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Autonomous power line detection and tracking system using UAVs

MICROPROCESSORS AND MICROSYSTEMS 2022年 94卷

作者： Schofield, Oscar Bowen Iversen, Nicolai Ebeid, Emad Univ Southern Denmark Maersk Mc Kinney Moller Inst SDU UAS Ctr Odense Denmark

A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection *** paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons;(2) a localisation system for pylon navigation and cable grasping;and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

关键词： Cloud services detection algorithms Autonomous missions Powerline inspection Drone framework

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A Novel Gait Phase detection Algorithm for Foot Drop Correction through Optimal Hybrid FES-Orthosis Assistance

A Novel Gait Phase Detection Algorithm for Foot Drop Correct...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jung, Pyeong-gook Huo, Weiguang Moon, Huiseok Amirat, Yacine Mohammed, Samer Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France Imperial Coll London Dept Mech Engn London England

ISBN: (纸本)9781728190778

As a life-threatening disease, stroke can lead to long-term problems affecting the patients' daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint's kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients' gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.

关键词： Legged locomotion Measurement units Trajectory tracking Iron Robustness Neuromuscular stimulation detection algorithms

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Optimality of Graph Scanning Statistic for Online Community detection

Optimality of Graph Scanning Statistic for Online Community ...

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IEEE International Symposium on Information Theory (ISIT)

作者： Xie, Liyan Xie, Yao Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538682098

Sequential change detection for graphs is a fundamental problem for streaming network data and has wide applications in social networks and power systems. Given fixed vertices and a sequence of random graphs, the objective is to detect the change-point where the underlying distribution of the random graph changes. In particular, we focus on the local change that only affects a small subgraph. We adopt the classical Erdos-Renyi model and revisit the generalized likelihood ratio (GLR) procedure. The scan statistic is computed by sequentially estimating the most-likely subgraph where the change happens. We provide theoretical analysis for the asymptotic optimality of the proposed procedure and we comment on generalizations to other random graph models. We demonstrate the efficiency of our detection algorithm using simulations.

关键词： Analytical models Social networking (online) Computational modeling Power systems detection algorithms Information theory

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Activity-Based Model using GPS Data and Google APIs 25

Activity-Based Model using GPS Data and Google APIs

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Tran, Yen Hashimoto, Naohisa Ando, Takafumi Sato, Toshihisa Konishi, Naoki Takeda, Yuji Akamatsu, Motoyuki Natl Inst Adv Ind Sci & Technol Digital Architecture Res Ctr Tsukuba Ibaraki 3058568 Japan Natl Inst Adv Ind Sci & Technol Human Ctr Mobil Res Ctr Tsukuba Ibaraki 3058568 Japan

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This study explores the potentials of utilizing GPS data for Activity-Based Models, under the condition that no additional information, such as travel diaries, is required. To extract activity details, we first developed a Time-Spatial Centroid-based Clustering algorithm to identify activity locations and times. Then a Home detection algorithm was used in combination with two APIs from Google Maps, namely Nearby Search and Place Details, to label each identified activity as either `home' or a specific activity type. Next, a Markov Chain Multinomial Logit Choice model was developed for the extracted activities that models the sequential relationship between consecutive activities. The approach was applied to a GPS dataset collected in Japan in 2020. The estimated parameters revealed how background factors, such as activity time and the person's age, and the previous activity associate with the current activity. Thus, GPS data alone can provide certain knowledge about activity-travel, which potentially benefit practices of travel demands forecasting.

关键词： Demand forecasting Clustering algorithms Markov processes Data models Internet detection algorithms Intelligent transportation systems

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Evaluation of the Potential of Automatic Naming Latency detection for Different Initial Phonemes during Picture Naming Task 43

Evaluation of the Potential of Automatic Naming Latency Dete...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Park, Sunghea Altermatt, Sven Beierlein, Sandra Widmer Blechschmidt, Anja Reymond, Claire Degen, Markus Rickert, Eliane Wyss, Sandra Kuntner, Katrin P. Hemm, Simone Univ Appl Sci & Arts Northwestern Switzerland Sch Educ Inst Special Educ & Psychol Windisch Switzerland Univ Appl Sci & Arts Northwestern Switzerland Sch Life Sci Inst Med Engn & Med Informat Windisch Switzerland Univ Appl Sci & Arts Northwestern Switzerland Acad Art & Design Inst Visual Commun Windisch Switzerland

