Image recognition of microplastic particles in marine sediments – planned activities

2020 ◽  
Author(s):  
Juho Junttila ◽  
Steffen Aagaard Sørensen ◽  
Thomas Haugland Johansen ◽  
Geir Wing Gabrielsen
Keyword(s):  
Image Recognition ◽  
Marine Sediments ◽  
Marine Sediment ◽  
Environmental Research ◽  
Data Sets ◽  
Plastic Pollution ◽  
Sediment Samples ◽  
Environmental Threat ◽  
Manual Classification

<p>Information about the distribution microplastics is crucial in marine environmental research. At present, plastic pollution is an environmental threat to the oceans and more than 90 % of microplastic particles are assumed to be deposited in the sediments on the ocean floor. An efficient way of identifying microplastic particles in marine sediments would result in improved understanding of microplastic distribution, inception, accumulation areas, and impact on marine ecosystems. Today, manual classification of microplastic particles using a microscope is time consuming. The goal of this study is to identify microplastic particles in marine sediment samples with the help of image recognition and machine learning. The possibility of using artificial microplastic particles will also be tested as a means of constructing comprehensive training sets. Existing algorithms already have been successful in classification of microfossils, which could be further developed for recognition of microplastic particles. Furthermore, hyperspectral analysis will be tested to determine the origin of the microplastic particles. Our overall goal is to train classifiers that in the future successfully can recognize different plastic objects in marine sediment samples and thereby replace the time-consuming manual classification task. Comparison between human based and machine based identifications for a large number of data sets will be made to test these classifiers.</p>

scholarly journals DISTRIBUSI SPASIAL FORAMINIFERA DI PERAIRAN TELUK CENDERAWASIH, PAPUA BARAT

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Eko Saputro ◽  
Lili Fauzielly ◽  
Imelda Rosalina Silalahi ◽  
Winatris Winatris
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Marine Sediments ◽  
Marine Sediment ◽  
Benthic Foraminifera ◽  
Planktonic Foraminifera ◽  
Sediment Samples ◽  
Enclosed Bay ◽  
Papua Barat ◽  
Good Environment

Sebanyak 20 sampel sedimen dari perairan Teluk Cenderawasih telah digunakan sebagai bahan studi foraminifera, yang bertujuan untuk mengetahui bagaimana sebaran spasial dan struktur komunitas foraminifera di perairan Teluk Cenderawasih. Hasil penelitian menunjukkan komposisi foraminifera planktonik yang terdiri dari 7 Genus dan 13 Spesies sedangkan foraminifera bentonik terdiri dari 57 Genus dan 87 Spesies. Foraminifera planktonik yang paling umum ditemukan karena muncul di seluruh sampel adalah genus Globigerinoides, terutama G. trilobus dan G. ruber. Sedangkan foraminifera bentonik didominasi oleh subordo Rotaliina, dan yang paling banyak ditemukan adalah genus Cibicidiodes dan Lenticulina. Keanekaragaman foraminifera planktonik dan bentonik termasuk dalam kategori tinggi dengan kisaran antara 0.82 – 0.90 (planktonik) dan 0.79 – 0.95 (bentonik). Kemerataan foraminifera planktonik dan bentonik juga termasuk dalam kategori tinggi dengan kisaran antara 0.83 – 0.99 (planktonik) dan 0.82–0.99 (bentonik). Sedangkan untuk dominasi foraminifera planktonik dan bentonik berada dalam kategori rendah dengan kisaran 0.10 – 0.18 (planktonik) dan 0.05 – 0.21 (bentonik). Hal ini menunjukkan bahwa Teluk Cendrawasih meskipun merupakan perairan yang semi tertutup, namun kondisinya masih sangat bagus bagi perkembangan foraminiferaKata Kunci : foraminifera, distribusi spasial, struktur komunitas, dan Teluk Cenderawasih A total of 20 marine sediment samples from Cenderawasih Bay waters have been used for foraminiferal study, . The purpose to describe the spatial distribution and structure of the foraminifera community in the waters of Cenderawasih Bay. The results indicate that marine sediments are composed of 7 genera and 13 species of planktonic foraminifera, and 57 genera and 87 species belong to benthic foraminifera. The most common planktonic foraminifera is Globigerinoides which is found in all location, particularly G. trilobus and G. ruber. Furthermore, benthonic foraminifera is dominated by subordo Rotaliina, particularly genera Cibicidoides and Lenticulina as the most common genera. Diversity of both Planktonic and benthonic foraminifera are categorized as high, the values are between 0.82 and 0.90, and between 0.79 and 0.95 respectively. Planktonic and benthonic foraminiferal evenness are also high with range value between 0.83 and 0.99 (planktonic), and between 0.82 and 0.99 (benthonic). In contrast, dominance of both foraminiferal type are low, between 0.10 and 0.18 for planktonic, and between 0.05 and 0.21 (benthonic).This indicates that despite a semi–enclosed bay, Cendrawasih Bay is still considered as a good environment for foraminiferal community. Keywords: foraminifera, spatial distribution, community structure, and Cenderawasih Bay.

