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Article | 23-April-2018

Single Image Dehazing Based on Deep Neural Network

This paper proposes a single image dehazing based on deep neural network that is to deal with haze image. In this paper, we build up a deep neural network to restore the hazy image.We test our method both objective and subjective and compare with classical method for dehazing. Our test shows that our method works better than the others in reducing Halo effect and also our method does well in restore colorful of input image. Finally, our method process faster.

Dewei Huang, Kexin Chen, Jianqiang Lu, Weixing Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 10–14

Article | 09-April-2018

The Prediction of Haze Based on BP Neural Network and Matlab

In this paper, the neural network theory is used to establish the BP neural network prediction system for the occurrence of haze. The corresponding parameters are determined by MATLAB language, and the effect of the model is tested by the prediction of Shijiazhuang area. The result shows the feasibility of the predictive model. So it’s valuable and has a bright future.

Ma Limei, Wang Fangwei

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 2, 107–119

Research Article | 10-April-2013

ADAPTIVE DYNAMIC CLONE SELECTION NEURAL NETWORK ALGORITHM FOR MOTOR FAULT DIAGNOSIS

A fault diagnosis method based on adaptive dynamic clone selection neural network (ADCSNN) is proposed in this paper. In this method the weights of neural network is encoded as the antibody, and the network error is considered as the antigen. The algorithm is then applied to fault detection of motor equipment. The experiments results show that the fault diagnosis method based on ADCS neural network has the capability in escaping local minimum and improving the algorithm speed, this gives better

Wu Hongbing, Lou Peihuang, Tang Dunbing

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 482–504

Research Article | 01-September-2014

RESEARCH ON THE CLASSIFICATION FOR FAULTS OF ROLLING BEARING BASED ON MULTI-WEIGHTS NEURAL NETWORK

A methodology based on multi-weights neural network (MWNN) is presented to identify faults of rolling bearing. With considerations of difficulties in analyzing rolling bearing vibration data, we analyzed how to extract time domain feature parameters of faults. Further, the time domain feature parameters extracted from fault signals are utilized to train multi-weights neural network for achieving an optimal coverage of fault feature space. Thus, faults of rolling bearing can

Yujian Qiang, Ling Chen, Liang Hua, Juping Gu, Lijun Ding, Yuqing Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1004–1023

Article | 14-October-2020

Analysis and Forecast of Urban Air Quality Based on BP Neural Network

many assumptions in the training process to achieve prediction, and finally achieves air quality the accuracy of the prediction needs to be further improved. The neural network has a good approximation effect in air quality prediction. It can continuously update the newly acquired air pollutant information to the neural network, update the prediction model in time, and improve the prediction accuracy. The neural network has a strong performance in air quality prediction. Dynamic adaptability and

Wenjing Wang, Shengquan Yang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 57–64

Article | 01-June-2015

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

A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented in this paper. This control method overcomes the defects existed in the traditional variable frequency induction motor driven hydraulic source, such as slow response, poor control precision, easy to overshoot. Permanent magnet synchronous motor driven constant pump hydraulic system is designed instead of common motor, energy saving, fast

Ma Yu

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1406–1423

Research Article | 27-December-2017

DEVELOPMENT OF NEURAL NETWORK-BASED ELECTRONIC NOSE FOR HERBS RECOGNITION

The ability to classify distinctive odor pattern for aromatic plants species provides significant impact in food industry especially for herbs. Each herbs species has a unique physicochemical and a distinctive odors. This project emphasizes on the techniques of artificial intelligence (AI) to distinguish distinctive odor pattern for herbs. Neural Network method has been exploited for the classification and optimization of various odor patterns. Based on AI techniques, Neural Network-based

