Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Investigating Optical Transport Network Performance:A Recurrence Plot Approach

In future optical transport networks,lightpath performance analysis is of great practical significance for fully automated management.In general,the quality of transmission(QoT)of lightpaths,measured by optical quality factor or optical signal-to-noise ratio,has a complex time-varying process,along with the interactions of the other lightpath state parameters(LSPs),such as transmit power,chromatic dispersion,polarization mode dispersion.Current studies are mostly focused on lightpath QoT estimation,but ignoring lightpath-level data analytics.In this case,our article proposes a novel lightpath performance analysis method considering recurrence plot(RP)and cross recurrence plot(CRP).Firstly,we give a detailed interpretation on the recurrence patterns of LSPs via a qualitative 2-D RP representation and its quantitative measure.It can potentially enable the accurate and fast lightpath failure detection with a low computational burden.On the other hand,CRP is devoted to modeling the relationships between lightpath QoT and LSPs,and the correlation degree is determined by a geometric mean of multiple indexes of cross recurrence quantification analysis.From the view of application,such CRP analysis can provide the effective knowledge sharing to guarantee more credible QoT prediction.Extensive experiments on a real-world optical network dataset have clearly demonstrated the effectiveness of our proposal.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.

Exploring the Multi-Layer Structural Properties of the Bus-Subway Transportation Network of Shanghai

Buses and subways are essential to urban public transportation systems and an important engine for activating high-quality urban development. Traditional multi-modal transportation networks focus on the structural feature mining of single-layer networks or each layer, ignoring the structural association of multi-layer networks. In this paper, we examined the multi-layer structural property of the bus-subway network of Shanghai at both global and nodal scales. A dual-layer model of the city’s bus and subway system was built. Single-layer complex network indicators were also extended. The paper also explored the spatial coupling properties of the city’s bus and subway system and identified its primary traffic nodes. It was found that 1) the dual-layer network increased the network’s connectivity to a certain extent and broke through the spatial limitation in terms of physical structure, making the connection between any two locations more direct. 2) The dual-layer network changed the topological characteristics of the transit network, increasing the centrality value and bit order in degree centrality, betweenness centrality, and closeness centrality to different degrees, and making each centrality tend to converge to the city center in spatial distribution. Enhancing the management of critical network nodes would help the integrated public transportation system operate more effectively and provide higher-quality services.

Femtosecond laser fabrication of 3D vertically aligned micro-pore network on thick-film Li_(4)Ti_(5)O_(12)electrode for high-performance lithium storage

The development of energy storage devices with high energy density relies heavily on thick film electrodes,but it is challenging due to the limited ion transport kinetics inherent in thick electrodes.Here,we report on the preparation of a directional vertical array of micro-porous transport networks on LTO electrodes using a femtosecond laser processing strategy,enabling directional ion rapid transport and achieving good electrochemical performance in thick film electrodes.Various three-dimensional(3D)vertically aligned micro-pore networks are innovatively designed,and the structure,kinetics characteristics,and electrochemical performance of the prepared ion transport channels are analyzed and discussed by multiple characterization and testing methods.Furthermore,the rational mechanisms of electrode performance improvement are studied experimentally and simulated from two aspects of structural mechanics and transmission kinetics.The ion diffusion coefficient,rate performance at 60 C,and electrode interface area of the laser-optimized 60-15%micro-porous transport network electrodes increase by 25.2 times,2.2 times,and 2.15 times,respectively than those of untreated electrodes.Therefore,the preparation of 3D micro-porous transport networks by femtosecond laser on ultra-thick electrodes is a feasible way to develop high-energy batteries.In addition,the unique micro-porous transport network structure can be widely extended to design and explore other high-performance energy materials.

Constructing low-dimensional perovskite network to assist efficient and stable perovskite solar cells

The use of low-dimensional(LD)perovskite materials is crucial for achieving high-performance perovskite solar cells(PSCs).However,LD perovskite films fabricated by conventional approaches give rise to full coverage of the underlying 3D perovskite films,which inevitably hinders the transport of charge carriers at the interface of PSCs.Here,we designed and fabricated LD perovskite structure that forms net-like morphology on top of the underlying three-dimensional(3D)perovskite bulk film.The net-like LD perovskite not only reduced the surface defects of 3D perovskite film,but also provided channels for the vertical transport of charge carriers,effectively enhancing the interfacial charge transfer at the LD/3D hetero-interface.The net-like morphological design comprising LD perovskite effectively resolves the contradiction between interfacial defect passivation and carrier extraction across the hetero-interfaces.Furthermore,the net-like LD perovskite morphology can enhance the stability of the underlying 3D perovskite film,which is attributed to the hydrophobic nature of LD perovskite.As a result,the net-like LD perovskite film morphology assists PSCs in achieving an excellent power conversion efficiency of up to 24.6%with over 1000 h long-term operational stability.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

Regulating the non-effective carriers transport for high-performance lithium metal batteries

