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Unlocking small gas field development in the Southern North Sea with the application of modern seismic imaging technologies

Unlocking small gas field development in the Southern North ...

引用

SPE Offshore Europe conference and Exhibition, OE 2013

作者： Darnet, Mathieu Van Loevezijn, Peter Hollman, Frans Wervelman, Rob Bruehl, Matthias Alperin, Juan Pi Shipp, Richard NAM B.v United Kingdom Shell Project and Technology B.v United Kingdom Shell U.K. Limited United Kingdom

ISBN: (纸本)9781629930992

The economics of small gas field developments in the Southern North Sea away from any existing infrastructure is particularly sensitive to subsurface uncertainties. Applying latest geophysical approaches is a key aspect of obtaining sufficient grip on these parameters and ensuring robust project economics. For the Alpha field, the structural uncertainty due to poor seismic imaging was identified as critical and therefore a dedicated seismic imaging project was undertaken. It involved re-processing the existing seismic data with the latest velocity modeling and imaging technologies, suchas Reverse Time Migration. It led to a clear improvement of the seismic character at objective level as well as a more consistent depth image. As a consequence, the expected volume of gas in place increased by 50% and additional reservoir targets were identified, considerably improving the project economics. In addition, a High Definition 3D seismic image of the shallow subsurface was for the first time successfully created over the area to assess any potential geohazards and ensured that the proposed and selected development concept had mitigations against these hazards and their consequences. Copyright 2013, Society of Petroleum Engineers.

关键词： Gas industry

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Fault line selection for small current grounding system based on ICEEMDAN and improved VGG 8

Fault line selection for small current grounding system base...

引用

8th IEEE International conference on Energy Internet, ICEI 2024

作者： Liu, Yadong Liu, Naiyu Zhang, Hao Cui, Yunhai Shi, Zhe Li, Chenggang Hu, Xiaochen Zhang, Dahai State Grid Jilin Electric Power Co. Ltd. Electric Power Research Institute Changchun China Beijing Jiaotong University School of Electrical Engineering Beijing China State Grid Jilin Electric Power Co. Ltd. Changchun China

ISBN: (纸本)9798331523558

In order to solve the problem of low accuracy of single-phase grounding fault line selection method in traditional low-current grounding system, this paper proposes a single-phase grounding fault line selection method for small-current grounding distribution network based on improved VGG. Firstly, the original signal is transformed into an Intrinsic Mode Function component by using the ICEEMDAN algorithm, and then the entropy value of each IMF component is calculated by using the distributed entropy algorithm and differentiated processing, and the processed IMF component is reconstructed to highlight the frequency domain characteristics and amplitude characteristics of the original data, and the influence of noise is removed to a certain extent. The processed zero-sequence current data is converted into a two-dimensional image by the Gramian Angular Field algorithm, and on the basis of retaining the data features, a single-phase grounding fault dataset is prepared, and the fault characteristics are more prominent, which is convenient for neural network processing. Then, the line selection results were obtained by improving the training iterative optimization parameters. Finally, an experimental platform was built by using Simulink, and the proposed algorithm was compared with two line selection methods. Experimental results show that the proposed algorithm has higher line selection accuracy and better noise immunity. © 2024 IEEE.

关键词： Electric grounding

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Traffic sign recognition in complex environments based on YOLOv5 2

Traffic sign recognition in complex environments based on YO...

引用

2nd International conference on Image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Feng, Fei Hu, Yu KaiLi University College of Big Data Engineering KaiLi China

ISBN: (纸本)9798350331417

To address the issue of difficulty in recognizing traffic signs in complex environments and the lack of accuracy, a traffic sign recognition model based on YOLOv5 in complex environments is proposed. Firstly, optimize the target detection layer and further reduce the size of the detection head to enhance the model's ability to extract features from small targets. Then, a cross-connection network is introduced into the network layer to effectively reduce the loss of feature information during network transmission. Finally, adaptive spatial feature fusion technology is used to strengthen the fusion of multi-scale feature information. Experimental results show that the traffic sign recognition in complex environments has reached 83.6%, 76.0%, and 82.2% in Precision, Recall and mAP@0.5 respectively. Compared with YOLOv5s, these three indicators have increased by 6.4%, 3.1%, and 4.7% respectively. Moreover, the model's parameter amount has decreased by 5.6%, making the model more lightweight. © 2023 IEEE.

关键词： adaptive spatial feature fusion complex environments optimize the target detection layer traffic sign recognition YOLOv5

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Distributed spatio-temporal multi-target association and tracking

Distributed spatio-temporal multi-target association and tra...

