The operation of distribution system with the components in deteriorating condition makes the system reliability worsen. It is important to find the solution for balancing failure cost and maintenance benefits such as...The operation of distribution system with the components in deteriorating condition makes the system reliability worsen. It is important to find the solution for balancing failure cost and maintenance benefits such as down-time and reliability. In this paper, time to replace the components in optimum condition based on constant-interval replacement mode is investigated. The optimal replacement time is mainly depended on component’s reliability and the cost ration of preventive replacement and failure replacement. In this paper, equipment inspection method and Weibull Analysis is applied to obtain the accurate reliability estimation. Weibull Analysis is applied with constant-interval replacement model to investigate the optimum replacement time for each component considering the different cost ratios. According to the quantitative results, the determination of the optimal replacement time (OPT) can minimize the total downtime and failure cost. Consequently, the reliability of the system is maximized and estimation also becomes more accurate due to sufficient approach.展开更多
One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understand...One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.展开更多
By using a Fourier series expansion method combined with Chew＇s perfectly matched layers （PMLs）, we analyze the frequency and quality factor of a micro-cavity on a two-dimensional photonic crystal is analyzed. Comp...By using a Fourier series expansion method combined with Chew＇s perfectly matched layers （PMLs）, we analyze the frequency and quality factor of a micro-cavity on a two-dimensional photonic crystal is analyzed. Compared with the results by the method without PML and finite-difference time-domain （FDTD） based on supercell approximation, it can be shown that by the present method with PMLs, the resonant frequency and the quality factor values can be calculated satisfyingly and the characteristics of the micro-cavity can be obtained by changing the size and permittivity of the point defect in the micro-cavity.展开更多
Transmitting a longitudinal wave and a traverse wave into a composite material in a molten state has been studied in the online control of the composite material which cannot be evaluated by a conventional ultrasonic ...Transmitting a longitudinal wave and a traverse wave into a composite material in a molten state has been studied in the online control of the composite material which cannot be evaluated by a conventional ultrasonic sensor as a final analysis, using the difference in the propagation characteristics of both modes. It is especially expected that measurement of the physical quantity which was not able to be conventionally measured can be performed by carrying out coincidence measurement of the ultrasonic wave in both modes. Therefore, in this research study, an ultrasonic probe, which can simultaneously transmit and receive a longitudinal wave and a traverse wave has been developed using an electromagnetic acoustic transducer (EMAT) because it has the advantage of measuring high temperature samples. In this study, two methods have been compared. The 1st method uses a traverse wave EMAT that travels in a vertical direction and a bar wave by which the low order mode is equivalent to longitudinal wave vibration. The other method is to carry out the mode conversion of the traverse wave by a traverse wave-EMAT. The longitudinal converted from the transverse wave are spread in the axis direction. As the experimental results of both optimizations of the drive conditions, it has been confirmed that the 2nd mode conversion method was promising. This paper reports about the trial process and the experimental results.展开更多
This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA)....This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.展开更多
A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to ...A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.展开更多
Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture t...Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns.This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling.Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations,enabling improved forecasting accuracy.Through extensive evaluation on two world real-datasets focused on renewable energy and electricity demand,we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks.By explicitly modeling variable interdependencies and incorporating temporal information,our method provides reliable probabilistic forecasts,crucial for effective decision-making and resource allocation in the energy sector.Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score(CRPS)by 2.1%-70.9%,Mean Absolute Error(MAE)by 4.4%-52.2%,and Root Mean Squared Error(RMSE)by 7.9%-53.4%over existing methods on two real-world datasets.展开更多
As mobile internet and Internet of Things technologies continue to advance,the application scenarios of peer-to-peer Internet of Drones(IoD)are becoming increasingly diverse.However,the development of IoD also faces s...As mobile internet and Internet of Things technologies continue to advance,the application scenarios of peer-to-peer Internet of Drones(IoD)are becoming increasingly diverse.However,the development of IoD also faces signifcant challenges,such as security,privacy protection,and limited computing power,which require technological innova-tion to overcome.For group secure communication,it is necessary to provide two basic services,user authentication and group key agreement.Due to the limited storage of IoD devices,group key negotiation requires lightweight calculations,and conventional schemes cannot satisfy the requirements of group communication in the IoD.To this end,a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD,which not only realizes the identity verifcation of user and group key negotiation,but also improves computational ef-ciency on each group member side.A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defned security requirements.More importantly,in our proposal,the computational cost on the user side remains unafected by the variability of the number of members participating in group communication,as members communicate in a non-interactive manner through broadcasting.As a result,the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes.Hence,this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.展开更多
Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image denoising.However,convolutional operations may change original distributions of noise in corrupted images,which may increa...Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image denoising.However,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image denoising.Using relations of surrounding pixels can effectively resolve this problem.Inspired by that,we propose a robust deformed denoising CNN(RDDCNN)in this paper.The proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding pixels.The EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed RDDCNN.To address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean image.Besides,we implement a blind denoising model.Experimental results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative analysis.Codes can be obtained at https://github.com/hellloxiaotian/RDDCNN.展开更多
In Shamir’s(t,n) threshold of the secret sharing scheme, a secret is divided into n shares by a dealer and is shared among n shareholders in such a way that (a) the secret can be reconstructed when there are t or mor...In Shamir’s(t,n) threshold of the secret sharing scheme, a secret is divided into n shares by a dealer and is shared among n shareholders in such a way that (a) the secret can be reconstructed when there are t or more than t shares;and (b) the secret cannot be obtained when there are fewer than t shares. In the secret reconstruction, participating users can be either legitimate shareholders or attackers. Shamir’s scheme only considers the situation when all participating users are legitimate shareholders. In this paper, we show that when there are more than t users participating and shares are released asynchronously in the secret reconstruction, an attacker can always release his share last. In such a way, after knowing t valid shares of legitimate shareholders, the attacker can obtain the secret and therefore, can successfully impersonate to be a legitimate shareholder without being detected. We propose a simple modification of Shamir’s scheme to fix this security problem. Threshold cryptography is a research of group-oriented applications based on the secret sharing scheme. We show that a similar security problem also exists in threshold cryptographic applications. We propose a modified scheme to fix this security problem as well.展开更多
In the industrial fields, many high temperature structures that require a non-destructive inspection exist. However, there are currently few sensors that can carry out non-destructive testing in a high temperature env...In the industrial fields, many high temperature structures that require a non-destructive inspection exist. However, there are currently few sensors that can carry out non-destructive testing in a high temperature environment. In particular, the ultrasonic sensor is normally not used at over 50 degrees Celsius. Also, a special sensor for high temperature is currently available, but there are various constraints;it has not yet reached a level that is useful in industry. Therefore, we have been developing a new sensor system using a long waveguide which can transmit an ultrasonic wave from a long distance. Especially, this study focuses on applying the developed technique to a pipe which is used in a nuclear power plant. Therefore, the best rectangular-shaped waveguide was studied and attempted to be wound around a pipe to be driven by an acoustic source of a guide wave. Finally, the L (0, 2) and T (0, 1)-mode guide waves were successfully detected by optimizing the shape of the opposite edge of the rectangular-shaped waveguide that could detect the reflected signal from an artificial defect machined into a test pipe.展开更多
This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability ...This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.展开更多
Physical Unclonable Functions(PUFs)can be seen as kind of hardware one-way functions,who are easily fabricated but difficult to clone,duplicate or predict.Therefore,PUFs with unclonable and unpredictable properties ar...Physical Unclonable Functions(PUFs)can be seen as kind of hardware one-way functions,who are easily fabricated but difficult to clone,duplicate or predict.Therefore,PUFs with unclonable and unpredictable properties are welcome to be applied in designing lightweight cryptography protocols.In this paper,a Basic Key Distribution Scheme(Basic-KDS)based on PUFs is firstly proposed.Then,by employing different deployment modes,a Random Deployment Key Distribution Scheme(RD-KDS)and a Grouping Deployment Key Distribution Scheme(GD-KDS)are further proposed based on the Basic-KDS for large scale wireless sensor networks.In our proposals,a sensor is not pre-distributed with any keys but will generate one by the embedded PUF when receiving a challenge from the gateway,which provides perfect resilience against sensor capture attacks.Besides,the unclonable and unpredictable properties of PUF guarantee the key uniqueness and two-way authentication.Analysis and experiment results show that our proposals have better performances in improving the resilience,secure-connectivity,and efficiency as compared to other schemes.展开更多
A smartphone-based context-aware augmentative and alternative communication(AAC) was applied was in order to enhance the user's experience by providing simple, adaptive, and intuitive interfaces. Various potential...A smartphone-based context-aware augmentative and alternative communication(AAC) was applied was in order to enhance the user's experience by providing simple, adaptive, and intuitive interfaces. Various potential context-aware technologies and AAC usage scenarios were studied, and an efficient communication system was developed by combining smartphone's multimedia functions and its optimized sensor technologies. The experimental results show that context-awareness accuracy is achieved up to 97%.展开更多
