scholarly journals A Study on the Shutter Time of a Surveillance Camera to Improve Speed Detection Accuracy of Vehicles on Highways and Inner-City Streets

2021 ◽  
Vol 31 (2) ◽  
pp. 43-50
Author(s):  
Keyword(s):  
Inner City ◽  
Detection System ◽  
Operating Time ◽  
Operation Time ◽  
Detection Accuracy ◽  
Vehicle Speed ◽  
Surveillance Camera ◽  
The World ◽  
Shutter Time

Detection of vehicle speed based on image processing technology recently has been found in many applications over the world. However, the accuracy of those methods has not been investigated taking into account of physical characteristics of the surveillance camera. Based on the operation time of the camera's optical sensor system including shutter time (ST) and sensor operating time, the accuracy of vehicle speed detection on highways as well as inner city streets can be significantly improved. The operation time of the camera’s sensor is essential for determination of frames over time in a vehicle surveillance system. Therefore, control of the shutter time ST will help a camera-based speed detection system to achieve much better accuracy.

INFLUENCE OF HEAT TREATMENT OF MOUNTAIN MASSIVE ON TEMPERATURE MODE OF GEOTHERMAL CIRCULATION SYSTEM

2018 ◽  
pp. 44-50
Author(s):  
Yu. P. Morozov
Keyword(s):  
Heat Transfer ◽  
Operating Time ◽  
Fluid Motion ◽  
Coolant Temperature ◽  
Circulation System ◽  
Thermal Processes ◽  
Temperature Mode ◽  
Heat Influx ◽  
Stationary Heat Transfer

Based on the solution of the problem of non-stationary heat transfer during fluid motion in underground permeable layers, dependence was obtained to determine the operating time of the geothermal circulation system in the regime of constant and falling temperatures. It has been established that for a thickness of the layer H <4 m, the influence of heat influxes at = 0.99 and = 0.5 is practically the same, but for a thickness of the layer H> 5 m, the influence of heat inflows depends significantly on temperature. At a thickness of the permeable formation H> 20 m, the heat transfer at = 0.99 has virtually no effect on the thermal processes in the permeable formation, but at = 0.5 the heat influx, depending on the speed of movement, can be from 50 to 90%. Only at H> 50 m, the effect of heat influx significantly decreases and amounts, depending on the filtration rate, from 50 to 10%. The thermal effect of the rock mass with its thickness of more than 10 m, the distance between the discharge circuit and operation, as well as the speed of the coolant have almost no effect on the determination of the operating time of the GCS in constant temperature mode. During operation of the GCS at a dimensionless coolant temperature = 0.5, the velocity of the coolant is significant. With an increase in the speed of the coolant in two times, the error changes by 1.5 times.

EFFECT OF OPERATING TIME OF THE WELLS IN THE STEADY DRAINAGE MODE BEFORE STOPPING ON THE RESULTS OF THE STUDY OF PRESSURE BUILD-UP CURVES

2017 ◽  
pp. 48-50
Author(s):  
E. F. Gilfanov
Keyword(s):  
Operating Time ◽  
Previous History ◽  
Operation Time ◽  
Pressure Recovery ◽  
Recovery Curve ◽  
History Of ◽  
Accuracy Of Results ◽  
Theoretical Pressure

Operation time of the well before stopping for investigating the pressure recovery curve in hydrodynamic studies is an important parameter affecting the quality and accuracy of results of research processing. Comparing the actual and theoretical pressure curves and the derivative, it’s possible to eliminate the uncertainty in the choice of previous history of the well operation.

Actual issues of valuation of businesses of the economic entity

2017 ◽  
Vol 31 (2) ◽  
pp. 156-162 ◽  
Author(s):  
O. V. Schneider
Keyword(s):  
Discount Rate ◽  
Accurate Prediction ◽  
Economic Activities ◽  
Business Valuation ◽  
Economic Entity ◽  
The World ◽  
Definition Of

The article summarizes the main approaches in the definition of business valuation the economic entity. In the process of business valuation, taking into account the risks of financial and economic activities necessary to obtain information on what stage the owner implements the business will receive income. The most difficult task is the impossibility of accurate prediction in determining the level of income and the determination of a discount rate capitalization of future incomes due to the instability of the economy, both in the country and around the world.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

Novel sliding hybrid microchip detection system for determination of whole blood phosphorus concentration

2021 ◽  
Vol 419 ◽  
pp. 129592
Author(s):  
Chin-Chung Tseng ◽  
Szu-Jui Chen ◽  
Song-Yu Lu ◽  
Chien-Hsuan Ko ◽  
Ju-Ming Wang ◽  
...  
Keyword(s):  
Whole Blood ◽  
Detection System ◽  
Phosphorus Concentration

scholarly journals Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rohit Kundu ◽  
Hritam Basak ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Ct Scan ◽  
Detection System ◽  
Network Models ◽  
Test Cases ◽  
Gompertz Function ◽  
Neural Network Models ◽  
Source Codes ◽  
Ensemble Strategy ◽  
The World ◽  
Long Time

AbstractCOVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

scholarly journals Anticipatory Non-Green-Phenomena Determination for Designing Eco-Design Products

2021 ◽  
Vol 13 (2) ◽  
pp. 621
Author(s):  
Hsin Rau ◽  
Mary Deanne M. Lagapa ◽  
Po-Hsun Chen
Keyword(s):  
Sustainable Development ◽  
Environmental Impact ◽  
Product Design ◽  
Determination Method ◽  
Washing Machine ◽  
Product Failure ◽  
Design Concepts ◽  
The World ◽  
World Business Council

The number of consumers with green awareness have grown these days and as a result they have turned to purchase eco-friendly products. For this reason, this study aims to propose a method for eco-design based on the anticipatory failure determination method to develop eco-design products. By using eco-design concepts adopted from the World Business Council for Sustainable Development, the process will limit the failures and issues related to environmental impact in product design. The proposed method for eco-design product in this study follows the following procedure. First, we analyze product failure. Second, we propose the determination of the non-green phenomenon of the failure. Thirdly, we integrate the intensified non-green phenomenon to generate non-green hypotheses and fourthly, we eliminate each non-green phenomenon hypothesis by introducing the contradiction matrix of TRIZ for obtaining solutions. Finally, we assess alternative eco-design solutions by evaluation. To verify the practicality of the new procedure, a washing machine is used as an example for illustration.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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