Low-level Errors Research Articles

Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas.

Influence of biomass type on its characteristics of convective heating and dehydration

The paper presents the results of experimental studies of the drying processes of forest biomass, which is promising for energy purposes, - branches of various types of wood: birch, linden, poplar. The experiments were carried out on an installation that provides a low level of error (no more than 8.5%) for measuring the main significant parameters and characteristics of the dehumidification process. According to the results of the experiments, the integral characteristics of the greatest interest (times of complete dehydration) of the drying process have been established. Based on the results of the experiments, a non-monotonic change in the rates of moisture removal was established (the values of Weva increase and decrease with time) at a low-temperature heating mode (at Tg<373 K). At the same time, under conditions of relatively high ambient temperatures (Tg≥373 K), such a change in Weva does not occur. The authors formulated a hypothesis explaining the physical mechanism of such a dependence of the mass rate of moisture removal (Weva) on time. An energy analysis of the drying efficiency of three types of biomass has been carried out. It has been shown that branches of birch, poplar and linden are most efficiently dried in conditions of relatively low temperatures (333≤Tg≤353 K). At the same time, it was found that for birch and poplar branches, an increase in the ambient temperature leads to a decrease in the efficiency of the drying process of woody biomass.

A comparative analysis of the characteristics of the water removal processes in preparation for incineration of typical wood waste and forest combustible materials

The article presents the results of the experimental studies of the processes of removing moisture from fairly typical and widespread forest combustible materials, and woodworking waste under conditions of their thermal preparation for combustion. Based on the results of the experiments, the main regularities of the drying processes of woody biomass under conditions of radiation-convective heating have been established. The integral characteristics (characteristic times) of the drying process of forest biomass under typical (for this technology) heating conditions have been determined. The experiments were carried out on equipment that provides a low level of error (less than 3.3%) for determining the main characteristics of the biomass dehydration process. The values of the mass rates of moisture removal (Weva) of four promising (for modern and future power engineering) and fairly common types of biomass have been established in the ambient temperature range from 333 to 393 K, which corresponds to the operating modes of the drying equipment of power plants. According to the results of the experiments, it was shown that at relatively moderate ambient temperatures (333≤ Tg ≤ 353 K), the type of wood has a rather significant effect on the characteristics and conditions of the dehumidification process. At the same time, the process of drying wood chips is much faster compared to other types of biomass. As the ambient temperature rises (at 373≤Tg ≤ 393 K), the influence of the type of biomass decreases. As a result of the experiments, a physical model of the process of removing moisture from the bulk layer of biomass was formulated. According to the results of the experiments, it was found that before burning forest biomass in boilers of thermal power plants, it is most advantageous to carry out drying in conditions of relatively moderate (at 333≤Tg ≤ 353 K) ambient temperatures. The studies carried out provide grounds for justifying the use of forest combustible material as fuel (or one of the components of the fuel mixture) at thermal power plants currently operating on coal.

Damping and tracking control of nanopositioning stages with double delayed position feedback.

This paper presents a new damping control scheme for piezoelectric nanopositioning stages with double delayed position feedback (DDPF). The DDPF in the inner loop is proposed to suppress vibration of the nanopositioning stage, which leads to a double time-delay system. A new numerical differential method is proposed to determine the parameters of the DDPF with pole placement. Then, a high-gain proportional-integral (PI) controller is designed in the outer loop to achieve a low level of tracking errors, which includes the hysteresis nonlinearity, disturbance, and modeling uncertainties. Experimental tests with various control schemes are conducted on a piezoelectric nanopositioning stage to verify the effectiveness of the proposed method. Experimental results reveal that the control bandwidth of the system is improved from 79Hz (with the PI controller), 416Hz (with the conventional delayed position feedback based controller), and 422Hz (with the recursive delayed position feedback based controller) to 483Hz (with the proposed controller).

Intra-Rater and Inter-Rater Reliability of Pressure Pain Algometry of the Sural and Tibial Nerves in Asymptomatic Elite Youth Footballers.

