The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learn...The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security operators.This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)attacks.The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained model.The methodology was validated on two benchmark datasets,CICIDS2017 and WUSTL-IIOT-2021.Extracted rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation Coefficient.Experimental results demonstrate that xAI-derived rules consistently outperform traditional static rules.Notably,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.展开更多
This paper deals with the experiemental study on the correlation between the geometrical parameters of electrical tree and corresponding partial discharge(PD)characteristic parameters in the course of eletrical tree a...This paper deals with the experiemental study on the correlation between the geometrical parameters of electrical tree and corresponding partial discharge(PD)characteristic parameters in the course of eletrical tree aging within cross linked polyethylene(XLPE)insulation.The electrical tree aging tests were performed on specimens removed from a section of 220 kV transmission cable.The PD macroscopic characteristic parameters were found to be significantly dependent on the corresponding geometrical parameters of eletrical tree channels in the course of aging of XLPE,and different kind of electrical tree has different characteristics,and there is obvious correlation between the type of electrical tree and the pre-applied power-frequency stress.Beside,using regression analysis,the expression of the relation between them were obtained,and from which it can be seen that there is significant nonlinear correlation between geometrical parameters of electrical tree and corresponding PD characteristic parameters in the course of aging of XLPE.Therefore,the aging degree of XLPE can be effectively evaluated by recognizing the changing regularity of the PD macroscopic characteristic parameters.展开更多
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
Because of its ease of implementation,a linear PID controller is generally used to control robotic manipulators.Linear controllers cannot effectively cope with uncertainties and variations in the parameters;therefore,...Because of its ease of implementation,a linear PID controller is generally used to control robotic manipulators.Linear controllers cannot effectively cope with uncertainties and variations in the parameters;therefore,nonlinear controllers with robust performance which can cope with these are recommended.The sliding mode control(SMC)is a robust state feedback control method for nonlinear systems that,in addition having a simple design,efficiently overcomes uncertainties and disturbances in the system.It also has a very fast transient response that is desirable when controlling robotic manipulators.The most critical drawback to SMC is chattering in the control input signal.To solve this problem,in this study,SMC is used with a boundary layer(SMCBL)to eliminate the chattering and improve the performance of the system.The proposed SMCBL was compared with inverse dynamic control(IDC),a conventional nonlinear control method.The kinematic and dynamic equations of the IRB-120 robot manipulator were initially extracted completely and accurately,and then the control of the robot manipulator using SMC was evaluated.For validation,the proposed control method was implemented on a 6-DOF IRB-120 robot manipulator in the presence of uncertainties.The results were simulated,tested,and compared in the MATLAB/Simulink environment.To further validate our work,the results were tested and confirmed experimentally on an actual IRB-120 robot manipulator.展开更多
One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse ...One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.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
This paper presents a new evolutionary method called in Harvest Season Artificial Bee Colony (HSABC) algorithm for solving constrained problems of Combined Economic and Emission Dispatch (CEED). The IEEE-30 bus system...This paper presents a new evolutionary method called in Harvest Season Artificial Bee Colony (HSABC) algorithm for solving constrained problems of Combined Economic and Emission Dispatch (CEED). The IEEE-30 bus system is adopted as a sample system for determining the best solutions of the CEED problems considering operational constraints. Running outs of designed programs for the HSABC show that applications of various compromised factors have different implications on the CEED’s results, that minimum cost computations are started at different values, and that increasing load demands have affected costs, pollutant emissions and generated powers.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
In this paper, a routing protocol for wireless sensor network, baptized energy based protocol (EBP) is proposed. Wireless sensor network presents many challenges and constraints, and one of the major constraints is th...In this paper, a routing protocol for wireless sensor network, baptized energy based protocol (EBP) is proposed. Wireless sensor network presents many challenges and constraints, and one of the major constraints is the routing problem. Due to the limited energy of sensor nodes, routing in this type of network shall perform efficiently to maximize the network lifetime. One of the proposed algorithms is the directional source aware routing protocol (DSAP) which, after simulation, showed a lot of limitations and drawbacks. The modified directional source aware routing protocol (MDSAP) was proposed by the authors of this paper to address some of the DSAP’s limitations but remains limited to a fixed topology, fixed source and stationary nodes. So EBP is proposed and operated under different scenarios and showed, after its simulation using TinyOS, many advantages in terms of load balancing, free looping, minimizing packet error rate and maximizing network lifetime.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
This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both f...This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both for background and target. The sithouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor ptane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual The centroids of the human body are catculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are tower than some threshotds, fall incident will be detected. Experiments with different failing direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.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
The theory of compressed sensing(CS)has been proposed to reduce the processing time and accelerate the scanning process.In this paper,the image recovery task is considered to outsource to the cloud server for its abun...The theory of compressed sensing(CS)has been proposed to reduce the processing time and accelerate the scanning process.In this paper,the image recovery task is considered to outsource to the cloud server for its abundant computing and storage resources.However,the cloud server is untrusted then may pose a considerable amount of concern for potential privacy leakage.How to protect data privacy and simultaneously maintain management of the image remains challenging.Motivated by the above challenge,we propose an image encryption algorithm based on chaotic system,CS and image saliency.In our scheme,we outsource the image CS samples to cloud for reduced storage and portable computing.Consider privacy,the scheme ensures the cloud to securely reconstruct image.Theoretical analysis and experiment show the scheme achieves effectiveness,efficiency and high security simultaneously.展开更多
