Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain ***,the existing methods for re...
详细信息
Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain ***,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training *** conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective ***,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining *** experimental results show that PURP retains strong prediction efficiency as the prediction period increases.
Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s *** the advent of distributed data collection and annotation,we can easily obtain and share such mul...
详细信息
Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s *** the advent of distributed data collection and annotation,we can easily obtain and share such multimodal ***,due to professional discrepancies among annotators and lax quality control,noisy labels might be *** research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the *** address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities ***,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network ***,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy *** conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.
Because the aerospace-ground Internet no longer relies on deploying infrastructure such as base stations,it has the advantage of all-weather full coverage services that traditional terrestrial networks do not ***,the ...
详细信息
Because the aerospace-ground Internet no longer relies on deploying infrastructure such as base stations,it has the advantage of all-weather full coverage services that traditional terrestrial networks do not ***,the traditional global navigation satellite system does not support communication *** newly developing aerospace network system is still in the construction stage,and there is no applicable solution *** communication technology is an important method to solve the contradiction between the low battery capacity of the Internet of things(IoT)node and the high energy consumption of *** is the development trend of the ***,the current passive technology based on Wi-Fi and other signals cannot achieve arbitrary communication due to the excitation signal acquisition *** solve the above two major problems,this paper proposes a passive system design for aerospace-ground IoT *** system can use the global navigation signal as excitation signal for backscatter *** the global navigation signal has the characteristics of all-weather and full coverage,this design solves the carrier acquisition problem in previous *** addition,this paper also proposes a low-power signal detection technology that can detect navigation signals with high precision on passive *** evaluate system performance through simulation *** experimental results show that the backscatter system based on global navigation satellite signals can realize efficient communication of IoT nodes.
In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signal...
详细信息
In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.
In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of sate...
详细信息
In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure *** edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.
Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient ...
详细信息
Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusio...
详细信息
Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.
Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Dr...
详细信息
This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in...
详细信息
The paper proposes an automated data exploration and analysis method based on Attribute Frequency Statistical Feature Ratio (AFSFR). It integrates AutoVis and Data Preprocessing Methods to design and develop AutoEDA-S...
详细信息
暂无评论