This paper studies the performance of Traffic Engineering (TE) signal protocols used for load balancing in Multi-Protocol Label Switching (MPLS) networks, namely;Constraint Based Routed Label Distribution Protocol LDP...This paper studies the performance of Traffic Engineering (TE) signal protocols used for load balancing in Multi-Protocol Label Switching (MPLS) networks, namely;Constraint Based Routed Label Distribution Protocol LDP (CR-LDP) and Resource Reservation Protocol (RSVP). Furthermore, the performance of an MPLS network uses these TE signal protocols is compared to that of a conventional Internet Protocol (IP) network. Different applications including voice, video, File Transfer Protocol (FTP) and Hyperlink Text Transfer Protocol (HTTP) are used for the performance evaluation. Simulation results show superior performance of the MPLS network with CR-LDP TE signal protocol in all tested applications.展开更多
We study the effect of strain on band structure and valley-dependent transport properties of graphene heterojunctions.It is found that valley-dependent separation of electrons can be achieved by utilizing strain and o...We study the effect of strain on band structure and valley-dependent transport properties of graphene heterojunctions.It is found that valley-dependent separation of electrons can be achieved by utilizing strain and on-site energies.In the presence of strain,the values of transmission can be effectively adjusted by changing the strengths of the strain,while the transport angle basically keeps unchanged.When an extra on-site energy is simultaneously applied to the central scattering region,not only are the electrons of valleys K and K'separated into two distinct transmission lobes in opposite transverse directions,but the transport angles of two valleys can be significantly changed.Therefore,one can realize an effective modulation of valley-dependent transport by changing the strength and stretch angle of the strain and on-site energies,which can be exploited for graphene-based valleytronics devices.展开更多
Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approac...Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approaches in Iran. Through comprehensive search, we explored main TE components researches include cell, scaffold, growth factor and bioreactor conducted in Iran. The field of TE and regenerative medicine in Iran dates back to the early part of the 1990 decade and the advent of stem cell researches. During past two decades, Iran was one of leader in stem cell research in Middle East. The next major step in TE was application and fabrication of scaffolds for TE in the early 2000s with focused on engineering bone and nerve tissue. Iranian researchers extensively used natural scaffolds in their studies and hybridized natural polymers and inorganic scaffolds. There are many universities and government research institutes are conducting active research on tissue-engineering technologies. Limitations to TE in Iran include property design and validation of bioreactors. In conclusion, in the last few years, fields of tissue engineering and regenerative medicine such as stem cell technology and scaffolds have progressed in Iran, but one of the biggest challenges for TE is bioreactors researches.展开更多
The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts....The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts.It is a vague and fuzzy concept for the wider community of engineers.The importance of remote sensing of temperature by measuring IR radiation has been recognized in a wide range of industrial,medical,and environ⁃mental uses.One of the major sources of errors in IR radiometry is the emissivity of the surface being measured.In real experiments,emissivity may be influenced by many factors:surface texture,spectral properties,oxida⁃tion,and aging of surfaces.While commercial blackbodies are prevalent,the much-needed grey bodies with a known emissivity,are unavailable.This study describes how to achieve a calibrated and stable emissivity with a blackbody,a perforated screen,and a reliable and linear novel IR thermal sensor,18 dubbed TMOS.The Digital TMOS is now a low-cost commercial product,it requires low power,and it has a small form factor.The method⁃ology is based on two-color measurements,with two different optical filters,with selected wavelengths conform⁃ing to the grey body definition of the use case under study.With a photochemically etched perforated screen,the effective emissivity of the screen is simply the hole density area of the surface area that emits according to the blackbody temperature radiation.The concept is illustrated with ray tracing simulations,which demonstrate the approach.Measured results are reported.展开更多
A hybrid control strategy integrating proportional derivative(PD)and the H-infinity control methodology is proposed for a serial two-link robotic manipulator with the goal of improving the tracking performance of the ...A hybrid control strategy integrating proportional derivative(PD)and the H-infinity control methodology is proposed for a serial two-link robotic manipulator with the goal of improving the tracking performance of the robot arm.The H-infinity controller has the ability to achieve a high performance and robustness in the presence of disturbances and uncertainties,while the PD controller is effective in stabilizing the manipulator.Simulation results using Matlab and Simulink show that the proposed hybrid controller,which integrates the advantages of both PD and H-infinity controllers,has the lowest rise time for the second link,the lowest settling time for the two links,the lowest peak time for both links,and the fastest decay of the error response.In addition,the hybrid control scheme also has the lowest mean square error value,with a 53.3%improvement over the H-infinity controller and a 91.8%improvement over the PD controller,indicating an improved trajectory tracking performance when compared with pure PD and pure H-infinity controllers,respectively.It was also found that the hybrid controller has the lowest integral absolute error,integral square error,integral time absolute error,and integral time square error for the second link,while the error values for the first link are satisfactory,showing a superior performance of the hybrid controller above the PD and H-infinity controllers,respectively.展开更多
