Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infec...Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.展开更多
Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management....Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.展开更多
An amorphous,colorless,and highly transparent star network polymer with a pentaerythritol core linking four PEG-block polymeric arms was synthesized from the poly(ethylene glycol)(PEG),pentaerythritol,and dichlorometh...An amorphous,colorless,and highly transparent star network polymer with a pentaerythritol core linking four PEG-block polymeric arms was synthesized from the poly(ethylene glycol)(PEG),pentaerythritol,and dichloromethane by Williamson reaction.FTIR and ~1H-NMR measurement demonstrated that the polymer repeating units were C[CH_2-OCH_2O-(CH_2CH_2O)_m-CH_2O-(CH_2CH_2O)_n-CH_2O]_4.The polymer host held well mechanical properties for pentaerythritol cross-linking.The gel polymer electrolytes based on Lithium pe...展开更多
This paper improves the modeling method for the device with characteristic family presented by L. O. Chua (1977) and results in the one-dimensional fluctuating canonical piecewise-linear model. It is an efficient mode...This paper improves the modeling method for the device with characteristic family presented by L. O. Chua (1977) and results in the one-dimensional fluctuating canonical piecewise-linear model. It is an efficient model. The algorithm for canonical piecewise-linear dynamic networks with one dimensional fluctuating model is discussed in detail.展开更多
5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and ...5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.展开更多
The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilizati...The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.展开更多
In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine...In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great progress.However,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of instructions.In this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese network.It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus.Then,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage.Finally,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability manually.We evaluate the accuracy,robustness,scalability and vulnerability search capability of firmVulSeeker.Results show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.展开更多
Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with...Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function （RBF） neural network is used, and the RBF neural network based on first-order reliability method （FORM） is to approximate the unknown implicit limit state functions and calculate the most probable point （MPP） with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.展开更多
Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantage...Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
This paper presents an analytical solution for the thermoelastic stress in a typical in-plane＇s thin-film micro- thermoelectric cooling device under different operating con- ditions. The distributions of the permissi...This paper presents an analytical solution for the thermoelastic stress in a typical in-plane＇s thin-film micro- thermoelectric cooling device under different operating con- ditions. The distributions of the permissible temperature fields in multilayered thin-films are analytically obtained, and the characteristics, including maximum temperature dif- ference and maximum refrigerating output of the thermo- electric device, are discussed for two operating conditions. Analytical expressions of the thermoelastic stresses in the layered thermoelectric thin-films induced by the tempera- ture difference are formulated based on the theory of mul- tilayer system. The results demonstrate that, the geometric dimension is a significant factor which remarkably affects the thermoelastic stresses. The stress distributions in layers of semiconductor thermoelements, insulating and support- ing membrane show distinctly different features. The present work may profitably guide the optimization design of high- efficiency micro-thermoelectric cooling devices.展开更多
First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SV...First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SVG, also the paper designs and develops the main controller of Network based composite power quality regulation device, based on RTDS, the real-time digital simulation model of The Device is established, and finally the prototype of the device is developed with the function of filter and split-phase compensation. The main controller determines the cooperative operation of both TSC and SVG, and the switching strategy of TSC. The simulation result in RTDS can verify the precision of the measure system and the validity of the control logic, the prototype has finished the type test according to the national standard.展开更多
The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. ...The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. The identification of model parameters of IGBT in PSPICE using BP neural network is illustrated.展开更多
