Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematica...Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.展开更多
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me...Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.展开更多
Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast ele...Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.展开更多
A signal is the entity that carries information. In the field of communication signal is the time varying quantity or functions of time and they are interrelated by a set of different equations, but some times process...A signal is the entity that carries information. In the field of communication signal is the time varying quantity or functions of time and they are interrelated by a set of different equations, but some times processing of signal is corrupted due to adding some noise in the information signal and the information signal become noisy. It is very important to get the information from corrupted signal as we use filters. In this paper, Butterworth filter is designed for the signal analysis and also compared with other filters. It has maximally flat response in the pass band otherwise no ripples in the pass band. To meet the specification, 6th order Butterworth filter was chosen because it is flat in the pass band and has no amount of ripples in the stop band.展开更多
Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.De...Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.展开更多
This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, ...This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment （Moodle）, using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture （SOA） methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer （REST）, as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.展开更多
Photovoltaics(PV)can convert sunlight into electricity by making use of the photovoltaic effect.Solar panels consist of photovoltaic cells made of semiconductor materials(such as silicon)to utilise the photovoltaic ef...Photovoltaics(PV)can convert sunlight into electricity by making use of the photovoltaic effect.Solar panels consist of photovoltaic cells made of semiconductor materials(such as silicon)to utilise the photovoltaic effect and convert sunlight into direct current(DC)electricity.Nowadays,PV has become the cheapest electrical power source with low price bids and low panel prices.The competitiveness makes it a potential path to mitigate the global warming.In this paper,we investigate the relationship of PC array output with irradiance and temperature,the performance of PV array over 24 hours period,and the simulation of PV micro grid by MATLAB simulation.展开更多
The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount o...The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.展开更多
In this paper,a new method for adjusting the current of three-phase voltage source DC-AC converter in orthogonal(DQ)reference frame is presented.In the DQ reference system,AC variable appears in the constant form of D...In this paper,a new method for adjusting the current of three-phase voltage source DC-AC converter in orthogonal(DQ)reference frame is presented.In the DQ reference system,AC variable appears in the constant form of DC,making the controller design the same as the DC-DC converter[1].It provides controllable gain benefits at the steady-state operating point,and finally realizes zero steady-state error[2].In addition,the creative analytical model is dedicated to building up a series of virtual quantities orthogonal to the actual single-phase system.In general,orthogonal imaginary numbers get the reference signal by delaying the real quantity by a quarter period.However,the introduction of such time delay makes the dynamic response of the system worse.In this paper,orthogonal quantities are generated from a virtual axis system parallel to the real axis,which can effectively improve the dynamic performance of traditional methods without increasing the complexity of controller structure.Through PSCAD simulation,the ideal experimental results are obtained.展开更多
The classical Wiener filter was engaged into identifying the linear structures,resulting in clear and incredible drawbacks in working with nonlinear integrated system.Currently,the Hermitian-Wiener system are suitable...The classical Wiener filter was engaged into identifying the linear structures,resulting in clear and incredible drawbacks in working with nonlinear integrated system.Currently,the Hermitian-Wiener system are suitable for unpredicted sub-system that consists of numerous and complex inputs.The system introduces a two-stage to analyze the subintervals where the output nonlinearities are noninvertible,through using the unknown orders and parameters.Finally,a practical strategy would be discussed to analyze the nonlinear parameters.展开更多
Patch antennas are small in size and suitable for microwave transmission,so they are widely used in small portable wireless devices.Multiple patch antennas are connected together to form an array antenna.Compared with...Patch antennas are small in size and suitable for microwave transmission,so they are widely used in small portable wireless devices.Multiple patch antennas are connected together to form an array antenna.Compared with the patch antenna,the array antenna has a higher directivity gain and can achieve better transmission performance.In this project,I will test the single patch antenna first,and then move to 2×1 antenna array.Finally,built a 2×2 antenna array,test and record their performance respectively.展开更多
Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign re...Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.展开更多
With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Thi...With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.展开更多
Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification frame...Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks(DNNs)with transfer learning,a customized DNN,and an optimized self-learning binary differential evolution(SLBDE)algorithm for feature selection and fusion.Leveraging computational techniques alongside medical imaging modalities,the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness.The methodology is evaluated on benchmark datasets,including ISIC 2017 and the Argentina Skin Lesion dataset,demonstrating superior accuracy,precision,and F1-score in melanoma detection.The proposed method achieved a classification accuracy of 98.5%,evaluated using an LSVM classifier on the Argentina Skin Lesion dataset,underscoring the robustness of the proposed methodology.The proposed approach offers a scalable and computationally efficient solution for automated skin lesion classification,thereby contributing to improved clinical decision-making and enhanced patient outcomes.By aligning artificial intelligence with radiation-based medical imaging and bioinformatics,this research advances dermatological computer-aided diagnosis(CAD)systems,minimizing misclassification rates and supporting early skin cancer detection.The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis,contributing to improved clinical decision-making and enhanced patient outcomes.展开更多
More than 30 years ago,a group of researchers in Tampere-Finland developed a thin foamed polymeric material for capacitive sensors.Such soft-voided films exhibited electrical charging characteristics,forming a powerfu...More than 30 years ago,a group of researchers in Tampere-Finland developed a thin foamed polymeric material for capacitive sensors.Such soft-voided films exhibited electrical charging characteristics,forming a powerful combination,which resulted in a smart-material with ferroelectric properties.The discovery of the electro-thermo mechanical film(ETMF)has sparked the curiosity of the electret community,leading to the development of several studies.At that time,ETMF became known as cellular electrets and,later,as ferroelectrets or piezoelectrets regarding their electromechanical properties.This paper provides a timeline review of the research on ferroelectrets produced in Brazil,between the years 1990 and 2020,towards demonstrating how the interest in the electret electrical charging mechanism has resulted in the use of ferroelectrets with well-controlled cavities for ultrasound applications.展开更多
Nowadays,the demand for advanced functional materials in transducer technology is growing rapidly.Piezoelectric materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)...Nowadays,the demand for advanced functional materials in transducer technology is growing rapidly.Piezoelectric materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice versa.They are interesting from both fundamental and application points of view.Ferrooelectrets(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric materials.They exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to now.In this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of ferroelectrets.We will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.展开更多
基金funded by Deanship of Research and Graduate Studies at King Khalid University.The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/556/45).
文摘Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.
基金the Deanship of Scientific Research at King Khalid University for funding this work through large group under grant number(GRP.2/663/46).
文摘Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
文摘A signal is the entity that carries information. In the field of communication signal is the time varying quantity or functions of time and they are interrelated by a set of different equations, but some times processing of signal is corrupted due to adding some noise in the information signal and the information signal become noisy. It is very important to get the information from corrupted signal as we use filters. In this paper, Butterworth filter is designed for the signal analysis and also compared with other filters. It has maximally flat response in the pass band otherwise no ripples in the pass band. To meet the specification, 6th order Butterworth filter was chosen because it is flat in the pass band and has no amount of ripples in the stop band.
文摘Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.
文摘This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment （Moodle）, using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture （SOA） methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer （REST）, as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.
文摘Photovoltaics(PV)can convert sunlight into electricity by making use of the photovoltaic effect.Solar panels consist of photovoltaic cells made of semiconductor materials(such as silicon)to utilise the photovoltaic effect and convert sunlight into direct current(DC)electricity.Nowadays,PV has become the cheapest electrical power source with low price bids and low panel prices.The competitiveness makes it a potential path to mitigate the global warming.In this paper,we investigate the relationship of PC array output with irradiance and temperature,the performance of PV array over 24 hours period,and the simulation of PV micro grid by MATLAB simulation.
文摘The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.
