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The Methods and Means of Efficiency Increasing the Linear optimization Problems Solving in Project Management

The Methods and Means of Efficiency Increasing the Linear Op...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Sergii K. Chernov Sergey D. Titov Ludmila S. Chernova Lubava S. Chernova Diana Zahorodnia Taras Lendiuk Admiral Makarov National University of Shipbuilding Mykolaiv Ukraine West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9781665426060

Modern computer combinatorial methods for solving problems linear optimization requires the development of algorithms that allow to obtain an approximate solution with a guaranteed estimate of the value of the objective function. It is important to simplify the mathematical model prepared for the beginning of computer implementation. Such expediency stimulates the development of effective and improvement of existing algorithms for preparation for computer calculations. The use of such algorithms will significantly reduce the computer time of calculations and reduce the hardware requirements for the computer. The presented work is devoted to the construction of a chain of efficient algorithms that simplify the original mathematical model of the problem and the implementation of its computer calculation. The aim of the work is to build effective algorithms and general principles of preparation for computer solution of LO problems with their illustration on different model problems.

关键词： Computational modeling Project management Approximation algorithms Search problems Linear programming Mathematical models Hardware

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Drone Trajectory optimization using Genetic Algorithm with Prioritized Base Stations 25

Drone Trajectory Optimization using Genetic Algorithm with P...

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25th IEEE international workshop on Computer Aided Modeling and Design of Communication Links and Networks (IEEE CAMAD)

作者： Qiao, Tianrui Sambo, Yusuf A. Imran, Muhammad A. Ahmad, Wasim Univ Elect Sci & Technol China Glasgow Coll UESTC Chengdu Peoples R China Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland

ISBN: (纸本)9781728163390

Drones have been widely applied to perform emergent tasks in the post-disaster scenario, due to their unique characteristics such as mobility, flexibility, and adaptivity to altitude. However, drones have limited energy capacity, which presents a major drawback in flight time and affects their performance in such scenarios. Hence, trajectory optimization has become a critical research problem for such applications of drones. In this paper, we present an optimal trajectory design for a single drone to ferry data from temporary Base Stations (BSs) deployed within a disaster zone to a fixed gateway node that is the point of origin and final destination for the drone flight. We have used a Genetic Algorithm (GA)-based approach that takes into account the shortest distance traveled and least time spent by the drone during flight. We also examine the case where some BSs have delay requirements that are unknown to the drone in advance. Simulation results show that the performance of our proposed GA-based approach matches that of the benchmark exhaustive search algorithm and the difference in computational time between the 2 algorithms increases with the number of BSs. Accordingly, our proposed algorithm has 96.4% lower computational time complexity compared to the benchmark exhaustive search algorithm when there are 12 BSs in the disaster area.

关键词： drone genetic algorithm trajectory optimization prioritized base Stations time-sensitive data

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1st international Conference on Intelligent Computing in Control and Communication, ICCC 2020

1st International Conference on Intelligent Computing in Con...

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1st international Conference on Intelligent Computing in Control and Communication, ICCC 2020

ISBN: (纸本)9789811584381

The proceedings contain 53 papers. The special focus in this conference is on Intelligent Computing in Control and Communication. The topics include: A Comparative Study of Unit Commitment Problem by Dynamic Programming and Genetic Algorithm;Robust Fuzzy Logic Controller for DC Motor Stability Enhancement Under Load Disturbances;optimal Scheduling for Delay Management in Railway Network Using Hybrid Bat Algorithm;optimal Rescheduling of Real Power to Mitigate Congestion Using Elephant Herd optimization;preface;A Fuzzy-Based STATCOM Controller for Grid Interconnection of Fuel System to Improve Power Quality;adiabatic Logic-Based Area- and Energy-Efficient Full Adder Design;Comparative Analysis of Rapid Single Flux Quantum (RSFQ) Circuit Technique Multipliers;techno-Economic Material Demand-Based Model in Plastic Waste Management Using Metaheuristic algorithms;breaking Down and Reduplication of Information in Cloud for Best Overall Performance and Protection;A Novel Modulating PID Controller for a Speed Control of BLDC Motor Adopting Flower Pollination Algorithm;shaped Beams from Circular Aperture Antennas;A Simple and Low-Cost HIL Solution for Control of Power Electronic Converters;improved Harmonic Profile of multilevel Inverter Topology with Shifted Carrier Modulation Technique;artificial Intelligence Based Control Methods for Speed Control of Wind Turbine Energy System;recent Trends and Challenges in Blockchain Technology;Comparison of Uplink Spectral Efficiency in Massive MIMO Systems;a Study on Static Call Admission Control Policies for Wireless Mobile Cellular Networks;Design of Low PDP Ternary Circuits Utilizing Carbon Nanotube Field-Effect Transistors;Detection and Estimation of Multiple Point Targets for LFM Echo Using Compressed Sensing;Design of Dumbbell-Shaped MIMO Antenna for Wearable applications.

