Individual Weighting Factors Research Articles

Bias-Compensated Sign Subband Adaptive Filter Algorithm With Individual Weighting Factors for Input Noise

To thoroughly exploit the decorrelation property of the traditional sign subband adaptive filter (SSAF), an individual weighting factors SSAF (IWF-SSAF) was presented. However, when the input of the adaptive filter is polluted by white Gaussian noise, the IWF-SSAF will produce estimation bias for unknown system identification. Thus, this brief proposes the bias-compensated IWF-SSAF (BC-IWF-SSAF) for resolving the above trouble. Especially, the unbiased criterion is applied to obtain compensation term for the conventional IWF-SSAF to alleviate the effect of noisy input. The proposed BC-IWF-SSAF both retains stability of impulse noise and decreases the estimated bias. The stable condition of the proposed BC-IWF-SSAF is achieved resorting to Price’s theorem with some reasonable assumptions and theorems. In addition, the complexity is given as well. Finally, simulations confirm the superiority of the proposed BC-IWF-SSAF algorithm.

Application of qualimetric methods for assessing quality of complex products

For the final product high-quality assessment in any application field, it is necessary to choose the optimal method and suitable measuring instruments for the right conditions. The paper reflects theoretical information about the atomic emission spectrometer, the advantages of using this device in the quantitative chemical analysis when monitoring surface waters. In addition, information on methods for evaluating such complex products as the atomic emission spectrometer is provided. The paper presents expert and mixed methods of qualimetry, describes their advantages and disadvantages. Based on them, a new method for evaluating the quality of the atomic emission spectrometer has been developed, which includes elements of the expert method, differential and complex, as well as a method for determining the consistency of experts. In addition, the methodology sets out requirements for experts who are members of the expert group. It is designed such factors as: individual and group weighting factor, assessment of the consistency of experts with the help of the coefficient of concordance, relative indicator of quality, the product quality level, the weighted average arithmetic. Based on the assessment of the last indicator, you can conclude that this product is better or worse than the basic one, or rank several products. Three atomic emission spectrometers were selected for testing this technique. One of them was selected as the base sample, with which the two evaluated samples were compared. They were ranked from best to worst, and their example shows the efficiency of this method.

Determinants of Neonatal Mortality in Kenya: Evidence from the Kenya Demographic and Health Survey 2014.

Background:Globally, there has been a marked decline in neonatal mortality and overall child mortality indicators from 1990 to date. In Kenya, neonatal deaths remain unacceptably high, contributing to 40% of under-five mortality rates (U5MR) making it an important health priority. The objective of this study was to identify the determinants of neonatal mortality in Kenya. An understanding of the determinants of neonatal mortality will provide evidence for better interventions to reduce these deaths.Methods:Neonatal deaths from singleton live-born infants were extracted from women’s dataset collected for the 5-year period preceding the study published in the Kenya Demographic and Health Survey (KDHS), 2014. Data were obtained from 18,951 births. There were 356 neonatal deaths recorded. Data were weighted using an individual weighting factor to adjust for the study design and reduce sample variability. Data were analyzed using SPSS version 20.0. Logistic regression was conducted to adjust for confounding factors.Results:Neonatal mortality rate was established at 19/1000 (95% CI: 16.8-20.7). Mothers with no education had higher odds of experiencing deaths of neonates with adjusted Odds Ratio (aOR)=2.201, 95% CI: 1.43-4.15,p=0.049) compared to mothers with higher education. Low Birth Weight (LBW) neonates were 3.2 times likely to die in the first 28 days (aOR=3.206, 95% CI: 1.85-12.08, p=0.006) compared to neonates with >3.5 kilograms at birth. Mothers who did not attend ANC during pregnancy and those who attended between 1-3 ANC visits had higher odds of losing their infants (aOR=3.348, 95% CI: 1.616-8.53, p=0.041, and aOR=2.316, 95% CI: 1.10-4.88, p=0.027) respectively, compared to mothers who attended >4 ANC visits.Conclusion and Global Health Implications:Improving maternal health and nutrition during pregnancy should be enhanced to ensure adequate weight gain and reduce instances of low birth weight. Community referrals and follow-up for expectant women to take up the requisite 4 ANC visits should be encouraged. Girls’ education should be emphasized to reduce the proportion of illiterate mothers.

Sparsity-aware SSAF algorithm with individual weighting factors: Performance analysis and improvements in acoustic echo cancellation

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.

