Reducing Response Time by Tilt Sensor using Neural Network Comparator

2019 ◽  
Vol 139 (9) ◽  
pp. 310-316
Author(s):  
Kosei Shimoo ◽  
Yukihisa Nanbu ◽  
Masahiro Teramura
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Tilt Sensor

scholarly journals AN ON-LINE NON-LEPTONIC NEURAL TRIGGER APPLIED TO AN EXPERIMENT LOOKING FOR BEAUTY

1994 ◽  
Vol 05 (05) ◽  
pp. 863-870
Author(s):  
C. BALDANZA ◽  
F. BISI ◽  
A. COTTA-RAMUSINO ◽  
I. D’ANTONE ◽  
L. MALFERRARI ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Enrichment Factor ◽  
Data Stream ◽  
Particle Production ◽  
Event Sample ◽  
On Line

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV π− on a Cu target, are presented. The neural trigger has been used to send on a special data stream (the Fast Stream) events to be analyzed with high priority. The non-leptonic signature uses microvertex detector data and was devised so as to enrich the fraction of events containing C3 secondary vertices (i.e, vertices having three tracks whith sum of electric charges equal to +1 or -1). The neural trigger module consists of a VME crate hosting two ETANN analog neural chips from Intel. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6–7.1 (depending on the event sample considered), which multiplied by that already provided by the standard non-leptonic trigger leads to a global C3 enrichment factor of ≈150. In the event sample selected by the neural trigger for the Fast Stream, 1 every ≈7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs.

scholarly journals STUDY OF A MA16-BASED VERTEX TRIGGER FOR THE CDF EXPERIMENT

1995 ◽  
Vol 06 (05) ◽  
pp. 681-692
Author(s):  
R. ODORICO
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Impact Parameter ◽  
Electronic Components ◽  
The Neural Network ◽  
Viable Solution ◽  
Impact Parameters ◽  
Input Variables ◽  
Better Than

A Neural Network trigger for [Formula: see text] events based on the SVT microvertex processor of experiment CDF at Fermilab is presented. It exploits correlations among track impact parameters and azimuths calculated by the SVT from the SVX microvertex detector data. The neural trigger is meant for implementation on the systolic Siemens microprocessor MA16, which has already been used in a neural-network trigger for experiment WA92 at CERN. A suitable set of input variables is found, which allows a viable solution for the preprocessing task using standard electronic components. The response time of the neural-network stage of the trigger, including preprocessing, can be estimated ~10 μs. Its precise value depends on the quantitative specifications of the output signals of the SVT, which is still in development. The performance of the neural-network trigger is found to be significantly better than that of a conventional trigger exclusively based on impact parameter data.

scholarly journals Research on Virtual Machine Response Time Prediction Method Based on GA-BP Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Guo ◽  
Shu Liu ◽  
Bin Zhang ◽  
Yongming Yan
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Virtual Machine ◽  
Bp Neural Network ◽  
Large Scale ◽  
Virtual Machines ◽  
Back Propagation ◽  
Prediction Method ◽  
Critical Issue ◽  
Machine Component

Cloud application provides access to large pool of virtual machines for building high-quality applications to satisfy customers’ requirements. A difficult issue is how to predict virtual machine response time because it determines when we could adjust dynamic scalable virtual machines. To address the critical issue, this paper proposes a prediction virtual machine response time method which is based on genetic algorithm-back propagation (GA-BP) neural network. First of all, we predict component response time by the past virtual machine component usage experience data: the number of concurrent requests and response time. Then, we could predict virtual machines service response time. The results of large-scale experiments show the effectiveness and feasibility of our method.

Artificial Neural Network Application in Optimal Coordination of Directional Overcurrent Protective Relays in Electrical Mesh Distribution Network

2015 ◽  
Vol 785 ◽  
pp. 48-52 ◽  
Author(s):  
Osaji Emmanuel ◽  
Mohammad Lutfi Othman ◽  
Hashim Hizam ◽  
Muhammad Murtadha Othman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Time ◽  
Short Circuit ◽  
Distribution Networks ◽  
Test System ◽  
Wrong Response ◽  
Optimal Coordination ◽  
Artificial Neural ◽  
Short Circuit Fault

Directional Overcurrent relays (DOCR) applications in meshed distribution networks (MDN), eliminate short circuit fault current due to the topographical nature of the system. Effective and reliable coordination’s between primary and secondary relay pairs ensures effective coordination achievement. Otherwise, the risk of safety of lives and installations may be compromised alongside with system instability. This paper proposes an Artificial Neural Network (ANN) approach of optimizing the system operation response time of all DOCR within the network to address miscoordination problem due to wrong response time among adjacent DOCRs to the same fault. A modelled series of DOCRs in a simulated IEEE 8-bus test system in DigSilent Power Factory with extracted data from three phase short circuit fault analysis adapted in training a custom ANN. Hence, an improved optimized time is produced from the network output to eliminate miscoordination among the DOCRs.

scholarly journals Embedding Convolution Neural Network-Based Defect Finder for Deployed Vision Inspector in Manufacturing Company Frontec

2020 ◽  
Vol 34 (08) ◽  
pp. 13164-13171
Author(s):  
Kyoung Jun Lee ◽  
Jun Woo Kwon ◽  
Soohong Min ◽  
Jungho Yoon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Response Time ◽  
Automobile Industry ◽  
Large Scale ◽  
Group Model ◽  
Quality Inspection ◽  
Circular Hough Transform ◽  
Learning Framework ◽  
Visual Geometry

In collaboration with Frontec, which produces parts such as bolts and nuts for the automobile industry, Kyung Hee University and Benple Inc. develop and deploy AI system for automatic quality inspection of weld nuts. Various constraints to consider exist in adopting AI for the factory, such as response time and limited computing resources available. Our convolutional neural network (CNN) system using large-scale images must classify weld nuts within 0.2 seconds with accuracy over 95%. We designed Circular Hough Transform based preprocessing and an adjusted VGG (Visual Geometry Group) model. The system showed accuracy over 99% and response time of about 0.14 sec. We use TCP / IP protocol to communicate the embedded classification system with an existing vision inspector using LabVIEW. We suggest ways to develop and embed a deep learning framework in an existing manufacturing environment without a hardware change.

scholarly journals PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yuanchang Zhong ◽  
Xu Huang ◽  
Pu Meng ◽  
Fachuan Li
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Pid Control ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Gas Pressure ◽  
Tracking Performance ◽  
Step Response ◽  
Pressure Regulator

The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor) of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

scholarly journals A Novel Optimization towards Higher Reliability in Predictive Modelling towards Code Reusability

2017 ◽  
Vol 7 (5) ◽  
pp. 2855
Author(s):  
Manoj H.M. ◽  
Nandakumar A.N.
Keyword(s):  
Neural Network ◽  
Software Engineering ◽  
Response Time ◽  
Real Time ◽  
Predictive Modelling ◽  
Least Square ◽  
Research Approach ◽  
Software Reusability ◽  
Remarkable Progress

Although, the area of software engineering has made a remarkable progress in last decade but there is less attention towards the concept of code reusability in this regards.Code reusability is a subset of Software Reusability which is one of the signature topics in software engineering. We review the existing system to find that there is no progress or availability of standard research approach toward code reusability being introduced in last decade. Hence, this paper introduced a predictive framework that is used for optimizing the performance of code reusability. For this purpose, we introduce a case study of near real-time challenge and involved it in our modelling. We apply neural network and Damped-Least square algorithm to perform optimization with a sole target to compute and ensure highest possible reliability. The study outcome of our model exhibits higher reliability and better computational response time.

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