Ankle injuries are highly prevalent in elite youth footballers and increase the mechanosensitivity of the local neural tissue, which may predispose athletes to re-injury and prolong rehabilitation periods. Increased neural mechanosensitivity presents clinically as altered pain pressure thresholds (PPTs) which are measured with pressure algometry. The purpose of this study was to determine the intra-rater and inter-rater reliability of PPTs of the ankle neural tissue in asymptomatic elite youth football players. Three raters utilised a digital algometer to evaluate the PPTs of the Sural and Tibial nervous tissue at the ankle of elite youth male footballers. Intraclass correlation coefficients (ICCs) with 95% confidence intervals (CI) were calculated to assess intra-rater and inter-rater reliability and Bland–Altman figures were plotted to enable visual evaluation of measurement error with a significance level of p < 0.05. Thirty-four players (16–18 years old) were assessed. Excellent intra-rater (Tibial ICC 0.88 (0.76–0.94); Sural ICC 0.89 (0.79–0.95)) and good inter-rater reliability (Tibial ICC 0.66 (0.40–0.82); Sural 0.71 (0.50–0.85)) was demonstrated. Bland–Altman plots demonstrated low levels of measurement error. Pressure algometry can be utilised clinically to accurately evaluate the PPTs of the Tibial and Sural nervous tissue at the ankle in asymptomatic elite male youth footballers.

3D cephalometric landmark detection by multiple stage deep reinforcement learning

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.

Local separation of potential field anomalies using equivalent sources: application for the 3-D structure of mantle uplift beneath Von Kármán crater, the Moon

SUMMARY The separation of regional-residual anomalies plays an important role in the processing of potential field anomalies for obtaining better understandings of the nature of the underground sources. Many methods have been developed to achieve the separation of anomalies that are of distinct wavelengths. On the other hand, fewer studies have addressed the separation of local anomalies from the observed potential field anomalies. In this paper, we introduce a new process for separating localized anomalies from the observations under the Cartesian and spherical coordinates. The separation is achieved using the equivalent source technique and an iterative inversion process which is to refine and finalize the separated local anomalies. Additionally, we introduce an inversion method for determining the equivalent sources that are of varying dimensions, as well as a quantitative measurement to assess the accuracy of the separation process. Verified with synthetic examples, the proposed method could extract arbitrary shaped local anomalies from the rest with low error levels. Subsequently, we apply the method to the construction of a 3-D model of the mantle uplift beneath the Von Kármán crater (VKC) on the Moon. The VKC is the landing site of the Chinese lunar exploration mission Chang'e 4, which lies in the northwestern portion of the South-Pole Aitken (SPA) basin on the far side of the Moon. Multiple generations of mare basalts are identified within the VKC, which indicates a complex geological history of the basin. Insights into the evolutionary history of this region can be obtained by investigating the deep crustal structure of the VKC using topographic and gravity data. Processed with the proposed method, the 3-D structure we obtain provides evidence for separated mantle uplifting events triggered by the two impact events that created the VKC and the Von Kármán M crater, respectively.

Tracking droplets in soft granular flows with deep learning techniques

The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The YOLO network was trained to recognize the droplets with synthetically prepared data, thereby bypassing the labor-intensive data acquisition process. In both applications, the trained YOLO + DeepSORT procedure performs with high accuracy on the real data from the fluid simulations, with low error levels in the inferred trajectories of the droplets and independently computed ground truth. Moreover, using commonly used desktop GPUs, the developed application is capable of analyzing data at speeds that exceed the typical image acquisition rates of digital cameras (30 fps), opening the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects, mostly but not exclusively, biological ones. Besides its practical applications, the procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems.

Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas.

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions.

Numerical model for atmospheric temperature prediction in the Pamplonita River Basin, Norte de Santander, Colombia

The work consists of the development of a computational model for the simulation of the temperatures, using a system of differential equations in partial derivatives, which solution was found by finite differences method, using the hypothesis of the isobaric coordinates, it means that the pressure is not a function but becomes an independent variable, in the new system, z is a function, more precisely a variable closely related to the geopotential ф. The data was obtained from the NCAR's Research Data Archive Database in January 2011 at different altitudes of 2 to 4572 meters above sea level (m.a.s.l.). The grid was structured with 10 spatial points on the x-axis, 8 spatial points on the y axis, and 5 sigma’s sigma's layers at thesame altitude evaluated, representing the 80 km² covering the Pamplonita River basin in the sector between Pamplona and Cucuta. The calculations obtained by the modeling system at the time of validation of the model have a low error level. The minimum error of validating the model was 0.17 %, obtained for the results of the model at 2 m.a.s.l.

