Search

  • Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • Obituary
  • original-paper
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • short-communication
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls
  • Architecture Civil Engineering Environment

 

Article

Research on Wireless Sensor Network Coverage Based on Improved Particle Swarm Optimization Algorithm

In order to improve the network coverage, this paper presents the research on wireless sensor network coverage based on improved particle swarm optimization algorithmfor wireless sensor nodes that are randomly deployed in a certain area.In this paper, we use the regional network coverage as the target objective function, and combine various improved particle swarm optimization algorithms to optimize the deployment location of all nodes to enhance the area coverage. The experimental results show

Li Changxing, Zhang Qing, Zhang Long-yao

International Journal of Advanced Network, Monitoring and Controls , ISSUE 4, 20–25

Article

VALVE BARREL POSITION CONTROL BASED ON SELF-TUNING FUZZY PID WITH PARTICLE SWARM OPTIMIZATION

This paper introduced the self-tuning fuzzy PID controller based on particle swarm optimization which aims to gain more precise control over the position of pneumatic proportional valve barrel, where particle swarm works to optimize the membership function, fuzzy rule and PID parameter in fuzzy control. The study fruits also include online optimization of the self-tuning fuzzy PID controller parameters. Comparing to the conventional control methodology, The self-tuning fuzzy PID controller with

Zhang Haiyan, Song Lepeng, Dong Zhiming

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1497–1515

Research Article

RANGED SUBGROUP PARTICLE SWARM OPTIMIZATION FOR LOCALIZING MULTIPLE ODOR SOURCES

A new algorithm based on Modified Particle Swarm Optimization (MPSO) that follows is a local gradient of a chemical concentration within a plume and follows the direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one

W. Jatmiko, W. Pambuko, A. Febrian, P. Mursanto, A. Muis, B. Kusumoputro, K. Sekiyama, T. Fukuda

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 411–442

Article

MODIFICATION OF PARTICLE SWARM OPTIMIZATION BY REFORMING GLOBAL BEST TERM TO ACCELERATE THE SEARCHING OF ODOR SOURCES

Particle Swarm Optimization (PSO) has been widely utilized for Odor Source Localization (OSL) purposes.There have been plenty of researches on this field. The latest research to modify original PSO were on the utilization of wind dynamics. In wind utilization research, the robot movement would be retarded if the robot movement direction is similar to the wind direction. Conform to the aforementioned method, this research proposed new modification on the global best term of PSO algorithm. There

D. Widiyanto, D. M. J. Purnomo, G. Jati, Aprinaldi Jasa Mantau, W. Jatmiko

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1410–1430

Article

A NEW KIND OF PSO: PREDATOR PARTICLE SWARM OPTIMIZATION

Mehdi Neshat, Mehdi Sargolzaei, Azra Masoumi, Adel Najaran

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 521–539

Article

OPPOSITION-BASED LEARNING PARTICLE SWARM OPTIMIZATION OF RUNNING GAIT FOR HUMANOID ROBOT

This paper investigates the problem of running gait optimization for humanoid robot. In order to reduce energy consumption and guarantee the dynamic balance including both horizontal and vertical stability, a novel running gait generation based on opposition-based learning particle swarm optimization (PSO) is proposed which aims at high energy efficiency with better stability. In the proposed scheme of running gait generation, a population initiation policy based on domain knowledge is employed

Liang Yang, Song Xijia, Chunjian Deng

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1162–1179

Research Article

A design of PID controllers using FRIT-PSO

This paper proposes the Fictitious Reference Iterative Tuning-Particle Swarm Optimization (PSO-FRIT) method to design PID controllers for control systems. The proposed method is an offline PID parameter tuning method and it is not necessary to derive any mathematical models of objected control systems. The proposed method is demonstrated by comparing with the FRIT method in numerical examples.

Takehito Azuma, Sohei Watanabe

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 1–6

Article

A METHOD TO DESIGN PID CONTROLLERS USING FRIT-PSO

This paper proposes the Fictitious Reference Iterative Tuning-Particle Swarm Optimization (FRIT-PSO) method to design PID controllers for feedback control systems. The proposed method is an offline PID parameter tuning method. Moreover it is not necessary to derive any mathematical models of objected control systems. The proposed method is demonstrated by comparing with the FRIT method in numerical examples and an experiment.

