scholarly journals Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo

2021 ◽  
Vol 6 (2) ◽  
pp. 192
Author(s):  
Qurotul Aini ◽  
Ninda Lutfiani ◽  
Hendra Kusumah ◽  
Muhammad Suzaki Zahran
Keyword(s):  
Object Recognition ◽  
Processing Speed ◽  
Discussion Section ◽  
Image Detection ◽  
Advantages And Disadvantages ◽  
Machine Learning Model ◽  
Previous Algorithm ◽  
The 1960S ◽  
High Level ◽  
Detection Processing

Object recognition and detection have been in request by numerous parties since Computer Vision innovation within the 1960s, both within the industrial and medical area. Since then, many studies have focused on object recognition and detection with various types of algorithm models that can recognize and detect objects in an image. However, not all of these algorithm models are efficient and effective in their application. Most of the previous algorithm models have a relatively high level of complexity. Here, the author tries to explain and introduce the YOLO (You only look once) algorithm model, which has a high enough image detection processing speed capability and accuracy that can compete with the previous algorithm models. There are several advantages and disadvantages of each version made, which are explained in the discussion section.

scholarly journals Forming Communicative Competence of Future Tesol Teachers by Microteaching (Based on British Experience)

2017 ◽  
Vol 7 (4) ◽  
pp. 7-15
Author(s):  
Natalya Bidyuk
Keyword(s):  
Higher Education ◽  
Communicative Competence ◽  
Scientific Discourse ◽  
Advantages And Disadvantages ◽  
The World ◽  
Three Stages ◽  
British Experience ◽  
The 1960S ◽  
High Level ◽  
British Education

Abstract The article deals with the analysis of the process of forming communicative competence of future TESOL students by means of microteaching based on the experience of leading British higher education institutions. It has been specified that the phenomenon of communicative competence in scientific discourse originated in the 1960s and connected with the prominent British, German and American scientific researchers (L. Bachman, R. Campbel, M. Canale, J. Habermas, M. Halliday, M. Swain et al.), who have produced their own implications on communicative competence. Based on their views it has been specified that the communicative competence should be acquired in all areas, namely, speaking, reading, listening, and writing. Therefore, it has been justified that microteaching is of significance, as it allows future TESOL teachers to develop the high level of communicative competence. It has been found out that microteaching was developed by Stanford University specialists and became adopted by many teacher education institutions in the world. The model of microteaching acquired in British education institutions has been described. It has been outlined that microteaching is seen as a simplified form of teaching, the main peculiarity of which means it is evaluated by peers/supervisors to provide a feedback. It has been indicated that the main aim of microteaching is to allow future TESOL teachers to determine their level of communicative competence most objectively. It has been clarified that future TESOL teachers should undergo three stages to form their communicative competence. The advantages and disadvantages of microteaching in the context of forming future TESOL teachers’ communicative competence have been presented. The most prominent advantages have been analyzed. The perspectives for further studies have been determined.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations, and Recommendations

2021 ◽  
Author(s):  
Lichao Xu ◽  
Szu-Yun Lin ◽  
Andrew W. Hlynka ◽  
Hao Lu ◽  
Vineet R. Kamat ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Data Exchange ◽  
Distributed Simulation ◽  
Distributed Data ◽  
Interactive Simulation ◽  
Integrated Simulation ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
High Level ◽  
Data Passing

AbstractThere has been a strong need for simulation environments that are capable of modeling deep interdependencies between complex systems encountered during natural hazards, such as the interactions and coupled effects between civil infrastructure systems response, human behavior, and social policies, for improved community resilience. Coupling such complex components with an integrated simulation requires continuous data exchange between different simulators simulating separate models during the entire simulation process. This can be implemented by means of distributed simulation platforms or data passing tools. In order to provide a systematic reference for simulation tool choice and facilitating the development of compatible distributed simulators for deep interdependent study in the context of natural hazards, this article focuses on generic tools suitable for integration of simulators from different fields but not the platforms that are mainly used in some specific fields. With this aim, the article provides a comprehensive review of the most commonly used generic distributed simulation platforms (Distributed Interactive Simulation (DIS), High Level Architecture (HLA), Test and Training Enabling Architecture (TENA), and Distributed Data Services (DDS)) and data passing tools (Robot Operation System (ROS) and Lightweight Communication and Marshalling (LCM)) and compares their advantages and disadvantages. Three specific limitations in existing platforms are identified from the perspective of natural hazard simulation. For mitigating the identified limitations, two platform design recommendations are provided, namely message exchange wrappers and hybrid communication, to help improve data passing capabilities in existing solutions and provide some guidance for the design of a new domain-specific distributed simulation framework.

Residual Religiosity on a Hull Council Estate

1989 ◽  
Vol 37 (3) ◽  
pp. 474-504
Author(s):  
Peter G. Forster
Keyword(s):  
Empirical Data ◽  
Sunday School ◽  
The 1960S ◽  
High Level

In the 1960s it was widely assumed that a process of secularisation was taking place and was likely to continue. Recent empirical data about a council estate, which among other things relate religious variables to age, are thought to throw light on such questions. These findings demonstrate that ‘religiosity’ tends to increase with age, but that for certain purposes important elements of residual religiosity remain. The decline in Sunday School is particularly noteworthy, and suggests a movement in the direction of a ‘voluntary’ church. Remaining churchgoers have a high level of commitment, suggesting that a move from church to sect can operate in the mainstream church.

