Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artific...Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artificial Neural Networks(ANN),Support Vector Regression(SVR),Multilayer Perceptron(MLP),and hybrid,along with fuzzy logic tools,were applied to predict the different properties like wavelength at maximum intensity(444 nm),crystallite size(17.50 nm),and optical bandgap(2.85 eV).While some other properties,such as energy density,power density,and charge transfer resistance,were also predicted with the help of datasets of 1000(80:20).In general,the energy parameters were predicted more accurately by hybrid models.The hydrothermal method was used to synthesize graphene oxide(GO)and zinc oxide(ZnO)nanocomposites.The increased surface area,conductivity,and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage.X-ray diffraction(XRD)confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO(12.8○)peaks.The scanning electron microscope(SEM)showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93μm.Energy-dispersive X-ray spectroscopy(EDX)confirmed the elemental composition,and Fouriertransform infrared spectroscopy(FTIR)revealed the impact of GO on functional groups and electrochemical behavior.Photoluminescence(PL)wavelength of(439 nm)and band gap of(2.81 eV)show that the material is suitable for energy applications in nanocomposites.Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis,characterization,fuzzy logic,and machine learning in this work.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find ...The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find it useful to have a tool that enables them to associate the faces in fiont of them with the account names they know. This paper proposes a method that enables a person to identify the account name of the person （＂target＂） in front of him/her using a smartphone. The attendees to a meeting exchange their identifiers （i.e., the account name） and GPS information using smartphones. When the user points his/her smartphone towards a target, the target＇s identifier is displayed near the target＇s head on the camera screen using AR （augmented reality）. The position where the identifier is displayed is calculated from the differences in longitude and latitude between the user and the target and the azimuth direction of the target from the user. The target is identified based on this information, the face detection coordinates, and the distance between the two. The proposed method has been implemented using Android terminals, and identification accuracy has been examined through experiments.展开更多
Disaster recovery (DR) and business continuity (BC) have been important areas of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business continuity, a fir...Disaster recovery (DR) and business continuity (BC) have been important areas of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business continuity, a firm must be able to recover from both man-made and natural disasters. This is especially true for maintaining and recovering the lifeline of the organization and its data. Although the literature has discussed the importance of disaster recovery and business continuity, there is not much known about how Information System Data Analytics Resilience (ISDAR) and the organization’s ability to recover from lost information. In this research, we take a step in this direction and analyze the relationship of IS personnel expertise on ISDAR and investigate Information System (IS) personnel understanding of the firm’s competitive priorities, IS Personnel understanding of business policies and objectives, IS personnel’s ability to solve business problems, IS personnel initiatives in changing business processes and their determination and attentiveness to focus on achieving confident leadership in data and analytics resilience. We collected data through a survey of IS and business managers from 302 participants. Our results show that there is evidence to support our hypothesis and that there may indeed be a relationship between these variables.展开更多
Purpose:This paper presents a new semi-automatic methodology for identifying inter-actor relationships by discerning viewpoints in non-social,political textual corpora.Although previous research has successfully disce...Purpose:This paper presents a new semi-automatic methodology for identifying inter-actor relationships by discerning viewpoints in non-social,political textual corpora.Although previous research has successfully discerned viewpoints,biases,and affiliations based on textual features,the task of relationship analysis in the absence of interactional data remains unaddressed.Design/methodology/approach:We introduce a new paradigm for topic representation as a dynamic,continuous,multi-viewpoint spectrum based on the representation of viewpoints as vectors that capture common topical themes.As a proof of concept,we applied this paradigm to scrutinize the inter-state relationships reflected in the speeches of the UN General Assembly Debate Corpus(UNGDC).Findings:The proposed paradigm effectively identifies discursive trends in UNGDC.Our analysis reveals common attitudes towards the topic and their prominence among different groups of actors and facilitates the analysis of relationships between actors through a quantitative representation of viewpoint similarity.The method also successfully captured temporal shifts in viewpoints and overall discourse trends,correlating with major geopolitical events.Research limitations:One limitation of this study is the method’s sensitivity to data sparsity,which can skew viewpoint representations in cases of low topic involvement.Practical implications:The proposed paradigm can be utilized by scholars in political science and other domains as a tool for semi-automated unsupervised textual analysis of various non-social textual sources,enabling the discovery of latent relationships between actors and the modeling of viewpoints in complex topics.Originality/value:This study presents a novel framework for unsupervised semi-automatic textual analysis of relationships in non-social corpora through a new approach for the representation of viewpoints as thematic vectors.展开更多
Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are ...Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.展开更多
Purpose:This paper introduces a novel perspective on academic excellence,focusing on a researcher’s consistent ability to produce highly-cited publications,and demonstrates its utility in distinguishing highachieving...Purpose:This paper introduces a novel perspective on academic excellence,focusing on a researcher’s consistent ability to produce highly-cited publications,and demonstrates its utility in distinguishing highachieving scientists compared to traditional scientometric indicators.Design/methodology/approach:We formulate this new perspective using a simple yet effective indicator termed the“Academic Midas Touch”(AMT).We then empirically analyze how AMT aligns with or diverges from popular scientometrics such as the H-index,i10-index,and citation counts.We further evaluate AMT’s effectiveness in identifying award-winning scientists,using these awards as a proxy for recognized academic excellence.Findings:Our empirical analysis reveals that the AMT offers a distinct measure of academic excellence that does not fully correlate with commonly used scientometrics.Furthermore,AMT favorably compares to these traditional metrics in its ability to accurately identify award-winning scientists.Research limitations:The AMT emphasizes short-term citation accumulation,thus it may overlook longterm dynamics such as“sleeping beauties”.Additionally,mindful parameter tuning and contextual interpretation within a specific discipline or a meaningful cohort of peers are necessary.Finally,the AMT does not seek to fully capture the multidimensional complexities of research excellence such as collaborations,mentoring,and societal impact.Practical implications:The findings suggest that AMT can serve as a valuable complementary tool for evaluating researchers,particularly in contexts such as excellence recognition,award nominations,grant applications,and faculty promotions,providing an under-explored view of a researcher’s consistent ability to produce highly-influential publications.Originality/value:This work introduces a unique conceptualization and measurement of academic excellence,shifting the focus from cumulative impact to the consistent propensity for producing highly-cited publications.The resulting AMT indicator provides a fresh perspective that complements existing scientometrics,offering a more nuanced understanding and recognition of research excellence.展开更多
A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing m...A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.展开更多
This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive ...This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.展开更多
The ZDPS-1A pico-satellites are the first satellites in China within the 1-10 kg mass range that are successfully operated on orbit. Unlike common pico-satellites, they are designed to be ＂larger but stronger＂ with ...The ZDPS-1A pico-satellites are the first satellites in China within the 1-10 kg mass range that are successfully operated on orbit. Unlike common pico-satellites, they are designed to be ＂larger but stronger＂ with more powerful platforms and unique payloads so as to bear a better promise for real applications. Through their space flight mission, the functionality and perform- ance of the two flight models are tested on orbit and validated to be mostly normal and in consistency with design and ground tests with only several inconforming occasions. Moreover, they have worked properly on orbit for one year so far, well exceed- ing their life expectancy of three months. Therefore, the space flight mission has reached all its goals, and verified that the design concept and the engineering process of the pico-satellites are sufficient in allowing them the desired functionality and perform- ance in, and the adaption to the launch procedure and the low-Earth orbit space environment. In the foreseeable future, the plat- form together with the design concept and the engineering process of the pico-satellites are expected to be applied to more com- plicated real space applications.展开更多
Based on test data from the hot forge experiments on Gleeble 1500, a Kumar type constitutive equation for 33Mn2V steel is established. Applying this constitutive equation in commercial FEM software of MSC/SuperForm 20...Based on test data from the hot forge experiments on Gleeble 1500, a Kumar type constitutive equation for 33Mn2V steel is established. Applying this constitutive equation in commercial FEM software of MSC/SuperForm 2005, the piercing process of 33Mn2V steel in Mannesmann mill is then simulated. The modeling results visualized the dynamic evolution of equivalent stress, especially inside the workpieee. It is shown that the non-uniform distribu- tion of stress on the internal and external surface of the workpiece is a distinct characteristic of processing tube pierc- ing. The numerical model was verified by comparing the values of calculated force parameters of the piercing process with those measured in laboratory eonditions. And it shows that the Kumar-type constitutive relationship meets the practical needs.展开更多
The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new...The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure.Compared with the typical strategy proposed in Motter–Lai(ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks.Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.展开更多