A. Che Soh, K. K.Chow, U. K. Mohammad Yusuf, A. J. Ishak, M. K. Hassan, S. Khamis

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 584–609

Article | 30-November-2018

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

lime rotary kiln is the main technology workers. Through the process system on-line monitoring state parameters to predict the current product quality, and it is as the basis for the adjustment of control parameters. The quality of products is greatly influenced by artificial judgment or off-line detection. In this paper, the ability of the neural network is used to deal with the nonlinear mapping problem, and the quality of the product is predicted based on the on-line monitoring state parameters

Lingli Zhu, Tingzhong Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 4, 83–86

research-article | 06-November-2020

Competitive fitness analysis using Convolutional Neural Network

through a filter that lets the larvae, but not the adults, though. To further improve Caenorhabditis fitness analysis, here we present (i) a fitness assay protocol involving separation of parental and offspring generations and (ii) an open-source model for image analysis based on a convolutional neural network (CNN). Machine learning models are very convenient for image analysis because they can be trained on a sample of pictures to analyze images of interest with a high output rate. The concept of

Joanna K. Palka, Krzysztof Fiok, Weronika Antoł, Zofia M. Prokop

Journal of Nematology, Volume 52 , 1–15

Article | 01-September-2015

SENSITIVITY ANALYSIS OF HIERARCHICAL HYBRID FUZZY - NEURAL NETWORK

To identify the important attributes of complex system, which is high-dimensional and contain both discrete and continuous variables, this paper proposes a sensitivity analysis method of hierarchical hybrid fuzzy - neural network. We derive the sensitivity indexes of discrete and continuous variables through the differential method. To verify the effectiveness of our method, this study employed a man-made example and a remote sensing image classification example to test the performance of our

Xing Haihua, Yu Xianchuan, Hu Dan, Dai Sha

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1837–1854

research-article | 25-August-2020

1/10th scale autonomous vehicle based on convolutional neural network

self-driving library written in Python for the implementation of the neural network. To analyze the AI aspect of the research, the paper by NVIDIA DAVE-2 (Bojarski et al., 2016) and ALVINN (Pomerleau, 1989) were the first attempts to build an operational CNN-based model. Analyzing different research work done, this research paper uses different heterogeneous and open-source material to build the autonomous vehicle (Blaga et al., 2018; Zhang and Mahale, 2018; Tian et al., 2018). It also reports the

Avishkar Seth, Alice James*, Subhas C. Mukhopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–17

Article | 16-December-2013

Applying Merging Convetional Marker and Backpropagation Neural Network in QR Code Augmented Reality Tracking

Usability of QR Code in Augmented Reality system has been used for digital content accessible publicly. However, QR Code in AR system still has imprecision tracking. In this article we propose merging QR Code within conventional marker and backpropagation neural network (BPNN) algorithm to recognizing QR Code Finder Pattern. The method which our chosen to approaching conventional marker. The result of BPNN testing, QRFP detected in perspective distortion with ID-encoded character length 78, 53

Gia M. Agusta, Khodijah Hulliyah, Arini, Rizal Broer Bahaweres

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 1918–1948

Article | 11-April-2018

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

Financial market dynamics forecasting has long been a focus of economic research. A hybridizing functional link artificial neural network (FLANN) and improved particle warm optimization (PSO) based on wavelet mutation (WM), named as IWM-PSO-FLANN, for forecasting the CSI 300 index is proposed in this paper. In the training model, it expands a wider mutation range while apply wavelet theory to the PSO, in order to exploring the solution space more effectively for better parameter solution. In

Tian Lu, Zhongyan Li

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 173–178

research-article | 30-November-2018

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 , Volume 12 , ISSUE 1, 1–10

Article | 01-December-2014

FILM THICKNESS MEASUREMENT OF MECHANICAL SEAL BASED ON CASCADED ARTIFICIAL NEURAL NETWORK RECOGNITION MODEL

, wavelet packet and kernel principal component analysis are used to extract the data features. Then cascaded decision is presented to improve the recognition rate of artificial neural network, by which the film thickness can be estimated accurately. With a set of tests, the results demonstrate that the method is effective. It can be widely used to take measurement of the film thickness in industrial field.

