The article examines the application of correlation analysis of experimental data in research into the process of extracting bioactive compounds and antioxidant activity in plant extracts from berries and grape pomace...The article examines the application of correlation analysis of experimental data in research into the process of extracting bioactive compounds and antioxidant activity in plant extracts from berries and grape pomace. The correlation analysis of the experimental data allowed the establishment of the second order statistical characteristics (autocorrelation function, intercorrelation function and correlation coefficient). Based on the correlation analysis of the experimental data, it was shown that the influencing factor and the measured parameters have zero correlation coefficients for all types of researched extracts. This indicates that they are not independent. Therefore, related mathematical models can be deduced.展开更多
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t...Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.展开更多
This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine ...This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine learning in cloud infrastructures,artificial intelligence techniques for big data analytics in cloud environments,and federated learning cloud systems are elucidated.Additionally,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is examined.Moreover,we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework(EEIBDM)established outside of every user in the cloud.IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room tem-peratures.Furthermore,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework.Finally,future directions for the expansion of this research are discussed.展开更多
Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for re...Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.展开更多
The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propaga...The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neurons of the second hidden layer. The paper presents the fundamental theory associated with this approach as well as a set of experimental results that evaluate the performance and accuracy of the proposed method against other methods found in the literature.展开更多
This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning...This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning in Chinese idioms. In this study, 60 students in grades three to six of an Elementary School in Nantou County of Taiwan participated in the research experiments. The results of this study reveal that the learning on Chinese idioms through the cloud-based E-learning assessment and remedial tutoring system is superior to the traditional reading to learn. The learning achievements of the students learning Chinese idioms by the proposed system are significantly improved. Therefore, the cloud-based E-learning teaching materials, methods of assessment, and remedial instruction are worth to develop and research.展开更多
In this paper, a facial feature extracting method is proposed to transform three-dimension （3D） head images of infants with deformational plagiocephaly for assessment of asymmetry. The features of 3D point clouds of...In this paper, a facial feature extracting method is proposed to transform three-dimension （3D） head images of infants with deformational plagiocephaly for assessment of asymmetry. The features of 3D point clouds of an infant＇s cranium can be identified by local feature analysis and a two-phase k-means classification algorithm. The 3D images of infants with asymmetric cranium can then be aligned to the same pose. The mirrored head model obtained from the symmetry plane is compared with the original model for the measurement of asymmetry. Numerical data of the cranial volume can be reviewed by a pediatrician to adjust the treatment plan. The system can also be used to demonstrate the treatment progress.展开更多
Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith th...Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith that are guaranteed for individual traffic classes, similarly as in weighted fair queueing. The paper describes a timed Petri net model of weighted priority queueing and uses discrete-event simulation of this model to obtain performance characteristics of simple queueing systems. The model is also used to analyze the effects of finite queue capacity on the performance of queueing systems.展开更多
Technology ranks second among the top topics on blogging. However, little research effort has been devoted to investigating “tech” blogs while it is interesting to provide insights on their network, as they may have...Technology ranks second among the top topics on blogging. However, little research effort has been devoted to investigating “tech” blogs while it is interesting to provide insights on their network, as they may have strong influence in the way that readers act and make decisions. In this vein, the paper records Greek “tech” blogs and their incoming links reported through their blogrolls. A directed network was created where blogs are noted as nodes and linked as directed arrows—arcs. The resulting network is analyzed using centrality and topological measurements and results are discussed in the social network analysis context. The network is a small world, with sparse density for a real-life social network and is low in-degree that shows off a kind of immaturity. Bloggers prefer to link to nodes with smaller degrees perhaps due to the fact that they are afraid of the growing competition thus they have reservations.展开更多