This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With th...This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With the expansion of large shopping malls and underground urban spaces(UUS),there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation,remodeling,and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps.The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site map of the up-to-date indoor site and recognizes complex indoor spaces based on zero-shot learning.This research specifically addresses two major challenges:the difficulty of detecting walls and floors due to complex patterns and the difficulty of spatial perception due to unknown obstacles.The proposed algorithm addresses the limitations of the existing foundation model,detects floors and obstacles without expensive sensors,and improves the accuracy of spatial recognition by combining floor detection,vanishing point detection,and fusion obstacle detection algorithms.The experimental results show that the algorithm effectively detects the floor and obstacles in various indoor environments,with F1 scores of 0.96 and 0.93 in the floor detection and obstacle detection experiments,respectively.展开更多
This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevale...This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.展开更多
The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threat...The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threats,necessitating robust IntrusionDetection Systems(IDS).Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems,especially when securing interconnected medical devices.This paper introduces SNN-IoMT(Stacked Neural Network Ensemble for IoMT Security),an AI-driven IDS framework designed to secure dynamic IoMT environments.Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron(MLP),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM),the model optimizes data management and integration while ensuring system scalability and interoperability.Trained on the WUSTL-EHMS-2020 and IoT-Healthcare-Security datasets,SNN-IoMT surpasses existing IDS frameworks in accuracy,precision,and detecting novel threats.By addressing the primary challenges in AI-driven healthcare systems,including privacy,reliability,and ethical data management,our approach exemplifies the importance of AI to enhance security and trust in IoMT-enabled healthcare.展开更多
In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial...In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Artifcial Intelligence (AI) is being applied to improve the efciency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the felds of AI that int...Artifcial Intelligence (AI) is being applied to improve the efciency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the felds of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the rel-evanceof the input features to the output of an AI model. File fragment classifcation is one of the vital issues of fle carving in Cyber Forensics (CF) and becomes challenging when the flesystem metadata is missing. Other major challenges it faces are: proliferation of fle formats, fle embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classifcation of fle fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a fle fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize fle fragment classifcation. To improve multinomial classifcation, a Multilayer Per-ceptronmodel is developed and optimized with fve hidden layers, each layer with i × n neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 fle types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the frst efort of applying XAI in CF for optimizing fle fragment classifcation.展开更多
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or...Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.展开更多
Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blo...Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.展开更多
The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from ...The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.展开更多
A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating thc cntire nonlinear system with the neural network （NN）, only the un-modeled part left over after the lincarization is a...A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating thc cntire nonlinear system with the neural network （NN）, only the un-modeled part left over after the lincarization is approximated. Compared with the conventional linear observer, the observer provides more accurate estimation of the state. The state estimation error is proved to asymptotically approach zero with the Lyapunov method. The simulation result shows that the proposed observer scheme is effective and has a potential application ability in the fault detection and identification （FDI）, and the state estimation.展开更多
The Chinese character library is one of the important data structures in the Chinese information Processing system.The behavior of the whole system depends directly on the reasonableness of design for its structure.Th...The Chinese character library is one of the important data structures in the Chinese information Processing system.The behavior of the whole system depends directly on the reasonableness of design for its structure.This paper expounds the structures of RAM-based Chinese character libraries,static and dynamic ,The paper offers a descriptive method for this behavior and inquires into some algorithms related to the structures mentioned above.展开更多
文摘This paper studies the performance of Traffic Engineering (TE) signal protocols used for load balancing in Multi-Protocol Label Switching (MPLS) networks, namely;Constraint Based Routed Label Distribution Protocol LDP (CR-LDP) and Resource Reservation Protocol (RSVP). Furthermore, the performance of an MPLS network uses these TE signal protocols is compared to that of a conventional Internet Protocol (IP) network. Different applications including voice, video, File Transfer Protocol (FTP) and Hyperlink Text Transfer Protocol (HTTP) are used for the performance evaluation. Simulation results show superior performance of the MPLS network with CR-LDP TE signal protocol in all tested applications.
