Ar Method Research Articles

Discussion on the Value of Wind-induced Vibration Coefficient of Independent Steel Tube Lightning Rod Structure Design

This study compared the reliability of the ISTLRs (independent steel tube lightning rod) whose structure was designed using WIVC (wind-induced vibration coefficient) in DL/T 5457-2012, GB50009-2012 and AR method. Based on the assembly diagram of the lightning rod in the lightning rod replacement project in a 220kV substation, the finite element model was established in ANSYS Workbench. The three WIVCs were substituted into the HD (horizontal displacement) of the structure design, and the reliability of structure designed from the three was compared. Suggestions were made on the value of the WIVC when designing an ISTLR structure.

Adaptive prediction model for effective electrical machine maintenance

Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

Isotope Systematics and Age of Authigenic Minerals in Shales of the Upper Riphean Inzer Formation, South Urals

Clay subfractions (SFs) with particles size of 2–5, 0.6–2.0, 0.3–0.6, 0.2–0.3, and 0.1–0.2 μm of two shale samples (Upper Riphean Inzer Formation, South Urals) are studied using transmission electron microscopy, X-ray diffractometry (XRD), and U–Pb, Sm–Nd, Rb–Sr, and K–Ar methods. The SFs are composed of low-temperature 1Md illite; quartz, chlorite, and 2M1 illite are observed only in some coarse SFs. Irrespective of the size, the clay particles are equant. The standardized illite crystallinity indices (CIS) of all SFs are typical of the dia(cata)genesis zone. The CIS value increases, the I002/I001 ratio of XRD patterns decreases, and the K content and K/Rb ratio increase with decreasing particle size of the SFs from 2–5 to 0.1–0.2 μm. Leaching with 1N HCl and 1N NH4OAc and U–Pb, Sm–Nd, and Rb–Sr analysis of untreated SFs, leachate, and residue allowed us to study the isotope systematic of mixing in mobile and silicate material of shales. The 208U/204Pb and 87Rb/86Sr ratios of leachates are lower and the 147Sm/144Nd ratio is higher than those of residues. The leachates are also characterized by less radiogenic Pb and Sr and more radiogenic Nd relative to residues. With decreasing size of SF particles, the U, Pb, Sm, Nd, and Sr contents of leachates gently decrease and the Rb content increases. The 87Rb/86Sr and 87Sr/86Sr ratios of leachates of fine SFs are significantly higher, whereas their 238U/204Pb ratio is lower in comparison with coarse SFs. What is more, the data points of residues of various SFs occur along the mixing lines in the 87Rb/86Sr‒87Sr/86Sr and 1/86Sr‒87Sr/86Sr plots. The data points of corresponding leachates also form linear trends in 238U/204Pb‒206Pb/204Pb, 206Pb/204Pb‒207Pb/204Pb, 147Sm/144Nd‒143Nd/144Nd, and 87Rb/86Sr‒87Sr/86Sr coordinates. Apparent Rb–Sr ages calculated from slopes of “inner isochrons” (“leachochrons”) as well as K-Ar ages gradually decrease from 835–836 and 721–773 Ma, respectively, for SFs of 2–5 μm and to 572‒580 and 555‒580 Ma, respectively, for SFs of 0.1–0.2 μm. Thus, the XRD and isotopic data indicate that both clay and mobile shale constituents are mixtures at least of two components, and the silicate phase contains authigenic illites of various ages. First-generation illite abundant in the coarse SFs of 2–5 μm and 0.6–2.0 μm was formed immediately after the deposition of the Inzer sediments and its age of 803–836 Ma is consistent with stratigraphic age of the Inzer Formation. The formation of this illite was facilitated either by lithostatic burial or intense horizontal fluid flow caused by tectonic inversion in the eastern regions of the Uralian paleobasin. The age of the second-generation illite is 572–580 Ma; it was formed as a result of vertical movements or renovation of the composition of the pore fluids during deformations and metamorphism of the South Urals related to the evolution of the Beloretsk metamorphic complex.