This paper studies the global stabilization problem by an output controller for a family of uncertain nonlinear systems satisfying some relaxed triangular-type conditions and with dynamics which may not be exactly kno...This paper studies the global stabilization problem by an output controller for a family of uncertain nonlinear systems satisfying some relaxed triangular-type conditions and with dynamics which may not be exactly known. Using a feedback domination design method, we explicitly construct a dynamic output compensator which globally stabilizes such an uncertain nonlinear system. The usefulness of our result is illustrated with an example.展开更多
In low earth orbit (LEO) satellite or missile communication scenarios, signals may experience extremely large Doppler shifts and have short visual time. Thus, direct sequence spread spectrum (DSSS) systems should be a...In low earth orbit (LEO) satellite or missile communication scenarios, signals may experience extremely large Doppler shifts and have short visual time. Thus, direct sequence spread spectrum (DSSS) systems should be able to achieve acquisition in a very short time in spite of large Doppler frequencies. However, the traditional methods cannot solve it well. This work describes a new method that uses a differential decoding technique for Doppler mitigation and a batch process of FFT (fast Fourier transform) and IFFT (invert FFT) for the purpose of parallel code phase search by frequency domain correlation. After the code phase is estimated, another FFT process is carried out to search the Doppler frequency. Since both code phase and Doppler frequency domains are searched in parallel, this architecture can provide acquisition fifty times faster than conventional FFT methods. The performance in terms of the probability of detection and false alarm are also analyzed and simulated, showing that a signal-to-noise ratio (SNR) loss of 3 dB is introduced by the differential decoding. The proposed method is an efficient way to shorten the acquisition time with slightly hardware increasing.展开更多
For direct sequence spread spectrum （DSSS） receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression （FDIS）. The key is...For direct sequence spread spectrum （DSSS） receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression （FDIS）. The key issue of this process is how to determine a threshold to eliminate interference in the frequency domain, which has been extensively studied. However, these previous methods are tedious or very complex. A simple and ef- ficient algorithm based on medians is proposed. The elimination threshold is only related to the median by a scale factor, which can be obtained by the numerical analysis. Simulation results show that the algorithm provides excellent narrow-band interfer- ence suppression while only slightly degrading the signal-to-noise ratio （SNR）. A one-pass algorithm using logarithmic segmentation is further derived to estimate medians with low computational complexity. Finally, the FDIS is implemented in a field programmable gate array （FPGA） of Xilinx. Experiments are carried out by connecting the FDIS FPGA to a DSSS receiver, and the results show that the receiver has an effective countermeasure for a 60 dB interference-to-signal ratio （ISR）.展开更多
The effects of Pr on the structure and magnetic properties of PrxFe60.5-xPt39.5 alloys （x = 0, 0.5, 1.0, and 1.5） were investigated. X-ray diffraction data indicated that the phase transition temperature of FePt bas...The effects of Pr on the structure and magnetic properties of PrxFe60.5-xPt39.5 alloys （x = 0, 0.5, 1.0, and 1.5） were investigated. X-ray diffraction data indicated that the phase transition temperature of FePt based alloys from disordered face-centered-cubic to ordered face-centered-tetragonal cubic decreases with the increase in Pr concentration. Pr plays the role of a grain refiner and it can enhance the exchange coupling between soft magnetic phase and hard magnetic phase. The results indicate that the replacement of Fe by Pr can significantly improve the remanence and coercivity of the Fe60.5Pt39.5 alloy. These results can be explained on the basis of phase transformation and microstructure. Both the remanence ratio and coercivity of the FePt based alloy as a function of the Pr content are increased by the optimum addition of 0.5 at.% Pr.展开更多
The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved ...The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.展开更多
Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong ...Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.展开更多
An SF6/CF4 cyclic reactive-ion etching （RIE） method is proposed to suppress the surface roughness and to opti- mize the morphology of Ge fin, aiming at the fabrication of superior Ge FinFETs for future CMOS technolo...An SF6/CF4 cyclic reactive-ion etching （RIE） method is proposed to suppress the surface roughness and to opti- mize the morphology of Ge fin, aiming at the fabrication of superior Ge FinFETs for future CMOS technologies. The surface roughness of the Ge after RIE can be sufficiently reduced by introducing SF6-O2 etching steps into the CF4-O2 etching process, while maintaining a relatively large ratio of vertical etching over horizontal etching of the Ge. As a result, an optimized rms roughness of 0.9nm is achieved for Ge surfaces after the SF6/CF4 cyclic etching with a ratio of greater than four for vertical etching over horizontal etching of the Ge, by using a proportion of 60% for SF6-O2 etching steps.展开更多
基金extend their gratitude to the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,for funding the publication of this work under the Ambitious Researcher program(Project No.KFU253806).