Dynamic modulus master curve construction of asphalt mixtures: Error analysis in different models and field scenarios

Asphalt mixture stiffness is a key property in any pavement long-life performance analysis; it is used to predict a material’s strain response for any given applied stress. To accurately describe experimental data, different mathematical models have been reported in literature, which allow for extrapolation to ranges not evaluated in laboratory tests. However, a precision analysis comparing each of them is necessary. This study evaluated 50 data points from five temperatures, ten loading frequencies, and 48 different asphalt mixtures to construct dynamic modulus master curves. Sigmoidal, Christensen-Anderson-Marasteanu (CAM), and Havriliak-Negami (HN) models were calibrated with three different target error functions, in addition to 2 Springs, 2 Parabolic creep elements, and 1 Dashpot (2S2P1D) to analyze their relative and absolute errors. In the first part, the mechanical analogs (2S2P1D and HN) achieved the best results. In the second part, the error added by the Williams-Landel-Ferry (WLF) and polynomial function, which describe the time–temperature shift factors used to construct the dynamic modulus master curves, was calculated; this suggested that the polynomial function better fits the master curves’ shift factors. In the third part, three Enhanced Integrated Climate Models (EICM) with different pavement depths and temperatures throughout the year together with the influence of vehicle speed and a depth equation were used to meet a reduced frequency interval that represents practical field conditions. Therefore, by filtering the errors found in part one using the field-representative reduced frequency interval, the fitting models (based on regression adjustments) can reach lower error levels closer to those of the mechanical analog models although still greater than those of the 2S2P1D model. In summary, it is possible to obtain a reliable pavement stiffness prediction using a less robust model as long as the dynamic modulus tested database is wide enough to express the desired reduced frequency range. Nevertheless, the use of a mechanical approach model such as 2S2P1D better describes the dynamic modulus experimental results, which leads to a more reliable prediction in addition to enabling characterization of the rheological behavior of asphalt materials.

Interrater and intrarater reliability of the single arm military press (SAMP) test for upper limb function in patients with non-specific neck pain

BackgroundPerformance measures that assess the upper limb disability (ULD) in patients with neck pain can provide useful information for making clinical decisions regarding the optimal management of those patients. The Single Arm Military Press (SAMP) test is a performance based ULD measure developed specifically for populations with neck pain. In this test, patients are asked to lift a 1 kg weight repetitively overhead for 30 s with repetitions counting as the score. Whilst the test has been shown to be acceptable and feasible for use by clinicians and patients, its reliability in a patient group is still unknown. ObjectiveTo assess the interrater, intrarater reliability and measurement error of the SAMP test in patients with non-specific neck pain (NSNP). MethodsA total of 210 patients with NSNP and 81 healthy subjects were recruited for this study. The Disabilities of the Arm, Shoulder and Hand (DASH) and the Neck Disability Index (NDI) were assessed at baseline to ensure eligibility of the participants. The SAMP test was assessed at baseline and repeated 4–7 days later. A VAS symptom score was used to establish the stability of the participants across time. Interrater, intrarater reliability and measurement error were evaluated using Interclass Correlation Coefficient (ICC2,1) and the standard error of measurement (SEM). ResultsThe ICCs for interrater and intrarater reliability for the SAMP test ranged from 0.993 to 0.996 in the patient group. The SEM was ≤1 and smaller than the Smallest Detectable Change (SDC) and Bland-Altman plot indicated that the test is accurate. ConclusionThe almost perfect interrater and intrarater reliability and low levels of measurement error indicate that the 1 kg SAMP test has potential for evaluating upper limb functional capacity in female patients with NSNP. Before the test can be fully recommended, further studies are required to evaluate the validity and responsiveness of the SAMP test in population with NSNP and other neck disorders.