Combining N2:Ar and Bayesian Methods to Quantify Underestimation and Uncertainty of Sediment Denitrification Determined by the Acetylene Inhibition Method

Accurately determining denitrification rates is essential for estimating the nitrogen budget in flooded ecosystems. The acetylene inhibition method (AIT) is a simple and common technique for measuring sediment denitrification. However, incomplete inhibition of nitrous oxide reduction or concurrent inhibition of nitrification may lead to underestimations of denitrification rates, whereas the N2:Ar ratio method with membrane inlet mass spectrometry (MIMS) provides an accurately way quantifying denitrification rates. The objective of this study was to compare sediment denitrification rates measured by AIT and MIMS, correspondingly quantify the underestimation and the uncertainty of AIT-measured denitrification by combining a Bayesian hierarchical method. A multilevel Bayesian framework was constructed to predict the true values of sediment denitrification based on the relationships between MIMS and AIT-measured denitrification. A Markov chain Monte Carlo (MCMC) algorithm was applied to obtain the targeted parameters and their associated uncertainties in the Bayesian. Results showed that both AIT and MIMS detected similar trends in denitrification responses to increasing NO3¯-N concentrations, but the values of denitrification measured by AIT were considerably lower than those of MIMS. These differences decreased rapidly with increasing NO3¯-N concentrations, then stabilized when NO3¯-N was greater than 2 mg N L−1 and AIT estimations of denitrification were about 21 times less than those of MIMS. The results were used to obtain a multilevel Bayesian model that quantified the underestimation and uncertainty of the AIT-based denitrification. We concluded that the AIT can be used to accurately estimate soil denitrification after quantification of system error and correction coefficients.

Analysis on the geochemical characteristics of Chachalongwa copper, lead and zinc polymetallic ores

The tectonic position of the Chachalongwa Cu, Pb, Zn polymetallic ores is located at the junction of the Qinqi - Kun orogenic system and the eastern Kunlun arc basin system. Through analysis of mineral assemblage, trace elements, rare earth elements, tectonic background and geochronology, the lithology, rock assemblages, geochemical characteristics of each ore-bearing strata in Xianqilongwa have been found out. It is found that the ore - bearing strata are volcanic rocks of the late Triassic Elashan Formation. As can be seen from the TAS diagram of principal elements, the magma evolution is in the direction of rich silicon, alkali, and poor magnesium, iron. the more that the magma differentiation, the higher that acid degree. From the diagram of rare earth partition mode, it can be found that the curve is inclined to the right, and is a negative Eu type of LREE enrichment. As the acidity of the rock increasing, the contents of elements such as Cu, Zn, Ni, Co, V, Ti, Mn and Sr are decreased, and compared with Taylor’s crustal abundance, the content of elements such as Pb, Zn, Cr, Ga and Sn increased gradually. According to the geological tectonic environment, it can be roughly determined that the tectonic environment is the volcanic rock area of the orogenic belt, it shows that the volcanic rocks is in the tectonic orogenic belt environment. The age obtained by Rb - Sr and K - Ar methods was 210 ± 14Ma - 235 ± 12Ma, it shows that the geological ages of volcanic activity is the late Triassic, and the period of volcanic activity is not so long.

The effects of oil and gold prices on oil-exporting countries

Gold and oil prices have wide impacts on the determinants of financial, commodity, labor markets and industrial production of countries because of high liquidity and tradability. Gold and oil price volatility implications are crucial for the economic growth of both emerging and developed countries across the world. By this motivation, in this paper, firstly, it is aimed to determine the asymmetric relationship between gold and oil prices which is strongly related with macroeconomic dynamics and business cycle structure of the countries. And secondly, their effects on the economic growth of the selected oil exporter countries (Canada, China, Kuwait, Mexico, Saudi Arabia, United Arab Emirates, United Kingdom, the United States of America) were examined by using MS-(V)AR methods. These methods are employed to test the impact of gold and oil price prices’ volatilities on each other, to identify the business cycle structures and make the effective economic policy inferences for each of the selected countries. Transition probabilities of the regimes emphasized the asymmetric behavior of business cycles. More critically, the findings revealed the importance of gold and oil prices volatilities on economic growth and oil prices have effective role on determining business cycle of the countries.

