Angle Detection Using a Continuously Rotating Gyro for Large Scale Profile Evaluation – Reversal Measurement for Eliminating Gyro Drift –

2018 ◽  
Vol 12 (4) ◽  
pp. 582-589 ◽  
Author(s):  
Tatsuya Kume ◽  
Masanori Satoh ◽  
Tsuyoshi Suwada ◽  
Kazuro Furukawa ◽  
Eiki Okuyama ◽  
...  
Keyword(s):  
Large Scale ◽  
Sufficient Accuracy ◽  
Experimental Results ◽  
Gyro Drift ◽  
Scale Profile

Profile evaluation by detecting tangential angles of the profile is competent for large objects because it inherently requires no reference, which is difficult to define with sufficient accuracy as the object becomes larger. We considered using a gyro for detecting the angles instead of an inclinometer or an autocollimator, which are conventionally used as angle detectors. A gyro can detect angles without angular reference; therefore, profiles can be evaluated without the limitation of a reference. However, angles detected by a gyro generally have considerable fluctuations to ensure accuracy in the μrad range, which is the same level as a highly precise inclinometer. In this work, we adopted a periodic reversal measurement using a rotating mechanism to eliminate fluctuations. Analysis and experimental results show that the angles of the gyro’s rotating axis against the earth’s rotating axis can be derived from the angular signals of two gyros rotating in counter directions, and that this method is effective for reducing the influences of fluctuations.

Computational fluid dynamics of rectangular external loop airlift reactor

2020 ◽  
Vol 18 (5-6) ◽  
Author(s):  
Shivanand M. Teli ◽  
Channamallikarjun S. Mathpati
Keyword(s):  
Large Scale ◽  
Lift Coefficient ◽  
Volume Ratio ◽  
Reynolds Stress Model ◽  
Airlift Reactor ◽  
Experimental Results ◽  
Turbulent Dispersion ◽  
Drag Forces ◽  
External Loop ◽  
Turbulent Models

AbstractThe novel design of a rectangular external loop airlift reactor is at present the most used large-scale reactor for microalgae culture. It has a unique future for a large surface to volume ratio for exposure of light radiation for photosynthesis reaction. The 3D simulations have been performed in rectangular EL-ALR. The Eulerian–Eulerian approach has been used with a dispersed gas phase for different turbulent models. The performance and applicability of different turbulent model’s i.e., K-epsilon standard, K-epsilon realizable, K-omega, and Reynolds stress model are used and compared with experimental results. All drag forces and non-drag forces (turbulent dispersion, virtual mass, and lift coefficient) are included in the model. The experimental values of overall gas hold-up and average liquid circulation velocity have been compared with simulation and literature results. It is seemed to give good agreements. For the different elevations in the downcomer section, liquid axial velocity, turbulent kinetic energy, and turbulent eddy dissipation experimental have been compared with different turbulent models. The K-epsilon Realizable model gives better prediction with experimental results.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

A New Algorithm with Distance Constraint for Large-Scale Profile Measurement

2006 ◽  
Vol 326-328 ◽  
pp. 159-162 ◽  
Author(s):  
Yong Qiang Wang ◽  
Nai Guang Lu ◽  
Wen Yi Deng ◽  
Ming Li Dong
Keyword(s):  
Large Scale ◽  
Profile Measurement ◽  
Distance Constraint ◽  
Scale Profile

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

scholarly journals Multi-Task Self-Supervised Learning for Disfluency Detection

2020 ◽  
Vol 34 (05) ◽  
pp. 9193-9200
Author(s):  
Shaolei Wang ◽  
Wangxiang Che ◽  
Qi Liu ◽  
Pengda Qin ◽  
Ting Liu ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Experimental Results ◽  
Training Data ◽  
Competitive Performance ◽  
Test Set ◽  
Full Dataset ◽  
Sentence Classification ◽  
Trained Network

Most existing approaches to disfluency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investigate methods for combining multiple self-supervised tasks-i.e., supervised tasks where data can be collected without manual labeling. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled news data, and propose two self-supervised pre-training tasks: (i) tagging task to detect the added noisy words. (ii) sentence classification to distinguish original sentences from grammatically-incorrect sentences. We then combine these two tasks to jointly train a network. The pre-trained network is then fine-tuned using human-annotated disfluency detection training data. Experimental results on the commonly used English Switchboard test set show that our approach can achieve competitive performance compared to the previous systems (trained using the full dataset) by using less than 1% (1000 sentences) of the training data. Our method trained on the full dataset significantly outperforms previous methods, reducing the error by 21% on English Switchboard.