On the improvement of methodology of tax planning on the regional level

The subject of this research is the methodology of planning tax revenues at the meso-level. Tax planning at the meso-level is a complex process, since each region of the Russian Federation has its individual set of factors that influence the actual amount of tax revenues. The problem of regional differentiation by the level of socioeconomic development leads to inaccurate planning due to the fact that the current methodology takes into account a limited number of contributing factors. Therefore, the existing methodology of planning tax revenues is viewed in through the prism of multifactorial nature of regional economy. The article employs the mathematical and empirical methods for identification of flaws in the existing methodology of planning tax revenues coupled with the problem of regional differentiation of by the level of socioeconomic development. As a result, the author presents a unified approach that allows improving existing methodology of planning tax revenues and balancing the effect upon the outcome of such planning that occurs due to regional differentiation by the level of socioeconomic development. The essence of this approach lies in creation of the multivariate model of planning tax revenues that takes into account the unlimited factors affecting the amount of tax revenues from the region. The results of the multivariate model indicate the low level of error in comparison to the calculated data with the actual data. Thus, the error for income tax totaled to 1.1%, for personal income tax to 1.2%, for excise taxes to 3.4%, and for MET to 1.4%. The acquired results indicate an improvement in the quality and efficiency of tax planning on the regional level. The presented model also illustrates the factors that affect the actual amount of tax revenues, which in turn, accurately points at the factors o0f errors in the plan values. This model may considerably combine the principles of multifactorial nature, integrity, and transparency of tax planning.

Fast protection strategy for DC transmission lines of MMC-based MT-HVDC grid

Multi-terminal high voltage DC (MT-HVDC) grid has broad application prospects in connecting different energy sources, asynchronous interconnection of power grids, remote load power supply, and other fields. At present, the key technologies that affect the development of MT-HVDC transmission system include swift fault identification and location in the DC line and its rapid isolation. Traditional fault monitoring relies on line communication, which cannot guarantee the rapidity and reliability of protection; moreover, it may even cause device damage. A fault identification scheme based on a single-terminal transient is presented in this paper. This scheme calculates the line inductance by using the rise rate of fault current at the initial stage of the fault, and determines the occurrence of the fault by comparing the observed line inductance with the set value, which lays a foundation for calculating the location of the fault point using distance protection. A simulation model on the PSCAD/EMTDC platform is built; the simulation example verifies that the proposed scheme can identify faults under dissimilar conditions while maintaining a low error level on the premise of no communication lines so as to meet the protection requirements of the MT-HVDC grid.

Truncated octree and its applications

Octree is a hierarchical data structure with many applications, especially in encoding unstructured point clouds. The depth of an octree is dependent of the scale of the input data and the desired resolution of the smallest voxels in the leaf nodes as well. Thus, it often requires a deep octree to maintain low level of geometric errors for large-scale sparse point clouds, which leads to high memory requirement and low access speed. This paper presents a new structure called truncated octree or T-Octree that truncates the octree by adaptively pruning the top hierarchy and represents the deep octree by a set of shallow sub-octrees. The structure is further extended to support random access of nodes and out-of-core streaming of large data sets. We also propose a variable length addressing scheme to adaptively choose the length of an octree’s node address based on the truncation level. As a result, T-Octree provides highly efficient query performance and can save storage without losing the original structure for sparse or clustered models and scenes. We demonstrate the efficacy and efficiency of the new structure on point cloud compression and scene query tasks for sparse or clustered data.

Multivariate Statistical Analysis for Training Process Optimization in Neural Networks-Based Forecasting Models

Data forecasting is very important for electrical analysis development, transport dimensionality, marketing strategies, etc. Hence, low error levels are required. However, in some cases data have dissimilar behaviors that can vary depending on such exogenous variables as the type of day, weather conditions, and geographical area, among others. Commonly, computational intelligence techniques (e.g., artificial neural networks) are used due to their generalization capabilities. In spite of the above, they do not have a unique way to reach optimal performance. For this reason, it is necessary to analyze the data’s behavior and their statistical features in order to identify those significant factors in the training process to guarantee a better performance. In this paper is proposed an experimental method for identifying those significant factors in the forecasting model for time series data and measure their effects on the Akaike information criterion (AIC) and the Mean Absolute Percentage Error (MAPE). Additionally, we seek to establish optimal parameters for the proper selection of the artificial neural network model.