Erqing Zhang, Pan Fu, Kesi LI, Xiaohui Li, Zhongrong Zhou

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1870–1889

Article | 30-November-2018

Research of Oil Pump Control Based On Fuzzy Neural Network PID Algorithm

control the oil pump accurately and stably, and is easy to cause system shock. With the development of intelligent control technology, such as fuzzy control, neural network control, and sensor technology, more and more control methods and control technology become more and more mature. This paper synthesizes the advantages of simple PID control, fuzzy control does not depend on model, and neural network control is adaptive. Fuzzy neural network PID control is used to control oil pump accurately, after

Chen Gong, Shengquan Yang

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 4, 63–68

Article | 07-May-2018

Research of Email Classification based on Deep Neural Network

algorithm is low. In allusion to the problem of poor accuracy of email classification based on naive bayes algorithm, scholars have proposed some new email classification algorithms. The email classification algorithm based on deep neural network is one kind of them. The deep neural network is an artificial neural network with full connection between layer and layer. The algorithm extracted the email feature from the training email samples and constructed a DNN with multiple hidden layers, the DNN

Wang Yawen, Yu Fan, Wei Yanxi

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 2, 17–21

Article | 02-April-2020

Research on House Price Prediction Based on Multi-Dimensional Data Fusion

;; So far, all values of the attribute have been expressed as One-Hot form. D. Data Fusion This section analyzes the problem of data fusion, that is, how to merge Large_Attributes, Small_Attributes, Interval_Attributes, and Enumerated_Attributes together. This thesis will propose a pixel-based data fusion method: first establish a pixel matrix; then use a fully connected neural network model to process the pixel matrix. 1) Create a pixel matrix This section aims to transform multiple attributes

Yonghui Yang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 1–8

Research Article | 27-December-2017

Hybrid Intelligent Method of Identifying Stator Resistance of Motorized Spindle

Aiming at the problem that changes of nonlinear dynamic resistance of stator affect the performance of speed sensorless vector control system, a hybrid computing intelligence approach is used in the identification of stator resistance of motorized spindle. The partial least squares (PLS) regression is combined with neural network to solve the problem of few samples and multi-correlation of variables in complicated data modeling. The PLS method is used to extract variable components from sample

Lixiu Zhang, Yuhou Wu, Ke Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 781–797

Article | 01-June-2016

ACOUSTIC EMISSION BASED DEFECTS MONITORING OF THREE-DIMENSIONAL BRAIDED COMPOSITES USING WAVELET NETWORK

In order to effectively differentiate all kinds of defects inside the composites, this paper carries out testing on the internal defects of three-dimensional (3d) braided composites by use of acoustic emission nondestructive detecting technology. It puts forward the processing method for acoustic emission signals for the internal defects of three-dimensional braided composites based on wavelet neural network (WNN). This method does wavelet transformation on real-time collected acoustic emission

Su Hua, Zhang Tianyuan, Zhang Ning

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 780–798

Article | 01-September-2015

RESEARCH ON LATERAL STABILITY OF FOUR HUBMOTOR- IN-WHEELS DRIVE ELECTRIC VEHICLE

This paper focuses on the problem of lateral stability of four hub-motor-in-wheels drive electric vehicle, 7 DOF (degrees of freedom) vehicle simulation model which is verified by field test is established based on Matlab/Simulink software. On basis of simulated model, BP neural network PID torque distribution controller of lateral stability is proposed. The sideslip angle at mass center and yaw rate are selected as the control variables, and the BP neural network PID torque distribution

Biao Jin, Chuanyang Sun, Xin Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1855–1875

Article | 10-April-2018

Research on Intelligent Monitoring Technology of Micro Hole Drilling

Aiming at the problem that the micro drills is easy to be broken in the process of drilling; it is difficult to detect the drill bit. The drilling torque signal is taken as the monitoring object. A new method for the on-line monitoring the micro-drill breakage based on BP neural network is proposed. After the three layer wavelet decomposition of the drilling torque signal, the energy feature vector is used as the input layer of the BP network, and the mapping model of the working state and the