Simulation of bright–dark soliton solutions of the Lonngren wave equation arising the model of transmission lines

2021 ◽  
pp. 2150484
Author(s):  
Asif Yokuş
Keyword(s):  
Wave Equation ◽  
Soliton Solutions ◽  
Stationary Wave ◽  
Dark Soliton ◽  
Traveling Wave Solution ◽  
Bright Soliton ◽  
Auxiliary Equation ◽  
Discussion Section ◽  
Auxiliary Equation Method ◽  
Advantages And Disadvantages

In this study, the auxiliary equation method is applied successfully to the Lonngren wave equation. Bright soliton, bright–dark soliton solutions are produced, which play an important role in the distribution and distribution of electric charge. In the conclusion and discussion section, the effect of nonlinearity term on wave behavior in bright soliton traveling wave solution is examined. The advantages and disadvantages of the method are discussed. While graphs representing the stationary wave are obtained, special values are given to the constants in the solutions. These graphs are presented as 3D, 2D and contour.

Numerical Simulations of Ocean Drilling System Behavior With a Surface or a Subsea BOP in Waves and Current

2008 ◽  
Author(s):  
Celso K. Morooka ◽  
Raphael I. Tsukada ◽  
Dustin M. Brandt
Keyword(s):  
System Behavior ◽  
Traditional System ◽  
Drilling Rig ◽  
Advantages And Disadvantages ◽  
Ocean Drilling ◽  
Sea Bottom ◽  
Drilling System ◽  
Drilling Rigs ◽  
High Level ◽  
Drilling Bit

Subsea equipment such as the drilling riser and the subsea Blow-Out Preventer (BOP) are mandatory in traditional systems used in deep sea drilling for ocean floor research and petroleum wellbore construction. The drilling riser is the vertical steel pipe that transfers and guides the drill column and attached drilling bit into a wellbore at the sea bottom. The BOP is used to protect the wellbore against uncontrolled well pressures during the offshore drilling operation. Presently, there is a high level of drilling activity worldwide and in particular in deeper and ultra-deeper waters. This shift in depth necessitates not only faster drilling systems but drilling rigs upgraded with a capacity to drill in the deep water. In this scenario, two general drilling systems are today considered as alternatives: the traditional system with the subsea BOP and the alternate system with the surface BOP. In the present paper, the two systems are initially described in detail, and a numerical simulation in time domain to estimate the system behavior is presented. Simulations of a floating drilling rig coupled with the subsea and surface BOP in waves and current are carried out for a comparison between the two methods. Results are shown for riser and BOP displacements. Critical riser issues for the systems are discussed, comparing results from both drilling system calculations. Conclusions are addressed showing advantages and disadvantages of each drilling system, and indicating how to correct the problems detected on each system.

scholarly journals A New Paradigm for Training Hyperactive Dopamine Transporter Knockout Rats: Influence of Novel Stimuli on Object Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Natalia P. Kurzina ◽  
Anna B. Volnova ◽  
Irina Y. Aristova ◽  
Raul R. Gainetdinov
Keyword(s):  
Object Recognition ◽  
Dopamine Transporter ◽  
Cognitive Task ◽  
Recognition Task ◽  
Long Term Memory ◽  
New Paradigm ◽  
Dopaminergic Transmission ◽  
Novel Objects ◽  
High Level ◽  
The Brain

Attention deficit hyperactivity disorder (ADHD) is believed to be connected with a high level of hyperactivity caused by alterations of the control of dopaminergic transmission in the brain. The strain of hyperdopaminergic dopamine transporter knockout (DAT-KO) rats represents an optimal model for investigating ADHD-related pathological mechanisms. The goal of this work was to study the influence of the overactivated dopamine system in the brain on a motor cognitive task fulfillment. The DAT-KO rats were trained to learn an object recognition task and store it in long-term memory. We found that DAT-KO rats can learn to move an object and retrieve food from the rewarded familiar objects and not to move the non-rewarded novel objects. However, we observed that the time of task performance and the distances traveled were significantly increased in DAT-KO rats in comparison with wild-type controls. Both groups of rats explored the novel objects longer than the familiar cubes. However, unlike controls, DAT-KO rats explored novel objects significantly longer and with fewer errors, since they preferred not to move the non-rewarded novel objects. After a 3 months’ interval that followed the training period, they were able to retain the learned skills in memory and to efficiently retrieve them. The data obtained indicate that DAT-KO rats have a deficiency in learning the cognitive task, but their hyperactivity does not prevent the ability to learn a non-spatial cognitive task under the presentation of novel stimuli. The longer exploration of novel objects during training may ensure persistent learning of the task paradigm. These findings may serve as a base for developing new ADHD learning paradigms.

scholarly journals Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

2020 ◽  
Author(s):  
Alexander J.E. Kell ◽  
Sophie L. Bokor ◽  
You-Nah Jeon ◽  
Tahereh Toosi ◽  
Elias B. Issa
Keyword(s):  
Object Recognition ◽  
Model Organism ◽  
High Standard ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Neuroscience ◽  
Brain Functions ◽  
Invariant Object Recognition ◽  
Small Model ◽  
High Level

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

scholarly journals EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS

2018 ◽  
Vol XLII-1 ◽  
pp. 347-354 ◽  
Author(s):  
F. Politz ◽  
M. Sester
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Good Alternative ◽  
Aerial Images ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Sensing Applications ◽  
High Level

<p><strong>Abstract.</strong> Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96<span class="thinspace"></span>% in an ALS and 83<span class="thinspace"></span>% in a DIM test set.</p>

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