scholarly journals Measurement of Fish School Backscattering Strength Directivity Using Omnidirectional Scanning Sonar

2013 ◽  
Vol 40 (3) ◽  
pp. 113-125
Author(s):  
Ryuzo TAKAHASHI ◽  
Kohji IIDA ◽  
Tohru MUKAI ◽  
Yasushi NISHIMORI
Keyword(s):  
Fish School ◽  
Backscattering Strength

scholarly journals Measurement of Fish School Backscattering Strength Directivity Using Omnidirectional Scanning Sonar

2016 ◽  
Vol 43 (3) ◽  
pp. 145-160
Author(s):  
Ryuzo TAKAHASHI ◽  
Koji IIDA ◽  
Tohru MUKAI ◽  
Yasushi NISHIMORI
Keyword(s):  
Fish School ◽  
Backscattering Strength

scholarly journals A method for reducing uncertainty in estimates of fish-school frequency response using data from multifrequency and multibeam echosounders

2009 ◽  
Vol 66 (6) ◽  
pp. 1155-1161 ◽  
Author(s):  
Laurent Berger ◽  
Cyrille Poncelet ◽  
Verena M. Trenkel
Keyword(s):  
Frequency Response ◽  
Beam Width ◽  
Multibeam Echosounder ◽  
Fish Schools ◽  
Fish School ◽  
Geographic Coordinate System ◽  
Backscattering Strength ◽  
Adaptive Thresholds ◽  
Exact Position ◽  
Using Data

Abstract Berger, L., Poncelet, C., and Trenkel, V. M. 2009. A method for reducing uncertainty in estimates of fish-school frequency response using data from multifrequency and multibeam echosounders. – ICES Journal of Marine Science, 66: 1155–1161. Fish schools can be insonified simultaneously with multifrequency echosounders (e.g. Simrad EK60s) and a multibeam echosounder (e.g. Simrad ME70). This paper presents a method for combining these data to improve estimates of the relative frequency response r(f) of fish schools. Values of r(f) are now commonly used to classify echoes in fishery surveys. The data from the roll- and pitch-stabilized, high-resolution ME70 are used to correct beam-width effects in the multifrequency EK60 data. First, knowing the exact position and orientation of the transducers and the position of the vessel, the echoes are placed into a common geographic coordinate system. Then, the EK60 data are rejected if they do not include a significant percentage of the fish school imaged with the multibeam echosounder. Echoes that exceed the overlap threshold are used to estimate the r(f). The proposed method is applied to simulated and actual data for sardine and mackerel schools in the Bay of Biscay to estimate their r(f) values. The results for different overlap thresholds are compared with the results of a different method, one that uses adaptive thresholds on volume-backscattering strength Sv. The proposed method reduces uncertainty in estimates of r(f) for schools with an overlap of greater than 80%, and it outperforms the Sv-thresholding technique.

scholarly journals Corrections of fish school area and mean volume backscattering strength by simulation of an omnidirectional multi-beam sonar

2018 ◽  
Vol 75 (4) ◽  
pp. 1496-1508 ◽  
Author(s):  
Vasilis Trygonis ◽  
Zacharias Kapelonis
Keyword(s):  
Three Dimensional ◽  
Simulated Data ◽  
Beam Width ◽  
Dependent Manner ◽  
Cross Sectional ◽  
Density Estimates ◽  
Fish Schools ◽  
Fish School ◽  
Backscattering Strength ◽  
The Difference

Abstract Fish school descriptors extracted from omnidirectional multi-beam data are biased due to beam width-related effects, and echotraces are distorted in a range-dependent manner that is a function of transducer intrinsic properties, as well as fish school characteristics. This work investigates a simulation approach that models the three-dimensional insonification of fish schools by an omnidirectional fishery sonar in order to assess the bias in measuring two key morphometric and energetic descriptors, namely the horizontal cross-sectional area of schools and their mean volume backscattering strength. Simulated fish schools of different sizes and backscattering properties were insonified at various ranges from the multi-beam transducer, outputting volume backscattering strength echograms. The simulated data were used to develop empirical models that correct the examined descriptors using only information extracted from the observed echotraces. Depending on the difference between the observed mean volume backscattering strength of a school and the echogram processing threshold, mean absolute percentage errors in measured area and volume backscatter reduced from 100.7% and 79.5% to 5.2% and 6.4%, respectively. The mean volume backscattering strength of a school is a key parameter for obtaining fish density estimates, and the results highlight the need for descriptor corrections to better interpret the multi-beam data.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Verification of the Backscattering Strength Based on the Swimming Behavior of Antarctic Krill

2020 ◽  
Vol 55 (3) ◽  
pp. 391-403
Author(s):  
Inwoo Han ◽  
Wooseok Oh ◽  
Hyoung Sul La ◽  
Seok-Gwan Choi ◽  
Sukyung Kang ◽  
...  
Keyword(s):  
Antarctic Krill ◽  
Swimming Behavior ◽  
Strength Based ◽  
Backscattering Strength

Application of the Improved Grey Model in Power Load Forecasting

2014 ◽  
Vol 575 ◽  
pp. 658-661 ◽  
Author(s):  
Yun Liang Wang ◽  
Qiao Yu Li
Keyword(s):  
Load Forecasting ◽  
Grey Model ◽  
Background Sequence ◽  
Fish School ◽  
Power Load ◽  
Power Load Forecasting ◽  
Improved Model

This paper presents an improved grey model used in power load forecasting. In order to overcome the limitation of the traditional grey model GM(1,1), vector θ is introduced to modify the calculating formula for background sequence value in grey model and build a more adaptable model. Using artificial fish school algorithm can solve the value of vector θ . It reflects that the improved model has higher accuracy of load forecasting and has wider application by cases analysis.

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