Yanhong Sun, Mei Tian

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 142–146

Article | 11-April-2018

Research on Combination Forecasting Model of Mine Gas Emission

This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t– norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used as the single prediction models. Finally, genetic algorithm and least square method were used to

Liang Rong, Chang Xintan, Jia Pengtao, Dong Dingwen

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 194–198

Research paper | 31-October-2017

APPLICATION OF BOX-JENKINS METHOD AND ARTIFICIAL NEURAL NETWORK PROCEDURE FOR TIME SERIES FORECASTING OF PRICES

compared with ANN (Artificial Neural Network) methodology. The results showed that ANN performed better than the ARIMA models in forecasting the prices.

Abhishek Singh, G. C. Mishra

Statistics in Transition New Series, Volume 16 , ISSUE 1, 83–96

Research Article | 24-February-2017

DATA MINING WORKSPACE AS AN OPTIMIZATION PREDICTION TECHNIQUE FOR SOLVING TRANSPORT PROBLEMS

prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.

Anastasiia KUPTCOVA, Petr PRŮŠA, Gabriel FEDORKO, Vieroslav MOLNÁR

Transport Problems, Volume 11 , ISSUE 3, 21–31

Article | 08-April-2018

The Research of Information Delay in the Neural Network Forecast Remote Control System

Aiming at the issues of random delay and delay uncertainty in both the before channel and feedback channel for network control system, the root causes of random delay influence closed-loop control system by case is analysis, and the predictive control method based on neural network to solve the feasibility of existence network random delay in control system closed-loop control has provided. Simulation results show that the method can reflect and predict the delay characteristics of between

Xu Shuping, Chen Yiwei, Cheng Xinhua, Su Xiaohui

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 1, 38–48

Article | 01-June-2016

NON INVASIVE ESTIMATION OF BLOOD UREA CONCENTRATION USING NEAR INFRARED SPECTROSCOPY

This paper details about the noninvasive estimation of Urea concentration in blood using near infrared spectroscopy (NIRS) and Artificial neural network based prediction model. The absorption spectrum of the urea has been studied experimentally in order to choose the wavelengths of peak absorption. For this purpose, IR absorption spectrum of 0.1M aqueous urea solution has been collected and analyzed in second overtone region of the near-infra red spectra using the Bruker tensor 27 FTIR

Swathi Ramasahayam, Shubhajit Roy Chowdhury

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 449–467

Article | 01-September-2015

COMPENSATION OF CAPACITIVE DIFFERENTIAL PRESSURE SENSOR USING MULTI LAYER PERCEPTRON NEURAL NETWORK

Capacitive differential pressure sensor (CPS), which converts an input differential pressure to an output current, is extremely used in different industries. Since the accuracy of CPS is limited due to ambient temperature variations and nonlinear dependency of input and output, compensation is necessary in industries that are sensitive to pressure measurement. This paper proposes a framework for designing of CPS compensation system based on Multi Layer Perceptron (MLP) neural network. Firstly

Payman Moallem, Mohammad Ali Abdollahi, S. Mehdi Hashemi

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1443–1463

Article | 30-November-2018

Research of Network Closed-loop Control System Based on the Model Predictive Control

control systems that are experiencing another problem. This paper based on the method of modify the control parameters of PID values to improve the stability of the remote control system, and propose intelligent remote control system design methods with adaptive function under a random delay based on neural network theory. III. QUESTION ANALYSIS Remote control single-link manipulator, set the sampling period in figure 1 is 0.05s, and take the delay value of 0.05s, the control information in time k

Xu Shuping, Wang Shuang, Guo yu, Su Xiaohui

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 4, 30–37

Article | 01-June-2015

BREATH ACETONE-BASED NON-INVASIVE DETECTION OF BLOOD GLUCOSE LEVELS

levels. Effects of pressure, temperature and humidity have been considered. Artificial Neural Network (ANN) has been used to extract features from the output waveform of the sensors. The system has been trained and tested with patient data in the blood glucose ranges from 80 mg/dl to 180 mg/dl. Using the proposed system, the blood glucose concentration has been estimated within an error limit of ±7.5 mg/dl.

