In order to use effectively renewable energy sources, we propose a new system, called Advanced Superconducting Power Conditioning System (ASPCS) that is composed of Superconducting Magnetic Energy Storage (SMES), Fuel...In order to use effectively renewable energy sources, we propose a new system, called Advanced Superconducting Power Conditioning System (ASPCS) that is composed of Superconducting Magnetic Energy Storage (SMES), Fuel Cell-Electrolyzer (FC-EL), hydrogen storage and DC/DC and DC/AC converters in connection with a liquid hydrogen station for fuel cell vehicles. The ASPCS compensates the fluctuating electric power of renewable energy sources such as wind and photovoltaic power generations by means of the SMES having characteristics of quick response and large Input-Output power, and hydrogen energy with FC-EL having characteristics of moderate response and large storage capacity. The moderate fluctuated power of the renewable energy is compensated by a trend forecasting method with the Artificial Neural Network. In case of excess of the power generation by the renewable energy to demand it is converted to hydrogen with EL. In contrast, shortage of the electric power is made up with FC. The faster fluctuation power that cannot be compensated by the forecasting method is effectively compensated by SMES. In the ASPCS, the SMES coil with an MgB2 conductor is operated at 20 K by using liquid hydrogen supplied from a liquid hydrogen tank of the fuel cell vehicle station. The necessary storage capacity of SMES is estimated as 50 MJ to 100 MJ depending on the forecasting time for compensating fluctuation power of the rated wind power generation of 5.0 MW. As a safety case, a thermosiphon cooling system is used to cool indirectly the MgB2 SMES coil by thermal conduction. In this paper, a trend forecasting result of output power of a wind power generation and the estimated storage capacity of SMES are reported.展开更多
Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE)...Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed.展开更多
A NiP/TiO2 composite film on carbon steel was prepared by electroless plating and sol-gel composite process. An artificial neural network was applied to optimize the prepared condition of the composite film. Corrosion...A NiP/TiO2 composite film on carbon steel was prepared by electroless plating and sol-gel composite process. An artificial neural network was applied to optimize the prepared condition of the composite film. Corrosion behavior of the NiP/TiO2 composite film was investigated by polarization resistance measurement, anode polarization, ESEM （environmental scanning electron microscopy） and EIS （electrochemical impedance spectroscopy） measurements. Results showed that the NiP/ TiO2 composite film has a good corrosion resistance in 0.5mol/L H2SO4 solution. The element valence of the composite film was characterized by XPS （X-ray photoelectron spectroscopy） spectrum, and an anticorrosion mechanism of the composite film was discussed.展开更多
The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to pr...The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works.The effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA system.The performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA system.Based on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)model.The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated.Then,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification.Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model.The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.展开更多
Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs...Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.展开更多
In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social...In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.展开更多
The sensitivity and fidelity of surface electromyography(sEMG)signal monitoring is critical for muscle status and fatigue assessment,prosthetic control,and gesture recognition.However,the incompatible skin-electrode i...The sensitivity and fidelity of surface electromyography(sEMG)signal monitoring is critical for muscle status and fatigue assessment,prosthetic control,and gesture recognition.However,the incompatible skin-electrode interface and complex electrophysiological environment restrict the sensitive acquisition and accurate analysis of sEMG signals.Focused on the impedance of the skin-electrode interface issue,we developed an interfacial gel electrode patch with a tunable hydrogen bond network to simultaneously achieve a conformal interface,suitable adhesion,and high conductivity for sEMG signal monitoring.By exploiting hydroxyethylidene diphosphonic acid(HEDP)and 2-hydroxyphosphono-acetic acid(HPAA)as hydrogen bonding regulators were introduced into the polyvinyl alcohol(PVA)-based hydrogel network to regulate the hydrogen bond cross-linking network.As