scholarly journals A Boosting Framework of Factorization Machine

2021 ◽  
pp. 2159036
Author(s):  
Jun Zhou ◽  
Longfei Li ◽  
Ziqi Liu ◽  
Chaochao Chen
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Low Rank ◽  
Inner Product ◽  
Adaptive Boosting ◽  
Rank Matrix ◽  
Factorization Machine ◽  
Fixed Rank ◽  
Low Rank Matrix

Recently, Factorization Machine (FM) has become more and more popular for recommendation systems due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions are learned as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank matrix can help improve the generalization ability of Factorization Machine. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machine (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will gradually increase its rank according to its performance until the performance does not grow. Extensive experiments are conducted to validate the proposed method on multiple large-scale datasets. The experimental results demonstrate that the proposed method can be more effective than the state-of-the-art Factorization Machines.

Dynamic Grid-Based Algorithms in DDM

2012 ◽  
Vol 433-440 ◽  
pp. 4297-4301
Author(s):  
Hui Ru Wang ◽  
Jing Ding
Keyword(s):  
Large Scale ◽  
Data Distribution ◽  
Experimental Results ◽  
Interactive Simulation ◽  
Distribution Management ◽  
Distributed Interactive Simulation ◽  
Dynamic Multicast ◽  
Grid Based

For large-scale distributed interactive simulation, it is important and difficult for data to communicate among thousands of objects. The purpose of the Data Distribution Management (DDM) service performs data filter and reduces irrelevant data between federations. Grid-based algorithm can only manage to filter part of irrelevant data. Experimental results show that, compare with normal grid-based algorithms, the dynamic multicast method can minimize.

The Random Element in Bird ‘Navigation’

1952 ◽  
Vol 29 (4) ◽  
pp. 532-560
Author(s):  
D. H. WILKINSON
Keyword(s):  
Experimental Evidence ◽  
Large Scale ◽  
Random Element ◽  
Random Search ◽  
Wild Birds ◽  
Experimental Results ◽  
Average Speed ◽  
Data Form ◽  
Coherent Picture

An analysis is presented of the results to be expected from experiments on the homing of wild birds if the only factor operating is random search. It is found that this model reproduces the experimental results and predicts values for the parameters involved in the theory which are inherently plausible and which are in quantitative accord with experimental evidence. Attention is paid to the dependence of percentage return on distance of release, to the dependence of the average speed of return on this distance, and to the distribution in time of the returns. These three sets of data form a coherent picture within the framework of the hypothesis of random search. Certain types of migration are also briefly considered. It is not suggested that this investigation proves that random search is indeed the mechanism by which the homing of wild birds is accomplished, but it is submitted that the large-scale experiments of the type considered here are not susceptible of the interpretation that a true navigational ability is involved.

Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction

2012 ◽  
pp. 225-252
Author(s):  
Nurcin Celik ◽  
Esfandyar Mazhari ◽  
John Canby ◽  
Omid Kazemi ◽  
Parag Sarfare ◽  
...  
Keyword(s):  
Execution Time ◽  
Large Scale ◽  
Time Synchronization ◽  
Computational Grid ◽  
Experimental Results ◽  
Time Interval ◽  
Computational Power ◽  
Large Scale Systems ◽  
Large Scale Simulations ◽  
Reduce Execution Time

Simulating large-scale systems usually entails exhaustive computational powers and lengthy execution times. The goal of this research is to reduce execution time of large-scale simulations without sacrificing their accuracy by partitioning a monolithic model into multiple pieces automatically and executing them in a distributed computing environment. While this partitioning allows us to distribute required computational power to multiple computers, it creates a new challenge of synchronizing the partitioned models. In this article, a partitioning methodology based on a modified Prim’s algorithm is proposed to minimize the overall simulation execution time considering 1) internal computation in each of the partitioned models and 2) time synchronization between them. In addition, the authors seek to find the most advantageous number of partitioned models from the monolithic model by evaluating the tradeoff between reduced computations vs. increased time synchronization requirements. In this article, epoch- based synchronization is employed to synchronize logical times of the partitioned simulations, where an appropriate time interval is determined based on the off-line simulation analyses. A computational grid framework is employed for execution of the simulations partitioned by the proposed methodology. The experimental results reveal that the proposed approach reduces simulation execution time significantly while maintaining the accuracy as compared with the monolithic simulation execution approach.

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