The relationship among renewable energy, economic growth, labor and capital formation in Italy

This study investigates the nexus between renewable energy consumption and economic in Italy in the 1970-2007 years. The unit root tests results point out that our four variables are integrated of order one. Cointegration analysis reach contradictory results. However, the long-run estimations reveal that, if renewable energy consumption increases by 1%, real GDP decreases by 0.23%. The Toda and Yamamoto approach shows that exists a unidirectional causal flow, running from renewable energy consumption to aggregate income, in line with the growth hypothesis. Moreover, these results are confirmed by Granger causality tests. While forecasts applications set out that VAR forecasts represent an improvement on simpler forecasts in more than half the comparisons, for each variable. In fact, VAR gives more accurate forecasts, over-performing the mean, RW, and AR methods.

A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system

BackgroundPatient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods.MethodsIn this study, in order to establish a simple method to explore ADEs in patients with kidney or liver injury as a background disease, the AR and proportional reporting ratio (PRR) signal detection methods were compared. We used oral medicine SRS data from the Japanese Adverse Drug Event Report database (JADER), and used AR as the proposed search method and PRR as the conventional method for comparison. “Rule count ≥ 3”, “min lift value > 1”, and “min conviction value > 1” were used as the AR detection criteria, and the PRR detection criteria were “Rule count ≥3”, “PRR ≥ 2”, and “χ2 ≥ 4”.ResultsIn patients with kidney injury, the AR method had a sensitivity of 99.58%, specificity of 94.99%, and Youden’s index of 0.946, while in patients with liver injury, the sensitivity, specificity, and Youden’s index were 99.57%, 94.87%, and 0.944, respectively. Additionally, the lift value and the strength of the signal were positively correlated.ConclusionsIt was suggested that computation using AR might be simple with the detection power equivalent to that of the conventional signal detection method as PRR. In addition, AR can theoretically be applicable to SRS other than JADER. Therefore, complicated conditions (patient’s background etc.) that must take factors other than the ADE into consideration can be easily explored by selecting the AR as the first screening for ADE exploration in pharmacovigilance using SRS.

A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System.

Background: Adverse events (AEs) can be caused not only by one drug but also by the interaction between two or more drugs. Therefore, clarifying whether an AE is due to a specific suspect drug or drug-drug interaction (DDI) is useful information for proper use of drugs. Whereas previous reports on the search for drug-induced AEs with signal detection using spontaneous reporting systems (SRSs) are numerous, reports on drug interactions are limited. This is because in methods that use “a safety signal indicator” (signal), which is frequently used in pharmacovigilance, a huge number of combinations must be prepared when signal detection is performed, and each risk index must be calculated, which makes interaction search appear unrealistic.Objective: In this paper, we propose association rule mining (AR) using large dataset analysis as an alternative to the conventional methods (additive interaction model (AI) and multiplicative interaction model (MI)).Methods: The data source used was the Japanese Adverse Drug Event Report database. The combination of drugs for which the risk index is detected by the “combination risk ratio (CR)” as the target was assumed to be true data, and the accuracy of signal detection using the AR methods was evaluated in terms of sensitivity, specificity, Youden's index, F-score.Results: Our experimental results targeting Stevens-Johnson syndrome indicate that AR has a sensitivity of 99.05%, specificity of 92.60%, Youden's index of 0.917, F-score of 0.876, AI has a sensitivity of 95.62%, specificity of 96.92%, Youden's index of 0.925, and F-score of 0.924, and MI has a sensitivity of 65.46%, specificity of 98.78%, Youden's index of 0.642, and F-score of 0.771. This result was about the same level as or higher than the conventional method.Conclusions: If you use similar calculation methods to create combinations from the database, not only for SJS, but for all AEs, the number of combinations would be so enormous that it would be difficult to perform the calculations. However, in the AR method, the “Apriori algorithm” is used to reduce the number of calculations. Thus, the proposed method has the same detection power as the conventional methods, with the significant advantage that its calculation process is simple.

