optical access network. The proposed archi-tecture provides FTTX high bandwidth which enables to give out 10Gbit/s per end-user. Increasing the subscribers in the future will cause massive congestion in the data transferred along the optical network. Our solution is using the wavelength division multiplexing PON (CWDM-PON) technology to achieve high bandwidth and enormous data transmission at the network access. Physical layer modifications are used in our model to provide satisfactory solution for the bandwidth needs. Thus high data rates can be achieved throughout the network using low cost technologies. Framework estimations are evaluated to prove the intended model success and reliability. Our argument that: this modification will submit a wide bandwidth suitable for the future Internet.

Next Generation Network Enabled by ZTE's Softswitch Technology

Softswitch technology integrates the su-periorities of both an intelligence net-work and the Internet, which embodiesits maturity and advancement. With ahierarchical network model, it effectivelysolves problems of evolution and convergenceof current communication networks. It also fol-