ISBN: (纸本)9781728111797

Naming latency (NL) represents the speech onset time after the presentation of an image. We recently developed an extended threshold-based algorithm for automatic NL (aNL) detection considering the envelope of the speech wave. The present study aims at exploring the influence of different manners (e.g., "m" and "p") and positions (e.g., "t" and "p") of articulation on the differences between manual NL (mNL) and aNL detection. Speech samples were collected from 123 healthy participants. They named 118 pictures in German, including different initial phonemes. NLs were manually (Praat, waveform and spectrogram) and automatically (developed algorithm) determined. To investigate the accuracy of automatic detections, correlations between mNLs and aNLs were analyzed for different initial phonemes. ANLs and mNLs showed a strong positive correlation and similar tendencies in initial phoneme groups. ANL mean values were shorter than the ones of mNLs. Nasal sounds (e.g., /m/) showed the largest and those for fricatives (e.g., /s/) the smallest difference. However, in fricatives, 39% of NLs were detected later by automatic detections than by manual detections, which led to a reduced mean difference with mNLs. The signal energy of the initial phonemes, i.e., if they are voiced or voiceless, influences the form of the speech envelope: initial high signal energy is often responsible for an early detection by the algorithm. Our study provides evidence of a similar tendency in mNL and aNL according to different positions of articulation in each initial phoneme group. ANLs are highly sensitive to detection of speech onsets across different initial phonemes. The dependency of the NL differences on the initial phonemes will lose importance during progress evaluations in aphasia patients if the relative changes for each picture are considered separately. Nevertheless, the algorithm will be further optimized by adapting its parameters for each initial phoneme group individually.

关键词： Correlation Prototypes Nose Manuals Biology Task analysis detection algorithms

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AFSar: An Anchor-Free SAR Target detection Algorithm Based on Multiscale Enhancement Representation Learning

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Wan, Huiyao Chen, Jie Huang, Zhixiang Xia, RunFan Wu, BoCai Sun, Long Yao, Baidong Liu, Xiaoping Xing, Mengdao Anhui Univ Sch Elect & Informat Engn Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China China Elect Technol Grp Corp Res Inst 38 Hefei 230088 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Peoples R China

Unlike optical images, synthetic aperture radar (SAR) images have unique characteristics, such as few samples, strong scattering, sparseness, multiple scales, complex interference and background, and inconspicuous target edge contour information. Current SAR target detection algorithms have difficulty in balancing accuracy and speed, and the performance of these algorithms is relatively limited, thus making it difficult to deploy practical applications. To this end, this article proposes AFSar, an innovative anchor-free SAR target detection algorithm based on multiscale enhancement representation learning. First, we introduce the latest anchor-free architecture YOLOX as the basic framework. Second, to reduce the computational complexity of the model and to improve the ability of multiscale feature extraction, we redesigned the lightweight backbone, namely, MobileNetV2S. Furthermore, we propose an attention enhancement PAN module, called CSEMPAN, which highlights the unique strong scattering characteristics of SAR targets by integrating channel and spatial attention mechanisms. Finally, in view of the multiscale and strong sparse characteristics of SAR targets, we propose a new target detection head, namely, ESPHead. ESPHead extracts the features of targets with different scales by using dilated convolution with different dilated rates, so as to enhance the detection ability of the model for targets with different scales. The results of ablation experiments on the SSDD dataset show that the mAP of our algorithm reaches 0.977, while the Flops is only 9.86 G, achieving state of the art.

关键词： Feature extraction Object detection Radar polarimetry Synthetic aperture radar detection algorithms Optical imaging Prediction algorithms Anchor-free deep learning enhancement representation learning multiscale learning synthetic aperture radar (SAR) target detection

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Clothoidal Mapping of Road Line Markings for Autonomous Driving High-Definition Maps

Clothoidal Mapping of Road Line Markings for Autonomous Driv...