Anand Thati, Arunangshu Biswas, Shubhajit Roy Chowdhury, Tapan Kumar Sau

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1244–1260

Article | 30-November-2018

Street View House Number Identification Based on Deep Learning

designed manually (such as SIFT, SURF, HOG, etc.), and the features of the artificial design are well interpreted. However, in the face of complex backgrounds, changing fonts and various deformations, it is rather troublesome and difficult to extract more general features[7]. The Convolutional Neural Network (CNN) is a multi-layered supervised learning neural network. Although the training process requires a large amount of data compared with the traditional method, the convolutional neural network can

Haoqi Yang, Hongge Yao

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 3, 47–52

Research Article | 01-June-2017

APPLICATION OF RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT EXCHANGE RATE WITH
FINANCIAL TIME SERIES

The literature indicates that exchange rates are largely unforecastable from the fact that the overwhelming majority of studies have employed linear models in forecasting exchange rates. In this paper, we applied Radial Basis Function Neural Network (RBFNN) to predict exchange rate, motived by the fact that RBFNN have the ability to implicitly detect complex nonlinear relationships between dependent and independent variables as it “learns” the relationship inherent in the exchange rate data

AI SUN, JUI-FANG CHANG

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 2, 308–326

Research Article | 13-December-2017

COOPERATIVE MULTI TARGET TRACKING USING MULTI SENSOR NETWORK

tracking system using multiple mobile sensors. For the purposes of surveillance and security, trackers use an Extended Kohonen neural network to track the moving targets in their environments. The proposed tracking algorithm can be used for single and multiple target tracking. A clustering algorithm is used in order to minimize the number of active trackers over time and hence save energy. An auction based algorithm is used for the purpose of optimizing the cooperation between trackers. Quantitative

Ahmed M. Elmogy, Fakhreddine O. Karray

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 716–734

Research paper | 31-October-2017

COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING

suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at

Germanas Budnikas

Statistics in Transition New Series, Volume 16 , ISSUE 2, 309–322

Research Article | 20-February-2013

Traffic Signal Control for Urban Trunk Road Based on Wireless Sensor Network and Intelligent Algorithm

Based on the analysis for the present research on controlling urban trunk road, a intelligent control method for urban trunk road based on wireless sensor and fuzzy neural network was proposed. In this method, we took two layers of WSN structure. The first one was data collecting layer, which consisted of traffic information collecting nodes and sink nodes. Data collecting layer was responsible for collecting vehicle information at single crossing, transmitted to the second layer after data

Peng Xiaohong, Mo Zhi, Liao Riyao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 352–367

Research Article | 01-March-2017

DEVELOPMENT OF SOFT SENSOR TO ESTIMATE MULTIPHASE FLOW RATES USING NEURAL NETWORKS AND EARLY STOPPING

This paper proposes a soft sensor to estimate phase flow rates utilizing common measurements in oil and gas production wells. The developed system addresses the limited production monitoring due to using common metering facilities. It offers a cost-effective solution to meet real-time monitoring demands, reduces operational and maintenance costs, and acts as a back-up to multiphase flow meters. The soft sensor is developed using feed-forward neural network, and generalization and network

Tareq Aziz AL-Qutami, Rosdiazli Ibrahim, Idris Ismail, Mohd Azmin Ishak

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 1, 199–222

Research Article | 11-January-2018

MEMS Seismic Sensor with FPAA-Based Interface Circuit for Frequency-Drift Compensation Using ANN

analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power

Ramesh Pawase, Dr. N.P. Futane

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–6

Article | 01-December-2016

OPTIMIZATION OF MODIFIED ROTAMETER USING HALL PROBE SENSOR WITH RESPECT TO LIQUID DENSITY AND ITS CALIBRATION USING ARTIFICIAL NEURAL NETWORK