The nondestructive inspection of a high temperature structure is required in order to guarantee its safety. However, there are no useful sensors for high temperature structures. Some of them cannot work at temperature...The nondestructive inspection of a high temperature structure is required in order to guarantee its safety. However, there are no useful sensors for high temperature structures. Some of them cannot work at temperatures over 50°C. Another concern is that they are too expensive to use. A sensing system, which can transmit and receive an ultrasonic wave that travels a long distance using a long waveguide, has been studied. We confirmed that the optimal guided ultrasonic wave could travel more than 10 m using an electromagnetic transducer (EMAT) with a thin Ni-sheet surrounded on the surface of the bar and a 2-mm diameter bar as the waveguide. However, we had the difficult problem of receiving the reflected ultrasonic wave from the inside of a test specimen. We tried to improve the trial inspection system using an acoustic horn. An experiment in which the temperature of the test block was heated to about 500°C has now been completed. Finally, the condition of the bend in the waveguide to pass without reflection was confirmed.展开更多
Objective and Impact Statement.There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms.We describe a novel rodent coil d...Objective and Impact Statement.There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms.We describe a novel rodent coil design to improve the focality for targeted stimulations in small rodent brains.Introduction.Transcranial magnetic stimulation(TMS)is becoming increasingly important for treating neuropsychiatric disorders and understanding brain mechanisms.Preclinical studies permit invasive manipulations and are essential for the mechanistic understanding of TMS effects and explorations of therapeutic outcomes in disease models.However,existing TMS tools lack focality for targeted stimulations.Notably,there has been limited fundamental research on developing coils capable of focal stimulation at deep brain regions on small animals like rodents.Methods.In this study,ferromagnetic cores are added to a novel angle-tuned coil design to enhance the coil performance regarding penetration depth and focality.Numerical simulations and experimental electric field measurements were conducted to optimize the coil design.Results.The proposed coil system demonstrated a significantly smaller stimulation spot size and enhanced electric field decay rate in comparison to existing coils.Adding the ferromagnetic core reduces the energy requirements up to 60%for rodent brain stimulation.The simulated results are validated with experimental measurements and demonstration of suprathreshold rodent limb excitation through targeted motor cortex activation.Conclusion.The newly developed coils are suitable tools for focal stimulations of the rodent brain due to their smaller stimulation spot size and improved electric field decay rate.展开更多
Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits.It is typically produced from oilseed crops such as soybean,rapeseed,palm oil,or a...Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits.It is typically produced from oilseed crops such as soybean,rapeseed,palm oil,or animal fats.However,its sustainability is debated,primarily because of the reliance on edible oil feedstocks and associated economic and environmental concerns.This study explores alternative,non-edible feedstocks,such as algae and jatropha,that do not compete with food production,offering increased sustainability.Despite their potential,these feedstocks are hindered by high production costs.To address these challenges,innovative approaches in feedstock assessment are imperative for ensuring the long-term viability of biodiesel as an alternative fuel.This review examines explicitly the application of deep learning techniques in selecting and evaluating biodiesel feedstocks.It focuses on their production processes and the chemical and physical properties that impact biodiesel quality.Our comprehensive analysis demonstrates that ANNs provide significant insights into the feedstock assessment process,emerging as a potent tool for identifying new correlations within complex datasets.By leveraging this capability,ANNs can significantly advance biodiesel research,producing more sustainable and efficient feedstock production.The study concludes by highlighting the substantial potential of ANN modeling in contributing to renewable energy strategies and expanding biodiesel research,underscoring its vital role in accelerating the development of biodiesel as a sustainable fuel alternative.展开更多
文摘The operation of distribution system with the components in deteriorating condition makes the system reliability worsen. It is important to find the solution for balancing failure cost and maintenance benefits such as down-time and reliability. In this paper, time to replace the components in optimum condition based on constant-interval replacement mode is investigated. The optimal replacement time is mainly depended on component’s reliability and the cost ration of preventive replacement and failure replacement. In this paper, equipment inspection method and Weibull Analysis is applied to obtain the accurate reliability estimation. Weibull Analysis is applied with constant-interval replacement model to investigate the optimum replacement time for each component considering the different cost ratios. According to the quantitative results, the determination of the optimal replacement time (OPT) can minimize the total downtime and failure cost. Consequently, the reliability of the system is maximized and estimation also becomes more accurate due to sufficient approach.
