scholarly journals Genetic Programming for algae detection in river images

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
B Verma
Keyword(s):  
Image Analysis ◽  
New Zealand ◽  
Genetic Programming ◽  
Real World ◽  
Ground Truth ◽  
Biological Application ◽  
Test Set ◽  
Fitness Value ◽  
Wide Range ◽  
F Measure

© 2015 IEEE. Genetic Programming (GP) has been applied to a wide range of image analysis tasks including many real-world segmentation problems. This paper introduces a new biological application of detecting Phormidium algae in rivers of New Zealand using raw images captured from the air. In this paper, we propose a GP method to the task of algae detection. The proposed method synthesises a set of image operators and adopts a simple thresholding approach to segmenting an image into algae and non-algae regions. Furthermore, the introduced method operates directly on raw pixel values with no human assistance required. The method is tested across seven different images from different rivers. The results show good success on detecting areas of algae much more efficiently than traditional manual techniques. Furthermore, the result achieved by the proposed method is comparable to the hand-crafted ground truth with a F-measure fitness value of 0.64 (where 0 is best, 1 is worst) on average on the test set. Issues such as illumination, reflection and waves are discussed.

scholarly journals Genetic Programming for algae detection in river images

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
B Verma
Keyword(s):  
Image Analysis ◽  
New Zealand ◽  
Genetic Programming ◽  
Real World ◽  
Ground Truth ◽  
Biological Application ◽  
Test Set ◽  
Fitness Value ◽  
Wide Range ◽  
F Measure

© 2015 IEEE. Genetic Programming (GP) has been applied to a wide range of image analysis tasks including many real-world segmentation problems. This paper introduces a new biological application of detecting Phormidium algae in rivers of New Zealand using raw images captured from the air. In this paper, we propose a GP method to the task of algae detection. The proposed method synthesises a set of image operators and adopts a simple thresholding approach to segmenting an image into algae and non-algae regions. Furthermore, the introduced method operates directly on raw pixel values with no human assistance required. The method is tested across seven different images from different rivers. The results show good success on detecting areas of algae much more efficiently than traditional manual techniques. Furthermore, the result achieved by the proposed method is comparable to the hand-crafted ground truth with a F-measure fitness value of 0.64 (where 0 is best, 1 is worst) on average on the test set. Issues such as illumination, reflection and waves are discussed.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis

2018 ◽  
Vol 26 (2) ◽  
pp. 177-212 ◽  
Author(s):  
Tomasz P. Pawlak ◽  
Krzysztof Krawiec
Keyword(s):  
Genetic Programming ◽  
Boolean Function ◽  
Performance Indicators ◽  
Mate Selection ◽  
Semantic Space ◽  
Symbolic Regression ◽  
Test Set ◽  
Theoretical Rationale ◽  
Fitness Test ◽  
Wide Range

Program semantics is a promising recent research thread in Genetic Programming (GP). Over a dozen semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of these operators are designed to exploit the geometric properties of semantic space, while others focus on making offspring effective, that is, semantically different from their parents. Only a small fraction of previous works aimed at addressing both of these features simultaneously. In this article, we propose a suite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation, and crossover. We present a theoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyze each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on a wide range of performance indicators, including best-of-run fitness, test-set fitness, and program size.

Natural enemies of bridal creeper Asparagus asparagoides in New Zealand

2008 ◽  
Vol 61 ◽  
pp. 362-367
Author(s):  
H.M. Harman ◽  
N.W. Waipara ◽  
C.J. Winks ◽  
L.A. Smith ◽  
P.G. Peterson ◽  
...  
Keyword(s):  
New Zealand ◽  
Natural Enemies ◽  
Invertebrate Fauna ◽  
Classical Biocontrol ◽  
Wide Range

Bridal creeper is a weed of natural and productive areas in the northern North Island of New Zealand A classical biocontrol programme was initiated in 20052007 with a survey of invertebrate fauna and pathogens associated with the weed in New Zealand Although bridal creeper was attacked by a wide range of generalist invertebrates their overall damage affected

scholarly journals CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 900
Author(s):  
Hanseob Kim ◽  
Taehyung Kim ◽  
Myungho Lee ◽  
Gerard Jounghyun Kim ◽  
Jae-In Hwang
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Human Perception ◽  
Ground Truth ◽  
Prior Work ◽  
Comparative Experiment ◽  
User Perception ◽  
Depth Distortion ◽  
Real Objects ◽  
Visual Artifacts

