Electric arc furnaces (EAFs) are important appliances in the steelmaking industry, but they are characterized by a nonlinear, dynamic, and stochastic nature. Due to this fact, EAFs can have a ...negative influence on power systems. Measures to mitigate such problems can be designed properly only with knowledge of the influence of the load on the system. Therefore, it is necessary to have accurate EAF models that reflect the complicated character of such loads. Researchers use different approaches for EAF modeling, such as stochastic process analysis, differential equation models, or neural networks. This article presents the application of three models based on artificial neural networks (ANNs) in EAF modeling along with an extension based on the stochastic moving average (MA) process. The goal was to provide ANN models that are simple in structure in comparison to, e.g., deep learning methods used by other researchers. The first two models are built on multilayer perceptron networks, and the third applies a nonlinear autoregressive exogenous model with the help of differential equation transformed into the Hammerstein-Wiener model. ANN models are improved by adding an MA ingredient. The article describes the measurement data, the design of each approach, and the results of the EAF modeling.
Load Forecasts are the primary factors which considered by electricity utility companies while planning power generation, power infrastructural development and load flows etc. Different forecasting ...techniques have been proposed from statistical to artificial intelligence-based models and the area of research is still growing. In our research work, considering the real time data of 33KV bus system which is having 34 buses and 54 lines. In this case, forecast the day ahead scheduling of various parameters such as load real power (Pload), voltage magnitude at each bus, apparent power flow between buses and total transmission losses for hourly basis and also forecasted the mentioned parameters for 5 days. The actual real time values are compared with forecasted values using two existing methods namely Extreme Learning Machine (ELM), moving average and proposed Moving Average–Extreme Learning Machine (MA-ELM) algorithm. In addition to this, forecasted the loads and losses for short term and long-term forecasting cases and verified through MATLAB programming.
Large biological variation hinders application of patient-based real-time quality control (PBRTQC). The effect of analyte ratios on the ability of PBRTQC to improve error detection was investigated.
...Four single analyte-ratio pairs (alanine aminotransferase ALT vs. ALT to aspartate aminotransferase ratio R
; creatinine Cr vs. Cr to cystatin C ratio R
; lactate dehydrogenase LDH vs. LDH to hydroxybutyrate dehydrogenase ratio R
; total bilirubin TB vs. TB to direct bilirubin ratio R
) were chosen for comparison. Various procedures, including four conventional algorithms (moving average MA, moving median MM, exponentially weighted moving average EWMA and moving standard deviation MSD) were assessed. A new algorithm that monitors the number of defect reports per analytical run (NDR) was also evaluated.
When a single analyte and calculated ratio used the same PBRTQC parameters, fewer samples were needed to detect systematic errors (SE) by taking ratios (p<0.05). Application of ratios in MA, MM and EWMA significantly enhanced their ability to detect SE. The influence of ratio on random error (RE) detection depended upon the analytes and PBRTQC parameters, as consistent advantage was not demonstrated. The NDR method performed well when appropriate parameters were used, but was only effective for unilateral SE. Rearrangement of sample order led to a significant deterioration of conventional algorithms' performance, while NDR remained almost unaffected.
For analytes with large variation and poor PBRTQC performance, using ratios as PBRTQC indexes may significantly improve performance and achieve better anti-interference ability, providing a new class of monitoring indicators for PBRTQC.
The primary task in cognitive radio is to dynamically explore the radio spectrum and reliably detect the co-existing licensed primary transmissions across a wide-band spectrum. This paper focuses on ...wavelet transform (WT) based wide-band sensing techniques, which identify the edges of the multiple frequency bands simultaneously. Novel edge detection algorithms are proposed based on continuous WT (CWT) and discrete WT (DWT) techniques, applied on wide-band power spectrum. In CWT based spectrum sensing, logarithmic scaling preceded by a thresholding is performed on the CWT coefficients to enhance the small modulus maxima values at the edges, resulting in better detection probability. Since the logarithmic scaling magnifies the spurious edges, the proposed algorithm increases the false alarm probability at high noise variance. To alleviate this problem, DWT based algorithms are proposed, where DWT performs simultaneous denoising and edge detection. To achieve good detection performance at poor SNR scenario, a moving average filtering strategy is adopted at different levels of DWT based algorithms and better performance is achieved even with lower scale value of DWT, thereby reducing the computation time. Comparative studies show that the proposed algorithms outperform the existing WT based edge detection algorithms in the dynamic and frequency selective channels as well.
This study introduces a new approach to enhance the measurement speed in the suggested system-on-chip (SoC) by incorporating quadratic least mean square (LMS) filtering with moving average (MA) and ...moving median (MM) filters at a low-averaging stage. The use of MA and MM filters in the frequency and time domains yields a significant enhancement in the reliability and accuracy of the Brillouin frequency shift (BFS). Our method, as demonstrated in this study, exhibits a significantly reduced latency, which ranges from 14 to 1563 times faster than Brillouin optical time-domain analysis (BOTDA) systems that rely on an oscilloscope and a computer for data processing, and the BOTDA system utilizing SoC with LMS filtering alone. Compared with LMS curve fitting alone, the implementation of this technique greatly enhances accuracy with low averages by 84%. These filters enable the identification of minor disruptions along the cable. The proposed method successfully detects small variations in Brillouin frequency while maintaining the spatial resolution of sensors. The deployed filters result in a spatial resolution ranging from 1.6 to 1.5 m, which demonstrates greater constancy than the normal range of 2-1.6 m. This study successfully employs filtering techniques in the frequency and time domains by incorporating low averaging and a broad dynamic range. The use of SoC technology in BOTDA improves measurement speed, accuracy, and the preservation of spatial resolution constancy. However, the measurable fiber length is limited due to ac coupling on the analog-to-digital converter board. The algorithm and dataset are publicly available at GitHub.
