scholarly journals On-line detection algorithm of ore grade change in grinding grading system

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 701-709
Author(s):  
Jianjun Zhao ◽  
Junwu Zhou
Keyword(s):  
Detection Method ◽  
Control Process ◽  
Detection Algorithm ◽  
Operating Conditions ◽  
Grading System ◽  
Process Data ◽  
Practical Applications ◽  
Grade Change ◽  
On Line ◽  
Point Detection

AbstractIn process industry control, process data is critical for both control and fault diagnosis. Timely detection of outliers and mutation point in process data can quickly adjust control parameters or discover potential failures throughout the system. Aiming at the shortcomings of the traditional mutation point detection method, such as the detection time delay and not being suitable for the process industrial data that mixed outliers, this paper proposes a mutation point and outliers detection method that is suitable for the grinding grading system using the wavelet method to analyze the “Efficient Scoring Vector.” In this algorithm, to distinguish between outliers and mutation points in the detection process, we propose a detection framework based on the relationship between Lipschitz index and wavelet coefficients. Under this framework, the detection algorithm proposed in this paper can detect outliers and mutation points simultaneously. The advantage of this is that the accuracy of the mutation point detection is not affected by the outliers. This means that the method can process grinding grading system process data containing outliers and mutation points under actual operating conditions and is more suitable for practical applications. Finally, the effectiveness and practicability of the proposed detection method are proved by simulation results.

scholarly journals Self-Similarity Based Corresponding-Point Extraction from Weakly Textured Stereo Pairs

2014 ◽  
Vol 2014 ◽  
pp. 1-20
Author(s):  
Min Mao ◽  
Kuang-Rong Hao ◽  
Yong-Sheng Ding
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Second Step ◽  
Self Similarity ◽  
Detection Mechanism ◽  
Matching Algorithm ◽  
Geometric Transformations ◽  
Point Set ◽  
Image Pairs ◽  
Point Detection

For the areas of low textured in image pairs, there is nearly no point that can be detected by traditional methods. The information in these areas will not be extracted by classical interest-point detectors. In this paper, a novel weakly textured point detection method is presented. The points with weakly textured characteristic are detected by the symmetry concept. The proposed approach considers the gray variability of the weakly textured local regions. The detection mechanism can be separated into three steps: region-similarity computation, candidate point searching, and refinement of weakly textured point set. The mechanism of radius scale selection and texture strength conception are used in the second step and the third step, respectively. The matching algorithm based on sparse representation (SRM) is used for matching the detected points in different images. The results obtained on image sets with different objects show high robustness of the method to background and intraclass variations as well as to different photometric and geometric transformations; the points detected by this method are also the complement of points detected by classical detectors from the literature. And we also verify the efficacy of SRM by comparing with classical algorithms under the occlusion and corruption situations for matching the weakly textured points. Experiments demonstrate the effectiveness of the proposed weakly textured point detection algorithm.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

scholarly journals Change Point Detection with Robust Control Chart

2011 ◽  
Vol 2011 ◽  
pp. 1-20
Author(s):  
Ng Kooi Huat ◽  
Habshah Midi
Keyword(s):  
Robust Control ◽  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Process ◽  
Change Point Detection ◽  
Process Data ◽  
To Come ◽  
Point Detection ◽  
Control Limits

Monitoring a process over time using a control chart allows quick detection of unusual states. In phase I, some historical process data, assumed to come from an in-control process, are used to construct the control limits. In Phase II, the process is monitored for an ongoing basis using control limits from Phase I. In Phase II, observations falling outside the control limits or unusual patterns of observations signal that the process has shifted from in-control process settings. Such signals trigger a search for assignable cause and, if the cause is found, corrective action will be implemented to prevent its recurrence. The purpose of this paper is to introduce a new methodology appropriate for constructing a robust control chart when a nonnormal or a contaminated data that may arise in phase I state. Through extensive Monte Carlo simulations, we examine the behaviors and performances of the proposed MM robust control chart when there is a process shift in mean.