文摘Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artificial Neural Networks(ANN),Support Vector Regression(SVR),Multilayer Perceptron(MLP),and hybrid,along with fuzzy logic tools,were applied to predict the different properties like wavelength at maximum intensity(444 nm),crystallite size(17.50 nm),and optical bandgap(2.85 eV).While some other properties,such as energy density,power density,and charge transfer resistance,were also predicted with the help of datasets of 1000(80:20).In general,the energy parameters were predicted more accurately by hybrid models.The hydrothermal method was used to synthesize graphene oxide(GO)and zinc oxide(ZnO)nanocomposites.The increased surface area,conductivity,and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage.X-ray diffraction(XRD)confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO(12.8○)peaks.The scanning electron microscope(SEM)showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93μm.Energy-dispersive X-ray spectroscopy(EDX)confirmed the elemental composition,and Fouriertransform infrared spectroscopy(FTIR)revealed the impact of GO on functional groups and electrochemical behavior.Photoluminescence(PL)wavelength of(439 nm)and band gap of(2.81 eV)show that the material is suitable for energy applications in nanocomposites.Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis,characterization,fuzzy logic,and machine learning in this work.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find it useful to have a tool that enables them to associate the faces in fiont of them with the account names they know. This paper proposes a method that enables a person to identify the account name of the person （＂target＂） in front of him/her using a smartphone. The attendees to a meeting exchange their identifiers （i.e., the account name） and GPS information using smartphones. When the user points his/her smartphone towards a target, the target＇s identifier is displayed near the target＇s head on the camera screen using AR （augmented reality）. The position where the identifier is displayed is calculated from the differences in longitude and latitude between the user and the target and the azimuth direction of the target from the user. The target is identified based on this information, the face detection coordinates, and the distance between the two. The proposed method has been implemented using Android terminals, and identification accuracy has been examined through experiments.
文摘Disaster recovery (DR) and business continuity (BC) have been important areas of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business continuity, a firm must be able to recover from both man-made and natural disasters. This is especially true for maintaining and recovering the lifeline of the organization and its data. Although the literature has discussed the importance of disaster recovery and business continuity, there is not much known about how Information System Data Analytics Resilience (ISDAR) and the organization’s ability to recover from lost information. In this research, we take a step in this direction and analyze the relationship of IS personnel expertise on ISDAR and investigate Information System (IS) personnel understanding of the firm’s competitive priorities, IS Personnel understanding of business policies and objectives, IS personnel’s ability to solve business problems, IS personnel initiatives in changing business processes and their determination and attentiveness to focus on achieving confident leadership in data and analytics resilience. We collected data through a survey of IS and business managers from 302 participants. Our results show that there is evidence to support our hypothesis and that there may indeed be a relationship between these variables.
基金part of the research project “What Are States Talking About?”(ISF Grant 2109/19),funded by the Israeli Science Foundation。
文摘Purpose:This paper presents a new semi-automatic methodology for identifying inter-actor relationships by discerning viewpoints in non-social,political textual corpora.Although previous research has successfully discerned viewpoints,biases,and affiliations based on textual features,the task of relationship analysis in the absence of interactional data remains unaddressed.Design/methodology/approach:We introduce a new paradigm for topic representation as a dynamic,continuous,multi-viewpoint spectrum based on the representation of viewpoints as vectors that capture common topical themes.As a proof of concept,we applied this paradigm to scrutinize the inter-state relationships reflected in the speeches of the UN General Assembly Debate Corpus(UNGDC).Findings:The proposed paradigm effectively identifies discursive trends in UNGDC.Our analysis reveals common attitudes towards the topic and their prominence among different groups of actors and facilitates the analysis of relationships between actors through a quantitative representation of viewpoint similarity.The method also successfully captured temporal shifts in viewpoints and overall discourse trends,correlating with major geopolitical events.Research limitations:One limitation of this study is the method’s sensitivity to data sparsity,which can skew viewpoint representations in cases of low topic involvement.Practical implications:The proposed paradigm can be utilized by scholars in political science and other domains as a tool for semi-automated unsupervised textual analysis of various non-social textual sources,enabling the discovery of latent relationships between actors and the modeling of viewpoints in complex topics.Originality/value:This study presents a novel framework for unsupervised semi-automatic textual analysis of relationships in non-social corpora through a new approach for the representation of viewpoints as thematic vectors.