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33rd IEEE Intelligent Vehicles Symposium (IEEE IV)

作者： Gallazzi, Barbara Cudrano, Paolo Frosi, Matteo Mentasti, Simone Matteucci, Matteo Politecn Milan Dept Elect Informat & Bioengn Pzza Leonardo da Vinci 32 Milan Italy

ISBN: (纸本)9781665488211

Lane-level HD maps are crucial for trajectory planning and control in current autonomous vehicles. For this reason, appropriate line models should be adopted to define them. Whereas mapping algorithms often rely on inaccurate representations, clothoid curves possess peculiar smoothness properties that make them desirable representations of road lines in control algorithms. We propose a multi-stage pipeline for the generation of lane-level HD maps from monocular vision relying on clothoidal spline models. We obtain measurements of the line positions using a line detection algorithm, and we exploit a graph-based optimization framework to reach an optimal fitting. An iterative greedy procedure reduces the model complexity removing unnecessary clothoids. We validate our system on a real-world dataset, which we make publicly available for further research at https://***. ***/datasets- and- tools/.

关键词： Trajectory planning Roads Pipelines Fitting Position measurement Splines (mathematics) detection algorithms

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Change detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Vinholi, Joao G. Palm, Bruna G. Silva, Danilo Machado, Renato Pettersson, Mats, I Aeronaut Inst Technol ITA Dept Telecommun BR-12228900 Sao Jose Dos Campos Brazil Blekinge Inst Technol BTH Dept Math & Nat Sci S-37179 Karlskrona Sweden Fed Univ Santa Catarina UFSC Dept Elect & Elect Engn BR-88040900 Florianopolis SC Brazil

This article presents two supervised change detection algorithms (CDAs) based on convolutional neural networks (CNNs) that use stacks of coregistered wavelength-resolution (WR) synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of CDAs. In particular, stacks of images with similar statistics can be obtained for ultrawideband (UWB) very high-frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image (DI) formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a DI. A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

关键词： Synthetic aperture radar Radar polarimetry Convolutional neural networks Monitoring detection algorithms Complexity theory Ultra wideband technology CARABAS-II classification convolutional neural network (CNN) deep learning remote sensing semantic segmentation

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An Improved Support Vector Machine Attack detection Algorithm for Industry Controls System

An Improved Support Vector Machine Attack Detection Algorith...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Shangqing Cui Dongqin Feng College of Control Science and Engineering Zhejiang University Hangzhou China

With the application of Internet technologies in the industrial field, industrial control systems (ICS) have the possibility of suffering malicious network attacks. Because ICS is widely used in critical infrastructure, designing attack detection algorithms for it can effectively reduce losses. Many papers have built physical models for cyber-attacks against ICS and the corresponding detection algorithms have been proposed. However, the detection performance can be further improved. This paper establishes the simulation attack on the Tennessee Eastman (TE) process and proposes an improved support vector machine (SVM) method to detect integrity attacks. This algorithm uses contribution plots and recursive feature elimination to select features. Compared with the principal component analysis (PCA) feature extraction, the detection accuracy is improved. After adding system dynamics to the algorithm, the time to detect attacks is advanced.

关键词： Support vector machines System dynamics Industrial control Heuristic algorithms Feature extraction detection algorithms Integrated circuit modeling

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Change Point detection with Neural Online Density-Ratio Estimator

Change Point Detection with Neural Online Density-Ratio Esti...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiuheng Wang Ricardo Augusto Borsoi Cédric Richard Jie Chen Université Côte d’Azur CNRS OCA Nice France Université de Lorraine CNRS CRAN Vandoeuvre-lès-Nancy France School of Marine Science and Technology Northwestern Polytechnical University Xi’an China

Detecting change points in streaming time series data is a long standing problem in signal processing. A plethora of methods have been proposed to address it, depending on the hypotheses at hand. Non-parametric approaches are particularly interesting as they do not make any assumption on the distribution of data or on the nature of changes. Nevertheless, leveraging recent advances in deep learning to detect change points in time series data is still challenging. In this paper, we propose a change point detection method using an online approach based on neural networks to directly estimate the density-ratio between current and reference windows of the data stream. A variational continual learning framework is employed to train the neural network in an online manner while retaining information learned from past data. This leads to a statistically-principled fully nonparametric framework to detect change points from streaming data. Experimental results with synthetic and real data illustrate the effectiveness of the proposed approach.

关键词： Deep learning Neural networks Time series analysis Signal processing Acoustics Speech processing detection algorithms

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