The Internet of Things (IoT) is a technological revolution that has changed everything we do and given us a new perspective on our daily lives, but despite the fact that numerous publications have focused on character...The Internet of Things (IoT) is a technological revolution that has changed everything we do and given us a new perspective on our daily lives, but despite the fact that numerous publications have focused on characterizing the many edges and technologies that make up an IoT system, the IoT ecosystem is still seen as too complex to be recognized as a stand-alone environment due to its significant diversity;hence, the objective of this research is to address such a complex environment in a way that highlights its components and distinguishes them both individually and in relation to their broader context. Therefore, the definition of IoT and its emergence were discussed and organized around the timeline of Internet development phases demonstrating that IoT has been a need that has accompanied the presence of the Internet since its early stages, and then its growth and impact were discussed and highlighted with estimates and numbers. On the technical side, each of the following groups, IoT components, protocols, and architectures, was defined, discussed, and grouped in such a way that their intergroup organization, as well as their placement and contribution to the overall ecosystem, was highlighted. This, in addition to the various examples mentioned throughout the discussion, will provide the reader with a better understanding of the Internet of Things and how deeply it has become entwined in our daily lives and routines as a result of its numerous applications.展开更多
Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical tasks.Spectral computed tomogr...Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical tasks.Spectral computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies.Therefore,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion classification.For issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory.Reconstruction results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise.For issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the CMs.Studies were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration detectors.Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data only.The CM-and HF-inputs can achieve the best AUC of 0.934 and 0.927.This texture themed study shows the insight that incorporating clinical important prior information,e.g.,tissue texture in this paper,into the medical imaging,such as the upstream image reconstruction,the downstream diagnosis,and so on,can benefit the clinical tasks.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
A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An advers...A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An adversary infers user’s locations from the trajectories, and gleans user’s private information through them via location-aware social networking applications and public available geographic data. In this paper, we propose a user proprietary obfuscate system to suit situations for position sharing and location privacy preserving in location-aware social network. Users transform the public available geographic data into personal obfuscate region maps with pre-defined profile to prevent the location leaking in stationary status. Our obfuscation with size restricted regions method tunes user’s transformed locations fitting into natural movement and prevents unreasonable snapshot locations been recorded in the trajectory.展开更多
基金funded under the Horizon Europe AI4CYBER Projectwhich has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No.101070450.
文摘The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security operators.This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)attacks.The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained model.The methodology was validated on two benchmark datasets,CICIDS2017 and WUSTL-IIOT-2021.Extracted rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation Coefficient.Experimental results demonstrate that xAI-derived rules consistently outperform traditional static rules.Notably,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.
基金Supported by the National Natural Science Foundation of China(59677018)
文摘This paper deals with the experiemental study on the correlation between the geometrical parameters of electrical tree and corresponding partial discharge(PD)characteristic parameters in the course of eletrical tree aging within cross linked polyethylene(XLPE)insulation.The electrical tree aging tests were performed on specimens removed from a section of 220 kV transmission cable.The PD macroscopic characteristic parameters were found to be significantly dependent on the corresponding geometrical parameters of eletrical tree channels in the course of aging of XLPE,and different kind of electrical tree has different characteristics,and there is obvious correlation between the type of electrical tree and the pre-applied power-frequency stress.Beside,using regression analysis,the expression of the relation between them were obtained,and from which it can be seen that there is significant nonlinear correlation between geometrical parameters of electrical tree and corresponding PD characteristic parameters in the course of aging of XLPE.Therefore,the aging degree of XLPE can be effectively evaluated by recognizing the changing regularity of the PD macroscopic characteristic parameters.
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Because of its ease of implementation,a linear PID controller is generally used to control robotic manipulators.Linear controllers cannot effectively cope with uncertainties and variations in the parameters;therefore,nonlinear controllers with robust performance which can cope with these are recommended.The sliding mode control(SMC)is a robust state feedback control method for nonlinear systems that,in addition having a simple design,efficiently overcomes uncertainties and disturbances in the system.It also has a very fast transient response that is desirable when controlling robotic manipulators.The most critical drawback to SMC is chattering in the control input signal.To solve this problem,in this study,SMC is used with a boundary layer(SMCBL)to eliminate the chattering and improve the performance of the system.The proposed SMCBL was compared with inverse dynamic control(IDC),a conventional nonlinear control method.The kinematic and dynamic equations of the IRB-120 robot manipulator were initially extracted completely and accurately,and then the control of the robot manipulator using SMC was evaluated.For validation,the proposed control method was implemented on a 6-DOF IRB-120 robot manipulator in the presence of uncertainties.The results were simulated,tested,and compared in the MATLAB/Simulink environment.To further validate our work,the results were tested and confirmed experimentally on an actual IRB-120 robot manipulator.