BD‐VSS individual weighting factor sign based adaptive filter control for power quality improvement in weak distribution grid

This study presents the control of distribution static compensator (DSTATCOM) using band-dependent variable step size (BD-VSS) individual weighting factor with sign error based adaptive filter for power quality improvement in a weak distribution grid. Here, the proposed algorithm is used for estimation of fundamental active weight components from the distorted load currents in order to generate the reference grid currents to mitigate the grid currents power quality issues. This new control algorithm is proposed for fast and accurate estimation of active weight components with low steady-state error, without dynamic oscillation at fast convergence speed. Moreover, the frequency locked loop-double self-tuning second-order generalised integrator based voltage filter is used to extract the positive sequence voltages of the distorted grid voltages for estimating the harmonics, and noise-free voltage unit templates. Therefore, the DSTATCOM with the proposed control algorithm is capable of mitigating the harmonic currents, providing reactive power compensation and operating at unity power factor in a weak distribution grid. Test results demonstrate the viability and robustness of the proposed control algorithm under balanced and unbalanced non-linear load conditions.

Comparative Evaluation of the Accuracy and Robustness of Various Information Processing Algorithms from Tripled Measuring Channels

Using the example of a three-channel system for measuring the level of filling of tanks of launch vehicles, we perform a comparative assessment of the accuracy and robustness of various algorithms for processing measurement information received from three measuring channels of the same type. We consider various processing algorithms of three equal-precision measurements: finding the arithmetic mean of three equal-precision measurements and a majority choice of three equal-precision measurements. For these algorithms, the probability density function of the measurement errors and confidence limits of the measurement error are found using the assumption of uniform or normal laws of the error distribution of three equally accurate measurements. A generalized algorithm for processing redundant information is proposed. The essence of the generalized algorithm is the addition of three equally accurate measurements taking into account individual weighting factors. Weight coefficients are selected based on the values of three equally accurate measurements: the minimum and maximum values are assigned the same weighting coefficient, and the median is assigned a coefficient which normalizes the sum of the three weighting factors to one. For a generalized algorithm by means of simulation modeling, the confidence limits of the measurement error are determined depending on the selected weighting factors. The robustness of the considered processing algorithms is evaluated for three equally accurate measurements performed on three measuring channels identical in design, with a random perturbation applied to one of the channels. Recommendations on the selection of weights for three equal measurements are given. For the fuel level measurement system under consideration, it is justified to use an algorithm for processing measurement information based on a majority choice of three equally accurate measurements.

Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.

Monte Carlo evaluations of methods of grade distribution in group projects: simpler is better

Group projects are widely used in higher education, but they can be problematic if all group members are given the same grade for a project to which they might not have contributed equally. Most scholars recommend addressing these problems by awarding individual grades, computing some kind of individual weighting factor (IWF) from peer and (sometimes) self-assessments, which is then multiplied by the group grade to generate an individual grade. Several variants of the IWF method have been proposed, sometimes with complex algorithms. However, theory suggests they are inaccurate and their accuracy has not been evaluated. This article uses Monte Carlo experiments to assess the accuracy of the original IWF method and variants proposed in the past decade. Findings show that the earlier, simpler methods work best and that self-assessments should definitely be avoided.

The Assimilation of Multi-Type Information for Seasonal Precipitation Forecasting Using Modular Neural Network

The rainfall occurrences are triggered by different types of climate sources not restricted to past precipitation values but may include climate indices such as El Nino/Southern Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation. In this paper, we investigated the effectiveness of assimilating two sources of inputs for heavy precipitation forecasting using modular neural network. The assimilated input was obtained by merging two input variable sources (climate indices and precipitation records) according to their individual weighting factor determined by correlation test. To simulate the hydrologic response using merged product, a modular neural network model was developed. The modular concept was implemented by separating the precipitation events based on seasonal monsoon and trained the subset of seasonal data using modular neural network. Four subsets of monthly precipitation data were sampled to evaluate modular neural network model at 1-month lead-time with single precipitation neural network model and multiple linear regression as benchmark models. The results show that the merging method can effectively assimilate information from two sources of inputs to improve the accuracy of heavy precipitation forecasting.

Delayless Individual-Weighting-Factors Sign Subband Adaptive Filter With Band-Dependent Variable Step-Sizes

Sign subband adaptive filter algorithms with individual weighting factors (IWF-SSAF) were recently proposed to achieve improved convergence performance in impulsive noise environments by fully utilizing the decorrelating property of subband adaptive filters. However, such sign subband adaptive filter (SSAF) algorithms may have an inherent signal path delay problem for real-time applications, and thus, can be restrictively applied. In this paper, a delayless IWF-SSAF with band-dependent variable step-sizes (BD-VSS), robust in impulsive noise environments, is proposed for real-time applications, whereby two delayless filter structures developed for the $\ell _2$ -norm-based SAF are employed along with the IWF-SSAF. In particular, a BD-VSS algorithm is also introduced for better convergence by applying the recent $\ell _1$ -norm minimization technique to respective subband. Finally, the performance of the proposed delayless IWF-SSAF with BD-VSS is verified in various impulsive interference environments such as system identification with impulsive noise, acoustic echo cancellation in a double-talk scenario, and active impulsive noise control.