The absence of control over carriers transport during electrochemical cycling,accompanied by the deterioration of the solid electrolyte interphase(SEI)and the growth of lithium dendrites,has hindered the development of lithium metal batteries.Herein,a separator complexion consisting of polyacrylonitrile(PAN)nanofiber and MIL-101(Cr)particles prepared by electrospinning is proposed to bind the anions from the electrolyte utilizing abundant effective open metal sites in the MIL-101(Cr)particles to modulate the transport of non-effective carriers.The binding effect of the PANM separator promotes uniform lithium metal deposition and enhances the stability of the SEI layer and long cycling stability of ultra-high nickel layered oxide cathodes.Taking PANM as the Li||NCM96 separator enables high-voltage cycling stability,maintaining 72%capacity retention after 800 cycles at a charging and discharging rate of 0.2 C at a cut-off voltage of 4.5 V and 0°C.Meanwhile,the excellent high-rate performance delivers a specific capacity of 156.3 mA h g^(-1) at 10 C.In addition,outstanding cycling performance is realized from−20 to 60°C.The separator engineering facilitates the electrochemical performance of lithium metal batteries and enlightens a facile and promising strategy to develop fast charge/discharge over a wide range of temperatures.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Coordinated Voltage Control of Distribution Network ConsideringMultiple Types of Electric Vehicles

The couple between the power network and the transportation network(TN)is deepening gradually with the increasing penetration rate of electric vehicles(EV),which also poses a great challenge to the traditional voltage control scheme.In this paper,we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV.In the first stage,the action of on-load tap changer and capacitor banks,etc.,are determined by optimal power flow calculation,and the node electricity price is also determined based on dynamic time-of-use tariff mechanism.In the second stage,multiple operating scenarios of multiple types of EVs such as cabs,private cars and buses are considered,and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory,Floyd-Warshall algorithm and traffic flow information.In the third stage,the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode.The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation,and the amount of regulation is calculated based on the fair voltage control mode of EVs.Finally,we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN.The simulation results show that the proposed scheme can mitigate voltage violations well.

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

5G Flexible Optical Transport Networks with Large-Capacity, Low-Latency and High-Efficiency

The fifth generation(5G) of mobile communications are facing big challenges, due to the proliferation of diversified terminals and unprecedented services such as internet of things(IoT), high-definition videos, virtual/augmented reality(VR/AR). To accommodate massive connections and astonish mobile traffic, an efficient 5G transport network is required. Optical transport network has been demonstrated to play an important role for carrying 5G radio signals. This paper focuses on the future challenges, recent studies and potential solutions for the 5G flexible optical transport networks with the performances on large-capacity, low-latency and high-efficiency. In addition, we discuss the technology development trends of the 5G transport networks in terms of the optical device, optical transport system, optical switching, and optical networking. Finally, we conclude the paper with the improvement of network intelligence enabled by these technologies to deterministic content delivery over 5G optical transport networks.

Evacuated tube transport technologies (ET3)^(tm):a maximum value global transportation network for passengers and cargo

Evacuated Tube Transport Technologies （ET3） offers the potential for more than an order of magnitude improvement in transportation efficiency, speed, cost, and effectiveness. An ET3 network may be optimized to sustainably displace most global transportation by car, ship, truck, train, and jet aircraft. To do this, ET3 standards should adhere to certain key principals： maximum value through efficiency, reliability, and simplicity; equal consideration for passenger and cargo loads; optimum size; high speed/high frequency operation; demand oriented; random accessibility; scalability; high granularity; automated control; full speed passive switching; open standards of implementation; and maximum use of existing capacities, materials, and processes.

Application of Neural Network in Fault Location of Optical Transport Network

Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.

Effect of seismic retrofit of bridges on transportation networks

The objective of this research is to determine the effect earthquakes have on the performance of transportation network systems.To do this,bridge fragility curves,expressed as a function of peak ground acceleration(PGA)and peak ground velocity(PGV),were developed.Network damage was evaluated under the 1994 Northridge earthquake and scenario earthquakes.A probabilistic model was developed to determine the effect of repair of bridge damage on the improvement of the network performance as days passed after the event.As an example,the system performance degradation measured in terms of an index,'Drivers Delay,'is calculated for the Los Angeles area transportation system,and losses due to Drivers Delay with and without retrofit were estimated.

Laplacian energy maximizationfor multi-layer air transportation networks

To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method ofmulti-layer air transportation networks is put forward based onLaplacian energy maximization. The effectiveness of takingLaplacian energy as a measure of network robustness isvalidated through numerical experiments. The flight routesaddificm optimization model is proposed with the principle ofmaximizing Laplacian energy. Three methods including thedepth-first search （DFS） algorithm, greedy algorithm andMonte-Carlo tree search （MCTS） algorithm are applied tosolve the proposed problem. The trade-off between systemperformance and computational efficiency is compared throughsimulation experiments. Finally, a case study on Chineseairport network （CAN） is conducted using the proposedmodel. Through encapsulating it into multi-layer infrastructurevia k-core decomposition algorithm, Laplacian energymaximization for the sub-networks is discussed which canprovide a useful tool for the decision-makers to optimize therobustness of the air transoortation network on different scales.