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A Modified Grasshopper optimization Algorithm

A Modified Grasshopper Optimization Algorithm

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Man Gao Hui Xu Jun Su Lingyu Yan Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

In order to solve the problems of Grasshopper optimization Algorithm (GOA) in the optimization process, such as easily falling into the local optimum and the slow convergence speed, a Modified Grasshopper optimization Algorithm (MGOA) is proposed. And the proposed MGOA improves the original fixed parameters to linear decreasing parameters in order to avoid the original algorithm from falling into the local optimum problem. At the same time, weighting calculation is introduced to improve the optimization accuracy of the MGOA algorithm. Additionally, the proposed MGOA is combined with the Simulated Annealing (SA) to improve the convergence speed of the original GOA algorithm. Both the unimodal test function and the multimodal test function are respectively used for simulation experiments, and experimental results show that, compared with the other five swarm intelligent algorithms, the proposed MGOA has significant advantages in aspects of accuracy, stability and convergence speed.

关键词： Annealing Conferences Data acquisition Simulated annealing Stability analysis optimization Convergence

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An Intrusion Detection Model Based on Improved Whale optimization Algorithm and XGBoost

An Intrusion Detection Model Based on Improved Whale Optimiz...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Xinlu Zong Ruicheng Li Zhiwei Ye Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

Hyperparameters optimization is an effective way to improve the detection performance of intrusion detection models based on machine learning. However, hyperparameters optimization is a typical NP-hard problem. Therefore, it is difficult to find the optimal parameters within an acceptable time by using traditional parameters optimization methods. To solve this problem, this paper proposed an intrusion detection model named IWOA-XGB which combines improved Whale optimization Algorithm (WOA) and eXtreme Gradient Boosting (XGBoost). Firstly, the spiral update maneuver of the original WOA is modified by setting the parameter $l$ to a linearly decreasing random value. Then, the improved WOA is used to optimize several valued parameters of tree booster in the XGBoost. Finally, the optimized XGBoost is applied for intrusion detection so that the performance of the intrusion detection model can be improved. To evaluate the efficacy of IWOA-XGB, the NSL-KDD intrusion detection data set is used for simulation tests. The simulation results show that compared to the original XGBoost and other evolutionary algorithms, IWOA-XGB could effectively improve the macro F1-score of the detection model, demonstrating good intrusion detection performance.

关键词： Spirals algorithms NP-hard problem Computational modeling Simulation Intrusion detection optimization methods

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8th international Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

8th International Conference on Scale Space and Variational ...

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8th international Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

ISBN: (纸本)9783030755485

The proceedings contain 45 papers. The special focus in this conference is on Scale Space and Variational Methods in Computer Vision. The topics include: Multiscale Registration;challenges for Optical Flow Estimates in Elastography;an Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation;low-Rank Registration of Images Captured Under Unknown, Varying Lighting;towards Efficient Time Stepping for Numerical Shape Correspondence;first Order Locally Orderless Registration;first-Order Geometric multilevel optimization for Discrete Tomography;bregman Proximal Gradient algorithms for Deep Matrix Factorization;Hessian Initialization Strategies for -BFGS Solving Non-linear Inverse Problems;inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting;quantisation Scale-Spaces;A Scaled and Adaptive FISTA Algorithm for Signal-Dependent Sparse Image Super-Resolution Problems;Convergence Properties of a Randomized Primal-Dual Algorithm with applications to Parallel MRI;wasserstein Generative Models for Patch-Based Texture Synthesis;sketched Learning for Image Denoising;Translating Numerical Concepts for PDEs into Neural Architectures;CLIP: Cheap Lipschitz Training of Neural Networks;variational Models for Signal Processing with Graph Neural Networks;synthetic Images as a Regularity Prior for Image Restoration Neural Networks;geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation;learning Local Regularization for Variational Image Restoration;Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations;on the Correspondence Between Replicator Dynamics and Assignment Flows;learning Linear Assignment Flows for Image Labeling via Exponential Integration;on the Geometric Mechanics of Assignment Flows for Metric Data Labeling;a Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration.

