Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

2018 ◽  
Vol 412 ◽  
pp. 191-200 ◽  
Author(s):  
Qing Tian ◽  
Dan Yang ◽  
Yuan Zhang ◽  
Hongquan Qu
Keyword(s):  
Optical Fiber ◽  
Warning System ◽  
Detection And Recognition

A new detection and recognition method for optical fiber pre-warning system

Optik ◽  
2017 ◽  
Vol 137 ◽  
pp. 209-219 ◽  
Author(s):  
Hongquan Qu ◽  
Tong Zheng ◽  
Liping Pang ◽  
Xuelian Li
Keyword(s):  
Optical Fiber ◽  
Warning System ◽  
Recognition Method ◽  
Detection And Recognition

A Vision-Based Lane Markings Detection and Recognition Method for Lane Departure Warning System

2011 ◽  
Vol 97-98 ◽  
pp. 1007-1011
Author(s):  
Xiao Feng Chen ◽  
Zhong Ke Shi
Keyword(s):  
Warning System ◽  
Least Square Method ◽  
Least Square ◽  
Driving Safety ◽  
Lane Departure Warning ◽  
Recognition Method ◽  
Lane Departure ◽  
Straight Line ◽  
Sequential Images ◽  
Detection And Recognition

Lane departure warning system is an important element in improving driving safety and the main difficulty of which is to detect and recognize lane markings quickly and precisely. In this paper, for satisfying the quickness and precision requirement of recognizing lane markings, a novel vision-based lane markings detection and recognition method, which mainly comprise of two steps, is proposed. The first step of which is to roughly determine the ROI of lane markings by means of projection, while the next is to precisely detect lane markings by combining Hough transform with the least square method to fit straight-line. Large amounts of experiment indicate that the proposed method can rapidly and precisely recognize lane markings from sequential images.

scholarly journals EARLY WARNING TERHADAP INSIDEN KEBAKARAN PADA OVER LAND BELT CONVEYOR PABRIK SEMEN MENGGUNAKAN LINEAR HEAT DETECTION SYSTEM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Ratna Ikawati ◽  
Widy Wibisono ◽  
Mulia F. Nasution ◽  
Ade Y Prasetya
Keyword(s):  
Optical Fiber ◽  
Early Warning ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Control Room ◽  
Belt Conveyor ◽  
Linear Heat ◽  
Early Warning Mechanism ◽  
Roller Temperature

The cement market is increasingly competitive with the rise of imported cement, which of course must be answered by cement producers among competition. One of the main processes that should not be disturbed is the supply of limestone from quarry to the factory. The safety of people, processes and equipment are certainly necessity for the production process to run smoothly. The problem that can occur in limestone supply is a breakdown on the Overland Belt Conveyor (OLBC) which is caused by a broken roller and causes the belt to tear and potentially fire. For this reason, prevention efforts need to be done with an early warning mechanism to provide alerts to interested parties. Early warning system can be applied to the process because it can detect the beginning of heat so that fires can be prevented. Therefore, it is necessary to install a sensor using optical fiber called a linear heat detector (LHD) on OLBC. Installing the LHD system on OLBC can effectively detect an increase in roller temperature which then becomes an early warning for operators in the control room 24 hours a day to prevent incidents that cause harm to the company due to belt fires or torn belts.

scholarly journals Scrutiny of Methods for Image Detection and Recognition of Different Species of Animals

2019 ◽  
Vol 8 (3S3) ◽  
pp. 151-160
Keyword(s):  
Preventive Measures ◽  
Real Life ◽  
Warning System ◽  
Residential Areas ◽  
Research Gaps ◽  
Image Detection ◽  
The Past ◽  
Animal Detection ◽  
Tracking And Recognition ◽  
Detection And Recognition

Animal detection-based study is useful in many real-life applications. Techniques involved in animal detection are useful in observing the locomotive behavior of the engaged animal and in result it prevent harmful interruption of animals in residential areas. There are some branches of research in animal detection. Some of these branches will therefore be discussed in this journal. Humans have developed many algorithms and techniques to gain a better understanding of animal behaviour. Therefore, for early preventive measures, these technologies can also serve as a warning system for humans from encroachment of dangerous wild animals. Such tasks can be reduced to three main branches, namely animal detection, tracking and recognition. Through these papers, new approaches for study and a variety of technologies/algorithms implemented in the past are identified and appropriate ways for solving the research gaps are suggested to fill the gap.

scholarly journals The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 129
Author(s):  
Zhiyong Sheng ◽  
Dandan Qu ◽  
Yuan Zhang ◽  
Dan Yang
Keyword(s):  
Optical Fiber ◽  
Embedded System ◽  
Real Time ◽  
Digital Signal Processor ◽  
Warning System ◽  
Vibration Signal ◽  
Identification Algorithm ◽  
The Real ◽  
Detection And Identification ◽  
Time Requirements

With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

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