scholarly journals A Comparison of the Effectiveness of the RRT, PRM, and Novel Hybrid RRT-PRM Path Planners

2021 ◽  
Vol 9 (12) ◽  
pp. 600-611
Author(s):  
Jacqueline Jermyn
Keyword(s):  
Path Length ◽  
Performance Metrics ◽  
Search Tree ◽  
Random Tree ◽  
The Novel ◽  
Search Area ◽  
Probabilistic Roadmap ◽  
Path Planner ◽  
Number Of Iterations ◽  
Path Number

Abstract: Sampling-based path planners develop paths for robots to journey to their destinations. The two main types of sampling-based techniques are the probabilistic roadmap (PRM) and the Rapidly Exploring Random Tree (RRT). PRMs are multi-query methods that construct roadmaps to find routes, while RRTs are single-query techniques that grow search trees to find paths. This investigation evaluated the effectiveness of the PRM, the RRT, and the novel Hybrid RRT-PRM methods. This novel path planner was developed to improve the performance of the RRT and PRM techniques. It is a fusion of the RRT and PRM methods, and its goal is to reduce the path length. Experiments were conducted to evaluate the effectiveness of these path planners. The performance metrics included the path length, runtime, number of nodes in the path, number of nodes in the search tree or roadmap, and the number of iterations required to obtain the path. Results showed that the Hybrid RRT-PRM method was more effective than the PRM and RRT techniques because of the shorter path length. This new technique searched for a path in the convex hull region, which is a subset of the search area near to the start and end locations. The roadmap for the Hybrid RRT-PRM could also be re-used to find pathways for other sets of initial and final positions. Keywords: Path Planning, Sampling-based algorithms, search tree, roadmap, single-query planners, multi-query planners, Rapidly Exploring Random Tree (RRT), Probabilistic Roadmap (PRM), Hybrid RRT-PRM

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

scholarly journals Probing Phenomenological Experiences Through Electroencephalography Brainwave Signals In Neuroarchitecture Study

2019 ◽  
Vol 6 (3) ◽  
pp. 11-20 ◽  
Author(s):  
Vickram Thevar Vijayan ◽  
Mohamed Rashid Embi
Keyword(s):  
Human Physiology ◽  
Built Environment ◽  
Performance Metrics ◽  
Positive Influence ◽  
The Novel ◽  
Daily Lives ◽  
Phenomenological Studies ◽  
The Impact ◽  
The Relationship ◽  
Area Of Study

Experiences are a part of our daily lives through our interactions with the environment around us. We live life through the realm of experiences, be it playing or working. As we encounter phenomena frequently, it is deduced that most of it comes from within the built environment, considering how most of our time is spent indoors. Hence, it is imperative that we understand the impact of the built environment on human physiology especially within the context of religious spaces which is largely attributed to phenomenological experiences. Despite the importance of understanding the impact of the built environment on human physiology, phenomenological studies that addresses this relationship are still lacking. This presents a gap which necessitates evidence to be provided in the form of phenomenological studies. Hence, this study attempts to address the gap by utilising evidential data with the utilisation of the portable electroencephalography (EEG) device. In doing so, the brainwave readings from four participants at the Tuanku Mizan Zainal Abidin Mosque were observed. Data from the EEG device in the form of brainwave signals was analysed through the performance metrics detection suite which focused on the possibility of analysing brainwave data through three phases of habitation. The findings detected relaxation performance metrics from the participants whilst being within the mosque prayer area, whereas the phases prior to entering and after leaving the mosque appears to have detected higher excitement and engagement levels. Thus, it could be deduced that the interior prayer area of the mosque appears to have had a positive influence on the participant's physiology. This study could contribute to the novel field of neuroarchitecture in Malaysia, an area of study at the threshold of neuroscience and architecture that could be significant in understanding the relationship between the built environment and human physiology.

scholarly journals An effective method for clustering-based web service recommendation

2022 ◽  
Vol 12 (2) ◽  
pp. 1571
Author(s):  
Ha Huy Cuong Nguyen ◽  
Bui Thanh Khiet ◽  
Van Loi Nguyen ◽  
Thanh Thuy Nguyen
Keyword(s):  
Web Services ◽  
Web Service ◽  
Performance Metrics ◽  
Research Problem ◽  
Quality Of Services ◽  
The Novel ◽  
Service Recommendation ◽  
Functional Relevance ◽  
Web Service Classification

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

scholarly journals A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images

2020 ◽  
Author(s):  
Tayyip Ozcan
Keyword(s):  
Transfer Learning ◽  
Performance Metrics ◽  
Experimental Studies ◽  
Large Family ◽  
Classification Performance ◽  
The Novel ◽  
X Ray ◽  
Learning Framework ◽  
Classification Feature ◽  
Novel Coronavirus

Abstract Coronavirus, a large family of viruses, causes illness in both humans and animals. The novel coronavirus (COVID-19) came up in Wuhan in December 2019. This deadly COVID-19 pandemic has become very fast-spreading and currently present in several countries worldwide. The timely detection of patients who have COVID-19 is vitally important. To this end, scientists are working on different detection methods.In this paper, a grid search (GS) and pre-trained model aided convolutional neural network (CNN) model is proposed to detect COVID-19 in X-Ray images. In the proposed method, the GS method is employed to optimize the hyperparameters of CNN, which directly affects classification performance. Three pre-trained CNN models (GoogleNet, ResNet18 and ResNet50), which can be used for classification, feature extraction and transfer learning purposes were used for transfer learning in this study. The proposed method was trained using the training and validation subdatasets of the collected dataset and detail evaluations are presented according to different performance metrics. According to the experimental studies, the best results were obtained with the GS and ResNet50 aided model.