a result,the balance of elastic modulus,adhesion,and electrical conductivity of PVA-HEDP-HPAA(PHH)hydrogel are achieved.The reliable electrodeskin interface is manipulated to achieve conformal contact by matching the elastic modulus,reducing the gap of electrode-skin interface by adhesion,and promoting ion and electron conduction by electrical conductivity.The PHH electrode patches exhibit a lower interfacial impedance(12.56 kΩ)and a signal-to-noise ratio of 38.09±1.28 dB for accurate analysis of muscle strength and evaluation of the fatigue state.With the assistance of the artificial neural network algorithm,seven gestures can be recognized with 100%prediction accuracy.The interfacial gel electrode patch contributes a bio-matching electrophysiological platform for prosthetic control,human–machine interaction,and clinical or athletic auxiliary monitoring.展开更多
In this paper,the relationship model between seawater environment,chemical composition and corrosion potential of low alloy steel is established and the distribution of corrosion potential of low alloy steel with chan...In this paper,the relationship model between seawater environment,chemical composition and corrosion potential of low alloy steel is established and the distribution of corrosion potential of low alloy steel with changes in key alloying elements is excavated.The research was carried out with the following steps:Firstly,the relationship model between corrosion potential of low alloy steel and its influencing factors was established by data dimension reduction and artificial neural network(ANN).Secondly,key alloying elements of experimental steels were selected out by Pearson correlation analysis,then the corrosion resistance element model was visualized to show the effect of key alloying elements on corrosion potential of low alloy steel.Finally,corrosion potential of low alloy steel with the change of key alloying elements was classified and visualized by classification method.The mining results can reflect the validity of the proposed mining methods to a certain extent and provide an intuitive data basis for the development of high-quality and low-cost low alloy steels.展开更多
文摘In order to use effectively renewable energy sources, we propose a new system, called Advanced Superconducting Power Conditioning System (ASPCS) that is composed of Superconducting Magnetic Energy Storage (SMES), Fuel Cell-Electrolyzer (FC-EL), hydrogen storage and DC/DC and DC/AC converters in connection with a liquid hydrogen station for fuel cell vehicles. The ASPCS compensates the fluctuating electric power of renewable energy sources such as wind and photovoltaic power generations by means of the SMES having characteristics of quick response and large Input-Output power, and hydrogen energy with FC-EL having characteristics of moderate response and large storage capacity. The moderate fluctuated power of the renewable energy is compensated by a trend forecasting method with the Artificial Neural Network. In case of excess of the power generation by the renewable energy to demand it is converted to hydrogen with EL. In contrast, shortage of the electric power is made up with FC. The faster fluctuation power that cannot be compensated by the forecasting method is effectively compensated by SMES. In the ASPCS, the SMES coil with an MgB2 conductor is operated at 20 K by using liquid hydrogen supplied from a liquid hydrogen tank of the fuel cell vehicle station. The necessary storage capacity of SMES is estimated as 50 MJ to 100 MJ depending on the forecasting time for compensating fluctuation power of the rated wind power generation of 5.0 MW. As a safety case, a thermosiphon cooling system is used to cool indirectly the MgB2 SMES coil by thermal conduction. In this paper, a trend forecasting result of output power of a wind power generation and the estimated storage capacity of SMES are reported.
基金the financial support of the National Natural Science Foundation of China(No.51371182)the National Program for the Young Top-notch Professionals and the Fundamental Research Funds for the Central Universities(N170205002)
文摘Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed.
文摘A NiP/TiO2 composite film on carbon steel was prepared by electroless plating and sol-gel composite process. An artificial neural network was applied to optimize the prepared condition of the composite film. Corrosion behavior of the NiP/TiO2 composite film was investigated by polarization resistance measurement, anode polarization, ESEM （environmental scanning electron microscopy） and EIS （electrochemical impedance spectroscopy） measurements. Results showed that the NiP/ TiO2 composite film has a good corrosion resistance in 0.5mol/L H2SO4 solution. The element valence of the composite film was characterized by XPS （X-ray photoelectron spectroscopy） spectrum, and an anticorrosion mechanism of the composite film was discussed.