Distribution feeder microgrid(DFM)built based on existing distributed feeder(DF),is a promising solution for modern microgrid.DFM contains a large number of heterogeneous devices that generate heavy network traffice a...Distribution feeder microgrid(DFM)built based on existing distributed feeder(DF),is a promising solution for modern microgrid.DFM contains a large number of heterogeneous devices that generate heavy network traffice and require a low data delivery latency.The information-centric networking(ICN)paradigm has shown a great potential to address the communication requirements of smart grid.However,the integration of advanced information and communication technologies with DFM make it vulnerable to cyber attacks.Adequate authentication of grid devices is essential for preventing unauthorized accesses to the grid network and defending against cyber attacks.In this paper,we propose a new lightweight anonymous device authentication scheme for DFM supported by named data networking(NDN),a representative implementation of ICN.We perform a security analysis to show that the proposed scheme can provide security features such as mutual authentication,session key agreement,defending against various cyber attacks,anonymity,and resilience against device capture attack.The security of the proposed scheme is also formally verified using the popular AVISPA(Automated Validation of Internet Security Protocols and Applications)tool.The computational and communication costs of the proposed scheme are evaluated.Our results demonstrate that the proposed scheme achieves significantly lower computational,communication and energy costs than other state-of-the-art schemes.展开更多
Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in emb...Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.展开更多
The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability t...The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.展开更多
The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded d...The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.展开更多
Objective To investigate the chemical compositions of Maxing Shigan Decoction(麻杏石甘汤,MXSGD)and elucidate its anti-influenza A virus(IAV)mechanism from prediction to validation.Methods Ultra high-performance liquid...Objective To investigate the chemical compositions of Maxing Shigan Decoction(麻杏石甘汤,MXSGD)and elucidate its anti-influenza A virus(IAV)mechanism from prediction to validation.Methods Ultra high-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was employed to analyze the chemical compositions of MXSGD.Network pharmacology theories were used to screen and identify shared targets of both the potential targets of active ingredients of MXSGD and IAV.A protein-protein interaction(PPI)network was then constructed,followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.The binding stability between core bioactive compounds and key targets was validated by molecular docking and dynamic simulations.A total of 24 BALB/c mice were infected with IAV to build IAV mouse models.After successful modelling,the mouse models were randomly divided into model,MXSGD high-dose(2.8 g/kg),MXSGD low-dose(1.4 g/kg),and oseltamivir(20.14 mg/kg)groups,with an additional normal mice as control group(n=6 per group).The treatments were administered by gavage daily between 8:00 a.m.and 10:00 a.m.for five consecutive days.Upon completion of the administration,the body weight ratio,lung index,protein content in the bronchoalveolar lavage fluid(BALF),and the levels of inflammatory factors including interleukin(IL)-6 and tumor necrosis factor(TNF)-αin mice were measured to preliminarily analyze the therapeutic efficacy of MXSGD against IAV infection.Furthermore,the expression levels of mechanistic target of rapamycin(mTOR),hypoxia inducible factor(HIF)-1α,and vascular endothelial growth factor(VEGF)proteins in the HIF-1 signaling pathway,which was enriched by network pharmacology,were detected by Western blot.Results A total of 212 chemical components in MXSGD were identified by the UPLC-MS/MS method.These chemical components can be classified into 9 primary categories and 31 secondary categories.After intersecting the chemical component targets with IAV-related targets,a total of 567 potential MXSGD components targeting IAV were identified.The construction of PPI network and the results of both GO and KEGG enrichment analyses revealed that the anti-IAV effects of MXSGD were associated with multiple pathways,including apoptosis,TNF,HIF-1,and IL-17 signaling pathways.The results of molecular docking demonstrated that the binding energies between the core compound 1-methoxyphaseollin and key targets including HIF-1α,mTOR,and VEGF were all lower than–5.0 kcal/mol.Furthermore,molecular dynamics simulations confirmed the structural stability of the resulting complexes.Animal experiments showed that compared with the normal controls,IAV-infected mice showed significantly reduced body weight ratio,markedly increased lung index,protein content in BALF,and the levels of inflammatory factors such as IL-6 and TNF-α(P<0.01),thereby causing damage to the lung tissue;consequently,the expression levels of mTOR,HIF-1α,and VEGF proteins in the lung tissues of these mice were significantly elevated(P<0.01).However,after MXSGD treatment,the mouse models presented a significant increase in body weight ratio,as well as marked decreases in lung index,protein content in BALF,and the levels of inflammatory factors including IL-6 and TNF-α(P<0.01).Furthermore,the therapy alleviated IAV-induced injuries and significantly downregulated the expression levels of mTOR,HIF-1α,and VEGF proteins in lung tissues(P<0.01 or P<0.05).Conclusion MXSGD exerts anti-IAV effects through multi-component,multi-target,and multi-pathway synergism.Among them,1-methoxyphaseollin is identified as a potential key component,which alleviates virus-induced lung injury and inflammatory response via the regulation of HIF-1 signaling pathway,providing experimental evidence for the clinical application of MXSGD.展开更多
文摘Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.
