In this work,an adaptive event-triggered control approach is developed for a virtual player(VP) to generate the human-like trajectories in the mirror game,a simple yet effective paradigm for studying interpersonal ***...
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In this work,an adaptive event-triggered control approach is developed for a virtual player(VP) to generate the human-like trajectories in the mirror game,a simple yet effective paradigm for studying interpersonal *** taking into account individual motor signature,an online control algorithm is designed to produce joint improvised motions with a human player or another virtual player while exhibiting some desired kinematic *** the proposed control algorithm,the control actions can be adaptively switched according to the movement status of ***,stability analysis of the VP model driven by the feedback controller is ***,the proposed control approach is validated by matching the experimental data.
This paper studies a rate-based TCP-Friendly Rate control（TFRC） mechanism, which is widely used in multimedia real-time services, and analyzes its basic workflow, throughput model and calculation of key parameters. ...
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This paper studies a rate-based TCP-Friendly Rate control（TFRC） mechanism, which is widely used in multimedia real-time services, and analyzes its basic workflow, throughput model and calculation of key parameters. In order to meet the demand of real-time service for network transmission, the bandwidth-delay product（BDP） is applied to the congestion control of TFRC as the network congestion warning signal, and the improved TFRC algorithm is proposed. The simulation results show that this method can achieve good results in the network transmission of real-time services, and its friendliness and smoothness are improved to a certain extent.
Determining the position and orientation of the camera is a crucial problem with the rapidly developing technology in visual Simultaneous Localization and Mapping(SLAM),augmented reality and 3 D *** the Pn P(perspecti...
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Determining the position and orientation of the camera is a crucial problem with the rapidly developing technology in visual Simultaneous Localization and Mapping(SLAM),augmented reality and 3 D *** the Pn P(perspective-n-point) problem is an effective method to calculate the pose of the camera and is also the most widely used method in many *** this paper,the methods for Pn P problem,including special Pn P problem and general Pn P problem are summarized ***,due to importance of performing Pn P methods in practical applications,ability to handle outliers for Pn P methods is ***,the main problems of the current researches on Pn P problem are presented.
In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on contextual information fusion to solve the problem of rea...
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In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on contextual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network *** experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow contextual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images.
3D formation drillability field is crucial for drilling optimization and control due to its vital role in describing the spatial formation environment. Conventional geostatistical and machine learning methods are intr...
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3D formation drillability field is crucial for drilling optimization and control due to its vital role in describing the spatial formation environment. Conventional geostatistical and machine learning methods are introduced to establish the ***, the modeling accuracy should be further improved to meet the high-level requirement of drilling engineering. In this paper, a novel deep learning-based spatial modeling method is proposed for 3D formation drillability field. First of all, the drilling process and its characteristics are described and analyzed. After that, long short-term memory（LSTM）, a deep learning method is proposed to establish the 3D formation drillability field model. The inputs of the model are the ground and depth coordinates and the output of the model is the formation drillability. Finally, 3D modeling and final test experiments are executed and the drilling data are from Xujiawei area, Northeast China. The results show the effectiveness of proposed method in modeling accuracy compared with four conventional methods（Random forest, Support vector regression, Scattered Interpolation, and Kriging）.
In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslid...
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In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%.
Alarm systems are commonly deployed in modern industrial facilities for monitoring of process ***,due to the presence of nuisance alarms and alarm floods,the efficiency of many real alarm systems is much *** that prob...
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Alarm systems are commonly deployed in modern industrial facilities for monitoring of process ***,due to the presence of nuisance alarms and alarm floods,the efficiency of many real alarm systems is much *** that problems,such as chattering alarms,redundant alarms,and false alarms,can be well solved,alarm floods are still difficult to *** an alarm flood situation,a number of alarms appear and overwhelm the plant operators;as a result,the operator may overlook the critical alarms and thus the situation may get *** paper studies the root cause analysis of alarm floods,and proposes a Few-Shot Learning(FSL) approach to diagnose faults under alarm flood situations based on alarm event sequences extracted from alarm and event(A&E) *** with the existing methods based on continuous-valued process data,the proposed method does not need to conduct feature selection or dimension reduction,and thus is more straightforward and computationally *** addition,the proposed method only requires a few shots of faulty data to train a fault diagnosis model,and thus shows better applicability in *** experimental results on the Vinyl Acetate Monomer(VAM) benchmark dataset showed the superiority of the proposed model against the state-of-the-art approaches.
In modern complex industrial processes,due to poor rationalization of alarm systems and the complexity of process interconnections,alarm floods are commonly *** floods are also identified as the main causes of many in...
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In modern complex industrial processes,due to poor rationalization of alarm systems and the complexity of process interconnections,alarm floods are commonly *** floods are also identified as the main causes of many industrial *** valid approach to deal with alarm floods is to mine meaningful alarm sequential patterns from alarm *** identified patterns can help to analyze root causes or to configure dynamic alarming *** this paper,a method based on the combination of ClaSP and Top-K is proposed to mine interesting alarm sequential patterns from historical alarm *** contributions of this study are twofold:1) A pattern mining approach is adapted to mine interesting patterns from alarm flood sequences;2) A pattern compression strategy is proposed to reduce pattern redundancy.A case study is presented to demonstrate the effectiveness of the proposed method.
In recent years,streaming media services have been widely used in data room management *** order to record the user operations,host machine alarms and reminder in real time,target detection algorithms for streaming me...
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In recent years,streaming media services have been widely used in data room management *** order to record the user operations,host machine alarms and reminder in real time,target detection algorithms for streaming media have played an important *** target detection methods,limited by detection speed,are not suitable for such real-time ***,in this paper,we applies SSD（Single Shot MultiBox Detector） algorithm to data room management ***,we constructs a host operating image data set,and then perform data amplification by adding noise and ***,the SSD model is used to train a ***,after obtaining the target detection results,the NMS（Non-Maximum Suppression）algorithm is used to avoid the redundant detection *** the same time,some improvement measures are put forward to solve the problem of small target,leading to the difficulty of feature extraction and low detection *** experimental results demonstrate that the model can detect and track the target better in the video and meet the requirements of the real-time performance and accuracy of the system.
Rate of penetration(ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages(...
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Rate of penetration(ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages(data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques(Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods(response surface method and support vector regression).
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