Simulation Study on Indoor Pollen Removal with Variable Exhaust Angle of an Air Purifier

2015 ◽  
Vol 643 ◽  
pp. 199-204
Author(s):  
Akinori Hashimoto ◽  
Toshiki Takahashi
Keyword(s):  
Removal Efficiency ◽  
Particle Motion ◽  
Graphics Processing Unit ◽  
Elevation Angle ◽  
Computational Time ◽  
Processing Unit ◽  
Simulation Research ◽  
Pollen Removal ◽  
Air Purifier ◽  
Turbulent Airflow

We calculate pollen grain trajectories in indoor airflow generated by an air purifier to investigate its pollen removal efficiency and effectiveness of the swinging louver at its air outlet. The air purifier has the directional airflow output vent on its top surface, and the elevation angle of the exhaust flow can be changed with time. The turbulent airflow field and particle motion are computed alternately. Since the turbulent calculation requires more computational time than the particle motion simulation, we need to accelerate the computation using graphics processing unit (GPU) to increase simulation research efficiency. As a consequence, the calculation of the indoor turbulent airflow and the particle trajectories on the GPU is 18 times faster than the same simulation on the CPU. It is found that variable exhaust angle enhances pollen removal efficiency by 6.9%. Moreover, it appears that we should swing louver from the upper corner of the ceiling to straight above the air purifier at higher angular velocity than 50 deg/s.

scholarly journals Acceleration of a CFD Code with a GPU

2010 ◽  
Vol 18 (3-4) ◽  
pp. 193-201 ◽  
Author(s):  
Dennis C. Jespersen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Graphics Processing ◽  
Work Done

The Computational Fluid Dynamics code OVERFLOW includes as one of its solver options an algorithm which is a fairly small piece of code but which accounts for a significant portion of the total computational time. This paper studies some of the issues in accelerating this piece of code by using a Graphics Processing Unit (GPU). The algorithm needs to be modified to be suitable for a GPU and attention needs to be given to 64-bit and 32-bit arithmetic. Interestingly, the work done for the GPU produced ideas for accelerating the CPU code and led to significant speedup on the CPU.

Feasibility of Ceiling-Mounted Assist Device of Air-Purifier for Removal of Airborne Allergic Pollen Grains

2015 ◽  
Vol 643 ◽  
pp. 205-208
Author(s):  
Toshiki Takahashi ◽  
Akinori Hashimoto ◽  
Shunsuke Tokoi ◽  
Makoto Goto
Keyword(s):  
Turbulent Flow ◽  
Removal Efficiency ◽  
Flow Rate ◽  
Particle Tracking ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Assist Device ◽  
Flow Calculation ◽  
Pollen Removal ◽  
Air Purifier

Feasibility of a ceiling-mounted assist device of the air-purifier for removal of airborne allergenic pollen grains is investigated by both turbulent flow and particle-tracking calculations. The device is mounted straight above the air-purifier and it collects suspended pollen grains in the exhaust flow of the air-purifier. It is found from the turbulent flow calculation that the flow rate of the assist device should be larger than that of the air-purifier. Otherwise the upward air flows around the assist device, and pollen grains move along the surrounding flow; they are never removed from the air. We also found about 40% improvement of the pollen removal efficiency by installing the assist device.

Brittle Intergranular Failure in Grain Aggregates - A Computer Simulation by Means of the Graphics Processing Unit

2009 ◽  
Vol 409 ◽  
pp. 386-389
Author(s):  
Miriam Kupková ◽  
Samuel Kupka
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Binary Variable ◽  
Cubic Lattice ◽  
Increasing Temperature ◽  
Graphics Processing

Within a model considered, each of bonds between contacting grains is treated as a two-state system and represented by a binary variable. Its two values refer to the two possible states of bond – intact or broken. A Monte Carlo simulation of fracture is carried out on a set of binary variables arranged to a cubic lattice. The transition from one configuration of broken bonds to another is governed by a Griffith-like energy associated with each of configurations. The results demonstrate i) the capability of the model to provide a useful information (e.g. the increase in roughness of fracture surface with increasing temperature, that is the transition from “brittle” to “plastic” failure), and ii) the advantage of simulation by using the graphics processing unit (saving of a computational time).

scholarly journals An Ensemble-based Supervised Machine Learning Framework for Android Ransomware Detection

2021 ◽  
Vol 18 (3A) ◽  
Author(s):  
Shweta Sharma ◽  
Rama Krishna ◽  
Rakesh Kumar
Keyword(s):  
Machine Learning ◽  
Graphics Processing Unit ◽  
Principal Component ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Processing Unit ◽  
Central Processing ◽  
Analysis Technique ◽  
Malicious Apps ◽  
Time Required

With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased. The Android-based smartphones occupy the largest market share among other mobile operating systems. The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes. Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks. Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model. After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively

scholarly journals Prediction of PM2.5 Purification Time of Air Purifier under High Elevation Angle and High Wind Speed

2021 ◽  
Vol 257 ◽  
pp. 01081
Author(s):  
WuFeng Jin ◽  
Cheng Wang ◽  
Chong Shi ◽  
Zhiqiang Wang
Keyword(s):  
Prediction Model ◽  
Elevation Angle ◽  
Influence Factors ◽  
High Elevation ◽  
Linear Regression Method ◽  
Simulation Research ◽  
Multiple Linear Regression Method ◽  
Purification Time ◽  
Air Purifier ◽  
The Relationship

In order to achieve the best purification effect of PM2.5 at different personnel positions, it is necessary to study the PM2.5 purification time at different personnel positions in the room when the influence factors change, and establish a prediction model of the purification time.In this paper, air purifier and room models were established for simulation research. Purification time of air purifier at different locations in the room was taken as regression data, and multiple linear regression method was adopted to obtain the relationship between each impact factor and PM2.5 purification time, and a prediction model of PM2.5 purification time at different personnel locations in the room was proposed.The results of this study provide a theoretical basis for putting forward the whole intelligent scheme of air purifier.

scholarly journals Image Classification using Parallel CPU and GPU Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 839-843
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
System Memory ◽  
Graphics Processing

Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

Sign in / Sign up

Export Citation Format

Share Document