文摘Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.
文摘Purpose:This paper introduces a novel perspective on academic excellence,focusing on a researcher’s consistent ability to produce highly-cited publications,and demonstrates its utility in distinguishing highachieving scientists compared to traditional scientometric indicators.Design/methodology/approach:We formulate this new perspective using a simple yet effective indicator termed the“Academic Midas Touch”(AMT).We then empirically analyze how AMT aligns with or diverges from popular scientometrics such as the H-index,i10-index,and citation counts.We further evaluate AMT’s effectiveness in identifying award-winning scientists,using these awards as a proxy for recognized academic excellence.Findings:Our empirical analysis reveals that the AMT offers a distinct measure of academic excellence that does not fully correlate with commonly used scientometrics.Furthermore,AMT favorably compares to these traditional metrics in its ability to accurately identify award-winning scientists.Research limitations:The AMT emphasizes short-term citation accumulation,thus it may overlook longterm dynamics such as“sleeping beauties”.Additionally,mindful parameter tuning and contextual interpretation within a specific discipline or a meaningful cohort of peers are necessary.Finally,the AMT does not seek to fully capture the multidimensional complexities of research excellence such as collaborations,mentoring,and societal impact.Practical implications:The findings suggest that AMT can serve as a valuable complementary tool for evaluating researchers,particularly in contexts such as excellence recognition,award nominations,grant applications,and faculty promotions,providing an under-explored view of a researcher’s consistent ability to produce highly-influential publications.Originality/value:This work introduces a unique conceptualization and measurement of academic excellence,shifting the focus from cumulative impact to the consistent propensity for producing highly-cited publications.The resulting AMT indicator provides a fresh perspective that complements existing scientometrics,offering a more nuanced understanding and recognition of research excellence.
基金Project supported by the National Natural Science Foundation of China (No 60802013)the Natural Science Foundation of Zhe-jiang Province, China (No Y106574)
文摘A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.
基金supported in part by the National Natural Science Foundation of China(61301228,61371091)the Fundamental Research Funds for the Central Universities(3132014212)
文摘This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.
基金Supported by National Nature Science Foundation of China （61074068, 60774009, 61034007）, and the Research Fund for the Doc- toral Program of Chinese Higher Education （200804220028）
基金National Natural Science Foundation of China (60904090)
文摘The ZDPS-1A pico-satellites are the first satellites in China within the 1-10 kg mass range that are successfully operated on orbit. Unlike common pico-satellites, they are designed to be ＂larger but stronger＂ with more powerful platforms and unique payloads so as to bear a better promise for real applications. Through their space flight mission, the functionality and perform- ance of the two flight models are tested on orbit and validated to be mostly normal and in consistency with design and ground tests with only several inconforming occasions. Moreover, they have worked properly on orbit for one year so far, well exceed- ing their life expectancy of three months. Therefore, the space flight mission has reached all its goals, and verified that the design concept and the engineering process of the pico-satellites are sufficient in allowing them the desired functionality and perform- ance in, and the adaption to the launch procedure and the low-Earth orbit space environment. In the foreseeable future, the plat- form together with the design concept and the engineering process of the pico-satellites are expected to be applied to more com- plicated real space applications.
基金Item Sponsored by Tianjin Natural Science Foundation of China(06YFJ MJC02200,11JCZDJC22600)
文摘Based on test data from the hot forge experiments on Gleeble 1500, a Kumar type constitutive equation for 33Mn2V steel is established. Applying this constitutive equation in commercial FEM software of MSC/SuperForm 2005, the piercing process of 33Mn2V steel in Mannesmann mill is then simulated. The modeling results visualized the dynamic evolution of equivalent stress, especially inside the workpieee. It is shown that the non-uniform distribu- tion of stress on the internal and external surface of the workpiece is a distinct characteristic of processing tube pierc- ing. The numerical model was verified by comparing the values of calculated force parameters of the piercing process with those measured in laboratory eonditions. And it shows that the Kumar-type constitutive relationship meets the practical needs.
文摘The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure.Compared with the typical strategy proposed in Motter–Lai(ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks.Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.
