Exponentially Weighted Moving Average Algorithm Research Articles

Real-Time Step Length Estimation in Indoor and Outdoor Scenarios.

In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is "sliding" forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.

Seismic velocity field estimation using sonic well logs decomposed by Singular Spectrum Analysis (SSA) and Exponentially Weighted Moving Average (EWMA) - A case of study in Recôncavo Basin, Brazil

The determination of an accurate seismic wavefield velocity propagating in the subsurface is a problem faced currently by geoscientists. However, velocity analysis on land seismic data is often compromised due to lack of a good signal-to-noise ratio, absence of guide reflectors, and low fold, which significantly affects the quality of the final velocity model and, consequently, the seismic image. This paper introduces an improvement in the root mean square (RMS) velocity model obtained from the Polo-Miranga seismic cube, located in the Recôncavo sedimentary basin in northeast of Brazil, using slowness well log data. The available vagarosity logs were smoothed using the Exponentially Weighted Moving Average (EWMA) and Singular Spectrum Analysis (SSA) to determine a new measurement that reflects the low frequency of the p-wave seismic velocity to be employed in the velocity analysis. Estimating the best parameter of the EWMA algorithm required some tests to smooth the slowness. At the same time, the first component of the sonic decomposition into three other curves was a reasonable way to estimate the velocity. Those techniques showed robust results and enhanced some reflectors in the 3D stack. Furthermore, the proposed methodology’s performance is validated through the normal moveout correction (NMO) simulation and on the time-slice image.

Research and Application of Network Anomaly Traffic Detection System

This paper puts forward a network security computing platform which based on the open source big data technology. This network anomaly traffic detection system includes a comprehensive and effective traffic anomaly detection algorithm. The algorithm combines the exponentially weighted moving average algorithm (EWMA) and the anomaly flow interval mapping algorithm two methods. The test experiments contain the physical cluster building parameters, the performance of the system and the DDoS attack verification. It is verified by the Tianjin Education metropolitan area network traffic and the NSFOCUS against denial attack event log service system (NSFOCUS ANTI-DDoS system). In Particular, the network anomaly traffic detection system can accurately identify the occurrence period of anomaly traffic such as the flow direction and the event priority. What’ more, it has strong fault tolerance which can achieve error recovery in a short time. All the test experiment results can verify that the system can effectively detect anomaly traffic and real-time monitor the dynamic network.

Eventual Convergence of the Reputation-Based Algorithm in IoT Sensor Networks.

Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of IoT networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in IoT networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.

Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks.

The aim of this paper is to introduce a NUT model (NUT: network-uncertainty-trust) that aids the decrease of the uncertainty of measurements in autonomous hybrid Internet of Things sensor networks. The problem of uncertainty in such networks is a consequence of various operating conditions and varied quality of measurement nodes, making statistical approach less successful. This paper presents a model for decreasing the uncertainty through the use of socially inspired metaphors of reputation, trust, and confidence that are the untapped latent information. The model described in the paper shows how the individual reputation of each node can be assessed on the basis of opinions provided by other nodes of the hybrid measurement network, and that this method allows to assess the extent of uncertainty the node introduces to the network. This, in turn, allows nodes of low uncertainty to have a greater impact on the reconstruction of values. The verification of the model, as well as examples of its applicability to air quality measurements are presented as well. Simulations demonstrate that the use of the model can decrease the uncertainty by up to 55% while using the EWMA (exponentially weighted moving average) algorithm, as compared to the reference one.

NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection

We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features (RFs) to efficiently use the Maximum Mean Discrepancy as a distance between distributions, furthermore exploiting recent optical hardware to compute high-dimensional RFs in near constant time. We show that our method is significantly faster than usual non-parametric methods for a given accuracy.

Multiscale modeling and run-to-run control of PECVD of thin film solar cells

In this work, we focus on the development of a multiscale modeling and run-to-run control framework with the purpose of improving thin film product quality in a batch-to-batch plasma-enhanced chemical vapor deposition (PECVD) manufacturing process. Specifically, at the macroscopic scale, gas-phase reaction and transport phenomena yield deposition rate profiles across the wafer surface which are then provided to the microscopic domain simulator in which the complex microscopic surface interactions that lead to film growth are described using a hybrid kinetic Monte Carlo algorithm. Batch-to-batch variability has prompted the development of an additional simulation layer in which an exponentially weighted moving average (EWMA) control algorithm operates between serial batch deposition sequences to adjust the operating temperature of the PECVD reactor to overcome drift in the electron density of the plasma. Application of the run-to-run (R2R) control system developed here is shown to reduce offset in the product thickness from 5% to less than 1% within 10 batches of reactor operation. Finally, we propose an extension of the EWMA algorithm to four independent, radial wafer zones in order to improve thickness uniformity in the presence of spatially non-uniform species concentrations. It is demonstrated that the produced thin films can be driven to the desired thickness set-point of 300 nm in less than 10 batches in the presence of both electron density drift and non-uniform deposition rate profiles.