Sunita Sinha, Nirupama Mandal

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 2204–2218

Research Article | 27-December-2017

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, Volume 7 , ISSUE 2, 837–855

Article | 01-June-2015

WSN BASED THERMAL MODELING: A NEW INDOOR ENERGY EFFICIENT SOLUTION

In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear

Yi Zhao, Valentin Gies, Jean-Marc Ginoux

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 869–895

Article | 01-September-2015

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant

Arun Jana, Nabarun Bhattacharyya, Rajib Bandyopadhyay, Bipan Tudu, Subhankar Mukherjee, Devdulal Ghosh, Jayanta Kumar Roy

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1730–1747

Research paper | 13-December-2017

SYSTEM IDENTIFICATION OF NONLINEAR AUTOREGRESSIVE MODELS IN MONITORING DENGUE INFECTION

This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, i.e. the final prediction error (FPE), Akaike’s Information Criteria (AIC), and Lipschitz number were used. Each of the models is divided into two approaches, which are unregularized approach and regularized approach. The findings indicate that NARMAX model with regularized approach yields

H. Abdul Rahim, F. Ibrahim, M. N. Taib

International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 4, 783–806

Article | 09-April-2018

Lidar Image Classification based on Convolutional Neural Networks

This paper presents a new method of recognition of lidar cloud point images based on convolutional neural network. This experiment uses 3D CAD ModelNet, and generates 3D point cloud data by simulating the scanning process of lidar. The data is divided into cells, and the distance is represented by gray values. Finally, the data is stored as grayscale images. Changing the number of cells dividing point cloud results in different experimental results. Experiments show that the proposed method has

Yang Wenhui, Yu Fan

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 158–162

Article | 07-May-2018

3D Target Recognition Based on Decision Layer Fusion

Ma Xing, Yu Fan, Yu Haige, Wei Yanxi, Yang Wenhui

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 19–22

Research Article | 15-February-2020

Thin Film Coated QCM-Sensors and Pattern Recognition Methods for Discrimination of VOCs

gas sensors with different chemical affinity towards VOC molecules. The sensitivity of the elaborated QCMbased sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds, such as; ethanol, benzene and chloroform. The sensors responses data have been used for the identification and quantification of VOCs. The principal component analysis (PCA) and the neural-network (NNs) pattern recognition analysis were used for the

Omar C. Lezzar, A. Bellel, M. Boutamine, S. Sahli, Y. Segui, P. Raynaud

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6

Research Article | 27-December-2017

PREDISTORTION SYSTEM IMPLEMENTATION BASED ON ANALOG NEURAL NETWORKS FOR LINEARIZING HIGH POWER AMPLIFIERS TRANSFER CHARACTERISTICS

B. Mulliez, E. Moutaye, H. Tap, L Gatet, F. Gizard

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 400–420

Research Article | 02-November-2017

DETECTION OF TRANSVERSE CRACKS IN A COMPOSITE BEAM USING COMBINED FEATURES OF LAMB WAVE AND VIBRATION TECHNIQUES IN ANN ENVIRONMENT

The detection, location and sizing of transverse cracks in a composite beam, by combining damage features of Lamb wave and vibration based techniques in artificial neural network (ANN) environment, using numerical finite element model, is discussed. Four damage features, time of flight (TOF) and amplitude ratio, which are Lamb wave based features and first and second natural frequencies, which are vibration based features were used as input to ANN. The output of ANN was crack location and depth

Ramadas C., Krishnan Balasubramaniam, M. Joshi, C.V. Krishnamurthy

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 4, 970–984

Article | 01-December-2012

NEURAL NETWORK BASED MULTISENSOR FUSION IN A NOVEL PERMANENT MAGNET MULTI-DOF ACTUATOR ORIENTATION DETECTION SYSTEM

Zheng LI

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 4, 911–927

Article | 11-April-2018

Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the over fitting problem, more samples are needed for model training. In this paper, the cross