文摘Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.
文摘An amorphous,colorless,and highly transparent star network polymer with a pentaerythritol core linking four PEG-block polymeric arms was synthesized from the poly(ethylene glycol)(PEG),pentaerythritol,and dichloromethane by Williamson reaction.FTIR and ~1H-NMR measurement demonstrated that the polymer repeating units were C[CH_2-OCH_2O-(CH_2CH_2O)_m-CH_2O-(CH_2CH_2O)_n-CH_2O]_4.The polymer host held well mechanical properties for pentaerythritol cross-linking.The gel polymer electrolytes based on Lithium pe...
基金Supported by National Natural Science Foundation of China
文摘This paper improves the modeling method for the device with characteristic family presented by L. O. Chua (1977) and results in the one-dimensional fluctuating canonical piecewise-linear model. It is an efficient model. The algorithm for canonical piecewise-linear dynamic networks with one dimensional fluctuating model is discussed in detail.
文摘5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.
文摘The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.
文摘In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great progress.However,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of instructions.In this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese network.It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus.Then,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage.Finally,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability manually.We evaluate the accuracy,robustness,scalability and vulnerability search capability of firmVulSeeker.Results show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.
文摘Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function （RBF） neural network is used, and the RBF neural network based on first-order reliability method （FORM） is to approximate the unknown implicit limit state functions and calculate the most probable point （MPP） with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grants No.2021B0909060002)National Natural Science Foundation of China(Grants No.62204219,62204140)Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401).
文摘Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported by the National Basic Research Program of China(2007CB607506)the Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China(111005)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(11121202)
文摘This paper presents an analytical solution for the thermoelastic stress in a typical in-plane＇s thin-film micro- thermoelectric cooling device under different operating con- ditions. The distributions of the permissible temperature fields in multilayered thin-films are analytically obtained, and the characteristics, including maximum temperature dif- ference and maximum refrigerating output of the thermo- electric device, are discussed for two operating conditions. Analytical expressions of the thermoelastic stresses in the layered thermoelectric thin-films induced by the tempera- ture difference are formulated based on the theory of mul- tilayer system. The results demonstrate that, the geometric dimension is a significant factor which remarkably affects the thermoelastic stresses. The stress distributions in layers of semiconductor thermoelements, insulating and support- ing membrane show distinctly different features. The present work may profitably guide the optimization design of high- efficiency micro-thermoelectric cooling devices.
文摘First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SVG, also the paper designs and develops the main controller of Network based composite power quality regulation device, based on RTDS, the real-time digital simulation model of The Device is established, and finally the prototype of the device is developed with the function of filter and split-phase compensation. The main controller determines the cooperative operation of both TSC and SVG, and the switching strategy of TSC. The simulation result in RTDS can verify the precision of the measure system and the validity of the control logic, the prototype has finished the type test according to the national standard.
文摘The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. The identification of model parameters of IGBT in PSPICE using BP neural network is illustrated.
基金This material is based upon work funded by the National Science Foundation EPSCoR Cooperative Agreement OIA-1757207。
文摘Distribution feeder microgrid(DFM)built based on existing distributed feeder(DF),is a promising solution for modern microgrid.DFM contains a large number of heterogeneous devices that generate heavy network traffice and require a low data delivery latency.The information-centric networking(ICN)paradigm has shown a great potential to address the communication requirements of smart grid.However,the integration of advanced information and communication technologies with DFM make it vulnerable to cyber attacks.Adequate authentication of grid devices is essential for preventing unauthorized accesses to the grid network and defending against cyber attacks.In this paper,we propose a new lightweight anonymous device authentication scheme for DFM supported by named data networking(NDN),a representative implementation of ICN.We perform a security analysis to show that the proposed scheme can provide security features such as mutual authentication,session key agreement,defending against various cyber attacks,anonymity,and resilience against device capture attack.The security of the proposed scheme is also formally verified using the popular AVISPA(Automated Validation of Internet Security Protocols and Applications)tool.The computational and communication costs of the proposed scheme are evaluated.Our results demonstrate that the proposed scheme achieves significantly lower computational,communication and energy costs than other state-of-the-art schemes.