Trajectory Planning for Abdominal Fine Needle Insertion Based on Insertion Angles

Fine needles can easily be deflected, making accurate needle insertion into a tumor difficult. In particular, it is difficult to insert the needle into a tumor in the lower abdomen because the needle has to pass through various tissues. Therefore, for lower abdominal needle insertion, we intend to develop a planning method for the optimal insertion path to minimize the deflection based on computed tomography images. In this letter, we analyze the deflection while performing needle insertion with axial rotation under conditions of various insertion angles into individual tissues composing the lower abdomen (pork loin and porcine small bowel) and develop a basic planning procedure to determine the optimal insertion path based on the analysis. From the results, we confirmed that the insertion angle should be minimized as much as possible. We then develop a planning method based on insertion angles into individual tissues the needle needs to pass through. We assumed that it is possible to determine the optimal insertion path by providing a weighting factor to the effect of the insertion angle depending on the depth of insertion, considering that the effect of the insertion angle on deflection in deep places of the body is smaller than the effect around the body surface. To verify the concept of the proposed planning procedure, we perform experiments with a simple model composed of two tissues. From the results, we show that the optimal insertion path can be planned by setting the optimal ratio of individual weighting factors.

Novel combination schemes of individual weighting factors sign subband adaptive filter algorithm

AbstractTo improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF‐SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step‐size IWF‐SSAF filter and a small step‐size one; and the second algorithm is to obtain a time‐varying step size for the IWF‐SSAF by combining a large step size and a small one. The minimization of the sum of the l1‐norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step‐size IWF‐SSAF filter to the small step‐size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms.

Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation

A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price's theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts.

Novel sign subband adaptive filter algorithms with individual weighting factors

This paper presents a new sign subband adaptive filter (SSAF) algorithm with an individual-weighting-factor (IWF) for each subband, instead of a common weighting factor in the original SSAF algorithm, called the IWF-SSAF. Each individual weighting factor only depends on the corresponding subband input signal power. Compared with the SSAF algorithm, the proposed approach fully utilizes the inherent decorrelating property of subband adaptive filter for colored inputs, leading to a better convergence performance. After that, to further enhance the performance of the IWF-SSAF in a sparse system, an improved proportionate IWF-SSAF (IWF-IPSSAF) algorithm is proposed. The proposed algorithms not only inherit the good robustness of sign algorithm against impulsive interferences, but also obtain a significant improvement in the performance as compared to their counterparts (i.e., SSAF and IPSSAF), in terms of the convergence rate and tracking capability. Besides, the IWF-IPSSAF algorithm has faster convergence rate than the IWF-SSAF algorithm for sparse impulse responses. Finally, the performances of two proposed algorithms are demonstrated in the system identification and the acoustic echo cancellation with double-talk.

Particle Swarm Optimization of the Multioscillatory LQR for a Three-Phase Four-Wire Voltage-Source Inverter With an <inline-formula> <tex-math notation="TeX">$LC$</tex-math></inline-formula> Output Filter

This paper presents evolutionary optimization of the linear quadratic regulator (LQR) for a voltage-source inverter with an LC output filter. The procedure involves particle-swarm-based search for the best weighting factors in the quadratic cost function. It is common practice that the weights in the cost function are set using the guess-and-check method. However, it becomes quite challenging, and usually very time-consuming, if there are many auxiliary states added to the system. In order to immunize the system against unbalanced and nonlinear loads, oscillatory terms are incorporated into the control scheme, and this significantly increases the number of weights to be guessed. All controller gains are determined altogether in one LQR procedure call, and the originality reported here refers to evolutionary tuning of the weighting matrix. There is only one penalty factor to be set by the designer during the controller synthesis procedure. This coefficient enables shaping the dynamics of the closed-loop system by penalizing the dynamics of control signals instead of selecting individual weighting factors for augmented state vector components. Simulational tuning and experimental verification (the physical converter at the level of 21 kVA) are included.

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

Summary The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30–60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods.

An approach to assign individual marks from a team mark: the case of Australian grading system at universities

This study uses a new approach to assign individual marks from a team mark using individual contributions to a teamwork product. A team member’s contribution to a teamwork product, in the form of an individual weighting factor, is calculated using team members’ co‐assessment. A comparison of the proposed approach with existing methods has been discussed with the help of a typical teamwork example. The approach has been refined to make it applicable for the Australian grading system at universities. It has been implemented in a large undergraduate engineering course to observe its effectiveness in practice. The results show that the method encourages teamwork, penalises below‐average contributions and rewards above‐average contributions. An analysis of a students’ perception survey shows that students prefer the approach over alternative approaches.