Junhua Ku, Kongduo Xing

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 72–77

Research Article | 13-December-2017

APPLICATION OF INTELLIGENT CONTROLLER IN A BALL AND BEAM CONTROL SYSTEM

Mohd Fuaad Rahmat, Herman Wahid, Norhaliza Abdul Wahab

International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 1, 45–60

Research paper | 12-December-2017

SIMULATION STUDIES ON A NEW INTELLIGENT SCHEME FOR RELATIVE HUMIDITY AND TEMPERATURE MEASUREMENT USING THERMISTORS IN 555 TIMER CIRCUIT

relative humidity and ambient temperature by an appropriately trained Artificial Neural Network (ANN). The performance of the arrangement has been evaluated by numerical simulation studies using thermistor data from manufacturer’s table. Significantly good results have been obtained.

Debangshu Dey, Sugata Munshi

International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 2, 217–229

Article | 02-April-2020

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

, the traditional survey situation is that the site construction surveyors scan the survey images generated by radar equipment one by one according to their expert knowledge. This traditional method has a large workload, a large human factor, and a certain rate of omission and error. In recent years, with the continuous improvement of GPU, the field of deep learning is booming. In 2006, Hinton[4] and other researcher proposed the concept of deep learning, using convolutional neural network (CNN) to

He Li, Yubian Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 44–53

Article | 14-October-2020

Hierarchical Image Object Search Based on Deep Reinforcement Learning

that can obtain high reward values in the environment, and find the best strategy to solve the problem in such constant interaction. Based on this idea, this paper use reinforcement learning technology to simulate the human visual attention mechanism. The agent is taught to change the shape of the bounding box and focus only on a significant part of the image at a time, and then extract its features through the convolutional neural network. Finally, the object of image positioning and

Wei Zhang, Hongge Yao, Yuxing Tan

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 65–72

Research Article | 12-December-2017

A LOW COST PORTABLE TEMPERATURE-MOISTURE SENSING UNIT WITH ARTIFICIAL NEURAL NETWORK BASED SIGNAL CONDITIONING FOR SMART IRRIGATION APPLICATIONS

Aman Tyagi, Arrabothu Apoorv Reddy, Jasmeet Singh, Shubhajit Roy Chowdhury

International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 1, 94–111

Article | 03-November-2017

PERFORMANCE ANALYSIS OF PATIENT SPECIFIC ELMAN-CHAOTIC OPTIMIZATION MODEL FOR FUZZY BASED EPILEPSY RISK LEVEL CLASSIFICATION FROM EEG SIGNALS

Dr.R HariKumar, T. Vijayakumar

International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 4, 612–635

Article | 01-September-2016

A NOVEL HYBRID LOCALIZATION METHOD FOR WIRELESS SENSOR NETWORK

components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the

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

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1323–1340

Research Article | 12-September-2018

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a

Hela Lassoued, Raouf Ketata, Slim Yacoub

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–15

Research Article | 30-April-2018

A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

Abstract This research aims at develop an MTB detection model from the FLVQ neural network to HGA-FLVQ model. In this research, the FLVQ method was developed through strengthening its initiation, in which the first cluster centers used as FLVQ input were optimized first by HGA. The results show that sensitivity and specificity of the HGA-FLVQ model reach 96.30 and 95.65%, whereas the sensitivity of an FLVQ method is 70.83%, and the sensitivity of an LVQ method is 87.50%. The specificity of an

N. Charibaldi, A. Harjoko, B. Hisyam

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–13

Article | 09-April-2018

Prediction of the Heat Load in Central Heating Systems Using GA-BP Algorithm

This paper presented the research on heat load prediction method of central heating system. The combined simulation data at Xi'an in January was used as the samples for training and predicting. This paper selected the daily average outdoor wind speed, the daily average outdoor temperature, date type, sunshine duration as input variables and the heating load value as output variable. After preprocessing of the historical data, the BP neural network algorithm and the GA-BP algorithm were