基金supported by the National Natural Science Foundation of China under Grants No.61534002,No.61761136015,No.61701095.
文摘Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the researchers supporting project(PNURSP2024R435)and this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.
基金This work was supported in part by the Ministry of Science and Technology of Taiwan,R.O.C.,the Grant Number of project 108-2218-E-194-007.
文摘The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.
基金Natural Science Foundation of Hunan Province(2025JJ80078)Open Fund of Hunan University of Chinese Medicine(21PTKF1005)。
文摘Objective To investigate the chemical compositions of Maxing Shigan Decoction(麻杏石甘汤,MXSGD)and elucidate its anti-influenza A virus(IAV)mechanism from prediction to validation.Methods Ultra high-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was employed to analyze the chemical compositions of MXSGD.Network pharmacology theories were used to screen and identify shared targets of both the potential targets of active ingredients of MXSGD and IAV.A protein-protein interaction(PPI)network was then constructed,followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.The binding stability between core bioactive compounds and key targets was validated by molecular docking and dynamic simulations.A total of 24 BALB/c mice were infected with IAV to build IAV mouse models.After successful modelling,the mouse models were randomly divided into model,MXSGD high-dose(2.8 g/kg),MXSGD low-dose(1.4 g/kg),and oseltamivir(20.14 mg/kg)groups,with an additional normal mice as control group(n=6 per group).The treatments were administered by gavage daily between 8:00 a.m.and 10:00 a.m.for five consecutive days.Upon completion of the administration,the body weight ratio,lung index,protein content in the bronchoalveolar lavage fluid(BALF),and the levels of inflammatory factors including interleukin(IL)-6 and tumor necrosis factor(TNF)-αin mice were measured to preliminarily analyze the therapeutic efficacy of MXSGD against IAV infection.Furthermore,the expression levels of mechanistic target of rapamycin(mTOR),hypoxia inducible factor(HIF)-1α,and vascular endothelial growth factor(VEGF)proteins in the HIF-1 signaling pathway,which was enriched by network pharmacology,were detected by Western blot.Results A total of 212 chemical components in MXSGD were identified by the UPLC-MS/MS method.These chemical components can be classified into 9 primary categories and 31 secondary categories.After intersecting the chemical component targets with IAV-related targets,a total of 567 potential MXSGD components targeting IAV were identified.The construction of PPI network and the results of both GO and KEGG enrichment analyses revealed that the anti-IAV effects of MXSGD were associated with multiple pathways,including apoptosis,TNF,HIF-1,and IL-17 signaling pathways.The results of molecular docking demonstrated that the binding energies between the core compound 1-methoxyphaseollin and key targets including HIF-1α,mTOR,and VEGF were all lower than–5.0 kcal/mol.Furthermore,molecular dynamics simulations confirmed the structural stability of the resulting complexes.Animal experiments showed that compared with the normal controls,IAV-infected mice showed significantly reduced body weight ratio,markedly increased lung index,protein content in BALF,and the levels of inflammatory factors such as IL-6 and TNF-α(P<0.01),thereby causing damage to the lung tissue;consequently,the expression levels of mTOR,HIF-1α,and VEGF proteins in the lung tissues of these mice were significantly elevated(P<0.01).However,after MXSGD treatment,the mouse models presented a significant increase in body weight ratio,as well as marked decreases in lung index,protein content in BALF,and the levels of inflammatory factors including IL-6 and TNF-α(P<0.01).Furthermore,the therapy alleviated IAV-induced injuries and significantly downregulated the expression levels of mTOR,HIF-1α,and VEGF proteins in lung tissues(P<0.01 or P<0.05).Conclusion MXSGD exerts anti-IAV effects through multi-component,multi-target,and multi-pathway synergism.Among them,1-methoxyphaseollin is identified as a potential key component,which alleviates virus-induced lung injury and inflammatory response via the regulation of HIF-1 signaling pathway,providing experimental evidence for the clinical application of MXSGD.