基金This work was supported in part by the Japanese Ministry of Education, Science, Sports and Culture under both the GrantAid of General Scientific Research (No. C-15560387)the 21st Century Center of Excellence (COE) Program.
文摘This paper studies the global stabilization problem by an output controller for a family of uncertain nonlinear systems satisfying some relaxed triangular-type conditions and with dynamics which may not be exactly known. Using a feedback domination design method, we explicitly construct a dynamic output compensator which globally stabilizes such an uncertain nonlinear system. The usefulness of our result is illustrated with an example.
基金Project(60904090) supported by the National Natural Science Foundation of China
文摘In low earth orbit (LEO) satellite or missile communication scenarios, signals may experience extremely large Doppler shifts and have short visual time. Thus, direct sequence spread spectrum (DSSS) systems should be able to achieve acquisition in a very short time in spite of large Doppler frequencies. However, the traditional methods cannot solve it well. This work describes a new method that uses a differential decoding technique for Doppler mitigation and a batch process of FFT (fast Fourier transform) and IFFT (invert FFT) for the purpose of parallel code phase search by frequency domain correlation. After the code phase is estimated, another FFT process is carried out to search the Doppler frequency. Since both code phase and Doppler frequency domains are searched in parallel, this architecture can provide acquisition fifty times faster than conventional FFT methods. The performance in terms of the probability of detection and false alarm are also analyzed and simulated, showing that a signal-to-noise ratio (SNR) loss of 3 dB is introduced by the differential decoding. The proposed method is an efficient way to shorten the acquisition time with slightly hardware increasing.
基金supported by the National Natural Science Foundation of China(60904090)
文摘For direct sequence spread spectrum （DSSS） receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression （FDIS）. The key issue of this process is how to determine a threshold to eliminate interference in the frequency domain, which has been extensively studied. However, these previous methods are tedious or very complex. A simple and ef- ficient algorithm based on medians is proposed. The elimination threshold is only related to the median by a scale factor, which can be obtained by the numerical analysis. Simulation results show that the algorithm provides excellent narrow-band interfer- ence suppression while only slightly degrading the signal-to-noise ratio （SNR）. A one-pass algorithm using logarithmic segmentation is further derived to estimate medians with low computational complexity. Finally, the FDIS is implemented in a field programmable gate array （FPGA） of Xilinx. Experiments are carried out by connecting the FDIS FPGA to a DSSS receiver, and the results show that the receiver has an effective countermeasure for a 60 dB interference-to-signal ratio （ISR）.
基金This work is financially supported by the National Natural Science Foundation of China (Nos.50261002 and 10574049).
文摘The effects of Pr on the structure and magnetic properties of PrxFe60.5-xPt39.5 alloys （x = 0, 0.5, 1.0, and 1.5） were investigated. X-ray diffraction data indicated that the phase transition temperature of FePt based alloys from disordered face-centered-cubic to ordered face-centered-tetragonal cubic decreases with the increase in Pr concentration. Pr plays the role of a grain refiner and it can enhance the exchange coupling between soft magnetic phase and hard magnetic phase. The results indicate that the replacement of Fe by Pr can significantly improve the remanence and coercivity of the Fe60.5Pt39.5 alloy. These results can be explained on the basis of phase transformation and microstructure. Both the remanence ratio and coercivity of the FePt based alloy as a function of the Pr content are increased by the optimum addition of 0.5 at.% Pr.
基金Projects(51634010,51676211) supported by the National Natural Science Foundation of ChinaProject(2017SK2253) supported by the Key Research and Development Program of Hunan Province,China
文摘The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.
文摘Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.
基金Supported by the National Basic Research Program of China under Grant No 2011CBA00607the National Natural Science Foundation of China under Grant No 61376097+1 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant No LR14F040001Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No20130091110025
文摘An SF6/CF4 cyclic reactive-ion etching （RIE） method is proposed to suppress the surface roughness and to opti- mize the morphology of Ge fin, aiming at the fabrication of superior Ge FinFETs for future CMOS technologies. The surface roughness of the Ge after RIE can be sufficiently reduced by introducing SF6-O2 etching steps into the CF4-O2 etching process, while maintaining a relatively large ratio of vertical etching over horizontal etching of the Ge. As a result, an optimized rms roughness of 0.9nm is achieved for Ge surfaces after the SF6/CF4 cyclic etching with a ratio of greater than four for vertical etching over horizontal etching of the Ge, by using a proportion of 60% for SF6-O2 etching steps.