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IEEE International conference on Acoustics, Speech and signal processing

作者： G. Ren I. D. Schizas V. Maroulas Dept. of EE Univ. of Texas at Arlington Arlington TX USA

ISBN: (纸本)9781467369985

Particle filtering is combined with sparse matrix decomposition techniques to address the problem of tracking multiple targets using nonlinear sensor observations measuring signal strength. The unknown number of targets may be time-varying, while sensors are spatially scattered. Norm-one regularized matrix factorization is employed to decompose the sensing data covariance matrix into sparse factors whose support facilitates the task of associating the targets with sensor measurements. The novel sensors-to-targets association scheme is developed using distributed optimization which is further integrated with particle filtering mechanisms to perform accurate tracking. Numerical tests demonstrate the tracking superiority of the proposed algorithm over alternative approaches.

关键词： Particle filtering distributed processing multi-target tracking sensor-to-targets association Particle filters Distributed processing Target tracking tracking targets matrix decomposition signal Intensity variance covariance matrix Sensors

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Automatic Gait Phases Detection in Parkinson Disease: A Comparative Study 42

Automatic Gait Phases Detection in Parkinson Disease: A Comp...

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42nd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Castano-Pino, Yor J. Gonzalez, Maria C. Quintana-Pena, Valentina Valderrama, Jaime Munoz, Beatriz Orozco, Jorge Navarro (SM), Andres CENIT i2t Res Ctr Cali Colombia Univ ICESI Cali Colombia Stavanger Univ Hosp Stavanger Ctr Age Related Med SESAM Stavanger Norway Fdn Valle Lili Ctr Invest Clin CIC Cali Colombia Fdn Valle Lili Neurol Dept Cali Colombia

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

Background: Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases: stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases. Objective: In this paper, we compared two techniques: wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals. Methods: Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained by both methods. Results: PD group showed significant reductions in speed (wavelet p=0.001, peak detection p<0.001) and significantly greater swing (wavelet p=0.003, peak detection p=0.005) and stance times (wavelet p=0.003, peak detection p=0.004). Hamming distances showed small differences between the signals obtained by both methods (16 to 18 signal points). A very strong correlation (Spearman rho > 0.8, p<0.05) was found between the spatiotemporal variables obtained by each signal processing technique. Conclusion: Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.

关键词： Wavelet analysis Cameras Legged locomotion Wavelet transforms Spatiotemporal phenomena signal processing Correlation

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License plate detection based on improved YOLOv5 object detection 5

License plate detection based on improved YOLOv5 object dete...

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5th International conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Minghan Gao, Bin Liu, Shutian Liu, Zhengjun Heilongjiang University College of Data Science and Technology Harbin150086 China Harbin Institute of Technology School of Physics Harbin150001 China

ISBN: (纸本)9798350373820

License plate detection is an important task in intelligent transport systems, which has a wide range of applications in vehicle management and public safety. Aiming at the current problems of low accuracy and efficiency and poor generalization ability of number plate detection whether based on traditional image processing methods or deep learning methods, we propose a number plate detection model based on improved YOLOv5 target detection. Firstly, MobileViTv3 is used to replace the backbone network of YOLOv5, which makes the model lighter while ensures the detection accuracy and allows the fusion of more features, then WIoU and AconC are used to further improve the accuracy of the target detection, and finally the Biformer bidirectional attention mechanism is introduced, which can help the model to better capture the correlation between the targets and the regional features, thus improving the detection of small and occluded targets. The experimental results of the article under the CCPD dataset show that the improved YOLOv 5 network achieves a detection accuracy of 9 9. 8%, a recall rate of 9 9. 9%, and an average precision of 99.5%, which is significantly better than other number plate detection methods. © 2024 IEEE.

关键词： Object detection

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Automated Evaluation of Non-Native English Pronunciation Quality: Combining Knowledge- and data-Driven Features at Multiple Time Scales 16

Automated Evaluation of Non-Native English Pronunciation Qua...

引用

16th Annual conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Black, Matthew P. Bone, Daniel Skordilis, Zisis I. Gupta, Rahul Xia, Wei Papadopoulos, Pavlos Chakravarthula, Sandeep Nallan Xiao, Bo Van Segbroeck, Maarten Kim, Jangwon Georgiou, Panayiotis G. Narayanan, Shrikanth S. Univ Southern Calif Signal Anal & Interpretat Lab Los Angeles CA 90089 USA Univ Southern Calif Informat Sci Inst Marina Del Rey CA USA Behav Informatix LLC Los Angeles CA USA