효율적인 하수관거 개량을 위한 의사결정모형의 개발

하수관거 개량사업의 주된 목적은 Inflow/Infiltration (I/I)를 제거 및 통수능력 확보이다. 최근 노후 하수관거의 개 보수 및 신설 사업이 활발히 이루어지고 있으나 현재의 사업들은 관거 데이터의 부족, 유량 및 수질 자료의 장기적인 측정 미비 등으로 인하여 효율적인 사업을 진행시키기에 무리가 있다. 본 연구에서는 하수관거 개량사업을 보다 효율적으로 진행시키기 위하여 Rehabilitation Weighting Model (RWM)과 Rehabilitation Priority Model (RPM)로 구성된 의사결정모형을 개발하였다. RWM은 시간 및 예산상의 제약으로 인하여 주요 지점에서만 관측되는 I/I를 상류의 각 관거별로 I/I를 산정하며, 관거별 I/I는 Analytic Hierarchy Process (AHP)를 통하여 산정된 8개 결함항목별 가중치에 따라서 결정된다. RPM은 Genetic Algorithm (GA)를 이용하여 소유역별 최적개량우선순위를 산정한다. 이것은 공사 기간 중 발생하는 I/I를 최소화시키기 위한 소유역별 공사 순서를 설정함으로써 하수처리장의 처리비용을 절감시킴으로써 하수관거 개량사업의 효율적인 시행을 위한 판단 기준을 제시해준다. The objective of sewer rehabilitation is to improve its function while eliminating inflow/infiltration (I/I) and insufficient carrying capacity (ICC). Such rehabilitation efforts, however, have not been particularly successful due to a lack of sewer data and unsystematic field practices. The present study aimed to solve these problems by developing a decision making model consisting of two models: the rehabilitation weighting model (RWM) and the rehabilitation priority model (RPM). In RWM, the I/I of each pipe in a drainage district is estimated according to various defects, with each defect given an individual weighting factor using an analytic hierarchy process (AHP). RPM determines the optimal rehabilitation priority (ORP) using a genetic algorithm (GA). The developed models can be used to overcome the problems associated with unsystematic practices and, in practice, as a decision making tool for urban sewer system rehabilitation.

The analysis of the impact of individual weighting factor on individual scores

In this study, category‐based self and peer assessment were applied twice in a semester in an Elementary Science Teaching Methods course in order to assess individual contributions of group members to group projects as well as to analyze the impact of Individual Weighting Factors (IWF) on individual scores and individual grades. IWF were calculated by applying Conway et al.'s (1993) formula. The resulting individual scores were analyzed to find out the impact of IWF on individual scores and individual grades. It was found that the differentiation of individual scores was very large in both applications and caused dramatic changes in some of the students' grades. For this reason, the IWF had to be scaled down to decrease its effect on individual grades. After scaling down the IWF, more acceptable distribution of student grades was obtained.

Application of the error propagation theory in estimates of static formation temperatures in geothermal and petroleum boreholes

Abstract We used the error propagation theory to calculate uncertainties in static formation temperature estimates in geothermal and petroleum wells from three widely used methods (line-source or Horner method; spherical and radial heat flow method; and cylindrical heat source method). Although these methods commonly use an ordinary least-squares linear regression model considered in this study, we also evaluated two variants of a weighted least-squares linear regression model for the actual relationship between the bottom-hole temperature and the corresponding time functions. Equations based on the error propagation theory were derived for estimating uncertainties in the time function of each analytical method. These uncertainties in conjunction with those on bottom-hole temperatures were used to estimate individual weighting factors required for applying the two variants of the weighted least-squares regression model. Standard deviations and 95% confidence limits of intercept were calculated for both types of linear regressions. Applications showed that static formation temperatures computed with the spherical and radial heat flow method were generally greater (at the 95% confidence level) than those from the other two methods under study. When typical measurement errors of 0.25 h in time and 5 °C in bottom-hole temperature were assumed for the weighted least-squares model, the uncertainties in the estimated static formation temperatures were greater than those for the ordinary least-squares model. However, if these errors were smaller (about 1% in time and 0.5% in temperature measurements), the weighted least-squares linear regression model would generally provide smaller uncertainties for the estimated temperatures than the ordinary least-squares linear regression model. Therefore, the weighted model would be statistically correct and more appropriate for such applications. We also suggest that at least 30 precise and accurate BHT and time measurements along with the respective errors should be obtained for a reliable application of the proposed regression procedure.