Optimization on Tensile Properties of Kenaf/Multi-walled CNT Hybrid Composites with Box-Behnken Design

Hybrid composites have recently become attractive to scholars due to their significant potential to overcome the drawbacks of monofiber composites. However, the simultaneous effects of independent parameters on the properties of these hybrid composites are still not clear as the previous studies have mostly focused on varying one parameter only. Therefore, a response surface methodology (RSM) is introduced. Accordingly, the present study attempts the modeling and optimization of four independent parameters on the kenaf/multi-walled carbon nanotubes (MWCNTs) hybrid composites by using three-level Box-Behnken RSM. The four parameters: kenaf fiber loading, NaOH concentration, MWCNTs loading and sonication time have been selected based on the past literature and their simultaneous effects on the tensile properties are studied. It was found that the optimized 10.029wt% of kenaf fiber loading, 4.057% NaOH concentration, 0.965wt% of MWCNTs loading and 2.970 h of sonication time predicted an optimum tensile strength and tensile modulus of 123.580 MPa and 16.084GPa respectively. Moreover, the validation through experiment of the predicted optimum results has been concluded and reveals that the results were reliable after considering the relatively low error levels. Besides, the response also displayed that the kenaf fiber loading had the highest degree of impact on the tensile properties compared to the other three parameters.

Bicycle Rider Behavior and Crash Involvement in Australia.

This research investigated how behaviours and attitudes of bicycle riders influence crash frequency and severity. The study recruited 1102 Australian bicycle riders for an online survey. The survey comprised questions on demographics, frequency of riding and the number and severity of traffic crashes during the last five years. The survey included the Cycling Behaviour Questionnaire and the Cyclist Risk Perception and Regulation Scale. Overall, there were low levels of errors and violations reported by participants indicating that these behaviours were on average never or rarely exhibited while riding a bicycle. Conversely, participants reported high levels of engagement in positive behaviours and reported high levels of traffic rule knowledge and risk perception. Higher rates of violations and errors were associated with increased crash likelihood, while higher rates of positive behaviours were associated with reduced rates of crash involvement in a period of 5 years. The findings highlight the relationship between errors, total crashes and crash severity Further promotion of positive behaviours amongst riders may also help to reduce the risk of crashes.

Improved prediction of litchi impact characteristics with an energy dissipation model

A model of elastic impact (EI) and a model that considers energy dissipation (CED model) were developed to describe the deformation behavior during litchi impact. In the CED model, dissipation energy is due to damping effect and the change of elastic modulus after impact. Drop tests of fruit onto a fixed plate were conducted with a pendulum device for three litchi cultivars (‘Nuomci’, ‘Guiwei’, and ‘Lvhebao’) to test the accuracy of the two models. The results show that the CED model better simulated the change of fruit velocity during impact and better predicted the occurrence time of maximum impact force and maximum deformation compared to the EI model. Both models accurately predicted the maximum impact force of ‘Guiwei’ litchi with all errors less than 5 %, but had errors up to 24.8 % for the prediction of the maximum impact force of ‘Nuomici’ and ‘Lvhebao’ litchis at high drop heights (drop height ≥ 400 mm). The CED model accurately predicted maximum deformation (errors less than 8 %) but the EI model over-predicted maximum deformation with errors range from 12.2 % to 39.2 % at all drop heights for the three tested cultivars. Both models predicted the contact time with low-level errors (<10 %). With a reliable theoretical model, the impact characteristics of litchis can be calculated and then applied to assess the fruit damage degree.

Using American Community Survey Data to Improve Estimates from Smaller U.S. Surveys Through Bivariate Small Area Estimation Models

Abstract We demonstrate the potential for borrowing strength from estimates from the American Community Survey (ACS), the largest US household survey, to improve estimates from smaller US household surveys. We do this using simple bivariate area-level models to exploit strong relationships between population characteristics estimated by the smaller surveys and ACS estimates of the same, or closely related, quantities. We illustrate this idea with two applications. The first shows impressive variance reductions for state estimates of health insurance coverage from the National Health Interview Survey when modeling these jointly with corresponding ACS estimates. The second application shows impressive variance reductions in ACS one-year county estimates of poverty of school-aged children from modeling these jointly with previous ACS five-year county estimates of school-age poverty. Simple theoretical calculations show how the amount of variance reduction depends on characteristics of the underlying data. In our applications, we examine three alternative bivariate models, starting with a simple bivariate Gaussian model. Since our applications involve modeling proportions, we also examine a bivariate binomial logit normal model, and an unmatched model that combines the Gaussian sampling model with the bivariate logit normal model for the population proportions. Given the strong relationships between the population characteristics estimated by the smaller surveys and the corresponding ACS estimates, and the low levels of sampling error in the ACS estimates, the models achieve large variance reductions even without using regression covariates drawn from auxiliary data sources.