This article focuses on the mathematical modelling of the extraction process of bioactive compounds from grape marc and berries (Aronia, rosehip, rowan, and hawthorn). The composition of the extraction medium (the con...This article focuses on the mathematical modelling of the extraction process of bioactive compounds from grape marc and berries (Aronia, rosehip, rowan, and hawthorn). The composition of the extraction medium (the concentration of the ethyl alcohol) served as a factor of influence. Furthermore, 8 experimental measured parameters were used as variables. The experimental results were processed using Hermite polynomials. In order to adapt the degree of the polynomial, the following conditions were imposed: high precision of the mathematical model by appealing to models on interval;obtaining a nominal model and two uncertain models (upper and lower);deduction of two predictive models, one superior and one inferior. It was found that the mathematical models based on Hermite polynomials do not provide explicit analytical expressions, although they allow the establishment of parameter values for any concentration of the extraction medium. In some cases, only high-grade polynomial models ensure the modelling error below 2%. Uncertain models (upper and lower 95%) include all experimental data. Predictive mathematical models (upper and lower) were established for a high prediction. The analytical expressions of the mathematical models on intervals are non-gaps, the coefficients having non-zero values. Dependencies between the measured parameters and the composition of the extraction solvent were analyzed, the results being presented through the calculation of a surface, with all the experimental values and their average values. Thus, it was found that polynomial mathematical models provide complete information for modelling the extraction processes of bioactive compounds of plant origin.展开更多
The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelera...The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.展开更多
This paper presents the notion of the global execution context of a task as a representational construct for analysing complexity in software evolution. Based on this notion a visual notation and a supporting tool are...This paper presents the notion of the global execution context of a task as a representational construct for analysing complexity in software evolution. Based on this notion a visual notation and a supporting tool are presented to support specification of a system’s global execution context. A system’s global execution context is conceived as an evolving network of use scenarios depicted by nodes and links designating semantic relationships between scenarios. A node represents either a base or a growth scenario. Directed links characterize the transition from one node to another by means of semantic scenario relationships. Each growth scenario is generated following a critique (or screening) of one or more base or reference scenarios. Subsequently, representative growth scenarios are compiled and consolidated in the global execution context graph. The paper describes the stages of this process, presents the tool designed to facilitate the construction of the global execution context graph and elaborates on recent practice and experience.展开更多
In dental panoramic images, the information on physical changes of alveolar bone or jaw bone is very important to diagnose several diseases. To detect such change, it is useful to compare two panoramic x-ray images ac...In dental panoramic images, the information on physical changes of alveolar bone or jaw bone is very important to diagnose several diseases. To detect such change, it is useful to compare two panoramic x-ray images acquired at different times. These two images are usually acquired with different conditions in terms of the positioning of the dental arch, and thus these images can be impaired from some geometrical changes related to the scale of the panoramic images and deformation of the teeth and jaw bones. As a result of this, it is very hard to make an accurate registration. To cope with this issue, we developed a dedicated image registration method to match these two images by a newly introduced non-rigid transformation method and registration method using the cross-correlation of localized regions. We evaluated our proposed method with several sets of two images acquired with different geometrical conditions. The material evaluated in this study was a skull phantom. The results of these experiments showed the validity and intrinsic ability of our proposed method in clinical examinations.展开更多
The purpose of this study is to remove the shadow of cervical vertebrae from dental panoramic x-ray images with a tomosynthesis method and improve the contrast of details in both the teeth and jaw bones. To measure th...The purpose of this study is to remove the shadow of cervical vertebrae from dental panoramic x-ray images with a tomosynthesis method and improve the contrast of details in both the teeth and jaw bones. To measure the shift-amount at each angular position that was required for reconstruction of panoramic x-ray images of the dental arch, strip images of a calibration phantom were acquired. Then, a shift-amount table was prepared from these images, and the other shift-amount table, which was used to reconstruct a panoramic image of the cervical vertebrae, was prepared by inverting the curve of the shift-amount table upside down. Using these two tables, images focused on the dental arch and cervical vertebrae of a patient were made with the original strip data of the patient. The shadow of the cervical vertebrae appearing on the image focused on the dental arch was removed using the two above-mentioned images and blurring functions defined at two focusing geometries. The validity of the proposed method was evaluated with clinically acquired data of two patients. The shadow of the cervical vertebrae was successfully eliminated, and the contrast of the front teeth and detailed structures of the jaw bones was improved. The results of the experiments showed that our proposed method was significantly effective in removing the shadow of the cervical vertebrae from conventional panoramic x-ray images.展开更多