Background New moving average quality control (MA QC) optimization methods have been developed and are available for laboratories. Having these methods will require a strategy to integrate MA QC and ...routine internal QC. Methods MA QC was considered only when the performance of the internal QC was limited. A flowchart was applied to determine, per test, whether MA QC should be considered. Next, MA QC was examined using the MA Generator (www.huvaros.com), and optimized MA QC procedures and corresponding MA validation charts were obtained. When a relevant systematic error was detectable within an average daily run, the MA QC was added to the QC plan. For further implementation of MA QC for continuous QC, MA QC management software was configured based on earlier proposed requirements. Also, protocols for the MA QC alarm work-up were designed to allow the detection of temporary assay failure based on previously described experiences. Results Based on the flowchart, 10 chemistry, two immunochemistry and six hematological tests were considered for MA QC. After obtaining optimal MA QC settings and the corresponding MA validation charts, the MA QC of albumin, bicarbonate, calcium, chloride, creatinine, glucose, magnesium, potassium, sodium, total protein, hematocrit, hemoglobin, MCH, MCHC, MCV and platelets were added to the QC plans. Conclusions The presented method allows the design and implementation of QC plans integrating MA QC for continuous QC when internal QC has limited performance.
Coordinated power demand management at residential or domestic levels allows energy participants to efficiently manage load profiles, increase energy efficiency and reduce operational cost. In this ...paper, a hierarchical coordination framework to optimally manage domestic load using photovoltaic (PV) units, battery-energy-storage-systems (BESs) and electric vehicles (EVs) is presented. The bidirectional power flow of EV with vehicle to grid (V2G) operation manages real time domestic load profile and takes appropriate coordinated action using its controller when necessary. The proposed system has been applied to a real power distribution network and tested with real load patterns and load dynamics. This also includes various test scenarios and prosumer's preferences e.g., with or without EV, number of EV owner, number of households, and prosumer's daily activities. This is a combined hybrid system for hierarchical coordination that consists of PV units, BES systems and EVs. The system performance was analyzed with different commercial EV types with charging/ discharging constraints and the result shows that the domestic load demand on distribution grid during the peak period has been reduced significantly. At the end, this proposed system's performance was compared with the prediction-based test techniques and the financial benefits were estimated.
In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) ...models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naive, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.
ABSTRAKSeorang investor pada umumnya berharap untuk membeli suatu saham dengan harga yang rendah dan menjual saham tersebut dengan harga yang lebih tinggi untuk memperoleh imbal hasil yang tinggi. ...Namun, kapan waktu yang tepat melakukannya menjadi tantangan tersendiri bagi para investor. Oleh sebab itu, dibutuhkan suatu model yang mampu menduga imbal hasil saham dengan baik, salah satunya adalah model autoregressive moving average (ARMA). Tujuan dari penelitian ini adalah untuk menerapkan model autoregressive (AR), model moving average (MA), atau model autoregressive moving average (ARMA) pada data observasi untuk menduga imbal hasil saham bank central asia (BCA). Terdapat empat prosedur dalam membangun sebuah model AR, MA atau ARMA. Pertama, data yang digunakan harus weakly stationary. Kedua, orde dari model harus diidentifikasi untuk memperoleh model yang terbaik. Ketiga, parameter setiap model harus ditentukan. Keempat, kelayakan model harus diperiksa dengan melakukan analisis residual untuk memperoleh model yang terbaik. Pada akhirnya, model ARMA (1,1) adalah model terbaik dan akurat dalam menduga imbal hasil saham BCA. ABSTRACTGenerally, investor always wish to be able to buy a stock at a low price and sell it at a higher price to obtain high returns. However, when is the best time to buy or sell it is a challenge for investor. Therefore, proper models are needed to predict a stock return, one of them is autoregressive moving average (ARMA) model. The first purpose of this paper is to apply the autoregressive (AR), moving average (MA) or ARMA models to the observations to predict stock returns. There are four procedures which is used to build an AR, MA, or ARMA model. First, the observations must be weakly stationary. Second, the order of the models must be identified to obtain the best model. Third, the unknown parameters of the models are estimated by maximum likelihood. Fourth, through residual analysis, diagnostic checks are performed to determine the adequacy of the model. In this paper, stock returns of BCA are used as data observation. Finally, the ARMA (1,1) model is the best model and appropriate to predict the stock returns BCA in the future.
Managers are driven to accomplish significantly higher levels of operational performance due to the difficulty of today's dynamic production environment. Typically, the precision of production ...facilities and the efficiency of manufacturing systems are significant variables in productivity. Thus, predicting machine performance has become an inevitable challenge for production managers. However, the question of how managers can reliably assess the effectiveness of equipments for resource allocation remains unaddressed properly. This issue has received little attention in previous research, but it is important in today's manufacturing environment. This study introduces a hybrid moving average - adaptive neuro-fuzzy inference system (MA-ANFIS) to predict the possible effectiveness of equipment. Three real-world problems are considered when developing and evaluating three distinct equipment effectiveness prediction models. The evaluation confirms that the hybrid MA-ANFIS model based on Gaussian membership function outperforms other developed models. This comprehensive solution is packaged as a decision support system. This aids production managers in evaluating the equipment effectiveness, and effectively improving equipment's performance to reduce time and cost of bus body building.
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