UDT 플로우 간 공평성 향상을 위한 혼잡도 기반의 가용대역폭 추정 기법

End to end 데이터 전송프로토콜에서 가용대역폭을 정확하게 추정하는 것은 매우 중요하다. UDT (UDP based Data Transfer)는 송신자에서 주기적으로 보낸 패킷 쌍을 이용하여 수신자에서 현재 링크의 최대 전송 가능한 속도 (MTR: Maximum Transfer Rate)를 추정하고 송신자에서 EWMA 알고리즘을 통해 MTR을 결정한다. 가용대역폭의 크기는 MTR과 현재 전송속도 차이로 구하기 때문에 정확한 가용대역폭 추정을 위해서는 정확한 MTR 추정이 우선시된다. 하지만 트래픽 영향으로 인한 혼잡상황에서 다수의 UDT 플로우가 경쟁하는 경우 심각한 공평성 문제를 초래한다. 본 논문에서는 혼잡도를 기반으로 한 MTR 추정 알고리즘을 제안한다. 혼잡도는 병목구간에서 트래픽 영향으로 인한 혼잡상태를 나타내는 상대적인 지수이며 패킷 쌍들의 도착간격을 이용하여 구할 수 있다. 그리고 혼잡도에 따라 EWMA 알고리즘을 세분화함으로써 실제에 근접한 가용대역폭 추정이 가능하다. Ns-2 시뮬레이션 실험 결과 제안기법은 다수의 경쟁플로우가 동일 링크를 점유하는 상황에서 데이터 전송거리에 따라 UDT 보다 확연하게 높은 공평성을 보이는 것을 확인하였다.

Levels of alarm thresholds of meningitis outbreaks in Hamadan Province, west of Iran.

Few studies have focused on syndromic data to determine levels of alarm thresholds to detection of meningitis outbreaks. The purpose of this study was to determine threshold levels of meningitis outbreak in Hamadan Province, west of Iran. Data on both confirmed and suspected cases of meningitis (fever and neurological symptom) form 21 March 2010 to 20 March 2012 were used in Hamadan Province, Iran. Alarm threshold levels of meningitis outbreak were determined using four different methods including absolute values or standard method, relative increase, statistical cutoff points and upper control limit of exponentially weighted moving average (EWMA) algorithm. Among 723 reported cases, 41 were diagnosed to have meningitis. Standard level of alarm thresholds for meningitis outbreak was determined as incidence of 5/100000 persons. Increasing 1.5 to two times in reported cases of suspected meningitis per week was known as the threshold levels according to relative increase method. An occurrence four cases of suspected meningitis per week that equals to 90th percentile was chosen as alarm thresholds by statistical cut off point method. The corresponding value according to EWMA algorithm was 2.57 i.e. three cases. Policy makers and staff of syndromic surveillance systems are highly recommended to apply the above different methods to determine the levels of alarm threshold.

How can sliding HyperLogLog and EWMA detect port scan attacks in IP traffic?

IP networks are constantly targeted by new techniques of denial of service attacks (SYN flooding, port scan, UDP flooding, etc), causing service disruption and considerable financial damage. The on-line detection of DoS attacks in the current high-bit rate IP traffic is a big challenge. We propose in this paper an on-line algorithm for port scan detection. It is composed of two complementary parts: First, a probabilistic counting part, where the number of distinct destination ports is estimated by adapting a method called ‘sliding HyperLogLog’ to the context of port scan in IP traffic. Second, a decisional mechanism is performed on the estimated number of destination ports in order to detect in real time any behavior that could be related to a malicious traffic. This latter part is mainly based on the exponentially weighted moving average algorithm (EWMA) that we adapted to the context of on-line analysis by adding a learning step (supposed without attacks) and improving its update mechanism. The obtained port scan detecting method is tested against real IP traffic containing some attacks. It detects all the port scan attacks within a very short time response (of about 30 s) and without any false positive. The algorithm uses a very small total memory of less than 22 kb and has a very good accuracy on the estimation of the number of destination ports (a relative error of about 3.25%), which is in agreement with the theoretical bounds provided by the sliding HyperLogLog algorithm.

An EWMA Algorithm With a Cycled Resetting (CR) Discount Factor for Drift and Fault of High-Mix Run-to-Run Control

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Run-to-run controllers based on the exponential weighted moving average (EWMA) statistic are probably the most frequently used for the quality control of certain semiconductor manufacturing process steps. The threaded-EWMA run-to-run control is an important stable control scheme. However, the process outputs will deviate largely in the first few runs of each cycle if the disturbance follows an IMA(1,1) series with deterministic linear drift and the thread has a long break length. In this paper, the output of the threaded-EWMA run-to-run control is derived, stability conditions are given, and the causes of large deviations in the first few runs of each cycle are found. Based on the analysis of system performance, a cycled resetting (CR) algorithm for discount factor is proposed to reduce the large deviations, as well as to achieve the minimum asymptotic variance control. Furthermore, how to deal with step fault is also discussed in this paper. By analyzing the influence of the fault, a discount factor resetting fault-tolerant (RFT) approach is proposed. Simulation study shows both the mean square error (MSE) and variance of the output by the proposed algorithm is about 30% to 50% lower than that of the algorithm with fixed discount factor in the process with and without oscillation. This verifies the effectiveness of the proposed approach. </para>