In the traditional fringe projection profilometry system,the projector and the camera light center are both spatially virtual *** spatial position relationships specified in the model are not easy to obtain,leading to...
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In the traditional fringe projection profilometry system,the projector and the camera light center are both spatially virtual *** spatial position relationships specified in the model are not easy to obtain,leading to inaccurate system parameters and affectingmeasurement *** paper proposes a method for solving the system parameters of the fringe projection profilometry system,and the spatial position of the camera and projector can be adjusted in accordance with the obtained calibration *** steps are as follows:First,in accordance with the conversion relationship of the coordinate system in the calibration process,the calculation formula of the vertical distance from the camera light center to the reference plane and the calculation formula of the distance between the projector and the camera light center are given ***,according to the projector calibration principle,the position of the projector light axis perpendicular to the reference plane is gained by comparing the parallel relationship between the reference plane coordinate system and the projector coordinate system’s ***,in order to fulfill the position restriction that the line between the projector light center and the camera light center must be parallel to the reference plane,the camera’s spatial location is adjusted so that the vertical distance between it and the reference plane tends to that between the projector light center and the reference *** finally,the three-dimensional(3D)reconstruction of the target object can be finished using the phase height model’s system parameters once the aforementioned position limitations are put into *** results demonstrate that the method improves the measurement accuracy,and verifies that it is effective and available in 3D shape measurement.
This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmi...
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This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.
Obfuscation techniques are frequently used in malicious programs to evade detection. However, current effective methods often require much memory space during training. This paper proposes a machine-learning-based sol...
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A solid electrolyte interphase(SEI)with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal ***,it is difficult to form such a SEI directly from an *** this work,a ...
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A solid electrolyte interphase(SEI)with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal ***,it is difficult to form such a SEI directly from an *** this work,a molecular crowding effect is based on the introduction of Zn(OTF)_(2)and Zn(ClO_(4))_(2)to 2 mol/L ZnSO_(4)*** and experiments indicate that the Zn(OTF)_(2)and Zn(ClO_(4))_(2)not only create a molecularly crowded electrolyte environment to promote the interaction of Zn^(2+)and OTF^(-),but also participate in the reduction to construct a robust and high ionic-conductive SEI,thus promoting metal zinc deposition to the(002)crystal *** this molecular crowding electrolyte,a high current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn as the anode for over 1000 *** in a temperature environment of-10℃,a current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn for over 200 ***//Bi_(2)S_(3)/VS4@C cells achieve a CE rate of up to 99.81%over 1000 ***,the utilization of a molecular crowding electrolyte is deemed a highly effective approach to fabricating a sophisticated SEI for a zinc anode.
The choice of cathode and anode materials for electrochromic devices plays a key role in the performance of electrochromic smart *** this research,WO_(3)/Ag and TiO_(2)/NiO composite thin films were separately prepare...
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The choice of cathode and anode materials for electrochromic devices plays a key role in the performance of electrochromic smart *** this research,WO_(3)/Ag and TiO_(2)/NiO composite thin films were separately prepared by the hydrothermal method combined with *** electrochromic properties of the single WO_(3) thin film were optimized,and TiO_(2)/NiO composite films showed better electrochromic performance than that of the single NiO ***_(3)/Ag and TiO_(2)/NiO composite films with excellent electrochromic properties were respectively chosen as the cathode and the anode to construct a WO_(3)/Ag‒TiO_(2)/NiO electrochromic *** response time(tc=4.08 s,tb=1.08 s),optical modulation range(35.91%),and coloration efficiency(30.37 cm^(2)·C^(-1))of this electrochromic device are better than those of WO_(3)-NiO and WO_(3)/Ag-NiO electrochromic *** work provides a novel research idea for the performance enhancement of electrochromic smart windows.
In this work,the upconversion luminescence（UCL） and temperature-sensing properties of SrGd2O4phosphors doped with Er3+,Yb3+were *** UCL performance was studied by adjusting the doping concentrations of Er3+,Yb3+.It ...
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In this work,the upconversion luminescence（UCL） and temperature-sensing properties of SrGd2O4phosphors doped with Er3+,Yb3+were *** UCL performance was studied by adjusting the doping concentrations of Er3+,Yb3+.It can be found that the intensity of red UCL is effectively improved by doping Yb3+*** band structures of SrGd2O4,SrGd2O4:5 mol%Er3+,and SrGd2O4:5 mol%Er3+,15 mol%Yb3+were calculated using the density functional theory（DFT） to verify the experimental *** thermal sensitivity of SrGd2O4:5 mol%Er3+,15 mol%Yb3+phosphors was studied using the novel fluorescence intensity ratio of non-thermally coupled energy levels（NTCL-FIR） technique based on the2H11/2and4F9/2energy *** analytical results reveal that the maximum values of Srare 1.69%/K（300 K）,1.18%/K（300 K） and 1.47%/K（300 K） respectively under 915,980 and 1550 nm *** Srvalues of novel NTCL-FIR are compared with that of thermal coupling of energy level（TCL） and other studies in the same *** results show that the thermometry sensitivity of red-emitting UCL materials can be enhanced by using the novel NTCL-FIR *** addition,the intense red UCL of SrGd2O4:Er3+,Yb3+phosphors have the advantage of deeply penetrating biological *** fore,the combination of novel NTCL-FIR and red-emitting SrGd2O4:Er3+,Yb3+phosphors could be conducive to temperature monitoring in vivo.
Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts...
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Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.
The paper presents a new calculation similarity method. It's called the constrained similarity method. Compared with the traditional cosine similarity and dot product method, the proposed method can maintain the s...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based *** researchers used data preprocessing techniques such as feature selection and normalization to overcome such *** most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider ***,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis ***,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,*** IDS models were implemented using the full and feature-selected copies of the datasets with and without *** models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art *** forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 *** RF models also achieved an excellent performance compared to recent *** results show that normalization and feature selection positively affect IDS ***,while feature sel
Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er soun...
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Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.
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