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

A CFD Study of Focused Extreme Wave Impact on Decks of Offshore Structures

2014 ◽  
Author(s):  
Xin Lu ◽  
Pankaj Kumar ◽  
Anand Bahuguni ◽  
Yanling Wu
Keyword(s):  
Real World ◽  
Offshore Structures ◽  
Air Gap ◽  
Irregular Waves ◽  
Linear Wave ◽  
Extreme Wave ◽  
Wave Impact ◽  
Loading Test ◽  
Wide Range ◽  
Wave Maker

The design of offshore structures for extreme/abnormal waves assumes that there is sufficient air gap such that waves will not hit the platform deck. Due to inaccuracies in the predictions of extreme wave crests in addition to settlement or sea-level increases, the required air gap between the crest of the extreme wave and the deck is often inadequate in existing platforms and therefore wave-in-deck loads need to be considered when assessing the integrity of such platforms. The problem of wave-in-deck loading involves very complex physics and demands intensive study. In the Computational Fluid Mechanics (CFD) approach, two critical issues must be addressed, namely the efficient, realistic numerical wave maker and the accurate free surface capturing methodology. Most reported CFD research on wave-in-deck loads consider regular waves only, for instance the Stokes fifth-order waves. They are, however, recognized by designers as approximate approaches since “real world” sea states consist of random irregular waves. In our work, we report a recently developed focused extreme wave maker based on the NewWave theory. This model can better approximate the “real world” conditions, and is more efficient than conventional random wave makers. It is able to efficiently generate targeted waves at a prescribed time and location. The work is implemented and integrated with OpenFOAM, an open source platform that receives more and more attention in a wide range of industrial applications. We will describe the developed numerical method of predicting highly non-linear wave-in-deck loads in the time domain. The model’s capability is firstly demonstrated against 3D model testing experiments on a fixed block with various deck orientations under random waves. A detailed loading analysis is conducted and compared with available numerical and measurement data. It is then applied to an extreme wave loading test on a selected bridge with multiple under-deck girders. The waves are focused extreme irregular waves derived from NewWave theory and JONSWAP spectra.

scholarly journals Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Zhao ◽  
E Ferdian ◽  
GD Maso Talou ◽  
GM Quill ◽  
K Gilbert ◽  
...  
Keyword(s):  
New Zealand ◽  
Interobserver Variability ◽  
Ground Truth ◽  
Automated Analysis ◽  
3D Echocardiography ◽  
Training Data ◽  
Patient Specific ◽  
Manual Analysis ◽  
Lv Mass ◽  
3D Echo

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation.  We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Comparing optical performance of a wide range of perovskite/silicon tandem architectures under real-world conditions

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Manvika Singh ◽  
Rudi Santbergen ◽  
Indra Syifai ◽  
Arthur Weeber ◽  
Miro Zeman ◽  
...  
Keyword(s):  
Solar Cells ◽  
Real World ◽  
Solar Spectrum ◽  
Photocurrent Density ◽  
Angle Of Incidence ◽  
Optical Study ◽  
Optical Performance ◽  
Tandem Solar Cells ◽  
Bottom Cell ◽  
Wide Range

Abstract Since single junction c-Si solar cells are reaching their practical efficiency limit. Perovskite/c-Si tandem solar cells hold the promise of achieving greater than 30% efficiencies. In this regard, optical simulations can deliver guidelines for reducing the parasitic absorption losses and increasing the photocurrent density of the tandem solar cells. In this work, an optical study of 2, 3 and 4 terminal perovskite/c-Si tandem solar cells with c-Si solar bottom cells passivated by high thermal-budget poly-Si, poly-SiOx and poly-SiCx is performed to evaluate their optical performance with respect to the conventional tandem solar cells employing silicon heterojunction bottom cells. The parasitic absorption in these carrier selective passivating contacts has been quantified. It is shown that they enable greater than 20 mA/cm2 matched implied photocurrent density in un-encapsulated 2T tandem architecture along with being compatible with high temperature production processes. For studying the performance of such tandem devices in real-world irradiance conditions and for different locations of the world, the effect of solar spectrum and angle of incidence on their optical performance is studied. Passing from mono-facial to bi-facial tandem solar cells, the photocurrent density in the bottom cell can be increased, requiring again optical optimization. Here, we analyse the effect of albedo, perovskite thickness and band gap as well as geographical location on the optical performance of these bi-facial perovskite/c-Si tandem solar cells. Our optical study shows that bi-facial 2T tandems, that also convert light incident from the rear, require radically thicker perovskite layers to match the additional current from the c-Si bottom cell. For typical perovskite bandgap and albedo values, even doubling the perovskite thickness is not sufficient. In this respect, lower bandgap perovskites are very interesting for application not only in bi-facial 2T tandems but also in related 3T and 4T tandems.

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