文摘In this paper,a new method for adjusting the current of three-phase voltage source DC-AC converter in orthogonal(DQ)reference frame is presented.In the DQ reference system,AC variable appears in the constant form of DC,making the controller design the same as the DC-DC converter[1].It provides controllable gain benefits at the steady-state operating point,and finally realizes zero steady-state error[2].In addition,the creative analytical model is dedicated to building up a series of virtual quantities orthogonal to the actual single-phase system.In general,orthogonal imaginary numbers get the reference signal by delaying the real quantity by a quarter period.However,the introduction of such time delay makes the dynamic response of the system worse.In this paper,orthogonal quantities are generated from a virtual axis system parallel to the real axis,which can effectively improve the dynamic performance of traditional methods without increasing the complexity of controller structure.Through PSCAD simulation,the ideal experimental results are obtained.
文摘The classical Wiener filter was engaged into identifying the linear structures,resulting in clear and incredible drawbacks in working with nonlinear integrated system.Currently,the Hermitian-Wiener system are suitable for unpredicted sub-system that consists of numerous and complex inputs.The system introduces a two-stage to analyze the subintervals where the output nonlinearities are noninvertible,through using the unknown orders and parameters.Finally,a practical strategy would be discussed to analyze the nonlinear parameters.
文摘Patch antennas are small in size and suitable for microwave transmission,so they are widely used in small portable wireless devices.Multiple patch antennas are connected together to form an array antenna.Compared with the patch antenna,the array antenna has a higher directivity gain and can achieve better transmission performance.In this project,I will test the single patch antenna first,and then move to 2×1 antenna array.Finally,built a 2×2 antenna array,test and record their performance respectively.
文摘Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.
文摘With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/283/46funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R748),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks(DNNs)with transfer learning,a customized DNN,and an optimized self-learning binary differential evolution(SLBDE)algorithm for feature selection and fusion.Leveraging computational techniques alongside medical imaging modalities,the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness.The methodology is evaluated on benchmark datasets,including ISIC 2017 and the Argentina Skin Lesion dataset,demonstrating superior accuracy,precision,and F1-score in melanoma detection.The proposed method achieved a classification accuracy of 98.5%,evaluated using an LSVM classifier on the Argentina Skin Lesion dataset,underscoring the robustness of the proposed methodology.The proposed approach offers a scalable and computationally efficient solution for automated skin lesion classification,thereby contributing to improved clinical decision-making and enhanced patient outcomes.By aligning artificial intelligence with radiation-based medical imaging and bioinformatics,this research advances dermatological computer-aided diagnosis(CAD)systems,minimizing misclassification rates and supporting early skin cancer detection.The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis,contributing to improved clinical decision-making and enhanced patient outcomes.
文摘More than 30 years ago,a group of researchers in Tampere-Finland developed a thin foamed polymeric material for capacitive sensors.Such soft-voided films exhibited electrical charging characteristics,forming a powerful combination,which resulted in a smart-material with ferroelectric properties.The discovery of the electro-thermo mechanical film(ETMF)has sparked the curiosity of the electret community,leading to the development of several studies.At that time,ETMF became known as cellular electrets and,later,as ferroelectrets or piezoelectrets regarding their electromechanical properties.This paper provides a timeline review of the research on ferroelectrets produced in Brazil,between the years 1990 and 2020,towards demonstrating how the interest in the electret electrical charging mechanism has resulted in the use of ferroelectrets with well-controlled cavities for ultrasound applications.
基金Financial support from the National Natural Science Foundation of China(No.12174102)the National Key Research and Development Program of China(No.2021YFC3001802)the Shanghai Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,and Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology is gratefully acknowledged.
文摘Nowadays,the demand for advanced functional materials in transducer technology is growing rapidly.Piezoelectric materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice versa.They are interesting from both fundamental and application points of view.Ferrooelectrets(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric materials.They exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to now.In this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of ferroelectrets.We will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.