文摘One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.
文摘By using a Fourier series expansion method combined with Chew＇s perfectly matched layers （PMLs）, we analyze the frequency and quality factor of a micro-cavity on a two-dimensional photonic crystal is analyzed. Compared with the results by the method without PML and finite-difference time-domain （FDTD） based on supercell approximation, it can be shown that by the present method with PMLs, the resonant frequency and the quality factor values can be calculated satisfyingly and the characteristics of the micro-cavity can be obtained by changing the size and permittivity of the point defect in the micro-cavity.
文摘Transmitting a longitudinal wave and a traverse wave into a composite material in a molten state has been studied in the online control of the composite material which cannot be evaluated by a conventional ultrasonic sensor as a final analysis, using the difference in the propagation characteristics of both modes. It is especially expected that measurement of the physical quantity which was not able to be conventionally measured can be performed by carrying out coincidence measurement of the ultrasonic wave in both modes. Therefore, in this research study, an ultrasonic probe, which can simultaneously transmit and receive a longitudinal wave and a traverse wave has been developed using an electromagnetic acoustic transducer (EMAT) because it has the advantage of measuring high temperature samples. In this study, two methods have been compared. The 1st method uses a traverse wave EMAT that travels in a vertical direction and a bar wave by which the low order mode is equivalent to longitudinal wave vibration. The other method is to carry out the mode conversion of the traverse wave by a traverse wave-EMAT. The longitudinal converted from the transverse wave are spread in the axis direction. As the experimental results of both optimizations of the drive conditions, it has been confirmed that the 2nd mode conversion method was promising. This paper reports about the trial process and the experimental results.
文摘This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.
文摘A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.
文摘Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns.This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling.Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations,enabling improved forecasting accuracy.Through extensive evaluation on two world real-datasets focused on renewable energy and electricity demand,we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks.By explicitly modeling variable interdependencies and incorporating temporal information,our method provides reliable probabilistic forecasts,crucial for effective decision-making and resource allocation in the energy sector.Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score(CRPS)by 2.1%-70.9%,Mean Absolute Error(MAE)by 4.4%-52.2%,and Root Mean Squared Error(RMSE)by 7.9%-53.4%over existing methods on two real-world datasets.
基金supported by the National Natural Science Founda-tion of China(Grants Nos.62172181,62272189,62072133)the Fundamental Research Funds for the Central Universities(No.CCNU19TS019)+1 种基金the Research Planning Project of National Language Committee(No.YB135-40)the Research Initiation Project of Zhejiang Lab(No.2022PD0AC02).
