ARM-based Behavior Tracking and Identification System for Grouphoused Pigs

2019 ◽  
Vol 12 (6) ◽  
pp. 554-565
Author(s):  
Xingqiao Liu ◽  
Jun Xuan ◽  
Fida Hussain ◽  
Chen Chong ◽  
Pengyu Li
Keyword(s):  
Monitoring System ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Identification System ◽  
Monitoring Method ◽  
Embedded Platform ◽  
Food And Water Intake ◽  
Behavior Tracking

Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.

Method for Grade Identification of Tobacco Based on Machine Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1487-1495
Author(s):  
Yan He ◽  
Haijun Wang ◽  
Shiping Zhu ◽  
Tao Zeng ◽  
Zhenzhen Zhuang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Machine Vision ◽  
Recognition Algorithm ◽  
Identification System ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern ◽  
Model Set

Abstract. Tobacco grading is the first step in the transfer of tobacco leaves from agricultural products to commodities and is key to determining the quality of tobacco. Manual grading is conventionally used for tobacco grading. However, it is time-consuming, expensive, and may require specialized labor. To overcome these limitations, a method for grade identification of tobacco leaves based on machine vision is proposed in this article. Based on a fuzzy pattern recognition algorithm, the tobacco leaf samples of the model set and prediction set could be classified by extracting appearance characteristics of the tobacco leaves. The identification system for tobacco leaves based on fuzzy pattern recognition was developed in MATLAB. The rate of correct grading was 85.81% and 80.23% for the modeling set and prediction set, respectively. This result shows that machine vision based automatic tobacco grading has a great advantage over manual grading, and this method can be explored for viable commercial use. Keywords: Fuzzy pattern recognition, Grade identification, Machine vision, Tobacco leaf.

Research on Construction and Function of Safety Monitoring Sys- tem for Integrated Construction Site Based on BIM

1970 ◽  
Vol 1 (1) ◽  
Author(s):  
Dan Xin
Keyword(s):  
Monitoring System ◽  
Safety Management ◽  
Safety Monitoring ◽  
Information Collection ◽  
Construction Site ◽  
Management Efficiency ◽  
Lack Of Information ◽  
Safety Monitoring System ◽  
And Function

The effective construction of safety monitoring system at construction site depends on perfect management system and advanced technical support. And the lack of information technology platform, resulting in reduced management efficiency, information is not accurate and other issues. Based on the construction site safety monitoring system to achieve the goal, to do a good job in advance prevention, to take the latest information collection technology RFID and BIM integrated comprehensive and effective monitoring of the construction site, constitute the main technology in the monitoring system, thus ensuring the construction site safety monitoring efficiency , Comprehensive, real-time, etc., on the management and technical two points to achieve the construction site safety monitoring, improve the quality of safety management.

scholarly journals Methodology: non-invasive monitoring system based on standing wave ratio for detecting water content variations in plants

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yunjeong Yang ◽  
Ji Eun Kim ◽  
Hak Jin Song ◽  
Eun Bin Lee ◽  
Yong-Keun Choi ◽  
...  
Keyword(s):  
Water Content ◽  
Monitoring System ◽  
Standing Wave ◽  
Magnetic Particles ◽  
Experimental Plant ◽  
Invasive Monitoring ◽  
Monitoring Method ◽  
Non Invasive ◽  
Content Variation ◽  
Stem Water

Abstract Background Water content variation during plant growth is one of the most important monitoring parameters in plant studies. Conventional parameters (such as dry weight) are unreliable; thus, the development of rapid, accurate methods that will allow the monitoring of water content variation in live plants is necessary. In this study, we aimed to develop a non-invasive, radiofrequency-based monitoring system to rapidly and accurately detect water content variation in live plants. The changes in standing wave ratio (SWR) caused by the presence of stem water and magnetic particles in the stem water flow were used as the basis of plant monitoring systems. Results The SWR of a coil probe was used to develop a non-invasive monitoring system to detect water content variation in live plants. When water was added to the live experimental plants with or without illumination under drought conditions, noticeable SWR changes at various frequencies were observed. When a fixed frequency (1.611 GHz) was applied to a single experimental plant (Radermachera sinica), a more comprehensive monitoring, such as water content variation within the plant and the effect of illumination on water content, was achieved. Conclusions Our study demonstrated that the SWR of a coil probe could be used as a real-time, non-invasive, non-destructive parameter for detecting water content variation and practical vital activity in live plants. Our non-invasive monitoring method based on SWR may also be applied to various plant studies.

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

Quality Monitoring System for Seismic While Drilling

2013 ◽  
Vol 318 ◽  
pp. 572-575
Author(s):  
Li Li Yu ◽  
Yu Hong Li ◽  
Ai Feng Wang
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Signal Analysis ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Quality Of Data ◽  
Whole Process ◽  
Engineering Parameters ◽  
Network Status

In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

Navigational Data Change Detection Based on Rasterizing Features & Attributes

2012 ◽  
Vol 256-259 ◽  
pp. 2279-2284
Author(s):  
Lian Ying Li ◽  
Zhang Huang ◽  
Xiao Lan Xu
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Vehicle Navigation ◽  
Digital Map ◽  
Navigation Data ◽  
Data Production ◽  
History Of ◽  
Map Data ◽  
Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

scholarly journals Monitoring Skeletal Anomalies in Big-Scale Sand Smelt, Atherina boyeri, as a Potential Complementary Tool for Early Detection of Effects of Anthropic Pressure in Coastal Lagoons