文摘One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.
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This paper presents a new evolutionary method called in Harvest Season Artificial Bee Colony (HSABC) algorithm for solving constrained problems of Combined Economic and Emission Dispatch (CEED). The IEEE-30 bus system is adopted as a sample system for determining the best solutions of the CEED problems considering operational constraints. Running outs of designed programs for the HSABC show that applications of various compromised factors have different implications on the CEED’s results, that minimum cost computations are started at different values, and that increasing load demands have affected costs, pollutant emissions and generated powers.
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In this paper, a routing protocol for wireless sensor network, baptized energy based protocol (EBP) is proposed. Wireless sensor network presents many challenges and constraints, and one of the major constraints is the routing problem. Due to the limited energy of sensor nodes, routing in this type of network shall perform efficiently to maximize the network lifetime. One of the proposed algorithms is the directional source aware routing protocol (DSAP) which, after simulation, showed a lot of limitations and drawbacks. The modified directional source aware routing protocol (MDSAP) was proposed by the authors of this paper to address some of the DSAP’s limitations but remains limited to a fixed topology, fixed source and stationary nodes. So EBP is proposed and operated under different scenarios and showed, after its simulation using TinyOS, many advantages in terms of load balancing, free looping, minimizing packet error rate and maximizing network lifetime.
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.
基金AcknowledgementsThis work is financially supported by the National Natural Science Foundation of China （61005015）, the third National Post-Doctoral Special Foundation of China （201003280）, and 2011 Shanshai city young teachers＇ subsidy scheme. The authors would like to thank the reviewers for their useful comments.
文摘This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both for background and target. The sithouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor ptane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual The centroids of the human body are catculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are tower than some threshotds, fall incident will be detected. Experiments with different failing direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.
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The theory of compressed sensing(CS)has been proposed to reduce the processing time and accelerate the scanning process.In this paper,the image recovery task is considered to outsource to the cloud server for its abundant computing and storage resources.However,the cloud server is untrusted then may pose a considerable amount of concern for potential privacy leakage.How to protect data privacy and simultaneously maintain management of the image remains challenging.Motivated by the above challenge,we propose an image encryption algorithm based on chaotic system,CS and image saliency.In our scheme,we outsource the image CS samples to cloud for reduced storage and portable computing.Consider privacy,the scheme ensures the cloud to securely reconstruct image.Theoretical analysis and experiment show the scheme achieves effectiveness,efficiency and high security simultaneously.
文摘The Internet of Things (IoT) is a technological revolution that has changed everything we do and given us a new perspective on our daily lives, but despite the fact that numerous publications have focused on characterizing the many edges and technologies that make up an IoT system, the IoT ecosystem is still seen as too complex to be recognized as a stand-alone environment due to its significant diversity;hence, the objective of this research is to address such a complex environment in a way that highlights its components and distinguishes them both individually and in relation to their broader context. Therefore, the definition of IoT and its emergence were discussed and organized around the timeline of Internet development phases demonstrating that IoT has been a need that has accompanied the presence of the Internet since its early stages, and then its growth and impact were discussed and highlighted with estimates and numbers. On the technical side, each of the following groups, IoT components, protocols, and architectures, was defined, discussed, and grouped in such a way that their intergroup organization, as well as their placement and contribution to the overall ecosystem, was highlighted. This, in addition to the various examples mentioned throughout the discussion, will provide the reader with a better understanding of the Internet of Things and how deeply it has become entwined in our daily lives and routines as a result of its numerous applications.
基金This work was partially supported by the NIH/NCI,No.CA206171.
文摘Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical tasks.Spectral computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies.Therefore,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion classification.For issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory.Reconstruction results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise.For issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the CMs.Studies were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration detectors.Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data only.The CM-and HF-inputs can achieve the best AUC of 0.934 and 0.927.This texture themed study shows the insight that incorporating clinical important prior information,e.g.,tissue texture in this paper,into the medical imaging,such as the upstream image reconstruction,the downstream diagnosis,and so on,can benefit the clinical tasks.
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A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An adversary infers user’s locations from the trajectories, and gleans user’s private information through them via location-aware social networking applications and public available geographic data. In this paper, we propose a user proprietary obfuscate system to suit situations for position sharing and location privacy preserving in location-aware social network. Users transform the public available geographic data into personal obfuscate region maps with pre-defined profile to prevent the location leaking in stationary status. Our obfuscation with size restricted regions method tunes user’s transformed locations fitting into natural movement and prevents unreasonable snapshot locations been recorded in the trajectory.