Augmented reality technology for preoperative planning and intraoperative navigation during hepatobiliary surgery: A review of current methods

BackgroundAugmented reality (AR) technology is used to reconstruct three-dimensional (3D) images of hepatic and biliary structures from computed tomography and magnetic resonance imaging data, and to superimpose the virtual images onto a view of the surgical field. In liver surgery, these superimposed virtual images help the surgeon to visualize intrahepatic structures and therefore, to operate precisely and to improve clinical outcomes. Data SourcesThe keywords “augmented reality”, “liver”, “laparoscopic” and “hepatectomy” were used for searching publications in the PubMed database. The primary source of literatures was from peer-reviewed journals up to December 2016. Additional articles were identified by manual search of references found in the key articles. ResultsIn general, AR technology mainly includes 3D reconstruction, display, registration as well as tracking techniques and has recently been adopted gradually for liver surgeries including laparoscopy and laparotomy with video-based AR assisted laparoscopic resection as the main technical application. By applying AR technology, blood vessels and tumor structures in the liver can be displayed during surgery, which permits precise navigation during complex surgical procedures. Liver transformation and registration errors during surgery were the main factors that limit the application of AR technology. ConclusionsWith recent advances, AR technologies have the potential to improve hepatobiliary surgical procedures. However, additional clinical studies will be required to evaluate AR as a tool for reducing postoperative morbidity and mortality and for the improvement of long-term clinical outcomes. Future research is needed in the fusion of multiple imaging modalities, improving biomechanical liver modeling, and enhancing image data processing and tracking technologies to increase the accuracy of current AR methods.

Repeatability of nasal allergen challenge results: Further validation of the allergic rhinitis clinical investigator collaborative protocols

BackgroundNasal allergen challenge (NAC) models have been used to study allergic rhinitis and new therapies. Symptoms and biological samples can be evaluated at time points after allergen exposure. ObjectiveTo verify protocol repeatability and adequate interval between allergen exposures. MethodsTen ragweed allergic participants were exposed to incrementally increasing dosages of ragweed allergen intranasally until they achieved a total nasal symptom score (TNSS) of 8 of 12 and a peak nasal inspiratory flow (PNIF) of 50% reduction or more from baseline. Three weeks later, participants were challenged with a cumulative dose equal to the sum of all the allergen doses received at screening. TNSS and PNIF were recorded at regular intervals, including a 24-hour assessment. A subsequent visit was conducted after a further 3 weeks. Nasal secretion samples were collected for cytokine and eosinophil quantification. ResultsNine participants completed all visits. TNSS and PNIF responses followed previous patterns, with an initial peak at 30 minutes followed by a gradual decline. Most participants reported similar patterns at both NAC visits, although some did not demonstrate the same phenotype at both visits. Some experienced a secondary symptom increase 24 hours after NAC. Eosinophil and cytokine sections followed a similar pattern at both NAC visits. ConclusionNAC is an adequate method for modeling AR in humans, demonstrating appropriate repeatability of symptoms, nasal mucosal eosinophil, and cytokines. The 24-hour time point, previously not studied in our model, may be beneficial in evaluation of long-acting medications. This three-week interval NAC model will be beneficial for studies in which before and after treatment comparisons are desired.

The analysis of dynamic characteristics and wind-induced displacement response of space Beam String Structure

The Beam String Structure structural system, also called BSS, has the advantages of lighter dead weight and greater flexibility. The wind load is the main design control factor. The dynamic characteristics and wind-induced displacement response of BSS are studied by the finite element method. The roof structure of the stadium roof of the Fuzhou Olympic Sports Center is the engineering background. 1)The numerical model was built by ANSYS, by shape finding, determine the initial stress state of structural members such as external cables; 2)From the analysis of dynamic characteristics, the main mode of vibration is the vibration of cables; 3)The wind speed spectrum of MATLAB generation structure is obtained by AR method, the structural response of the structure under static wind load and fluctuating wind load is calculated. From the analysis result, considering the equivalent static wind load of BSS , the design of adverse wind is not safe, and the fluctuating wind load should be taken into account.

Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks

This study presents the classification of cervical disc herniation patient and healthy persons by using muscle fatigue information. Cervical disc herniation patients suffer from neck pain and muscle fatigue in the neck increases these aches. Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this study surface Electromyography (EMG) signals were used to examine muscle fatigue. EMG signals were obtained from Trapezius and Sternocleidomastoid (SCM) muscles in the cervical region of 10 control subject and 10 cervical disc herniation patients. Surface EMG was preferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue was determined using Median and Mean frequency values obtained by Fourier Transform and Welch methods. Feature extraction was the third step which was performed by Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN) was used for classification. Training and test data were created by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigated on patient classification using muscle fatigue information. The best results were obtained by AR method with %99 classification accuracy. Also, the best results were obtained by DWT with %100 classification accuracy for SCM muscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMG signals by providing high accuracy classification with artificial intelligence methods for cervical disc herniation disease. Besides, it is shown that muscle fatigue distinguishes cervical disc herniation patients from healthy people.

Observations of the Singlets of Higher-Degree Modes Based on the OSE

In this study, we select 18 SG (superconducting gravimeter) records from 15 GGP stations and 99 vertical, 69 horizontal components of IRIS broad-band seismograms during 2004 Sumatra Earthquake to detect the splitting of higher-degree Earth’s free oscillations modes (0S4, 0S7~0S10, 2S4, 1S5, 2S5, 1S6) and 12 inner-core sensitive modes (25S2, 27S2, 6S3, 9S3, 13S3, 15S3, 11S4, 18S4, 8S5, 11S5, 23S5, 16S6) by using OSE (optimal sequence estimation) method which only considers self-coupling. Results indicate that OSE can completely isolate singlets of high-degree modes in time-domain, effectively resolve the coupled multiplets independently, and reduce the possibility of mode mixing and end effect, showing that OSE could improve some signals’ signal-to-noise ratio. Comparing the results of SG records with seismic data sets suggests that the number of SG records is inadequate to detect all singlets of higher modes. Hence we mainly select plentiful seismograms of IRIS to observe the multiplets of higher modes. We estimate frequencies of the singlets using AR method and evaluate the measurement error using bootstrap method. Besides, we compared the observations with the predictions of PREM-tidal model. This study demonstrates that OSE is effective in isolating singlets of Earth’s free oscillations with higher modes. The experimental results may provide constraints to the construction of 3D Earth model.

Active and passive recovery influence responses of luteinizing hormone and testosterone to a fatiguing strength loading.

The purpose of this study was to examine the acute hormonal and muscular responses to a strenuous strength loading [bilateral leg press (LP) 10 × 10 1RM] followed by loading-specific active (AR, n = 7, LP 10 × 10 × 30% 1RM) or passive (PR, n = 11, seated) recovery. The subjects were men age: 26 ± 4 years, height: 174 ± 8cm, body mass: 75 ± 13kg. After control measurements, experimental measurements were conducted at pre- and post-loading as well as post-recovery and next morning. A significantly higher absolute concentration (p < 0.05) of serum luteinizing hormone (LH) was observed in AR than PR at next morning while no differences were observed in serum testosterone (T), cortisol (C) or sex hormone binding globulin (SHBG). Significant differences in relative hormonal responses to the loading were observed at next morning with greater responses observed in AR than in PR in terms of LH, and T (p < 0.05). Maximal bilateral isometric force (MVC) and countermovement jump height (CMJ) decreased significantly (p < 0.001) from the control measurements in both AR and PR but returned to control levels by next morning. No between-group differences were observed in mean absolute or relative changes in MVC or CMJ. From a hormonal perspective, the present AR method appears to have had some favorable effects following the strenuous strength loading; however, acute decreases in muscular force production did not significantly differ between groups. These results provide insight into the development of training programs that may help to support the performance of individuals involved in strenuous tasks.