scholarly journals RTL Implementation of White Balance Algorithm Based on ZYNQ

2020 ◽  
Vol 3 (2) ◽  
pp. 12
Author(s):  
Jie Zhou ◽  
Keyao Li
Keyword(s):  
Control Process ◽  
Gain Coefficient ◽  
Detection Algorithm ◽  
World Model ◽  
White Balance ◽  
White Point ◽  
The Common ◽  
The Cost ◽  
Point Detection ◽  
Automatic White Balance

 In view of the common automatic white balance algorithm complexity is too high and the characteristics of the hardware to realize real-time. This paper combines gray world model and the advantage of white point detection algorithm, an adaptive control process is used to calculate gain coefficient and to picture the three-component white balance correction. On this basis to realize the automatic white balance based on the zynq RTL implementation of the algorithm. Compared with the traditional method, the cost of hardware is reduced, and the efficiency and flexibility of the algorithm are improved. Experimental results show that the algorithm can run smoothly and realize accurate correction of off-color images.

scholarly journals Data Streams Oriented Outlier Detection Method: A Fast Minimal Infrequent Pattern Mining

2021 ◽  
Author(s):  
ZhongYu Zhou ◽  
DeChang Pi
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Pattern Mining ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Mining Method ◽  
Telemetry Data ◽  
Process Data ◽  
Mining Data Streams

Outlier detection is a common method for analyzing data streams. In the existing outlier detection methods, most of methods compute distance of points to solve certain specific outlier detection problems. However, these methods are computationally expensive and cannot process data streams quickly. The outlier detection method based on pattern mining resolves the aforementioned issues, but the existing methods are inefficient and cannot meet requirements of quickly mining data streams. In order to improve the efficiency of the method, a new outlier detection method is proposed in this paper. First, a fast minimal infrequent pattern mining method is proposed to mine the minimal infrequent pattern from data streams. Second, an efficient outlier detection algorithm based on minimal infrequent pattern is proposed for detecting the outliers in the data streams by mining minimal infrequent pattern. The algorithm proposed in this paper is demonstrated by real telemetry data of a satellite in orbit. The experimental results show that the proposed method not only can be applied to satellite outlier detection, but also is superior to the existing methods.

scholarly journals Research on Collision Detection Method of Contact On-line Measurement

2021 ◽  
Vol 2095 (1) ◽  
pp. 012079
Author(s):  
Hongfang Qi ◽  
Runqi Guo
Keyword(s):  
Collision Detection ◽  
Detection Method ◽  
Structural Characteristics ◽  
Detection Algorithm ◽  
Discrete Method ◽  
Simple Geometry ◽  
Line Measurement ◽  
On Line ◽  
Grid Division ◽  
Speed And Accuracy

Abstract In order to avoid collision and improve the safety of on-line measurement, a contact on-line measurement collision detection method is studied. Firstly, according to the structural characteristics of the probe and workpiece, the dynamic collision detection between the probe and workpiece is transformed into static collision detection by using the discrete method, and then the grid division of the collision detection space is carried out by using the space division method. Finally, the dynamic collision detection between the probe and workpiece is transformed into the intersection judgment between simple geometry, and according to different collision accuracy requirements, Hierarchical collision detection combining rough detection and fine detection is carried out. Experimental results show that the hierarchical collision detection algorithm has high detection speed and accuracy.

One Point Detection Method of a Rotary Kiln Axis and its Wireless Sensor Receiving System

2014 ◽  
Vol 536-537 ◽  
pp. 304-308
Author(s):  
Zhi Gang Li ◽  
Jian Wang
Keyword(s):  
Rotary Kiln ◽  
Detection Method ◽  
Measuring Method ◽  
Wireless Transmission ◽  
Wireless Sensor ◽  
Hardware System ◽  
On Line ◽  
The One ◽  
Point Detection ◽  
Measurement And Calculation Method

Measurement device for rotary kiln axis demands high positioning accuracy, considering the disadvantage of its complex way of measurement and its wired transmission, an on-line measuring method of rotary kiln using the one-point measuring method at each rotary kiln support is put forward. On the basis of it, a wireless transmitting and receiving hardware system is built, and a software is designed to send the information to the PC using the short distance wireless transmission techniques, then the fitting center of the whole axis is formed. The advantages of the method are that measurement and calculation method is simple, and the information transmission is fast.

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