scholarly journals Estrus Detection Methods in Dairy Animals- Advances and the Prospects: A Review

2021 ◽  
Author(s):  
A.R. Madkar ◽  
P. Boro ◽  
M. Abdullah
Keyword(s):  
Detection System ◽  
Management Strategies ◽  
Reproductive Potential ◽  
Dairy Herd ◽  
Detection Methods ◽  
Reproductive Efficiency ◽  
Detection Rates ◽  
Estrus Detection ◽  
Dairy Animals ◽  
Automatic Detection System

Fertility over the past few decades is of serious concern in the dairy industry. Fertility of a dairy herd is determined by composite factors, which in turn depends upon effective management strategies. The reproductive potential of the animals need to be exploited to its maximum to achieve optimum production in a herd. The single most important factor that limits the establishment of pregnancy and survival of the embryo in dairy cattle and buffaloes and thereby reproductive efficiency of a herd is proper estrus detection, Pedometer or activity meter is a motion switches devices within which steps followed by animals are recorded. Activity meters can be attached to the neck or leg of cows and they may be read by a receiver and processed by computer in a milking parlour. By implementing automatic detection system, heat detection rates can be improved, for improving reproductive efficiency. The activity monitoring techniques can also be used to detect the silent ovulation which is helpful for improving efficiency and accuracy of estrus.

Subgrade Compaction Boundary Layer Detection Method and Detection System

2013 ◽  
Vol 639-640 ◽  
pp. 1259-1264
Author(s):  
Ji Xiang Zhong
Keyword(s):  
Boundary Layer ◽  
Density Distribution ◽  
Detection System ◽  
Detection Methods ◽  
Dense Layer ◽  
Distribution Law ◽  
Compression Pressure ◽  
Vertical Zone ◽  
Automatic Detection System ◽  
Compaction Density

At present, the degree of compaction is the main criterion for subgrade compaction quality.It is the relative expression of the compacted density,Just average.It does not adequately reflect the subgrade compaction layer vertical compaction density distribution law. Compaction boundary layer micro-unit compression pressure on in the process of pressure transmission decreases gradually until they reach the critical formation pressure dense layer. Compaction by detecting the boundary layer vertical zone layer densification, to calculate the boundary thickness, to draw isodense of densification. a clear reproduction of the compacted layer vertical compaction density distribution law. This paper describes the detection principles and detection methods of the compaction boundary layer. describes in detail functional structure and system design of the vehicle automatic detection system used to detect compaction boundary layer of each vertical zone.

The Method of Recognizing Navigation Lights in the Runway Debris Visual Automatic Detection System

2011 ◽  
Vol 121-126 ◽  
pp. 2333-2337
Author(s):  
Zhi Jing Yu ◽  
Feng Ze Lang ◽  
Xiao Jing Guo
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
Estimation Method ◽  
Automatic Detection ◽  
Detection Methods ◽  
Experimental Result ◽  
Scanning System ◽  
Result Show ◽  
Automatic Detection System ◽  
System Orientation

Runway debris visual automatic detection system is one of key measures that ensure efficient and safe airport operation. It can automatically recognize runway debris with visual detection methods, and determine the orientation of scanning system with attitude estimation method, then realize automatically detect debris. In this paper, the recognizing method of fixed feature navigation lights that are used to calculate system orientation are researched on. Runway area and background are detached by recognizing runway lines. Navigation lights are recognized and matched by the method of image matching. Experimental result show, runway can be separated and recognized from complex environment, and navigational lights can be matched quickly in the algorithm. The reliable fixed feature is supplied for followed determining scanning system orientation and located debris.

scholarly journals FACE DETECTION ON GRAYSCALE AND COLOR IMAGES USING COMBINED CASCADE OF CLASSIFIERS

2014 ◽  
pp. 61-71
Author(s):  
Yuriy Kurylyak ◽  
Ihor Paliy ◽  
Anatoly Sachenko ◽  
Amine Chohra ◽  
Kurosh Madani
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Space ◽  
Color Images ◽  
Detection Methods ◽  
Color Segmentation ◽  
Detection Rates ◽  
Verification Method ◽  
High Processing Speed

The paper describes improved face detection methods for grayscale and color images using the combined cascade of classifiers and skin color segmentation. The combined cascade with proposed face candidates’ verification method allows achieving one of the best detection rates on CMU test set and a high processing speed suitable for a video flow processing. It’s also shown that the mixture of color spaces is more efficient during the skin color segmentation than the application of one color space. A lot of experiments are made to choose rational parameters for the developed face detection system in order to improve the detection rate, false positives’ number and system’s speed.

scholarly journals A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhihui Wang ◽  
Sook Yoon ◽  
Shan Juan Xie ◽  
Yu Lu ◽  
Dong Sun Park
Keyword(s):  
Machine Learning ◽  
Random Vector ◽  
Detection System ◽  
Pedestrian Detection ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Functional Link ◽  
Detection Rates ◽  
Standard Scale

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

The Effect of Introducing Estrus Detection System on Hanwoo Industry

2019 ◽  
Vol 46 (2) ◽  
pp. 168-187
Author(s):  
Ho Young Rho ◽  
Jun Byeong Hwang ◽  
Ye Bon Cha ◽  
Hong Seok Seo ◽  
Chung Hyeon Kim ◽  
...  
Keyword(s):  
Detection System ◽  
Estrus Detection

Survey on Safety Devices Using IoT

2020 ◽  
pp. 450-453
Keyword(s):  
Traffic Congestion ◽  
Detection System ◽  
Text Message ◽  
Emergency Situation ◽  
Heart Beat ◽  
Advanced Technology ◽  
Night Time ◽  
Remote Areas ◽  
Alert Message ◽  
Automatic Detection System

Traffic congestion is becoming a huge problem, which is arising due to vehicle failure or accidents. Transportation and use of advanced technology has great importance in society and that has made many of our lives much easier. By automatic accident detection and alerting GSM & GPS based technology can be used to overcome these problems. Where as in case of Child and Women there are very few efficient security and safety measures adopted. Now in India the safety for women has become a major issue while travelling. Nowadays women think twice before taking any steps out of their homes, especially in the night time. Hence, this is unfortunately, the sad reality of our country and also due to various crimes like child abuse, rape, dowry deaths, trafficking and many more. At the time of women facing unsecured situations, there is a need to ensure safety while travelling. Hence automatic detection system needs to be established where one can send alert message to the police station or the relatives which detects the current location of the required ones by use of such technologies the women and children can get protection. Mainly in remote areas children use bicycles as means of transport from several years and nowadays, despite due to the large vailability of new and faster means, the bicycle users is not decreased. Despites the cyclists find difficult to travel within them and other vehicles find difficult to find them during night time. In case of any emergency situation faced at unknown remote areas the cyclist can send their location to required ones to help them. In this paper, report the survey on the existing mechanism for detecting locations, and sending signals and to collect parameters such as temperature of the human body, heart beat etc. using sensors. With the help of GPS and GSM we can track the location of the child, women or vehicle. Hence, by these we can save the life of person’s being injured in various locations by sending a text message using IOT technologies

scholarly journals Research on automatic detection system of encoder accuracy based on PID algorithm

2021 ◽  
Vol 1754 (1) ◽  
pp. 012233
Author(s):  
Han Hou ◽  
Guohua Cao ◽  
Hongchang Ding ◽  
Changfu Zhao ◽  
Aijia Wang
Keyword(s):  
Detection System ◽  
Automatic Detection ◽  
Pid Algorithm ◽  
Automatic Detection System

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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