40 Ar/ 39 Ar age of cryptochron C2r.2r-1 as recorded in a lava sequence within the Ko'olau volcano (Hawaii, USA)

Abstract The 40 Ar/ 39 Ar method was used to refine the dating of a volcanic sequence drilled in the Ko'olau volcano which recorded a period of anomalously shallow inclinations. Previously, this sequence was dated using the unspiked K-Ar method at ≈ 2.1 Ma (Laj et al., 2000). This age was questioned by Ozawa et al. (2005) based on unspiked K-Ar ages of 2.40 and 2.41 Ma for two stratigraphically younger lava flows. Our new 40 Ar/ 39 Ar isochron results date this period of low inclination between 2.52 ± 0.10 and 2.40 ± 0.17 Ma. The combination of our two most reliable ages allows us to propose an age of 2.46 ± 0.13 Ma for the magnetic anomaly that we attribute to the cryptochon C2r.2r-1. This age agrees with previous estimates on the Halawa section of 2.46 ± 0.12 Ma (Singer, 2014) and the Sao Goncalo profile of 2.46 ± 0.08 Ma (Holm et al., 2008; Knudsen et al., 2009). Our 40 Ar/ 39 Ar ages confirm the unspiked K-Ar dating results of Ozawa et al. (2005), which were corrected for the mass-fractionation effect. Along with published data, our new ages permit to bracket the Makapuu late-shield stage between 2.6 and 2.2 Ma.

다중 채널 융합 기법을 이용한 DTV 기반 수동형 레이다의 표적 인식 방법

In this paper, we proposed airborne target recognition using multi-channel combining method in DTV-based passive radar. By combining multi-channel signals, we obtained the HRRP with sufficient range resolution. HRRP was obtained by AR method or zero-padding. From the obtained HRRP, we extracted scattering centers by CLEAN algorithm using the gradient descent. We extracted feature vectors and performed target recognition after training neural network using the extracted feature vectors. To verify performance of proposed methods, we assumed frequency bands of three broadcasting transmitters operated in Korea(Mt. Gwan-ak, Mt. Yong-moon, Kyeon-wol-ak) and used full scale 3D CAD model of four targets. Also we compared the target recognition performance of the proposed method with that of using only single-channel of three broadcasting transmitters. As a result, proposed methods showed better performance than using only single-channel at three broadcasting transmitters.

2D autoregressive model‐based dynamic correlated massive MU‐MIMO channel simulator

To design a dynamic correlated channel simulator which can precisely depict the space–time correlation properties and be suitable for the massive multi-user multiple-input multiple-output (MU-MIMO) system, a 2D autoregressive (2D AR) model-based method is proposed. Specifically, by exploiting stationarity in space–time domain, the authors use 2D AR method to create the space–time channel matrices of every user separately. In this way, the space–time dimension of the simulated channel can be modified flexibly without rebuilding the 2D AR model, and spatial correlation matrix with excessively big size is not necessary to guarantee the accuracy of the proposed simulator, making it suitable for massive MU-MIMO system. Simulation results verify the advantages of the proposed simulator.

Multi‐step forecasting in the presence of breaks

AbstractThis paper analyzes the relative performance of multi‐step AR forecasting methods in the presence of breaks and data revisions. Our Monte Carlo simulations indicate that the type and timing of the break affect the relative accuracy of the methods. The iterated autoregressive method typically produces more accurate point and density forecasts than the alternative multi‐step AR methods in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real‐time US output and inflation series shows that the alternative multi‐step methods only episodically improve upon the iterated method.

Omnet Sales Digital Protection with Box Jenkins and Delphi Method (case Study: PT Media Virtual Indonesia)

To be able to develop sales turnover digital newspaper Jawa Pos, PT. Media Virtual Indonesia requires an appforecasting a sales turnover. The method used in forecasting the quantitative turnover using ARIMA method (BoxJenkins) while for forecasting the skin using Delphi method. The results showed ARIMA AR method became an optionbecause it has the smallest MSE error that is ARIMA AR 3525.36963, ARIMA MA 3803.33228, while ARIMA ARMA4176.79443. While with the Delphi method, the main problem is the increase in selling power of Digital Java Posnewspaper, while the results obtained at each stage is Phase 1: Which region will be the concentration of sales:Bandung City with points 7. Stage 2 How to introduce the product to the surrounding community? : Held a LocalEvent.