ISBN: (纸本)9781510817906

Automatically evaluating pronunciation quality of non-native speech has seen tremendous success in both research and commercial settings, with applications in L2 learning. In this paper, submitted for the INTERSPEECH 2015 Degree of Nativeness Sub-Challenge, this problem is posed under a challenging cross corpora setting using speech data drawn from multiple speakers from a variety of language backgrounds (L1) reading different English sentences. Since the perception of non-nativeness is realized at the segmental and suprasegmental linguistic levels, we explore a number of acoustic cues at multiple time scales. We experiment with both data-driven and knowledge-inspired features that capture degree of nativeness from pauses in speech, speaking rate, rhythm/stress, and goodness of phone pronunciation. One promising finding is that highly accurate automated assessment can be attained using a small diverse set of intuitive and interpretable features. Performance is further boosted by smoothing scores across utterances from the same speaker;our best system significantly outperforms the challenge baseline.

关键词： Behavioral signal processing (BSP) computational paralinguistics Goodness of Pronunciation (GOP) speech assessment non-native speech prosody challenge

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CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOWN NON-targets FOR PLDA-SVM SCORING

CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOW...

引用

IEEE International conference on Acoustics, Speech and signal processing

作者： Wei Rao Man-Wai Mak Dept. of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong SAR China

ISBN: (纸本)9781479928941

Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers and claimants only. We have previously demonstrated that better performance can be achieved by incorporating the information of background speakers in the scoring process via speaker-dependent SVMs. This is achieved by defining a PLDA score space with dimension equal to the number of training i-vectors for each target speaker. The new protocol in NIST 2012 SRE permits systems to use the information of other target-speakers (called known non-targets) in each verification trial. In this paper, we exploit this new protocol to enhance the performance of PLDA-SVM scoring by using the score vectors of both known and unknown non-targets as the impostor class data to train the speaker-dependent SVMs. Because some target speakers have one enrollment utterance only, which results in severe imbalance in the speaker- and impostor-class data for SVM training. This paper shows that if the enrollment utterance is sufficiently long, a number of target-speaker i- vectors can be generated by an utterance partitioning and resampling ii- technique, resulting in much better scoring SVMs. Results on NIST 2012 SRE demonstrate the advantages of pooling the known and unknown non-targets for training the SVMs and that the resampling techniques can help the SVM training algorithm to find better decision boundaries for those speakers with only a small number of enrollment utterances.

关键词： Loudspeakers Scoring Scoring Unknown speaker-dependent Support Vector Network Series Relay NIST Functional training

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The Group Assignment Problem

The Group Assignment Problem

引用

signal and data processing of small targets 2003

作者： Poore, Aubrey B. Gadaleta, Sabino Numerica Corporation PO Box 251246 Fort Collins CO 80527-1246

Multiple frame data association, whether it is based on multiple hypothesis tracking or multi-dimensional assignment problems, has established itself as the method of choice for difficult tracking problems, principally due to the ability to hold difficult data association decisions in abeyance until additional information is available. Over the last twenty years, these methods have focused on one-to-one assignments, many-to-one, and many-to-many assignments. Group tracking, on the other hand, introduces new complexity into the association process, especially if some soft decision making capability is desired. Thus, the goal of this work is to combine multiple grouping hypotheses for each frame of data (tracks or measurements) with matching these hypotheses across multiple frames of data using one-to-one, many-to-one, or many-to-many assignments to determine the correct hypothesis on each frame of data and connectivity across the frames. The resulting formulation is sufficiently general to cover four broad classes of problems in multiple target tracking, namely (a) group cluster tracking, (b) pixel (clump) IR cluster tracking, (c) the merged measurement problem, and (d) MHT for track-to-track fusion. What is more, the cluster assignment problem for either two or multiple dimensions represents a generalized data association problem in the sense that it reduces to the classical assignment problems when there are no overlapping groups or clusters. The formulation of the assignment problem for resolved object tracking and candidate group methods for use in multiple frame group tracking are briefly reviewed. Then, three different formulations of the group assignment problem are developed.

关键词： data reduction

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New Techniques of Multiple Maneuvering targets Passive Tracking by Single Observer Based on Information Fusion

New Techniques of Multiple Maneuvering Targets Passive Track...

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International conference on signal processing Proceedings (ICSP)

作者： Jiegui Wang Jingqing Luo Electron. Eng. Inst. Hefei

The key techniques of multiple maneuvering targets passive tracking by single observer are discussed. Firstly, a new adaptive passive tracking initiation algorithm based on information fusion is proposed. Secondly, a new data association algorithm is proposed. Based on the information fusion of multiple features, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective

关键词： Target tracking Frequency diversity Multidimensional systems Radio frequency Neural networks Computer simulation Algorithm design and analysis Extraterrestrial measurements Azimuth Radiofrequency integrated circuits

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