Differential-Evolution-based Weights Fine Tuning Mechanism for GRU to Predict 5G Traffic Flow

2021 ◽  
Author(s):  
Min-Yan Tsai ◽  
Hsin-Hung Cho
Keyword(s):  
Differential Evolution ◽  
Traffic Flow ◽  
Fine Tuning ◽  
Tuning Mechanism

scholarly journals Differential Evolution: A Survey and Analysis

2018 ◽  
Vol 8 (10) ◽  
pp. 1945 ◽  
Author(s):  
Tarik Eltaeib ◽  
Ausif Mahmood
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Based ◽  
Vital Role ◽  
Fine Tuning ◽  
Control Parameters ◽  
Hybrid Techniques ◽  
Global Optimizer

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

Functional analysis of the SUMOylation pathway in Drosophila

2008 ◽  
Vol 36 (5) ◽  
pp. 868-873 ◽  
Author(s):  
Ana Talamillo ◽  
Jonatan Sánchez ◽  
Rosa Barrio
Keyword(s):  
Functional Analysis ◽  
Fine Tuning ◽  
Model Systems ◽  
Post Translational Modification ◽  
Reversible Process ◽  
Cellular Processes ◽  
Tuning Mechanism ◽  
Sumo Conjugation

SUMOylation, a reversible process used as a ‘fine-tuning’ mechanism to regulate the role of multiple proteins, is conserved throughout evolution. This post-translational modification affects several cellular processes by the modulation of subcellular localization, activity or stability of a variety of substrates. A growing number of proteins have been identified as targets for SUMOylation, although, for many of them, the role of SUMO conjugation on their function is unknown. The use of model systems might facilitate the study of SUMOylation implications in vivo. In the present paper, we have compiled what is known about SUMOylation in Drosophila melanogaster, where the use of genetics provides new insights on SUMOylation's biological roles.

scholarly journals Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Betania Hernández-Ocaña ◽  
Ma. Del Pilar Pozos-Parra ◽  
Efrén Mezura-Montes ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Mechanical Design ◽  
Differential Evolution Algorithm ◽  
Fine Tuning ◽  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Planar Mechanisms ◽  
Bacterial Foraging ◽  
Evolution Algorithm ◽  
Parameter Values

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Increase of SARS-CoV 3CL peptidase activity due to macromolecular crowding effects in the milieu composition

2010 ◽  
Vol 391 (12) ◽  
Author(s):  
Debora N. Okamoto ◽  
Lilian C.G. Oliveira ◽  
Marcia Y. Kondo ◽  
Maria H.S. Cezari ◽  
Zoltán Szeltner ◽  
...  
Keyword(s):  
Rna Virus ◽  
Antiviral Agents ◽  
Fine Tuning ◽  
Host Cells ◽  
Respiratory Syndrome Virus ◽  
Peptidase Activity ◽  
Buffer Solutions ◽  
Tuning Mechanism ◽  
Positive Strand Rna Virus ◽  
Positive Strand Rna

Abstract The 3C-like peptidase of the severe acute respiratory syndrome virus (SARS-CoV) is strictly required for viral replication, thus being a potential target for the development of antiviral agents. In contrast to monomeric picornavirus 3C peptidases, SARS-CoV 3CLpro exists in equilibrium between the monomer and dimer forms in solution, and only the dimer is proteolytically active in dilute buffer solutions. In this study, the increase of SARS-CoV 3CLpro peptidase activity in presence of kosmotropic salts and crowding agents is described. The activation followed the Hofmeister series of anions, with two orders of magnitude enhancement in the presence of Na2SO4, whereas the crowding agents polyethylene glycol and bovine serum albumin increased the hydrolytic rate up to 3 times. Kinetic determinations of the monomer dimer dissociation constant (K d) indicated that activation was a result of a more active dimer, without significant changes in K d values. The activation was found to be independent of substrate length and was derived from both k cat increase and K m decrease. The viral peptidase activation described here could be related to the crowded intracellular environment and indicates a further fine-tuning mechanism for biological control, particularly in the microenvironment of the vesicles that are induced in host cells during positive strand RNA virus infection.