A new method for calculation of non-relativistic energy spectrum of Coulomb three-body systems with two identical particles has been developed. The novelty of the method is the introduction of an expansion of the wave...A new method for calculation of non-relativistic energy spectrum of Coulomb three-body systems with two identical particles has been developed. The novelty of the method is the introduction of an expansion of the wave function on harmonic oscillator (HO) functions with different sizes in the Jacobi coordinates instead of only one unique size parameter in the traditional approach. The method presented obeys the principles of antisymmetry and translational invariance. The theoretical formulation has been illustrated by evaluation of ground state energies of a number of Coulomb three-body systems with two identical particles for zero HO excitation energy. The analytical solution of this problem in case of only one size parameter has been derived. The obtained results show significant advantage of the base with different sizes over the traditional approach for investigation of the bound state problem of quantum systems.展开更多
This paper is addressed to the problem of Galilei invariant basis construction for identical fermions systems. The recently introduced method for spurious state elimination from expansions in harmonic oscillator basis...This paper is addressed to the problem of Galilei invariant basis construction for identical fermions systems. The recently introduced method for spurious state elimination from expansions in harmonic oscillator basis [1] 08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F005200650066003400310034003400340037003600360038000000 is adopted and applied to bound states of two particles system with Coulomb potential description. Traditional expansions in this case demonstrate the extremely well-known slow convergence, and hence this is the best problem with known exact solutions for the test of the method. Obtained results demonstrate the significant simplification of the problem and fast convergence of expansions. We show that the application of this general method is very efficient in a test case of the energy spectrum calculation problem of two particles with different masses interacting with Coulomb potential.展开更多
Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread ap...Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.展开更多
The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved...The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.展开更多
In ubiquitous computing,data should be able to be accessed from any location,and the correctness of data becomes vital during the communication.Suppose that many users sign different messages respectively,before forwa...In ubiquitous computing,data should be able to be accessed from any location,and the correctness of data becomes vital during the communication.Suppose that many users sign different messages respectively,before forwarding or sending these messages,then the verifier must spend a lot of computing time to verify their signatures.Consequently,the aggregate signature scheme is an effective method of improving efficiency in this kind of systems,which provides the convenience for the verifier.In this paper,we propose a new certificateless aggregate signature scheme which is efficient in generating a signature and verification.This scheme is provably secure under the extended computational Diffie-Hellman assumption.展开更多
文摘The article examines the application of correlation analysis of experimental data in research into the process of extracting bioactive compounds and antioxidant activity in plant extracts from berries and grape pomace. The correlation analysis of the experimental data allowed the establishment of the second order statistical characteristics (autocorrelation function, intercorrelation function and correlation coefficient). Based on the correlation analysis of the experimental data, it was shown that the influencing factor and the measured parameters have zero correlation coefficients for all types of researched extracts. This indicates that they are not independent. Therefore, related mathematical models can be deduced.
文摘Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
文摘This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine learning in cloud infrastructures,artificial intelligence techniques for big data analytics in cloud environments,and federated learning cloud systems are elucidated.Additionally,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is examined.Moreover,we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework(EEIBDM)established outside of every user in the cloud.IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room tem-peratures.Furthermore,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework.Finally,future directions for the expansion of this research are discussed.
文摘Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.
文摘The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neurons of the second hidden layer. The paper presents the fundamental theory associated with this approach as well as a set of experimental results that evaluate the performance and accuracy of the proposed method against other methods found in the literature.
