scholarly journals Application-Specific Evaluation of a Weed-Detection Algorithm for Plant-Specific Spraying

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7262
Author(s):  
Thijs Ruigrok ◽  
Eldert van Henten ◽  
Johan Booij ◽  
Koen van Boheemen ◽  
Gert Kootstra
Keyword(s):  
Evaluation Method ◽  
Detection System ◽  
Field Performance ◽  
Detection Algorithm ◽  
Image Evaluation ◽  
Labor Costs ◽  
Weed Detection ◽  
Image Metrics ◽  
Plant Level ◽  
Application Specific

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals Color Image Evaluation for Small Space Based on FA and GEP

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Li Deng ◽  
Sui-Huai Yu ◽  
Wen-Jun Wang ◽  
Jun-Xuan Chen ◽  
Guo-Chang Liu
Keyword(s):  
Evaluation System ◽  
Frequency Conversion ◽  
Evaluation Method ◽  
Color Image ◽  
Gene Expression Programming ◽  
Image Evaluation ◽  
Evaluation Tool ◽  
Small Space ◽  
Entropy Weight ◽  
Fast Evaluation

Aiming at the problem that color image is difficult to quantify, this paper proposes an evaluation method of color image for small space based on factor analysis (FA) and gene expression programming (GEP) and constructs a correlation model between color image factors and comprehensive color image. The basic color samples of small space and color images are evaluated by semantic differential method (SD method), color image factors are selected via dimension reduction in FA, factor score function is established, and by combining the entropy weight method to determine each factor weights then the comprehensive color image score is calculated finally. The best fitting function between color image factors and comprehensive color image is obtained by GEP algorithm, which can predict the users’ color image values. A color image evaluation system for small space is developed based on this model. The color evaluation of a control room on AC frequency conversion rig is taken as an example, verifying the effectiveness of the proposed method. It also can assist the designers in other color designs and provide a fast evaluation tool for testing users’ color image.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

scholarly journals Rancangan sistem pengukuran kinerja untuk penentuan key performance indicator dengan metode Balance scorecard

2020 ◽  
Vol 12 (2) ◽  
pp. 248
Author(s):  
Lucky Setiawan ◽  
Yuliani Fauziah
Keyword(s):  
Performance Measurement ◽  
Balanced Scorecard ◽  
Evaluation Method ◽  
Corporate Strategy ◽  
Performance Indicator ◽  
Production Costs ◽  
Success Factor ◽  
Key Performance Indicator ◽  
Labor Costs ◽  
The Balanced Scorecard

This research adopts the Vision and Mission of a textile company headquartered in Tangerang. This company produces producing polo shirts, golf shirts, track suits, sweat shirts, and pants using materials such as single jersey, pique, lace in cotton, polyester, and others. The company's vision and mission can represent other textile companies that have the same processes and products. The characteristics of textile companies that have processes with human skill factors become critical points that are difficult to replace with robot technology. This has an impact on the needs of a large number of human resources in the industry. The increase in labor costs which always increases every year has a very significant impact on production costs. Thus, expansion is one of the critical factors to be used as a top management strategy in determining the current KPI items. The Balanced Scorecard is a performance evaluation method that can meet these expectations. The design of company performance measurement in this study begins with the translation of the company's vision and mission, secondly, making a proposed corporate strategy using the PQCDSME approach (profit, quality, Cost, Delivery, Service, Moral, Environment). Third, calcify the company's strategy into the perspective of the balanced scorecard. Fourth, the decision making process to determine KPI items using the brainstorming method. Fifth, determining the critical success factor and key performance indicator. Other factors are also compared between the achievement of the previous year as an illustration of determining a target. So the target given will be in accordance with the SMART rules (speciefic, measurable, achievable, realistic and timely). The results are obtained with 4 criteria (financial, consumer, internal business processes, learning and growth). This performance measurement produced 19 key performance indicators, consisting of 6 financial perspective indicators, 1 customer perspective indicator, 7 internal business process perspective indicators, and 5 growth and learning perspective indicators.

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

scholarly journals A Novel Wavelet-Based Algorithm for Detection of QRS Complex

2019 ◽  
Vol 9 (10) ◽  
pp. 2142 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Hung-Yu Chang ◽  
Yan-Hua Huang ◽  
Cheng-Yu Yeh
Keyword(s):  
Evaluation Method ◽  
Decision Rules ◽  
Detection Algorithm ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Frequency Components ◽  
Noise Evaluation ◽  
Complex Detection ◽  
Almost All

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

Research on TV Goniometer Object Extraction Algorithm Based on Threshold Segmentation

2014 ◽  
Vol 889-890 ◽  
pp. 1093-1098
Author(s):  
He Chen ◽  
Nan Li ◽  
Tian Chen Huang ◽  
Rong Xia Duan
Keyword(s):  
Optimal Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Field Of View ◽  
Object Extraction ◽  
Gray Level ◽  
Line Extraction ◽  
Standard Signal ◽  
Extraction Algorithm ◽  
Target Extraction

In the TV goniometer detection system, to play the signal and field of view points line extraction is a key link in the process of parameter detection. Combination of target processing requirements, this article will target extraction algorithm based on gray level threshold and edge detection algorithm is studied, and through the experimental analysis to select the optimal algorithm was applied to the detection of TV goniometer; According to the characteristics of the standard signal and view points, lines, and put forward the corresponding methods of target recognition, and is verified through experiments

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