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 159
Author(s):  
Chiara Leone ◽  
Francesca De Luca ◽  
Eleonora Ciccotti ◽  
Arianna Martini ◽  
Clara Boglione
Keyword(s):  
Early Detection ◽  
Habitat Degradation ◽  
Anthropogenic Impacts ◽  
Coastal Lagoons ◽  
Ecological Status ◽  
Detection Methods ◽  
Anthropogenic Pressures ◽  
Morphological Examination ◽  
Skeletal Anomalies

Mediterranean coastal lagoons are increasingly affected by several threats, all concurrently leading to habitat degradation and loss. Methods based on fish for the assessment of the ecological status are under implementation for the Water Framework Directive requirements, to assess the overall quality of coastal lagoons. Complementary tools based on the use of single fish species as biological indicators could be useful as early detection methods of anthropogenic impacts. The analysis of skeletal anomalies in the big-scale sand smelt, Atherina boyeri, from nine Mediterranean coastal lagoons in Italy was carried out. Along with the morphological examination of fish, the environmental status of the nine lagoons was evaluated using a method based on expert judgement, by selecting and quantifying several environmental descriptors of direct and indirect human pressures acting on lagoon ecosystems. The average individual anomaly load and the frequency of individuals with severe anomalies allow to discriminate big-scale sand smelt samples on the basis of the site and of its quality status. Furthermore, a relationship between skeletal anomalies and the environmental quality of specific lagoons, driven by the anthropogenic pressures acting on them, was found. These findings support the potentiality of skeletal anomalies monitoring in big-scale sand smelt as a tool for early detection of anthropogenic impacts in coastal lagoons of the Mediterranean region.

Using complementary remote detection methods for retrofitted eco-passages: a case study for monitoring individual koalas in south-east Queensland

2016 ◽  
Vol 43 (5) ◽  
pp. 369 ◽  
Author(s):  
C. E. Dexter ◽  
R. G. Appleby ◽  
J. P. Edgar ◽  
J. Scott ◽  
D. N. Jones
Keyword(s):  
Road Construction ◽  
Primary Objective ◽  
Correlated Data ◽  
Camera Traps ◽  
Detection Methods ◽  
Urban Landscapes ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Phascolarctos Cinereus ◽  
The Individual

Context Vehicle-strike has been identified as a key threatening process for koala (Phascolarctos cinereus) survival and persistence in Australia. Roads and traffic act as barriers to koala movement and can impact dispersal and metapopulation dynamics. Given the high cost of wildlife mitigation structures such as purpose-built fauna-specific underpasses or overpasses (eco-passages), road construction and management agencies are constantly seeking cost-effective strategies that facilitate safe passage for fauna across roads. Here we report on an array of detection methods trialled to verify use of retrofitted road infrastructure (existing water culverts or bridge underpasses) by individual koalas in fragmented urban landscapes in south-east Queensland. Aims The study examined whether the retrofitting of existing road structures at six sites facilitated safe passage for koalas across roads. Our primary objective was to record utilisation of retrofitted infrastructure at the level of the individual. Methods We used a combination of existing monitoring methods such as GPS/VHF collars, camera traps, sand plots, and RFID tags, along with a newly developed animal-borne wireless identification (WID) tag and datalogging system, specifically designed for this project, to realise the study aims. Key results We were able to verify 130 crossings by koalas involving a retrofitted structure or a road surface over a 30-month period by using correlated data from complementary methods. We noted that crossings were generally uncommon and mostly undertaken by only a subset of our tagged individuals at each site (21% overall). Conclusions An important element of this study was that crossing events could be accurately determined at the level of the individual. This allowed for detailed assessment of eco-passage usage, rather than the more usual approach of simply recording species’ presence. Implications This study underscores the value of identifying the constraints of each individual monitoring method in relation to site conditions. It also highlights the benefits of contingency planning to limit data loss (i.e. using more than one method to collect data). We suggest an approach that uses complementary monitoring methods has significant advantages for researchers, particularly with reference to improving understanding of whether eco-passages are meeting their prescribed conservation goals.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

Development of On-Line Monitoring System and Aging Characteristics Research of Switchgear

2013 ◽  
Vol 330 ◽  
pp. 364-367
Author(s):  
Shu Xin Liu ◽  
Yun Dong Cao ◽  
Chun Guang Hou ◽  
Yang Liu ◽  
Xiao Ming Liu
Keyword(s):  
Epoxy Resin ◽  
Monitoring System ◽  
Estimation Method ◽  
Current Transformer ◽  
Insulation Material ◽  
Electrical Characteristics ◽  
Infrared Transmission ◽  
Lifetime Estimation ◽  
Monitoring Method ◽  
On Line

For improving reliable operation of switchgear in power system, an approach for on-line monitoring the insulation characteristic and bus-bar temperature rising of the switchgear is proposed in this paper. Through comparing several existing temperature measurement methods for monitoring temperature rising elevation at bus-bas, a new design of temperature monitoring method is proposed. It adopts quick-magnetic saturated current transformer, temperature sensor and infrared transmission to solve the problem of high voltage isolation. The epoxy resin insulation material which is commonly used in switchgear its aging mechanism data is not complete, seriously restrict on-line monitoring for switchgear, so thousands hours of aging experiment is done on switchgear, systematic study various electrical characteristics variation law on the gradual aging process of epoxy resin insulation materials. Therefore, study on the aging characteristics of switchgearinsulation and its lifetime estimation method is the key technology to understand agingmechanism better, search for new fault diagnostic method and the way to extend theuseful lifetime of switchgear. At last, the system runs in real system and the result shows the on-line monitoring system is stable and reliable which can be provide reference for on-line monitoring system design of switchgear.

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