文摘This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning in Chinese idioms. In this study, 60 students in grades three to six of an Elementary School in Nantou County of Taiwan participated in the research experiments. The results of this study reveal that the learning on Chinese idioms through the cloud-based E-learning assessment and remedial tutoring system is superior to the traditional reading to learn. The learning achievements of the students learning Chinese idioms by the proposed system are significantly improved. Therefore, the cloud-based E-learning teaching materials, methods of assessment, and remedial instruction are worth to develop and research.
文摘In this paper, a facial feature extracting method is proposed to transform three-dimension （3D） head images of infants with deformational plagiocephaly for assessment of asymmetry. The features of 3D point clouds of an infant＇s cranium can be identified by local feature analysis and a two-phase k-means classification algorithm. The 3D images of infants with asymmetric cranium can then be aligned to the same pose. The mirrored head model obtained from the symmetry plane is compared with the original model for the measurement of asymmetry. Numerical data of the cranial volume can be reviewed by a pediatrician to adjust the treatment plan. The system can also be used to demonstrate the treatment progress.
文摘Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith that are guaranteed for individual traffic classes, similarly as in weighted fair queueing. The paper describes a timed Petri net model of weighted priority queueing and uses discrete-event simulation of this model to obtain performance characteristics of simple queueing systems. The model is also used to analyze the effects of finite queue capacity on the performance of queueing systems.
文摘Technology ranks second among the top topics on blogging. However, little research effort has been devoted to investigating “tech” blogs while it is interesting to provide insights on their network, as they may have strong influence in the way that readers act and make decisions. In this vein, the paper records Greek “tech” blogs and their incoming links reported through their blogrolls. A directed network was created where blogs are noted as nodes and linked as directed arrows—arcs. The resulting network is analyzed using centrality and topological measurements and results are discussed in the social network analysis context. The network is a small world, with sparse density for a real-life social network and is low in-degree that shows off a kind of immaturity. Bloggers prefer to link to nodes with smaller degrees perhaps due to the fact that they are afraid of the growing competition thus they have reservations.
文摘This article focuses on the mathematical modelling of the extraction process of bioactive compounds from grape marc and berries (Aronia, rosehip, rowan, and hawthorn). The composition of the extraction medium (the concentration of the ethyl alcohol) served as a factor of influence. Furthermore, 8 experimental measured parameters were used as variables. The experimental results were processed using Hermite polynomials. In order to adapt the degree of the polynomial, the following conditions were imposed: high precision of the mathematical model by appealing to models on interval;obtaining a nominal model and two uncertain models (upper and lower);deduction of two predictive models, one superior and one inferior. It was found that the mathematical models based on Hermite polynomials do not provide explicit analytical expressions, although they allow the establishment of parameter values for any concentration of the extraction medium. In some cases, only high-grade polynomial models ensure the modelling error below 2%. Uncertain models (upper and lower 95%) include all experimental data. Predictive mathematical models (upper and lower) were established for a high prediction. The analytical expressions of the mathematical models on intervals are non-gaps, the coefficients having non-zero values. Dependencies between the measured parameters and the composition of the extraction solvent were analyzed, the results being presented through the calculation of a surface, with all the experimental values and their average values. Thus, it was found that polynomial mathematical models provide complete information for modelling the extraction processes of bioactive compounds of plant origin.
文摘The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.
文摘This paper presents the notion of the global execution context of a task as a representational construct for analysing complexity in software evolution. Based on this notion a visual notation and a supporting tool are presented to support specification of a system’s global execution context. A system’s global execution context is conceived as an evolving network of use scenarios depicted by nodes and links designating semantic relationships between scenarios. A node represents either a base or a growth scenario. Directed links characterize the transition from one node to another by means of semantic scenario relationships. Each growth scenario is generated following a critique (or screening) of one or more base or reference scenarios. Subsequently, representative growth scenarios are compiled and consolidated in the global execution context graph. The paper describes the stages of this process, presents the tool designed to facilitate the construction of the global execution context graph and elaborates on recent practice and experience.