Bingqing Guo, Jin Xu, Ling Cheng, Lei Chen

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 137–141

Research Article | 15-February-2020

Analysis of Foot-pressure Data to Classify Mobility Pattern

Goutam Chakraborty, Tetsuhiro Dendou

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6

Article | 01-March-2015

ACOUSTIC-PHONETIC FEATURE BASED DIALECT IDENTIFICATION IN HINDI SPEECH

Shweta Sinha, Aruna Jain, S. S. Agrawal

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 235–254

Article | 02-November-2017

Normalization techniques for gas sensor array as applied to classification for black tea

different normalization techniques were applied on electronic nose data. Finally the comparison of classification accuracy is presented with different normalization techniques using back-propagation multilayer perceptron (BP-MLP) neural network.

Bipan Tudu, Bikram Kow, Nabarun Bhattacharyya, Rajib Bandyopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 1, 176–189

Article | 01-September-2015

THE USE OF ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF THE PERCEPTION OF SOUND BY THE HUMAN AUDITORY SYSTEM

through the modeling of the physical auditory system and the biological neural network of the primary auditory cortex using artificial neural networks. The behavior of artificial neural networks both during and after the training process has also been found to mimic that of biological neural networks and this method will be shown to have certain advantages over previous methods in the modeling of auditory perception. This work will describe the nature of artificial neural networks and investigate

D. Riordan, P. Doody, J. Walsh

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1806–1836

Article | 01-December-2012

VISUAL ATTENTION MODEL WITH ADAPTIVE WEIGHTING OF CONSPICUITY MAPS FOR BUILDING DETECTION IN SATELLITE IMAGES

and evaluated against existing solutions in the literature. A novel adaptive algorithm then chooses the best weighting scheme based on a similarity error to ensure the best performance of the attention model in a given context. Finally, a neural network is trained to predict the set of weights provided by the best weighting scheme for the context of the image in which buildings are to be detected. The solution provides encouraging results on a set of 50 satellite images.

A.-M. Cretu, P. Payeur

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 4, 742–766

Research paper | 10-April-2013

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 448–465

Research Article | 23-May-2019

PREDICTIVE MICROBIOLOGY OF FOOD

models, transfer models, Artificial Neural Network, interactions between species, and single cell models. The process of creating a mathematical model requires coordination of work and the knowledge of: microbiology, statistics, mathematics, chemistry, process engineering and computer and web science. It also requires appropriate hardware and software. There are four stages in the construction of a mathematical model: planning; data collection and analysis; mathematical description; validation and

Elżbieta Rosiak, Katarzyna Kajak-Siemaszko, Monika Trząskowska, Danuta Kołożyn-Krajewska

Postępy Mikrobiologii - Advancements of Microbiology, Volume 57 , ISSUE 3, 229–243

Research paper | 25-July-2017

Functional correlates of brain aging: beta and gamma components of event-related band responses

The brain as a system with gradually declining resources by age maximizes its performance by neural network reorganization for greater efficiency of neuronal processes which is reflected in changes of event-related band responses (ERBRs) for sensory stimuli. Whether changes of high-frequency components of event-related responses are related to plasticity in neural recruitment during stabilization of sensory/cognitive mechanisms accompanying aging or are underlying pathological changes remains

Mario Christov, Juliana Dushanova

Acta Neurobiologiae Experimentalis, Volume 76 , ISSUE 2, 98–109

Article | 01-June-2016

A MACHINE VISION SYSTEM FOR ESTIMATION OF THEAFLAVINS AND THEARUBIGINS IN ORTHODOX BLACK TEA

Amitava Akuli, Abhra Pal, Gopinath Bej, Tamal Dey, Arunangshu Ghosh, Bipan Tudu, Nabarun Bhattacharyya, Rajib Bandyopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 709–731

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