As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of sys...This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.展开更多
The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a block...The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a blockchain-enabled manufacturing collaboration framework is proposed,with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P)collaboration.First,a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain.Second,an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants.Third,a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information.It facilitates automation of the matching process while protecting users'privacy via blockchainbased smart contracts.Finally,simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4%compared to the Bayesian game-based approach,makes all participants profitable,and achieves 90%fairness of enterprises.展开更多
Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always cre...Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always credible in practice.Some studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of collusion.In this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct.In addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute resolution.Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.展开更多
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute...The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.展开更多
A learning-based approach for solving wall shear stresses from Shear-Sensitive Liquid Crystal Coating(SSLCC) color images is presented in this paper. The approach is able to learn and establish the mapping relationshi...A learning-based approach for solving wall shear stresses from Shear-Sensitive Liquid Crystal Coating(SSLCC) color images is presented in this paper. The approach is able to learn and establish the mapping relationship between the SSLCC color-change responses in different observation directions and the shear stress vectors, and then uses the mapping relationship to solve wall shear stress vectors from SSLCC color images. Experimental results show that the proposed approach can solve wall shear stress vectors using two or more SSLCC images, and even using only one image for symmetrical flow field. The accuracy of the approach using four or more observations is found to be comparable to that of the traditional multi-view Gauss curve fitting approach;the accuracy is slightly reduced when using two or fewer observations. The computational efficiency is significantly improved when compared with the traditional Gauss curve fitting approach, and the wall shear stress vectors can be solved in nearly real time. The learning-based approach has no strict requirements on illumination direction and observation directions and is therefore more flexible to use in practical wind tunnel measurement when compared with traditional liquid crystal-based methods.展开更多
This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework ...This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.展开更多
It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a signific...It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.展开更多
BACKGROUND It has been reported that deep learning-based reconstruction(DLR)can reduce image noise and artifacts,thereby improving the signal-to-noise ratio and image sharpness.However,no previous studies have evaluat...BACKGROUND It has been reported that deep learning-based reconstruction(DLR)can reduce image noise and artifacts,thereby improving the signal-to-noise ratio and image sharpness.However,no previous studies have evaluated the efficacy of DLR in improving image quality in reduced-field-of-view(reduced-FOV)diffusionweighted imaging(DWI)[field-of-view optimized and constrained undistorted single-shot(FOCUS)]of the pancreas.We hypothesized that a combination of these techniques would improve DWI image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.AIM To evaluate the efficacy of DLR for image quality improvement of FOCUS of the pancreas.METHODS This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021.We evaluated three types of FOCUS examinations:FOCUS with DLR(FOCUS-DLR+),FOCUS without DLR(FOCUS-DLR−),and conventional FOCUS(FOCUS-conv).The three types of FOCUS and their apparent diffusion coefficient(ADC)maps were compared qualitatively and quantitatively.RESULTS FOCUS-DLR+(3.62,average score of two radiologists)showed significantly better qualitative scores for image noise than FOCUS-DLR−(2.62)and FOCUS-conv(2.88)(P<0.05).Furthermore,FOCUS-DLR+showed the highest contrast ratio and 600 s/mm^(2)(0.72±0.08 and 0.68±0.08)and FOCUS-DLR−showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2(0.62±0.21 and 0.62±0.21)(P<0.05),respectively.FOCUS-DLR+provided significantly higher ADCs of the pancreas and lesion(1.44±0.24 and 3.00±0.66)compared to FOCUS-DLR−(1.39±0.22 and 2.86±0.61)and significantly lower ADCs compared to FOCUS-conv(1.84±0.45 and 3.32±0.70)(P<0.05),respectively.CONCLUSION This study evaluated the efficacy of DLR for image quality improvement in reduced-FOV DWI of the pancreas.DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution.The denoising level of DWI can be controlled to make the images appear more natural to the human eye.However,this study revealed that DLR did not ameliorate pancreatic distortion.Additionally,physicians should pay attention to the interpretation of ADCs after DLR application because ADCs are significantly changed by DLR.展开更多
For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibi...For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.展开更多
Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax...Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.展开更多
In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illn...In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.展开更多
As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent task...As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.展开更多
With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum resources.Spectrum auctions came into being,which can effectively utilize spectrum resources....With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum resources.Spectrum auctions came into being,which can effectively utilize spectrum resources.Because of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be guaranteed.Most scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and buyers.Researchers begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction rules.But these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this paper.Unlike traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum auctions.In addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum auctions.Finally,security analysis and performance evaluation of our protocol are discussed.展开更多
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
基金supported in part by the National Science Fund for Excellent Young Scholars of China(62222317)the National Science Foundation of China(62303492)+3 种基金the Major Science and Technology Projects in Hunan Province(2021GK1030)the Science and Technology Innovation Program of Hunan Province(2022WZ1001)the Key Research and Development Program of Hunan Province(2023GK2023)the Fundamental Research Funds for the Central Universities of Central South University(2024ZZTS0116)。
文摘This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.
