SPECIALIZED MEASURES OF LEAF OUTLINES AND HIERARCHICAL PERCEPTRONS IN AN AUTOMATIC IDENTIFICATION OF PLANTS

2004 ◽  
Vol 15 (08) ◽  
pp. 1171-1186 ◽  
Author(s):  
WOJCIECH BORKOWSKI ◽  
LIDIA KOSTRZYŃSKA
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Real Data ◽  
Automatic Identification ◽  
Identification System ◽  
Data Sets ◽  
Identification Algorithm ◽  
Plant Identification ◽  
Neural Network Classifiers ◽  
Computer Identification

The development of an efficient image-based computer identification system for plants or other organisms is an important ambitious goal, which is still far from realization. This paper presents three new methods potentially usable for such a system: fractal-based measures of complexity of leaf outline, a heuristic algorithm for automatic detection of leaf parts — the blade and the petiole, and a hierarchical perceptron — a kind of neural network classifier. The next few sets of automatically extractable features of leaf blades, encompassed those presented and/or traditionally used, are compared in the task of plant identification using the simplest known "nearest neighbor" identification algorithm, and more realistic neural network classifiers, especially the hierarchical. We show on two real data sets that the presented techniques are really usable for automatic identification, and are worthy of further investigation.

scholarly journals An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning

2021 ◽  
Vol 11 (5) ◽  
pp. 2415
Author(s):  
Yulong Feng ◽  
Wei Xiao ◽  
Teng Wu ◽  
Jianwei Zhang ◽  
Jing Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Automatic Identification ◽  
Data Sets ◽  
Identification Algorithm ◽  
Brain Functions ◽  
Data Measurement ◽  
Weak Magnetic Fields ◽  
Space Projection ◽  
Artificial Neural Network Ann

Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm. The results show that the method can recognize the blink artifacts from the original detected MEG data, providing a feasible MEG data-processing approach that can potentially be implemented automatically and simultaneously with MEG data measurement.

scholarly journals Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

2021 ◽  
Vol 2 ◽  
Author(s):  
Chengjie Li ◽  
Lidong Zhu ◽  
Zhongqiang Luo ◽  
Zhen Zhang ◽  
Yilun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Bp Neural Network ◽  
Source Separation ◽  
Optimal Solution ◽  
Blind Signal Separation ◽  
Automatic Identification ◽  
Identification System ◽  
Data Set ◽  
Processing Power

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

scholarly journals Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

2019 ◽  
Vol 9 (1) ◽  
pp. 6705-6714
Keyword(s):  
Neural Network ◽  
Medicinal Plants ◽  
Leaf Shape ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Plant Identification ◽  
Learning Techniques ◽  
Whale Optimization ◽  
Medical Plant

Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional binary phase encoding (2DBPE). Finally, a whale optimization with deep neural network (DNN) classifier is used to characterize the type of plants. Accuracy in identification of any ayurvedic plant leaf is achieved by understanding and extracting the plant features. The main objective of the proposed EAC-AMP approach is to increase the accuracy of classifier. MATLAB experimental analysis showed better results such as accuracy, sensitivity and specificity.

The Automated Risk Estimation for the Navigation of Autonomous Ships by Learning with Navigation Feature

2020 ◽  
pp. 2041003
Author(s):  
Wei Chian Tan ◽  
Kie Hian Chua ◽  
Yanling Wu
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Risk Estimation ◽  
Nearest Neighbor Search ◽  
Mathematical Representation ◽  
Automatic Identification ◽  
Identification System ◽  
North West ◽  
The North ◽  
Neighbor Search

This work presents a data-driven approach for the automated risk estimation of the voyage of a vessel or ship. While the industry is moving from a compliance-based framework with existing rules to a risk-based one, there is also a need to monitor the risk of a vessel from the perspective of the navigation. This is of even higher importance for the case of autonomous ships. Built based on the state-of-the-art mathematical representation, the navigation feature, each existing voyage is transformed into a corresponding series of points in [Formula: see text]-dimensional space. During the stage of pre-processing, given a set of historical Automatic Identification System (AIS) data, those records that belong to the same vessel within a certain period of time are taken as a voyage and mapped to the corresponding space of the navigation feature. After the pre-processing and during the online monitoring, the current trajectory of the vessel is transformed into the corresponding representation in the same way. Based on a nearest-neighbor search scheme, the distance from the nearest neighbor is taken as the risk of the current voyage. In other words, the deviation from the closest route in the historical data is taken as the risk. The developed method has demonstrated encouraging performance on a set of challenging historical AIS data from the Australian Maritime Safety Authority, covering three regions in the Australian territory, namely, the Bass Strait, the Great Australian Bight and the North West.

scholarly journals Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data

2020 ◽  
Vol 10 (11) ◽  
pp. 4010 ◽  
Author(s):  
Kwang-il Kim ◽  
Keon Myung Lee
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Gear ◽  
Trajectory Data ◽  
Data Set ◽  
Prediction Module ◽  
Fishing Gears ◽  
Fully Connected

Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

scholarly journals Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations

2017 ◽  
Vol 70 (4) ◽  
pp. 699-718 ◽  
Author(s):  
Donggyun Kim ◽  
Katsutoshi Hirayama ◽  
Tenda Okimoto
Keyword(s):  
Distributed Algorithms ◽  
Search Algorithm ◽  
Real Data ◽  
Stochastic Search ◽  
Automatic Identification ◽  
Identification System ◽  
Local Search Algorithm ◽  
Ship Collision ◽  
Stochastic Search Algorithm ◽  
Made In

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover.