Takehito Azuma

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1876–1895

Article

A NOVEL HYBRID LOCALIZATION METHOD FOR WIRELESS SENSOR NETWORK

Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main

Wang Jun, Zhang Fu, Ren Tiansi, Chen Xun, Liu Gang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1323–1340

Article

FPGA BASED MAXIMUM POWER POINT TRACKER OF PARTIALLY SHADED SOLAR PHOTOVOLTAIC ARRAYS USING MODIFIED ADAPTIVE PERCEPTIVE PARTICLE SWARM OPTIMIZATION

The paper presents a Field Programmable Gate Array (FPGA) based tracker to accurately track the maximum power point (MPP) of a photovoltaic (PV) array. The tracking logic realized on FPGA is based on a modified version of Adaptive Perceptive Particle Swarm Optimization (APPSO) technique. Photovoltaic generation systems use MPP tracker because the photovoltaic array exhibits multiple maxima in the power voltage characteristic under partially shaded conditions. Compared to PSO, the APPSO offers

Shubhajit Roy Chowdhury, Dipankar Mukherjee, Hiranmay Saha

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 661–675

Article

OPTIMAL ANALOG WAVELET BASES CONSTRUCTION USING HYBRID OPTIMIZATION ALGORITHM

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming

Hongmin Li, Yigang He, Yichuang Sun

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1918–1942

Article

A Disparateness-Aware Scheduling using K-Centroids Clustering and PSO Techniques in Hadoop Cluster

, and network time. By integrating these three parameters, the optimized fitness function is employed for Particle Swarm Optimization (PSO) to select the computing node. Failure may occur after completion of the successful execution in the network. To improve the fault tolerance service, the migration of the cluster is focused on the particular failure node. This can recomputed the node by PSO and the corresponding optimal node is predicted. The experimental results exhibit better scheduling length

E. Laxmi Lydia, M.Ben Swarup

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 34–46

Article

PSO ALGORITHM FOR SINGLE AND MULTIPLE ODOR SOURCES LOCALIZATION PROBLEMS: PROGRESS AND CHALLENGE

known as Particle Swarm Optimization (PSO) to solve these problems. The experiment conducted using PSO shows that PSO able to localize the odor source in the same condition where single agent failed. However, PSO still need to be modified before it can be use widely. This paper shows modification that has been proposed by the authors to enhance it’s ability. The research also has been push to solve multiple odor sources using parallel localization. To verify proposed method, software simulator was

W. Jatmiko, F. Jovan, R.Y.S. Dhiemas, M.S. Alvissalim, A. Febrian, D. Widiyanto, D.M.J. Purnomo, H.A. Wisesa, T. Fukuda, K. Sekiyama

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1431–1478

Research Article

ERROR MODELING AND COMPENSATION OF 3D SCANNING ROBOT SYSTEM BASED ON PSO-RBFNN

In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct

Jianhong Qi, Jinda Cai

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 837–855

Article

The Application of Improved PSO Algorithm in the Geometric Constraint Solving

Geometric constraint solving is a hot topic in the constraint design research field. Particle swarm optimization (PSO) is a method to solve the optimization problem from the biological population’s behavior characteristics. PSO is easy to diverge and fall into the local optimum. There are various kinds of improvements. In addition to improving some performance, the corresponding cost is paid. In this paper, a particle swarm optimization algorithm based on the geese is adopted to solve the

Tian Wei, Zhu Xiaogang

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 116–119

Article

AMN-PSO METHOD FOR JAMMING UNMANNED AERIAL VEHICLE NETWORK

UAVs are attracting more and more attentions for their versatilities and low costs. This paper focuses on their security and considers launching jamming attacks on them. We firstly formulate the UAVs jamming problem. Secondly the PSO (Particle Swarm Optimization) algorithm is introduced and new metrics like AJRL (Area for jamming a receiving link) and NJRL (Number of AJRLs) are defined. Then we provide a new jamming method AMN-PSO (Achieving Maximal NJRL based on PSO) for UAVs jamming attack