scholarly journals URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES

2018 ◽  
Vol XLII-4 ◽  
pp. 499-506 ◽  
Author(s):  
M. V. Peppa ◽  
D. Bell ◽  
T. Komar ◽  
W. Xiao
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Flow Analysis ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Harmonic Mean ◽  
Fine Tuning ◽  
Road Users ◽  
Car Detection ◽  
Traffic Flow Analysis

<p><strong>Abstract.</strong> Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2<span class="thinspace"></span>% precision, 58.5<span class="thinspace"></span>% recall and 73.4<span class="thinspace"></span>% harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4<span class="thinspace"></span>%), recall (68.8<span class="thinspace"></span>%) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.</p>

scholarly journals Segmentation of White Chali Arecanuts using Soft Computing Methods

2020 ◽  
Vol 9 (7) ◽  
pp. 1049-1055
Keyword(s):  
Differential Evolution ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Search Algorithms ◽  
Fine Tuning ◽  
Experimental Result ◽  
Multilevel Thresholding ◽  
Low Contrast ◽  
Morphological Segmentation ◽  
Grading Systems

Performance of computer vision based grading systems is remarkably affected by the efficiency of object segmentation. The automatic segmentation of low contrast objects is a challenging task in various fruit and nut grading systems. In this paper background elimination of white chali arecanut images is carried out using morphological segmentation. The fine-tuning of edge threshold for morphological segmentation is achieved by obtaining threshold values from multilevel thresholding of original grayscale image. The best figure ground segmentation is selected by a network trained using shape parameters of the ground truth masks. The performance of morphological segmentation is evaluated for the best figure ground segmentations using precision, recall and F-scores. Comparison of segmentation performance is done by employing multilevel thresholding based on Otsu, Fuzzy c-mean, Harmony search, Differential Evolution and Cuckoo Search algorithms. The experimental result shows that, multilevel thresholding using Differential Evolution and Cuckoo Search algorithms yield best results for the fine-tuning of edge thresholds and hence the better segmentation performance of the white chali arecanuts

Building of Fine-Tuning Mechanism Posture and Kinematics Model for Shield Segment Erector

2013 ◽  
Vol 634-638 ◽  
pp. 3737-3740
Author(s):  
Ying Chao Yuan ◽  
Yu Zhang
Keyword(s):  
Parallel Mechanism ◽  
Fine Tuning ◽  
Kinematics Analysis ◽  
Lie Groupoids ◽  
Kinematic Synthesis ◽  
Kinematics Model ◽  
Tuning Mechanism ◽  
And Performance ◽  
Segment Erector

The fine-tuning posture 3-SPS-1-S-type mechanism’s kinematics model and position mechanism for segment erector is built by creating general stiffness motion and revolution motion’s posture parameterized representations based on the Lie groupoids theory’s kinematic synthesis, the duality between parallel mechanism structure and performance and PRY angle coordination system’s kinematics analysis.

scholarly journals Brain Tumour Detection using Fine-Tuning Mechanism for Magnetic Resonance Imaging

2018 ◽  
Vol 150 ◽  
pp. 06025
Author(s):  
Mohammed S.H. Al-Tamimi ◽  
Nur Hafizah Ghazali ◽  
Norfadila Mahrom ◽  
Nurulhuda Ghazali ◽  
Ghazali Sulong
Keyword(s):  
Brain Tumour ◽  
Detection Method ◽  
Experimental Tests ◽  
Fine Tuning ◽  
Qualitative Assessment ◽  
Cerebral Tissue ◽  
Tumour Detection ◽  
Tuning Mechanism ◽  
Tumour Diagnosis ◽  
Brain Tumour Diagnosis

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

scholarly journals Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3459 ◽  
Author(s):  
Shidrokh Goudarzi ◽  
Mohd Kama ◽  
Mohammad Anisi ◽  
Seyed Soleymani ◽  
Faiyaz Doctor
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Superior Performance ◽  
Traffic Information ◽  
Series Data ◽  
Internet Of Vehicles ◽  
Belief Network ◽  
Self Organizing

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

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