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A Hybrid Rice optimization Algorithm with Ant System for Feature Selection

A Hybrid Rice Optimization Algorithm with Ant System for Fea...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Zhiwei Ye Zhe Shu Shiuin Liu Xiaoyu Xia School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

Feature selection is an important processing step of data mining and aims at selecting a subset with high quality. The feature subset is obtained by reducing the irrelevant and redundant features based on maintaining satisfactory accuracy. Many algorithms might be trapped in local optima on solving high-dimensional feature selection. Hybrid rice optimization (HRO) is a newly presented algorithm, its population is divided into three types such as the maintainer, restorer, and sterile line to simulate the breeding process of Chinese hybrid rice. Three main stages are designed to update the restorer and sterile line. However, the maintainer line is not updated in HRO, which degrades the performance of the algorithm on high-dimensional feature selection. In this paper, a hybrid model named HROAS is proposed for feature selection by combining HRO with ant system (AS). AS is embedded as an operator in HRO to update the maintainer line due to its unique updating mechanism. The performance of HROAS is evaluated on nine standard benchmark datasets and compared with several classic and novel algorithms. The experimental results demonstrate the outperformance of HROAS.

关键词： Measurement Sociology Diversity reception Data acquisition Feature extraction Classification algorithms Data mining

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A Band Selection Method for Hyperspectral Image Based on Binary Coded Hybrid Rice optimization Algorithm

A Band Selection Method for Hyperspectral Image Based on Bin...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Zhiwei Ye Shiqin Liu Xinlu Zong Zhe Shu Xiaoyu Xia School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

Hyperspectral image analysis is one of the most important topics in the field of remote sensing. As the band dimension of hyperspectral image increases, it may cause a curse of dimensionality. Band selection could choose a subset of bands that is the most effective for classification recognition to achieve dimensionality reduction. Furthermore, binary coding is more suitable for solving the band selection problem. Therefore, a band selection method for hyperspectral image based on binary coded hybrid rice optimization algorithm is proposed in the paper. The band selection problem is solved as a combinatorial optimization problem by defining an objective function based on the classification accuracy and the number of bands. The proposed method is compared with other nature-inspired algorithms on Indian Pines, Salinas, Kennedy Space Center, and Pavia University hyperspectral datasets. Experimental results demonstrate that the proposed method achieves satisfactory results in terms of performance and execution time for band selection.

关键词： Dimensionality reduction Image analysis Conferences Data acquisition Linear programming Encoding Classification algorithms

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An Artificial Intelligence Approach for Time Series Next Generation applications

An Artificial Intelligence Approach for Time Series Next Gen...

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IEEE international Conference on Communications (IEEE ICC) / workshop on NOMA for 5G and Beyond

作者： Dridi, Aicha Ibn Khedher, Hatem Moungla, Hassine Afifi, Hossam Telecom SudParis Saclay Inst Polytech Paris CNRS UMR 5157 Paris France Univ Paris LIPADE F-75006 Paris France Univ Paris Saclay Univ Paris Sud Cent Supelec Lab Signaux & SystCNRS Gif Sur Yvette France

ISBN: (纸本)9781728150895

With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.

关键词： Internet of Things (IoT) Artificial Intelligence (AI) Deep Learning (DL) Long Short-Term Memory (LSTM) Convolutional Neural Network (CNN) Time Series Prediction (TSP) Cloud applications

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Method of Selecting and Determining the Free Parameters of Swarm Intelligent algorithms for Optimizing Solutions in GIS

Method of Selecting and Determining the Free Parameters of S...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Dmytro Uhryn Vasyl Lytvyn Taras Lendiuk Chernivtsi faculty of the National Technical University “Kharkiv Polytechnic Institute” Chernivtsi Ukraine Lviv Polytechnic National University Lviv Ukraine West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9781665426060

The article proposes information technology for solving the discrete optimization problems in Geographic Information Systems (GIS). The information model of geographic data (geodata) was further developed, which consists of a formalized combination of their spatial and attributive components. It takes into account relational, semantic and frame models for representing the knowledge of the attributive component in order to unify the branch geodata. The conceptual model of decision support in sectoral GIS has been further developed, which takes into account the information model of geodata and the dynamics of their change, the hierarchy of tasks of sectoral GIS, the input operational data, the function of preferences and the criterion of decision-making. A new method of decision support for GIS construction was developed on the base of the proposed conceptual model. The method of selection and determination of free parameters of swarm intelligence algorithms is developed to increase the effectiveness of GIS. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. Based on this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted in the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters for individual swarm algorithms were found.

关键词： Training Machine learning algorithms Q-learning Semantics Data models Task analysis Particle swarm optimization

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