ZHANG Yu, LIU Feng, HAN Jie

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 2042–2064

Article

INTELLIGENT NEURAL NETWORK CONTROL STRATEGY OF HYDRAULIC SYSTEM DRIVEN BY SERVO MOTOR

Ma Yu

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1406–1423

Review

MULTI-VARIABLE OPTIMIZATION MODELS FOR BUILDING ENVELOPE DESIGN USING ENERGYPLUS SIMULATION AND METAHEURISTIC ALGORITHMS

functions is not available [9]. Thus, the most used optimization algorithms in this matter are derivate-free, simulation-based ones, which provide the iterative improvement of the solution until the fulfillment of a stop criterion. Among these methods, the dominant ones are the metaheuristic stochastic population-based algorithms [5], such as particle swarm optimization [10], differential evolution [11, 12] or genetic algorithms [9, 13, 14, 15]. More recently, optimization-based selection approaches

Krzysztof GRYGIEREK, Joanna FERDYN-GRYGIEREK

Architecture, Civil Engineering, Environment , ISSUE 2, 81–90

Research paper

KNOWLEDGE-BASED MODELING FOR PREDICTING CANE SUGAR CRYSTALLIZATION STATE

, based on support vector machine with particle swarm optimization, to improve the predictive accuracy and generalization capacity. Furthermore, the intelligent system is tested using a self-regulating intelligent comprehensive monitoring and controlling platform that represents the cane sugar process. Results demonstrate the feasibility of the system for predicting the crystallization state in a real cane sugar process.

Yanmei Meng, Xian Yu, Haiping He, Zhihong Tang, Xiaochun Wang, Jian Chen

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 942–965

research-article

Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

Nomenclatures SE state estimation PS power system PSN power system networks PSO–NN particle swarm optimization–neural network DPN distribution power network RDPSNs radial distribution power system networks DMS distribution management system GTP graph theoretic procedure algorithm DSSE distribution system state estimation model WLS weighted least square WLAV weighted least absolute value PMUs phasor measurement units ANN artificial neural network

Husham Idan Hussein, Ghassan Abdullah Salman, Ahmed Majeed Ghadban

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–10

Article

Forecasting CSI 300 index using a Hybrid Functional Link Artificial Neural Network and Particle Swarm Optimization with Improved Wavelet Mutation

Tian Lu, Zhongyan Li

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 173–178

Article

EXPERIMENTAL AND THEORETICAL VALIDATION METHOD FOR ESTIMATION OF STRAIGHTNESS DEVIATION AND ASSOCIATED UNCERTAINTY IN CNC-CMM MEASUREMENT

3D using slab surface by CNC-CMM at the Egyptian national metrology institute (NIS). The work has been investigated experimentally and theoretically analyzed. The straightness deviation and its uncertainty results from 2D measurement have been estimated experimentally. The straightness deviation result of the 3D discrete points measurements have been analyzed theoretically using the standard Particle Swarm Optimization (PSO) algorithm. The probability density distribution of the measured

Salah H.R. Ali, M.A.H. Khalafalla, Ihab H. Naeim, Sarwat Z.A. Zahwi

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 397–418

Research Article

LOAD AWARE CHANNEL ESTIMATION AND CHANNEL SCHEDULING FOR 2.4GHZ FREQUENCY BAND BASED WIRELESS NETWORKS FOR SMART GRID APPLICATIONS

efficient data communication. During this scheme, if node attains a channel it must wait for a network reconfiguration time for moving to next channel. Hence, during this time other nodes are allowed for moving to the new channel. Secondly, for moving to the new channel load aware channel estimation is proposed to assess the possibility of traffic weight assignment at each channel. Finally, the Particle swarm optimization (PSO) based collision avoiding multiple-channel based superframe scheduling is

Vikram K, Sarat Kumar Sahoo

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 879–902

No Record Found..
Page Actions