文摘As mobile internet and Internet of Things technologies continue to advance,the application scenarios of peer-to-peer Internet of Drones(IoD)are becoming increasingly diverse.However,the development of IoD also faces signifcant challenges,such as security,privacy protection,and limited computing power,which require technological innova-tion to overcome.For group secure communication,it is necessary to provide two basic services,user authentication and group key agreement.Due to the limited storage of IoD devices,group key negotiation requires lightweight calculations,and conventional schemes cannot satisfy the requirements of group communication in the IoD.To this end,a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD,which not only realizes the identity verifcation of user and group key negotiation,but also improves computational ef-ciency on each group member side.A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defned security requirements.More importantly,in our proposal,the computational cost on the user side remains unafected by the variability of the number of members participating in group communication,as members communicate in a non-interactive manner through broadcasting.As a result,the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes.Hence,this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.
基金Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021A1515110079Fundamental Research Funds for the Central Universities,Grant/Award Number:D5000210966+1 种基金Basic Research Plan in Taicang,Grant/Award Number:TC2021JC23Key Project of NSFC,Grant/Award Number:61836016。
文摘Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image denoising.However,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image denoising.Using relations of surrounding pixels can effectively resolve this problem.Inspired by that,we propose a robust deformed denoising CNN(RDDCNN)in this paper.The proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding pixels.The EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed RDDCNN.To address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean image.Besides,we implement a blind denoising model.Experimental results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative analysis.Codes can be obtained at https://github.com/hellloxiaotian/RDDCNN.
文摘In Shamir’s(t,n) threshold of the secret sharing scheme, a secret is divided into n shares by a dealer and is shared among n shareholders in such a way that (a) the secret can be reconstructed when there are t or more than t shares;and (b) the secret cannot be obtained when there are fewer than t shares. In the secret reconstruction, participating users can be either legitimate shareholders or attackers. Shamir’s scheme only considers the situation when all participating users are legitimate shareholders. In this paper, we show that when there are more than t users participating and shares are released asynchronously in the secret reconstruction, an attacker can always release his share last. In such a way, after knowing t valid shares of legitimate shareholders, the attacker can obtain the secret and therefore, can successfully impersonate to be a legitimate shareholder without being detected. We propose a simple modification of Shamir’s scheme to fix this security problem. Threshold cryptography is a research of group-oriented applications based on the secret sharing scheme. We show that a similar security problem also exists in threshold cryptographic applications. We propose a modified scheme to fix this security problem as well.
文摘In the industrial fields, many high temperature structures that require a non-destructive inspection exist. However, there are currently few sensors that can carry out non-destructive testing in a high temperature environment. In particular, the ultrasonic sensor is normally not used at over 50 degrees Celsius. Also, a special sensor for high temperature is currently available, but there are various constraints;it has not yet reached a level that is useful in industry. Therefore, we have been developing a new sensor system using a long waveguide which can transmit an ultrasonic wave from a long distance. Especially, this study focuses on applying the developed technique to a pipe which is used in a nuclear power plant. Therefore, the best rectangular-shaped waveguide was studied and attempted to be wound around a pipe to be driven by an acoustic source of a guide wave. Finally, the L (0, 2) and T (0, 1)-mode guide waves were successfully detected by optimizing the shape of the opposite edge of the rectangular-shaped waveguide that could detect the reflected signal from an artificial defect machined into a test pipe.
文摘This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.
基金This work is supported by the National Natural Science Foundation of China(under grant 61902163)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(under grant 17KJD520003,19KJB520033)the Research Startup Foundation of Jinling Institute of Technology(under grant JIT-B-201639,JIT-B-201726,JIT-B-202001).
文摘Physical Unclonable Functions(PUFs)can be seen as kind of hardware one-way functions,who are easily fabricated but difficult to clone,duplicate or predict.Therefore,PUFs with unclonable and unpredictable properties are welcome to be applied in designing lightweight cryptography protocols.In this paper,a Basic Key Distribution Scheme(Basic-KDS)based on PUFs is firstly proposed.Then,by employing different deployment modes,a Random Deployment Key Distribution Scheme(RD-KDS)and a Grouping Deployment Key Distribution Scheme(GD-KDS)are further proposed based on the Basic-KDS for large scale wireless sensor networks.In our proposals,a sensor is not pre-distributed with any keys but will generate one by the embedded PUF when receiving a challenge from the gateway,which provides perfect resilience against sensor capture attacks.Besides,the unclonable and unpredictable properties of PUF guarantee the key uniqueness and two-way authentication.Analysis and experiment results show that our proposals have better performances in improving the resilience,secure-connectivity,and efficiency as compared to other schemes.