文摘In dental panoramic images, the information on physical changes of alveolar bone or jaw bone is very important to diagnose several diseases. To detect such change, it is useful to compare two panoramic x-ray images acquired at different times. These two images are usually acquired with different conditions in terms of the positioning of the dental arch, and thus these images can be impaired from some geometrical changes related to the scale of the panoramic images and deformation of the teeth and jaw bones. As a result of this, it is very hard to make an accurate registration. To cope with this issue, we developed a dedicated image registration method to match these two images by a newly introduced non-rigid transformation method and registration method using the cross-correlation of localized regions. We evaluated our proposed method with several sets of two images acquired with different geometrical conditions. The material evaluated in this study was a skull phantom. The results of these experiments showed the validity and intrinsic ability of our proposed method in clinical examinations.
文摘The purpose of this study is to remove the shadow of cervical vertebrae from dental panoramic x-ray images with a tomosynthesis method and improve the contrast of details in both the teeth and jaw bones. To measure the shift-amount at each angular position that was required for reconstruction of panoramic x-ray images of the dental arch, strip images of a calibration phantom were acquired. Then, a shift-amount table was prepared from these images, and the other shift-amount table, which was used to reconstruct a panoramic image of the cervical vertebrae, was prepared by inverting the curve of the shift-amount table upside down. Using these two tables, images focused on the dental arch and cervical vertebrae of a patient were made with the original strip data of the patient. The shadow of the cervical vertebrae appearing on the image focused on the dental arch was removed using the two above-mentioned images and blurring functions defined at two focusing geometries. The validity of the proposed method was evaluated with clinically acquired data of two patients. The shadow of the cervical vertebrae was successfully eliminated, and the contrast of the front teeth and detailed structures of the jaw bones was improved. The results of the experiments showed that our proposed method was significantly effective in removing the shadow of the cervical vertebrae from conventional panoramic x-ray images.
文摘A new method for calculation of non-relativistic energy spectrum of Coulomb three-body systems with two identical particles has been developed. The novelty of the method is the introduction of an expansion of the wave function on harmonic oscillator (HO) functions with different sizes in the Jacobi coordinates instead of only one unique size parameter in the traditional approach. The method presented obeys the principles of antisymmetry and translational invariance. The theoretical formulation has been illustrated by evaluation of ground state energies of a number of Coulomb three-body systems with two identical particles for zero HO excitation energy. The analytical solution of this problem in case of only one size parameter has been derived. The obtained results show significant advantage of the base with different sizes over the traditional approach for investigation of the bound state problem of quantum systems.
文摘This paper is addressed to the problem of Galilei invariant basis construction for identical fermions systems. The recently introduced method for spurious state elimination from expansions in harmonic oscillator basis [1] 08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F005200650066003400310034003400340037003600360038000000 is adopted and applied to bound states of two particles system with Coulomb potential description. Traditional expansions in this case demonstrate the extremely well-known slow convergence, and hence this is the best problem with known exact solutions for the test of the method. Obtained results demonstrate the significant simplification of the problem and fast convergence of expansions. We show that the application of this general method is very efficient in a test case of the energy spectrum calculation problem of two particles with different masses interacting with Coulomb potential.
文摘Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
文摘The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.
基金supported by the National Science Council under Grant No.NSC100-2221-E-005-062 and NSC 100-2221-E-468-014
文摘In ubiquitous computing,data should be able to be accessed from any location,and the correctness of data becomes vital during the communication.Suppose that many users sign different messages respectively,before forwarding or sending these messages,then the verifier must spend a lot of computing time to verify their signatures.Consequently,the aggregate signature scheme is an effective method of improving efficiency in this kind of systems,which provides the convenience for the verifier.In this paper,we propose a new certificateless aggregate signature scheme which is efficient in generating a signature and verification.This scheme is provably secure under the extended computational Diffie-Hellman assumption.