基金National Natural Science Foundation of China(Grant No.11574067)。
文摘We study the effect of strain on band structure and valley-dependent transport properties of graphene heterojunctions.It is found that valley-dependent separation of electrons can be achieved by utilizing strain and on-site energies.In the presence of strain,the values of transmission can be effectively adjusted by changing the strengths of the strain,while the transport angle basically keeps unchanged.When an extra on-site energy is simultaneously applied to the central scattering region,not only are the electrons of valleys K and K'separated into two distinct transmission lobes in opposite transverse directions,but the transport angles of two valleys can be significantly changed.Therefore,one can realize an effective modulation of valley-dependent transport by changing the strength and stretch angle of the strain and on-site energies,which can be exploited for graphene-based valleytronics devices.
文摘Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approaches in Iran. Through comprehensive search, we explored main TE components researches include cell, scaffold, growth factor and bioreactor conducted in Iran. The field of TE and regenerative medicine in Iran dates back to the early part of the 1990 decade and the advent of stem cell researches. During past two decades, Iran was one of leader in stem cell research in Middle East. The next major step in TE was application and fabrication of scaffolds for TE in the early 2000s with focused on engineering bone and nerve tissue. Iranian researchers extensively used natural scaffolds in their studies and hybridized natural polymers and inorganic scaffolds. There are many universities and government research institutes are conducting active research on tissue-engineering technologies. Limitations to TE in Iran include property design and validation of bioreactors. In conclusion, in the last few years, fields of tissue engineering and regenerative medicine such as stem cell technology and scaffolds have progressed in Iran, but one of the biggest challenges for TE is bioreactors researches.
文摘The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts.It is a vague and fuzzy concept for the wider community of engineers.The importance of remote sensing of temperature by measuring IR radiation has been recognized in a wide range of industrial,medical,and environ⁃mental uses.One of the major sources of errors in IR radiometry is the emissivity of the surface being measured.In real experiments,emissivity may be influenced by many factors:surface texture,spectral properties,oxida⁃tion,and aging of surfaces.While commercial blackbodies are prevalent,the much-needed grey bodies with a known emissivity,are unavailable.This study describes how to achieve a calibrated and stable emissivity with a blackbody,a perforated screen,and a reliable and linear novel IR thermal sensor,18 dubbed TMOS.The Digital TMOS is now a low-cost commercial product,it requires low power,and it has a small form factor.The method⁃ology is based on two-color measurements,with two different optical filters,with selected wavelengths conform⁃ing to the grey body definition of the use case under study.With a photochemically etched perforated screen,the effective emissivity of the screen is simply the hole density area of the surface area that emits according to the blackbody temperature radiation.The concept is illustrated with ray tracing simulations,which demonstrate the approach.Measured results are reported.