Investigation of Branch Accessibility with a Robotic Pruner for Pruning Apple Trees

2021 ◽  
Vol 64 (5) ◽  
pp. 1459-1474
Author(s):  
Azlan Zahid ◽  
Long He ◽  
Daeun Choi ◽  
James Schupp ◽  
Paul Heinemann
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Path Length ◽  
Computation Time ◽  
Tree Canopy ◽  
Random Tree ◽  
Target Point ◽  
List Type ◽  
Apple Trees ◽  
End Effector

HighlightsA branch accessibility simulation was performed for robotic pruning of apple trees.A virtual tree environment was established using a kinematic manipulator model and an obstacle model.Rapidly-exploring random tree (RRT) was combined with smoothing and optimization for improved path planning.Effects on RRT path planning of the approach angle of the end-effector and cutter orientation at the target were studied.Abstract. Robotic pruning is a potential solution to reduce orchard labor and associated costs. Collision-free path planning of the manipulator is essential for successful robotic pruning. This simulation study investigated the collision-free branch accessibility of a six rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear cutter end-effector. A virtual environment with a simplified tall spindle tree canopy was established in MATLAB. An obstacle-avoidance algorithm, rapidly-exploring random tree (RRT), was implemented for establishing collision-free paths to reach the target pruning points. In addition, path smoothing and optimization algorithms were used to reduce the path length and calculate the optimized path. Two series of simulations were conducted: (1) performance and comparison of the RRT algorithm with and without smoothing and optimization, and (2) performance of collision-free path planning considering different approach poses of the end-effector relative to the target branch. The simulations showed that the RRT algorithm successfully avoided obstacles and allowed the manipulator to reach the target point with 23 s average path finding time. The RRT path length was reduced by about 28% with smoothing and by 25% with optimization. The RRT smoothing algorithm generated the shortest path lengths but required about 1 to 3 s of additional computation time. The lowest coefficient of variation and standard deviation values were found for the optimization method, which confirmed the repeatability of the method. Considering the different end-effector approach poses, the simulations suggested that successfully finding a collision-free path was possible for branches with no existing path using the ideal (perpendicular cutter) approach pose. This study provides a foundation for future work on the development of robotic pruning systems. Keywords: Agricultural robotics, Collision-free path, Manipulator, Path planning, Robotic pruning, Virtual tree environment.

Behavioral analysis of anxiolytic action of phenazepam in conditions of an acute psychogenic stress (predator presentation) in Danio rerio

2021 ◽  
Vol 19 (1) ◽  
pp. 71-78
Author(s):  
Andrei A. Lebedev ◽  
Aleksandr S. Devyashin ◽  
Aleksandra A. Blazhenko ◽  
Sergei V. Kazakov ◽  
Viktor A. Lebedev ◽  
...  
Keyword(s):  
Danio Rerio ◽  
Path Length ◽  
Stress Test ◽  
Control Group ◽  
The Novel ◽  
Highly Sensitive ◽  
Psychogenic Stress ◽  
Pharmacological Substance ◽  
Dose Dependent ◽  
Dose Dependent Effect

We studied the effect of benzodiazepine anxiolytic phenazepam after the predator presentation to Danio rerio. The test of novelty was used: the fish was placed first in a beaker with a dissolved pharmacological substance (or H2O) and then into a novel tank for 6 min, where the trajectory, the path length, the number of movements to the upper part of the novel tank, the number and time of the pattern of freezing were measured. It is shown that, in response to the novelty of tank, the fish are reacted by submerging to the bottom, increasing the frizing, and reducing the number of movements to the upper half of the novel tank. After phenazepam administration, the fish were not only in the lower, but also in the upper part of the novel tank. The average path length did not change significantly in the range of doses used. The number and time of the frizing, as well as the time spent in the lower part of the novel tank, decreased more than 2 times compared to the control group of animals and showed a dose-dependent effect. The number of movements to the upper part of the novel tank for the experiment increased significantly from 1 in the control to 57 after phenazepam in a dose of 1 mg/l. At the same time, the number of movements of fish to the upper part of the novel tank significantly increased more than 2 times from 3th min of the experiment with the use of phenazepam in a dose of 1 mg/l. Predator presentation (Hypsophrys nicaraguensis) caused an increase in the number of freezing (temporary immobilization) and a decrease in the length of the trajectory of movement in the novel tank as compared with the Danio rerio control group. Phenazapam at a dose of 1 mg/l removed the effects of a predator, while exhibiting a typical effect: the number of movements to the upper part of the tank during the experiment significantly increased to 30; the time at the bottom of the tank was halved. It was concluded that the novelty stress test and the test with a predator are highly sensitive for studying anxiety-phobic reactions in Danio rerio.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 333
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

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