DIRECT SPECIATION OF SULFUR IN MARINE SEDIMENT BY HIGH-RESOLUTION PIXE

2000 ◽  
Vol 10 (01n02) ◽  
pp. 47-56 ◽  
Author(s):  
KUNIKO MAEDA ◽  
TOMOTAKE HASEGAWA ◽  
HIROMI HAMANAKA ◽  
KENICHI HASEGAWA ◽  
MASARU MAEDA
Keyword(s):  
High Resolution ◽  
Marine Sediments ◽  
Marine Sediment ◽  
Chemical Shifts ◽  
Sediment Samples ◽  
Chemical States

Chemical shifts of Kα1,2 line of sulfur in marine sediments were measured with in-air high-resolution PIXE in order to examine the possibility of direct speciation of sulfur in such environmental substances. Change of chemical states of sulfur along the depth in the sediments was observed. Oxidation of the sediment samples by air was also examined. Problems to be improved for exact speciation are discussed.

Distillation of total reduced inorganic sulfur from marine sediment samples v1

2021 ◽  
Author(s):  
Patrick D Larkin ◽  
Sebastian Rubiano-Rincon
Keyword(s):  
Marine Sediments ◽  
Marine Sediment ◽  
Sediment Samples ◽  
Inorganic Sulfur

This procedure can be used to precipitate H2S as Ag2S from the Total Reduced Inorganic Sulfur (TRIS) fraction of marine sediments.

Radiocarbon dating of lacustrine and marine sediments from the Bunger Hills, East Antarctica

1994 ◽  
Vol 6 (3) ◽  
pp. 375-378 ◽  
Author(s):  
Martin Melles ◽  
Sergey R. Verkulich ◽  
Wolf-D. Hermichen
Keyword(s):  
Organic Carbon ◽  
Marine Sediments ◽  
Marine Sediment ◽  
Radiocarbon Dating ◽  
Sediment Samples ◽  
Water Lake ◽  
Antarctic Marine ◽  
The Antarctic ◽  
Marine Basins ◽  
Fresh Water Lake

Radiocarbon dating was carried out on the total organic carbon of 19 lacustrine and marine sediment samples from the Bunger Hills. The results indicate that radiocarbon contamination is negligible throughout two sediment sequences from a fresh water lake. In contrast, two sequences from marine basins are irregularly influenced by the Antarctic Marine Reservoir Effect, which today amounts to more than 1000 years, depending on the degree of dilution with meltwater. All dated sediments were deposited during Holocene time.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Manual versus Automatic Classification of Laryngeal Lesions based on Vascular Patterns in CE+NBI Images

2020 ◽  
Vol 6 (3) ◽  
pp. 70-73
Author(s):  
Nazila Esmaeili ◽  
Alfredo Illanes ◽  
Axel Boese ◽  
Nikolaos Davaris ◽  
Christoph Arens ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Automatic Classification ◽  
Vascular Pattern ◽  
Objective Interpretation ◽  
Laryngeal Lesions ◽  
Malignant Lesions ◽  
Manual Classification

AbstractLongitudinal and perpendicular changes in the blood vessels of the vocal fold have been related to the advancement from benign to malignant laryngeal cancer stages. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) provides intraoperative realtime visualization of vascular pattern in Larynx. The evaluation of these vascular patterns in CE+NBI images is a subjective process leading to differentiation difficulty and subjectivity between benign and malignant lesions. The main objective of this work is to compare multi-observer classification versus automatic classification of laryngeal lesions. Six clinicians visually classified CE+NBI images into benign and malignant lesions. For the automatic classification of CE+NBI images, we used an algorithm based on characterizing the level of the vessel’s disorder. The results of the manual classification showed that there is no objective interpretation, leading to difficulties to visually distinguish between benign and malignant lesions. The results of the automatic classification of CE+NBI images on the other hand showed the capability of the algorithm to solve these issues. Based on the observed results we believe that, the automatic approach could be a valuable tool to assist clinicians to classifying laryngeal lesions.

Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules

2021 ◽  
pp. 1-1
Author(s):  
Adam Kiersztyn ◽  
Pawe Karczmarek ◽  
Krystyna Kiersztyn ◽  
Witold Pedrycz
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Information Granules

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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