基金Project supported by the Changwon National University(2013-2014),Korea
文摘A smartphone-based context-aware augmentative and alternative communication(AAC) was applied was in order to enhance the user's experience by providing simple, adaptive, and intuitive interfaces. Various potential context-aware technologies and AAC usage scenarios were studied, and an efficient communication system was developed by combining smartphone's multimedia functions and its optimized sensor technologies. The experimental results show that context-awareness accuracy is achieved up to 97%.
文摘The nondestructive inspection of a high temperature structure is required in order to guarantee its safety. However, there are no useful sensors for high temperature structures. Some of them cannot work at temperatures over 50°C. Another concern is that they are too expensive to use. A sensing system, which can transmit and receive an ultrasonic wave that travels a long distance using a long waveguide, has been studied. We confirmed that the optimal guided ultrasonic wave could travel more than 10 m using an electromagnetic transducer (EMAT) with a thin Ni-sheet surrounded on the surface of the bar and a 2-mm diameter bar as the waveguide. However, we had the difficult problem of receiving the reflected ultrasonic wave from the inside of a test specimen. We tried to improve the trial inspection system using an acoustic horn. An experiment in which the temperature of the test block was heated to about 500°C has now been completed. Finally, the condition of the bend in the waveguide to pass without reflection was confirmed.
基金supported by the NSF grant ECCS-1631820,NIH grants MH112180,MH108148,MH103222a Brain and Behavior Research Foundation grant.
文摘Objective and Impact Statement.There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms.We describe a novel rodent coil design to improve the focality for targeted stimulations in small rodent brains.Introduction.Transcranial magnetic stimulation(TMS)is becoming increasingly important for treating neuropsychiatric disorders and understanding brain mechanisms.Preclinical studies permit invasive manipulations and are essential for the mechanistic understanding of TMS effects and explorations of therapeutic outcomes in disease models.However,existing TMS tools lack focality for targeted stimulations.Notably,there has been limited fundamental research on developing coils capable of focal stimulation at deep brain regions on small animals like rodents.Methods.In this study,ferromagnetic cores are added to a novel angle-tuned coil design to enhance the coil performance regarding penetration depth and focality.Numerical simulations and experimental electric field measurements were conducted to optimize the coil design.Results.The proposed coil system demonstrated a significantly smaller stimulation spot size and enhanced electric field decay rate in comparison to existing coils.Adding the ferromagnetic core reduces the energy requirements up to 60%for rodent brain stimulation.The simulated results are validated with experimental measurements and demonstration of suprathreshold rodent limb excitation through targeted motor cortex activation.Conclusion.The newly developed coils are suitable tools for focal stimulations of the rodent brain due to their smaller stimulation spot size and improved electric field decay rate.
文摘Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits.It is typically produced from oilseed crops such as soybean,rapeseed,palm oil,or animal fats.However,its sustainability is debated,primarily because of the reliance on edible oil feedstocks and associated economic and environmental concerns.This study explores alternative,non-edible feedstocks,such as algae and jatropha,that do not compete with food production,offering increased sustainability.Despite their potential,these feedstocks are hindered by high production costs.To address these challenges,innovative approaches in feedstock assessment are imperative for ensuring the long-term viability of biodiesel as an alternative fuel.This review examines explicitly the application of deep learning techniques in selecting and evaluating biodiesel feedstocks.It focuses on their production processes and the chemical and physical properties that impact biodiesel quality.Our comprehensive analysis demonstrates that ANNs provide significant insights into the feedstock assessment process,emerging as a potent tool for identifying new correlations within complex datasets.By leveraging this capability,ANNs can significantly advance biodiesel research,producing more sustainable and efficient feedstock production.The study concludes by highlighting the substantial potential of ANN modeling in contributing to renewable energy strategies and expanding biodiesel research,underscoring its vital role in accelerating the development of biodiesel as a sustainable fuel alternative.