Supervised Networks That Self-Organize Class Outputs

1997 ◽  
Vol 9 (3) ◽  
pp. 637-648 ◽  
Author(s):  
Ramesh R. Sarukkai
Keyword(s):  
Neural Network ◽  
Mapping Function ◽  
Real Data ◽  
Learning Problems ◽  
Data Sets ◽  
Neural Network Learning ◽  
Network Learning ◽  
Unseen Data ◽  
Class Labels ◽  
Self Organizing

Supervised, neural network, learning algorithms have proved very successful at solving a variety of learning problems; however, they suffer from a common problem of requiring explicit output labels. In this article, it is shown that pattern classification can be achieved, in a multilayered, feedforward, neural network, without requiring explicit output labels, by a process of supervised self-organization. The class projection is achieved by optimizing appropriate within-class uniformity and between-class discernibility criteria. The mapping function and the class labels are developed together iteratively using the derived self organizing backpropagation algorithm. The ability of the self-organizing network to generalize on unseen data is also experimentally evaluated on real data sets and compares favorably with the traditional labeled supervision with neural networks. In addition, interesting features emerge out of the proposed self-organizing supervision, which are absent in conventional approaches.

scholarly journals Performance Analysis of a Medium Frequency Offshore Grid for Identification Of Vessels Sailing on High Density Maritime European Routes

2017 ◽  
Vol 24 (4) ◽  
pp. 18-26
Author(s):  
Alfonso López ◽  
Miguel Gutiérrez ◽  
Andrés Ortega ◽  
Cristina Puente ◽  
Alejandro Morales ◽  
...  
Keyword(s):  
Grid Method ◽  
Real Data ◽  
Satellite System ◽  
Simulation Software ◽  
High Density ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Distributed Data ◽  
Medium Frequency

Abstract The paper analyses the performance of an Automatic Vessel Identification System on Medium Frequency (AVISOMEF), which works with the Grid Method (GM) on high density maritime European routes using real data and uniformly distributed data. Compared to other systems, AVISOMEF is a novelty, as it is not a satellite system, nor is it limited by a given coverage distance, in contrast to the Automatic Identification System (AIS), though in exceptional circumstances it leans towards it. To perform the analysis, special simulation software was developed. Moreover, a number of maritime routes along with their traffic density data were selected for the study. For each route, two simulations were performed, the first of which based on the uniform traffic distribution along the route, while the second one made use of real AIS data positioning of vessels sailing on the selected routes. The obtained results for both simulations made the basis for formulating conclusions regarding the capacity of selected routes to support AVISOMEF.

scholarly journals AUTOMATIC IDENTIFICATION OF ARCHAEOLOGICAL ARTIFACTS ON THE EXCAVATION SITE

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1347-1353
Author(s):  
E. Alby ◽  
V. Desbiolles ◽  
M. Lecocq
Keyword(s):  
Neural Network ◽  
Automatic Identification ◽  
Data Sets ◽  
Strong Constraint ◽  
Neural Network Learning ◽  
Archaeological Data ◽  
Gis Tools ◽  
Excavation Site ◽  
Learning Data ◽  
Archaeological Objects

Abstract. Archaeological data is processed to ensure that it can be easily accessed and used. The integration of the documentation into GIS tools is carried out in the post-excavation phase. The final documents are completed on the basis of intermediate documents made on the excavation site. Time during the excavation is precious and any action that takes time is questioned to allow to devote a maximum of resources to the most important tasks. Many tasks are associated with a traditional paper entry. The aim of this study is to experiment with the use of means of automating the management of archaeological documents in order to minimize the repetition of recording acts of different kind. The integration of computer technology in the field is gradually being achieved through the use of tablets, but their use on the excavation site remains a strong constraint. The first task of this automation lies in the possibility of identifying objects of interest during the excavation. In order to make this recognition of archaeological entities possible it is necessary to ask when they are easily identifiable: in the excavation report. The hypothesis formulated here is that excavation reports can be used as a source for creating learning data sets of neural networks dedicated to the recognition of archaeological objects on site. Two important steps in automating the integration of archaeological data are presented here, the extraction of images and their semantics from excavation reports and the learning process of a neural network for the recognition of archaeological entities at the site of their discovery. The extraction of images and the identification of what they contain allows to enrich neural network learning datasets. Tests have been made to validate the ability of such tools to reliably identify particular objects. We chose CNN to test the ability to recognize archaeological objects in an excavation context. It is an image-based network. What is sought here is the ability to recognize an object for a neural network.

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