基金supported in part by the National Natural Science Foundation of China(62273310)the Natural Science Foundation of Zhejiang Province of China(LY22F030006,LZ24F030009)
文摘The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a blockchain-enabled manufacturing collaboration framework is proposed,with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P)collaboration.First,a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain.Second,an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants.Third,a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information.It facilitates automation of the matching process while protecting users'privacy via blockchainbased smart contracts.Finally,simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4%compared to the Bayesian game-based approach,makes all participants profitable,and achieves 90%fairness of enterprises.
基金supported by the National Key R&D Program of China (No.2020YFB1005500)the Leading-edge Technology Program of Jiangsu Natural Science Foundation (No.BK20202001)+1 种基金the Fundamental Research Funds for the Central Universities (No.XJSJ23040)the Postdoctoral Science Foundation of Jiangsu Province (No.2021K596C)。
文摘Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always credible in practice.Some studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of collusion.In this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct.In addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute resolution.Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
文摘The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
基金co-supported by the National Natural Science Foundation of China(No.11602107)the Natural Science Foundation of Jiangsu Province of China(No.BK20150733)。
文摘A learning-based approach for solving wall shear stresses from Shear-Sensitive Liquid Crystal Coating(SSLCC) color images is presented in this paper. The approach is able to learn and establish the mapping relationship between the SSLCC color-change responses in different observation directions and the shear stress vectors, and then uses the mapping relationship to solve wall shear stress vectors from SSLCC color images. Experimental results show that the proposed approach can solve wall shear stress vectors using two or more SSLCC images, and even using only one image for symmetrical flow field. The accuracy of the approach using four or more observations is found to be comparable to that of the traditional multi-view Gauss curve fitting approach;the accuracy is slightly reduced when using two or fewer observations. The computational efficiency is significantly improved when compared with the traditional Gauss curve fitting approach, and the wall shear stress vectors can be solved in nearly real time. The learning-based approach has no strict requirements on illumination direction and observation directions and is therefore more flexible to use in practical wind tunnel measurement when compared with traditional liquid crystal-based methods.
文摘This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.
基金supported by National Basic Research Program of China (NO 2012CB316002)China’s 863 Project (NO 2014AA01A703)+2 种基金National Major Projec (NO. 2014ZX03003002-002)Program for New Century Excellent Talents in University (NCET-13-0321)Tsinghua University Initiative Scientific Research Program (2011THZ02-2)
文摘It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.
文摘BACKGROUND It has been reported that deep learning-based reconstruction(DLR)can reduce image noise and artifacts,thereby improving the signal-to-noise ratio and image sharpness.However,no previous studies have evaluated the efficacy of DLR in improving image quality in reduced-field-of-view(reduced-FOV)diffusionweighted imaging(DWI)[field-of-view optimized and constrained undistorted single-shot(FOCUS)]of the pancreas.We hypothesized that a combination of these techniques would improve DWI image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.AIM To evaluate the efficacy of DLR for image quality improvement of FOCUS of the pancreas.METHODS This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021.We evaluated three types of FOCUS examinations:FOCUS with DLR(FOCUS-DLR+),FOCUS without DLR(FOCUS-DLR−),and conventional FOCUS(FOCUS-conv).The three types of FOCUS and their apparent diffusion coefficient(ADC)maps were compared qualitatively and quantitatively.RESULTS FOCUS-DLR+(3.62,average score of two radiologists)showed significantly better qualitative scores for image noise than FOCUS-DLR−(2.62)and FOCUS-conv(2.88)(P<0.05).Furthermore,FOCUS-DLR+showed the highest contrast ratio and 600 s/mm^(2)(0.72±0.08 and 0.68±0.08)and FOCUS-DLR−showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2(0.62±0.21 and 0.62±0.21)(P<0.05),respectively.FOCUS-DLR+provided significantly higher ADCs of the pancreas and lesion(1.44±0.24 and 3.00±0.66)compared to FOCUS-DLR−(1.39±0.22 and 2.86±0.61)and significantly lower ADCs compared to FOCUS-conv(1.84±0.45 and 3.32±0.70)(P<0.05),respectively.CONCLUSION This study evaluated the efficacy of DLR for image quality improvement in reduced-FOV DWI of the pancreas.DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution.The denoising level of DWI can be controlled to make the images appear more natural to the human eye.However,this study revealed that DLR did not ameliorate pancreatic distortion.Additionally,physicians should pay attention to the interpretation of ADCs after DLR application because ADCs are significantly changed by DLR.
基金Supported by National S&T Major Project of China(2013ZX03003002-003)
文摘For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.
文摘Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.
基金support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.
文摘As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government.(MSIT)(2021R1A2B5B02087169)the National Natural Science Foundation of China(Nos.62072092,62072093+4 种基金U1708262)the China Postdoctoral Science Foundation(No.2019M653568)the Key Research and Development Project of Hebei Province(No.20310702D)the Natural Science Foundation of Hebei Province(No.F2020501013)the Fundamental Research Funds for the Central Universities(No.N2023020)。
文摘With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum resources.Spectrum auctions came into being,which can effectively utilize spectrum resources.Because of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be guaranteed.Most scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and buyers.Researchers begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction rules.But these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this paper.Unlike traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum auctions.In addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum auctions.Finally,security analysis and performance evaluation of our protocol are discussed.