基金We wish to thank the financial support from the National Natural Science Foundation of China for the project No.51476120from the Nat-ural Science Foundation of Liaoning Province for the project No.2020-CSLH-43+1 种基金Mr.Liang Tong also thanks the support from the China Schol-arship Council(CSC)and the Fonds de Recherche du Québec-Nature et Technologies(FRQNT)for the PBEEE fellowship(No.203790)Yi Zong also thanks to the International Network Programmne supported by the Danish Agency for Higher Education and Science(No.8073-00026B)for the project PRESS-Proactive Energy Management Systems for Power-to-Heat and Power-to-Gas Solutions.We also appreciate Dr.Feng Ye for his assistance on artificial neural network programming.
文摘The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works.The effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA system.The performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA system.Based on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)model.The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated.Then,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification.Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model.The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.
基金supported by the National Natural Science Foundation of China(Grant No.62076042)the National Key Research and Development Plan of China,Key Project of Cyberspace Security Governance(Grant No.2022YFB3103103)the Key Research and Development Project of Sichuan Province(Grant Nos.2022YFS0571,2021YFSY0012,2021YFG0332,and 2020YFG0307)。
文摘Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.
基金The authors acknowledge the funding support ofFRGS/1/2021/ICT07/UTAR/02/3 and IPSR/RMC/UTARRF/2020-C2/G01 for this study.
文摘In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.
基金supported by the National Natural Science Foundation of China(grant nos.21874056 and 52003103)the National Key R&D Program of China(grant no.2016YFC1100502).
文摘The sensitivity and fidelity of surface electromyography(sEMG)signal monitoring is critical for muscle status and fatigue assessment,prosthetic control,and gesture recognition.However,the incompatible skin-electrode interface and complex electrophysiological environment restrict the sensitive acquisition and accurate analysis of sEMG signals.Focused on the impedance of the skin-electrode interface issue,we developed an interfacial gel electrode patch with a tunable hydrogen bond network to simultaneously achieve a conformal interface,suitable adhesion,and high conductivity for sEMG signal monitoring.By exploiting hydroxyethylidene diphosphonic acid(HEDP)and 2-hydroxyphosphono-acetic acid(HPAA)as hydrogen bonding regulators were introduced into the polyvinyl alcohol(PVA)-based hydrogel network to regulate the hydrogen bond cross-linking network.As a result,the balance of elastic modulus,adhesion,and electrical conductivity of PVA-HEDP-HPAA(PHH)hydrogel are achieved.The reliable electrodeskin interface is manipulated to achieve conformal contact by matching the elastic modulus,reducing the gap of electrode-skin interface by adhesion,and promoting ion and electron conduction by electrical conductivity.The PHH electrode patches exhibit a lower interfacial impedance(12.56 kΩ)and a signal-to-noise ratio of 38.09±1.28 dB for accurate analysis of muscle strength and evaluation of the fatigue state.With the assistance of the artificial neural network algorithm,seven gestures can be recognized with 100%prediction accuracy.The interfacial gel electrode patch contributes a bio-matching electrophysiological platform for prosthetic control,human–machine interaction,and clinical or athletic auxiliary monitoring.
基金financially supported by the National Environmental Corrosion Platform of Chinathe National Key Research and Development Program of China(No.2017YFB0702100)the National Natural Science Foundation of China(No.51871024)。
文摘In this paper,the relationship model between seawater environment,chemical composition and corrosion potential of low alloy steel is established and the distribution of corrosion potential of low alloy steel with changes in key alloying elements is excavated.The research was carried out with the following steps:Firstly,the relationship model between corrosion potential of low alloy steel and its influencing factors was established by data dimension reduction and artificial neural network(ANN).Secondly,key alloying elements of experimental steels were selected out by Pearson correlation analysis,then the corrosion resistance element model was visualized to show the effect of key alloying elements on corrosion potential of low alloy steel.Finally,corrosion potential of low alloy steel with the change of key alloying elements was classified and visualized by classification method.The mining results can reflect the validity of the proposed mining methods to a certain extent and provide an intuitive data basis for the development of high-quality and low-cost low alloy steels.
