scholarly journals An Assembled Detector Based on Geometrical Constraint for Power Component Recognition

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3517
Author(s):  
Zheng Ji ◽  
Yifan Liao ◽  
Li Zheng ◽  
Liang Wu ◽  
Manzhu Yu ◽  
...  
Keyword(s):  
Low Cost ◽  
Operation Mode ◽  
Real Data ◽  
Training Sample ◽  
Power Line ◽  
Geometrical Constraint ◽  
Detection Model ◽  
Access Structures ◽  
Cascade Classifiers ◽  
Uav Images

The intelligent inspection of power lines and other difficult-to-access structures and facilities has been greatly enhanced by the use of Unmanned Aerial Vehicles (UAVs), which allow inspection in a safe, efficient, and high-quality fashion. This paper analyzes the characteristics of a scene containing power equipment and the operation mode of UAVs. A low-cost virtual scene is created, and a training sample for the power-line components is generated quickly. Taking a vibration-damper as the main object, an assembled detector based on geometrical constraint (ADGC) is proposed and is used to analyze the virtual dataset. The geometric positional relationship is used as the constraint, and the Faster Region with Convolutional Neural Network (R-CNN), Deformable Part Model (DPM), and Haar cascade classifiers are combined, which allows the features of different classifiers, such as contour, shape, and texture to be fully used. By combining the characteristics of virtual data and real data using UAV images, the power components are detected by the ADGC. The result produced by the detector with relatively good performance can help expand the training set and achieve a better detection model. Moreover, this method can be smoothly transferred to other power-line facilities and other power-line scenarios.

scholarly journals Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Wenzhi Cui ◽  
Lu Liu ◽  
Jian Xu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Large Scale ◽  
Low Cost ◽  
Human Microbiome ◽  
Amplicon Sequencing ◽  
Training Sample ◽  
Rrna Gene ◽  
16S Amplicon Sequencing ◽  
Cross Platform ◽  
Functional Profiles

Abstract Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

A General Low Cost UAV Solution for Power Line Tracking

2020 ◽  
pp. 525-530
Author(s):  
Antonio J. Dantas Filho ◽  
Alexandre C. B. Ramos ◽  
Leandro D. de Jesus ◽  
Hildebrando F. de Castro Filho ◽  
Felix Mora-Camino
Keyword(s):  
Low Cost ◽  
Power Line ◽  
Line Tracking

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

2018 ◽  
Author(s):  
Quang Thanh Tran ◽  
Li Jun Hao ◽  
Quang Khai Trinh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Low Cost ◽  
Real Data ◽  
Exponential Smoothing ◽  
High Accuracy ◽  
Training Data ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

scholarly journals IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 31-37 ◽  
Author(s):  
Shan Huang ◽  
Zuxun Zhang ◽  
Jianan He ◽  
Tao Ke
Keyword(s):  
Coordinate System ◽  
Low Cost ◽  
Reference Image ◽  
Model Parameters ◽  
Camera Model ◽  
Orientation Data ◽  
Network Generation ◽  
Free Network ◽  
Uav Images ◽  
Image Network

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

scholarly journals UNMANNED AERIAL VEHICLE APPLICATIONS OF 3D MODELLING, VISUALIZATION AND PARAMETER CALCULATIONS

2018 ◽  
Vol XLII-5 ◽  
pp. 649-652 ◽  
Author(s):  
V. Lambey ◽  
A. D. Prasad
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
3D Modelling ◽  
Aerial Photogrammetry ◽  
Average Accuracy ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Institute Of Technology ◽  
Uav Images ◽  
Economical Alternative

<p><strong>Abstract.</strong> Photogrammetric surveying with the use of Unmanned Aerial Vehicles (UAV) have gained vast popularity in short span. UAV have the potential to provide imagery at an extraordinary spatial and temporal resolution when coupled with remote sensing. Currently, UAV platforms are fastest and easiest source of data for mapping and 3D modelling. It is to be considered as a low-cost substitute to the traditional airborne photogrammetry. In the present study, UAV applications are explored in terms of 3D modelling, visualization and parameter calculations. National Institute of Technology Raipur, Raipur is chosen as study area and high resolution images are acquired from the UAV with 85% overlap. 3D model is processed through the point cloud generated for the UAV images. The results are compared with traditional methods for validation. The average accuracy obtained for elevation points and area is 97.99% and 97.75%. The study proves that UAV based surveying is an economical alternative in terms of money, time and resources, when compared to the classical aerial photogrammetry methods.</p>

scholarly journals Industrial Production of Poly-β-hydroxybutyrate from CO2: Can Cyanobacteria Meet this Challenge?

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 323 ◽  
Author(s):  
Roberta Carpine ◽  
Giuseppe Olivieri ◽  
Klaas J. Hellingwerf ◽  
Antonino Pollio ◽  
Antonio Marzocchella
Keyword(s):  
Low Cost ◽  
Operation Mode ◽  
Commercial Production ◽  
Continuous Operation ◽  
Attractive Alternative ◽  
Bacterial Fermentation ◽  
Plastic Materials ◽  
Phb Production ◽  
Global Concern ◽  
Environmentally Friendly Process

The increasing impact of plastic materials on the environment is a growing global concern. In regards to this circumstance, it is a major challenge to find new sources for the production of bioplastics. Poly-β-hydroxybutyrate (PHB) is characterized by interesting features that draw attention for research and commercial ventures. Indeed, PHB is eco-friendly, biodegradable, and biocompatible. Bacterial fermentation processes are a known route to produce PHB. However, the production of PHB through the chemoheterotrophic bacterial system is very expensive due to the high costs of the carbon source for the growth of the organism. On the contrary, the production of PHB through the photoautotrophic cyanobacterium system is considered an attractive alternative for a low-cost PHB production because of the inexpensive feedstock (CO2 and light). This paper regards the evaluation of four independent strategies to improve the PHB production by cyanobacteria: (i) the design of the medium; (ii) the genetic engineering to improve the PHB accumulation; (iii) the development of robust models as a tool to identify the bottleneck(s) of the PHB production to maximize the production; and (iv) the continuous operation mode in a photobioreactor for PHB production. The synergic effect of these strategies could address the design of the optimal PHB production process by cyanobacteria. A further limitation for the commercial production of PHB via the biotechnological route are the high costs related to the recovery of PHB granules. Therefore, a further challenge is to select a low-cost and environmentally friendly process to recover PHB from cyanobacteria.

scholarly journals Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2984
Author(s):  
Yue Mu ◽  
Tai-Shen Chen ◽  
Seishi Ninomiya ◽  
Wei Guo
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Yield Prediction ◽  
Learning Approaches ◽  
Size Estimation ◽  
Detection Model ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Location Map ◽  
Tomato Detection

Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction.

scholarly journals Vision-based SLAM system for MAVs in GPS-denied environments

2017 ◽  
Vol 9 (4) ◽  
pp. 283-296 ◽  
Author(s):  
Sarquis Urzua ◽  
Rodrigo Munguía ◽  
Antoni Grau
Keyword(s):  
Sensory Input ◽  
Low Cost ◽  
Real Data ◽  
Micro Aerial Vehicle ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Payload Capacity ◽  
The World ◽  
Aerial Vehicle ◽  
Metric Scale

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.

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