文摘A hybrid control strategy integrating proportional derivative(PD)and the H-infinity control methodology is proposed for a serial two-link robotic manipulator with the goal of improving the tracking performance of the robot arm.The H-infinity controller has the ability to achieve a high performance and robustness in the presence of disturbances and uncertainties,while the PD controller is effective in stabilizing the manipulator.Simulation results using Matlab and Simulink show that the proposed hybrid controller,which integrates the advantages of both PD and H-infinity controllers,has the lowest rise time for the second link,the lowest settling time for the two links,the lowest peak time for both links,and the fastest decay of the error response.In addition,the hybrid control scheme also has the lowest mean square error value,with a 53.3%improvement over the H-infinity controller and a 91.8%improvement over the PD controller,indicating an improved trajectory tracking performance when compared with pure PD and pure H-infinity controllers,respectively.It was also found that the hybrid controller has the lowest integral absolute error,integral square error,integral time absolute error,and integral time square error for the second link,while the error values for the first link are satisfactory,showing a superior performance of the hybrid controller above the PD and H-infinity controllers,respectively.
基金supported by Kyonggi University Research Grant 2024.
文摘This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With the expansion of large shopping malls and underground urban spaces(UUS),there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation,remodeling,and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps.The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site map of the up-to-date indoor site and recognizes complex indoor spaces based on zero-shot learning.This research specifically addresses two major challenges:the difficulty of detecting walls and floors due to complex patterns and the difficulty of spatial perception due to unknown obstacles.The proposed algorithm addresses the limitations of the existing foundation model,detects floors and obstacles without expensive sensors,and improves the accuracy of spatial recognition by combining floor detection,vanishing point detection,and fusion obstacle detection algorithms.The experimental results show that the algorithm effectively detects the floor and obstacles in various indoor environments,with F1 scores of 0.96 and 0.93 in the floor detection and obstacle detection experiments,respectively.
基金Türkiye Bilimsel ve Teknolojik Arastırma Kurumu。
文摘This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.
文摘The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threats,necessitating robust IntrusionDetection Systems(IDS).Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems,especially when securing interconnected medical devices.This paper introduces SNN-IoMT(Stacked Neural Network Ensemble for IoMT Security),an AI-driven IDS framework designed to secure dynamic IoMT environments.Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron(MLP),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM),the model optimizes data management and integration while ensuring system scalability and interoperability.Trained on the WUSTL-EHMS-2020 and IoT-Healthcare-Security datasets,SNN-IoMT surpasses existing IDS frameworks in accuracy,precision,and detecting novel threats.By addressing the primary challenges in AI-driven healthcare systems,including privacy,reliability,and ethical data management,our approach exemplifies the importance of AI to enhance security and trust in IoMT-enabled healthcare.
文摘In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
文摘Artifcial Intelligence (AI) is being applied to improve the efciency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the felds of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the rel-evanceof the input features to the output of an AI model. File fragment classifcation is one of the vital issues of fle carving in Cyber Forensics (CF) and becomes challenging when the flesystem metadata is missing. Other major challenges it faces are: proliferation of fle formats, fle embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classifcation of fle fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a fle fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize fle fragment classifcation. To improve multinomial classifcation, a Multilayer Per-ceptronmodel is developed and optimized with fve hidden layers, each layer with i × n neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 fle types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the frst efort of applying XAI in CF for optimizing fle fragment classifcation.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
基金funded by Scientific Research Deanship at University of Hail-Saudi Arabia through Project Number RG-23092.
文摘Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.
基金supported by the Large Group Project under grant number(RGP2/473/46).
文摘Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.
文摘The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.
文摘A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating thc cntire nonlinear system with the neural network （NN）, only the un-modeled part left over after the lincarization is approximated. Compared with the conventional linear observer, the observer provides more accurate estimation of the state. The state estimation error is proved to asymptotically approach zero with the Lyapunov method. The simulation result shows that the proposed observer scheme is effective and has a potential application ability in the fault detection and identification （FDI）, and the state estimation.
文摘The Chinese character library is one of the important data structures in the Chinese information Processing system.The behavior of the whole system depends directly on the reasonableness of design for its structure.This paper expounds the structures of RAM-based Chinese character libraries,static and dynamic ,The paper offers a descriptive method for this behavior and inquires into some algorithms related to the structures mentioned above.
