Development of a Biologically-Based System for Detection and Tracking of Airborne Herbicides

1993 ◽  
Vol 7 (2) ◽  
pp. 404-410 ◽  
Author(s):  
Kassim Al-Khatib ◽  
Gaylord I. Mink ◽  
Guy Reisenauer ◽  
Robert Parker ◽  
Halvor Westberg ◽  
...  
Keyword(s):  
Computer Model ◽  
High Sensitivity ◽  
Weather Conditions ◽  
Weather Data ◽  
Indicator Plant ◽  
Field Exposure ◽  
Detection And Tracking ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Herbicide Drift

This study was designed to develop a protocol for using a biologically-based system to detect and tract airborne herbicides. Common bean, lentil, and pea were selected for their quasi-diagnostic sensitivity to chlorsulfuron, thifensulfuron, metsulfuron, tribenuron, paraquat, glyphosate, bromoxynil, 2,4-D, and dicamba. Plants were grown in the greenhouse at Prosser, WA, and placed at 25 exposure sites at weekly intervals between Apr. 2 and Oct. 15, 1991. After 1 wk of field exposure plants were brought back and observed for herbicide symptoms over a 28-d period. Symptoms that developed were compared with symptoms caused by disease, insects, adverse weather conditions, and herbicides applied at different rates under controlled conditions on these species. In addition, if herbicide symptoms were observed, herbicide spray records and weather data in the area were used in a computer model to determine the source of potential herbicide drift. This study demonstrates that indicator plant species selected for high sensitivity to herbicides can be used to monitor the occurrence of herbicide movement.

scholarly journals Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather

2021 ◽  
Vol 11 (7) ◽  
pp. 3018
Author(s):  
Shih-Lin Lin ◽  
Bing-Han Wu
Keyword(s):  
Kalman Filter ◽  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Weather Data ◽  
Stereo Images ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
On The Road

A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.

Enhancing the Performance of Vehicle Passenger Detection under Adverse Weather Conditions Using Augmented Reality-Based Machine Learning Approach

2021 ◽  
pp. 036119812110312
Author(s):  
Jaeyun Lee ◽  
Sangcheol Kang ◽  
Jaedeok Lim ◽  
Seong Geon Kim ◽  
Changmo Kim
Keyword(s):  
Augmented Reality ◽  
Traffic Congestion ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Training Data ◽  
Weather Data ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Adverse Weather Conditions ◽  
Adverse Weather

In response to extreme traffic congestion in metropolitan areas that causes unnecessarily long travel times, high fuel consumption, and excessive greenhouse gas emissions, transportation agencies have implemented various strategies to mitigate traffic congestion. Managed lanes—one of the measures applied worldwide—provide benefits to road users and operating agencies by integrating advanced technologies such as electronic and dynamic tolling systems. However, those agencies already implementing or considering implementing the managed lane strategy are seeking a solution to effectively and properly charge toll rates based on vehicle occupancy and penalize violating vehicles. Vehicle passenger detection systems (VPDSs) have been developed and evaluated worldwide, but limitations still inhibit their full implementation. This study confirms that the performance of the deep learning algorithm, a core VPDS technology, declines under certain adverse weather conditions because of lack of training data sets. The performance of the “you only look once” (YOLOv3) model trained with a normal weather data set decreased by as much as 8.5% when it was tested for adverse weather conditions. In this study, augmented reality (AR) models are developed to enhance the accuracy of vehicle passenger detection (VPDA) by the VPDS by training the algorithm with AR images representing virtual adverse weather conditions. Models trained with AR image sets of various weather categories (fog, rain, and snow) attained VPDA enhanced by up to 7.9%. The final model significantly improves VPDA under adverse weather conditions. The proposed models could be considered for implementation with road weather information systems under adverse weather conditions.

scholarly journals Ship Handling in Unprotected Waters: A Review of New Technologies in Escort Tugs to Improve Safety

2021 ◽  
Vol 2 (1) ◽  
pp. 46-62
Author(s):  
Santiago Iglesias-Baniela ◽  
Juan Vinagre-Ríos ◽  
José M. Pérez-Canosa
Keyword(s):  
High Performance ◽  
New Technologies ◽  
Weather Conditions ◽  
Hull Form ◽  
Best Available Technologies ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
New Type ◽  
Technological Challenge ◽  
And Performance

It is a well-known fact that the 1989 Exxon Valdez disaster caused the escort towing of laden tankers in many coastal areas of the world to become compulsory. In order to implement a new type of escort towing, specially designed to be employed in very adverse weather conditions, considerable changes in the hull form of escort tugs had to be made to improve their stability and performance. Since traditional winch and ropes technologies were only effective in calm waters, tugs had to be fitted with new devices. These improvements allowed the remodeled tugs to counterbalance the strong forces generated by the maneuvers in open waters. The aim of this paper is to perform a comprehensive literature review of the new high-performance automatic dynamic winches. Furthermore, a thorough analysis of the best available technologies regarding towline, essential to properly exploit the new winches, will be carried out. Through this review, the way in which the escort towing industry has faced this technological challenge is shown.

Statistical analysis and critical review of navigational accidents in adverse weather conditions

2018 ◽  
Vol 163 ◽  
pp. 502-517 ◽  
Author(s):  
N.P. Ventikos ◽  
A.D. Papanikolaou ◽  
K. Louzis ◽  
A. Koimtzoglou
Keyword(s):  
Statistical Analysis ◽  
Critical Review ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Adverse Weather
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