Sewer system flow components identification using signal processing

2010 ◽  
Vol 62 (1) ◽  
pp. 106-114
Author(s):  
F. A. Dorval ◽  
B. Chocat ◽  
E. Emmanuel ◽  
G. Lipeme Kouyi
Keyword(s):  
Signal Processing ◽  
Time Series Data ◽  
Continuous Model ◽  
Series Data ◽  
Time Step ◽  
Sewer System ◽  
Long Time ◽  
Recorded Data ◽  
Flow Components ◽  
Main Components

The development of a continuous model to simulate the behaviour of sewer systems requires detailed information on each component of the flows contributing to the global discharge. In this paper authors investigate a novel method based on signal processing and long time series data implemented with a 2 min time step (flow rate, conductivity, pH and turbidity) in order to identify the dry weather components in a separated stormwater sewer system draining an industrial catchment. The wavelet analysis is applied to the recorded data to identify main components in dry weather flow after the removing of the signal noise. This paper highlights also a method to detect inflow into sewer system and shows how hydrological modelling can be used to characterise the relevant components. These techniques could be used as a basis for several applications.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

News Article Based Industry Risk Index Prediction for Industry-Specific Evaluation

2021 ◽  
Author(s):  
Kyungwon Kim ◽  
Kyoungro Yoon
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Evaluation Method ◽  
Time Series Data ◽  
Risk Index ◽  
News Article ◽  
Series Data ◽  
Specific Interest ◽  
Analysis Model ◽  
Long Time

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants

2011 ◽  
Vol 15 (3) ◽  
pp. 336-344 ◽  
Author(s):  
Daisaku Kimura ◽  
◽  
Manabu Nii ◽  
Takafumi Yamaguchi ◽  
Yutaka Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Regression ◽  
Time Series Data ◽  
Numerical Data ◽  
The State ◽  
Series Data ◽  
Target System ◽  
Chemical Plants ◽  
Domain Experts ◽  
Recorded Data

In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult to realize. In this paper, a technique of diagnosing target systems based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.

scholarly journals Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

2020 ◽  
Vol 245 ◽  
pp. 07001
Author(s):  
Laura Sargsyan ◽  
Filipe Martins
Keyword(s):  
Time Series ◽  
Control System ◽  
Distribution System ◽  
Large Scale ◽  
High Energy Physics ◽  
Time Series Data ◽  
High Energy ◽  
Series Data ◽  
Test Version ◽  
Long Time

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

scholarly journals Nonlinear Reaction functions: Evidence from India

2019 ◽  
Vol 8 (1) ◽  
pp. 111-132
Author(s):  
Taufeeq Ajaz
Keyword(s):  
Time Series Data ◽  
Policy Preferences ◽  
Series Data ◽  
Output Gap ◽  
Reaction Function ◽  
Reaction Functions ◽  
Reserve Bank ◽  
Show Evidence ◽  
Long Time ◽  
Monetary Policy Reaction Function

Abstract This paper uses time-series data from India and tests for asymmetries in policy preferences of the Reserve Bank of India (the Central Bank of India, hereafter RBI). The results show evidence in favour of preference asymmetries in monetary policy reaction function in India and hence nonlinearities in the Taylor-rule. Evidence of both recession avoidance preference (RAP) as well as inflation avoidance preference (IAP) is established. And it is found that RAP is dominant over IAP, thus confirming nonlinearities in reaction function which in the present case turns out to be concave in inflation and output gap. Further, the results indicate preference asymmetries in both the objectives. The coefficient weights to positive and negative inflation and output gap differ over long time horizons thus confirming asymmetric policy preferences. Specifically the RBI seems to be more averse to a negative output gap (contraction) as compared to an equal positive gap. In addition, the RBI appears to be more averse to a positive inflation gap as compared to an equal negative gap.

scholarly journals Roadmap on signal processing for next generation measurement systems

2021 ◽  
Vol 33 (1) ◽  
pp. 012002
Author(s):  
Dimitris K Iakovidis ◽  
Melanie Ooi ◽  
Ye Chow Kuang ◽  
Serge Demidenko ◽  
Alexandr Shestakov ◽  
...  
Keyword(s):  
Signal Processing ◽  
Time Series Data ◽  
Research Field ◽  
Series Data ◽  
Next Generation ◽  
Research Attention ◽  
Measurement Systems ◽  
Scientific Disciplines ◽  
Wide Range ◽  
Future Challenges

Abstract Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

scholarly journals Time-Aware Multi-Scale RNNs for Time Series Modeling

2021 ◽  
Author(s):  
Zipeng Chen ◽  
Qianli Ma ◽  
Zhenxi Lin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multiple Scales ◽  
Multivariate Time Series ◽  
Human Motion ◽  
Series Data ◽  
Time Step ◽  
Multi Scale ◽  
Important Time ◽  
Time Aware

Multi-scale information is crucial for modeling time series. Although most existing methods consider multiple scales in the time-series data, they assume all kinds of scales are equally important for each sample, making them unable to capture the dynamic temporal patterns of time series. To this end, we propose Time-Aware Multi-Scale Recurrent Neural Networks (TAMS-RNNs), which disentangle representations of different scales and adaptively select the most important scale for each sample at each time step. First, the hidden state of the RNN is disentangled into multiple independently updated small hidden states, which use different update frequencies to model time-series multi-scale information. Then, at each time step, the temporal context information is used to modulate the features of different scales, selecting the most important time-series scale. Therefore, the proposed model can capture the multi-scale information for each time series at each time step adaptively. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on multivariate time series classification and human motion prediction tasks. Furthermore, visualized analysis on music genre recognition verifies the effectiveness of the model.

scholarly journals Reassessment of long-period constituents for tidal predictions along the German North Sea coast and its tidally influenced rivers

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1363-1379
Author(s):  
Andreas Boesch ◽  
Sylvin Müller-Navarra
Keyword(s):  
North Sea ◽  
Time Series Data ◽  
Series Data ◽  
Tide Gauges ◽  
Mean Values ◽  
Long Period ◽  
Long Time ◽  
Harmonic Representation ◽  
Sea Coast ◽  
North Sea Coast

Abstract. The harmonic representation of inequalities (HRoI) is a procedure for tidal analysis and prediction that combines aspects of the non-harmonic and the harmonic method. With this technique, the deviations of heights and lunitidal intervals, especially of high and low waters, from their respective mean values are represented by superpositions of long-period tidal constituents. This article documents the preparation of a constituents list for the operational application of the harmonic representation of inequalities. Frequency analyses of observed heights and lunitidal intervals of high and low water from 111 tide gauges along the German North Sea coast and its tidally influenced rivers have been carried out using the generalized Lomb–Scargle periodogram. One comprehensive list of partial tides is realized by combining the separate frequency analyses and by applying subsequent improvements, e.g. through manual inspections of long time series data. The new set of 39 partial tides largely confirms the previously used set with 43 partial tides. Nine constituents are added and 13 partial tides, mostly in the close neighbourhood of strong spectral components, are removed. The effect of these changes has been studied by comparing predictions with observations from 98 tide gauges. Using the new set of constituents, the standard deviations of the residuals are reduced on average by 2.41 % (times) and 2.30 % (heights) for the year 2016. The new set of constituents will be used for tidal analyses and predictions starting with the German tide tables for the year 2020.

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