A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM

Liu, Xingwei; Fang, Xuming; Qin, Zhenhua; Ye, Chun; Xie, Miao

doi:10.1007/s10922-010-9188-3

Cited by 46 publications

(20 citation statements)

References 22 publications

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“…In our case we are interested in generating a disturbance in the brain through an external stimulus signal and to capture the presence of response to this signal in the EEG signal cloud. These properties of chaos theory has been successfully used in various applications such as investigation of seismic activities (Yang et al 2012), development of radar technology (Willsey et al 2011) and analysis of internet activities (Liu et al 2011). Furthermore, chaotic nature of available data for quality control (Torres et al 2002) and reliability and maintenance control (Chouikhi et al 2014) in manufacturing industry has been addressed in similar ways.…”

Section: Bio-signal Identification Techniquesmentioning

confidence: 99%

Bio-signal-based geometric modeling application for physically disabled users

Akgündüz

2016

J Intell Manuf

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This paper discusses the challenges in achieving bio-signal-based design environments. While the main motivation of this paper was to provide a user interface for physically disabled people to express their artistic natures, a special emphasis is given on graphical user interface design where bio-signals are the single input source. Among three bio-signal sources investigated-electromyography, electrooculography and electroencephalography (EEG)-stimulus-based human-computer interaction design (EEG feature extraction method) is found to be the most promising for achieving design environments to perform complex tasks. In the proposed stimulus-based brain-computer-interaction application, the user communication with a computer is achieved by coupling intended functionalities with stimuli signals on the computer screen. Constant focus on the intended command stimulates the brain. In return, the brain releases a response signals (steady state visual evoked potential). In theory, brain's response signals and the stimulus signals are identical. Once successfully identified, the presence of a signal pattern that is identical to the one of the alternative stimulus signals (paired with a command in a user interface) indicates the intention of a user. Since each option is associated with a unique signal pattern, multiple options can simultaneously be offered to users. The main challenge of working with stimulus signals is that the response signals are weak and they are buried inside of highly polluted EEG signals that include brain's natural activities. In this paper, we introduce a signal processing algorithm based on Lorenz systems of differential equations for identifying the source of stimulus signals. Our experiments strongly suggest that bio-B Ali Akgunduz

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Section: Bio-signal Identification Techniquesmentioning

confidence: 99%

Bio-signal-based geometric modeling application for physically disabled users

Akgündüz

2016

J Intell Manuf

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“…Considering the collected sensor data as a time series [3], the linear regression method is used to fill or predict the missing variables [4][5] [6]. There are also many nonlinear methods such as least squares [7], support vector regression [8], or neural network model [9] are used. Generally speaking, most of these reconstruction methods only consider the time character of the missing data, the reconstruction accuracies are not high when the data acquisition interval is larger or the data is non-stably changed.…”

Section: Introductionmentioning

confidence: 99%

Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks

Zhao¹,

Fang²

2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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Due to external interference or fault, the collected sensor data is often missed or abnormal. It's significant to reconstruct the missing data, especially the large-scale missing data. In this paper, a missing sensor data reconstruction method based on the adaptively updated dictionary is presented. The K-SVD algorithm is used to train the historical data frames which are collected at different time to generate the original dictionary atoms. Moreover, in order to meet the realtime, continuous characteristics of sensor data, an adaptive dictionary update algorithm is studiedwhich. It calculates the correlation between the current reconstructed data frame and the largest weight frame in the training dictionary to update the dictionary. The experimental results are fully analyzed by the open data set. The results show that the proposed method has higher reconstructed precision especially the interval of data frames which is more than 60 minutes compared with other commonly used methods .

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“…This study managed bandwidth allocation on max-min fairness queue scheduling using a time constraint condition. Liu, et al [8] has predicted network traffic by using chaos theory and Support Vector Machine (SVM). This research used campus data including wired and wireless.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Tsukamoto for SARIMA On Automation of Bandwidth Allocation

Bustoni¹,

Permanasari²,

Hidayah³

et al. 2017

ijacsa

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Abstract-The wireless network is used in different fields to enhance information transfer between remote areas. In the education area, it can support knowledge transfer among academic member including lecturers, students, and staffs. In order to achieve this purpose, the wireless network is supposed to be well managed to accommodate all users. Department of Electrical Engineering and Information Technology UGM sets wireless network for its daily campus activity manually and monitor data traffic at a time then share it to the user. Thus, it makes bandwidth sharing becomes less effective. This study, build a dynamic bandwidth allocation management system which automatically determines bandwidth allocation based on the prediction of future bandwidth using by implementing Seasonal Autoregressive Integrated Moving Average (SARIMA) with the addition of outlier detection since the result more accurate. Moreover, the determination of fixed bandwidth allocation was done using Fuzzy Logic with Tsukamoto Inference Method. The results demonstrate that bandwidth allocations can be classified into 3 fuzzy classes from quantitative forecasting results. Furthermore, manual and automatic bandwidth allocation was compared. The result on manual allocation MAPE was 70,76% with average false positive value 56 MB, compared to dynamic allocation using Fuzzy Logic and SARIMA which has MAPE 38,9% and average false positive value around 13,84 MB. In conclusion, the dynamic allocation was more effective in bandwidth allocation than manual allocation.

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A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM

Cited by 46 publications

References 22 publications

Bio-signal-based geometric modeling application for physically disabled users

Bio-signal-based geometric modeling application for physically disabled users

Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks

Fuzzy Logic Tsukamoto for SARIMA On Automation of Bandwidth Allocation

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