Short-term Accuracy Research Articles

Information fusion algorithm of GNSS / INS integrated navigation system

Inertial navigation system (INS) has high short-term accuracy, while satellite navigation positioning system (GNSS) has high long-term accuracy. The integrated navigation system integrates two or more different navigation systems with hardware and software, and uses their respective advantages to make up for the shortcomings of a single system. In this paper, a dual-antenna GNSS/MIMU integrated navigation system is designed, and experimental research is performed on different integrated navigation systems. Algorithms of GNSS satellite positioning and dual-antenna attitude measurement, strapdown inertial navigation attitude calculation and update, and integrated navigation filtering are studied. Static and dynamic experiments are performed to study the performance of the system under different GNSS/MIMU combinations. The experimental results show that the combined system has smaller errors in terms of horizontal position, heading angle, pitch angle, and roll angle.

Improved Particle Filter-Based Matching Method With Gravity Sample Vector for Underwater Gravity-Aided Navigation

Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness.

Cost-Effectiveness of Noninvasive Screening for Alcohol-Related Liver Fibrosis.

Alcohol-related liver disease is often undetected until irreversible late-stage decompensated disease manifests. Consequently, there is an unmet need for effective and economically reasonable pathways to screen for advanced alcohol-related fibrosis. We used real-world data from a large biopsy-controlled study of excessive drinkers recruited from primary and secondary care, to evaluate the cost-effectiveness of four primary care initiated strategies: (1) routine liver function tests with follow-up ultrasonography for test-positives, (2) the enhanced liver fibrosis (ELF) test with hospital liver stiffness measurement (LSM) for positives, (3) a three-tier strategy using the Forns Index to control before strategy 2, and (4) direct referral of all to LSM. We used linked decision trees and Markov models to evaluate outcomes short term (cost-per-accurate diagnosis) and long term (quality-adjusted life-years [QALYs]). For low-prevalence populations, ELF with LSM follow-up was most cost-effective, both short term (accuracy 96%, $196 per patient) and long term (incremental cost-effectiveness ratio [ICER] $5,387-$8,430/QALY), depending on whether diagnostic testing had lasting or temporary effects on abstinence rates. Adding Forns Index decreased costs to $72 per patient and accuracy to 95%. The strategy resulted in fewer QALYs due to more false negatives but an ICER of $3,012, making this strategy suited for areas with restricted access to ELF and transient elastography or lower willingness-to-pay. For high-prevalence populations, direct referral to LSM was highly cost-effective (accuracy 93%, $297 per patient), with ICERs between $490 and $1,037/QALY. Noninvasive screening for advanced alcohol-related fibrosis is a cost-effective intervention when different referral pathways are used according to the prevalence of advanced fibrosis. Patients in the primary health care sector should be tested with the ELF test followed by LSM if the test was positive, whereas direct referral to LSM is highly cost-effective in high-prevalence cohorts.

Nonparametric Bootstrap Technique to Improve Positional Accuracy in Mobile Robots With Differential Drive Mechanism

Wheeled mobile robots (WMRs) inevitably experience positional inaccuracy, which is caused by a number of factors including design imperfections, problems with component fabrication, sensor errors, and electromechanical malfunction. We have previously proposed an odometry-based technique that reduces positional inaccuracy in WMRs driven with standard wheels. This technique combines high sampling rates and short-term accuracy, and calculates necessary lateral and longitudinal corrections by using linear regression to model the relationship between positional inaccuracy and angular velocities of the robot's wheels. This technique can do so without the precision of measurement required by other techniques. In this paper, we discuss how a nonparametric bootstrap approach can be used to find both interval and point estimates of the modified angular velocities required to alleviate the positional inaccuracy of the WMR. First, the robot travels along a path recommended by the odometry-based error reduction technique. Then, the positional and angular errors of the robot at the stop point are measured. Next, these measurements are used to estimate angular velocities, providing the necessary confidence intervals (prediction). Results from these calculations could be incorporated into the robot program to modify the movement along a given path (validation). To show viability, the bootstrap technique was applied to a prototype mobile robot while the robot was programmed to move along an unseen trajectory. Results indicate that, for this typical unseen path, the bootstrap technique is capable of improving real-time positional systematic and non-systematic inaccuracy with acceptable levels of precision compared to the linear regression technique under the normality assumption. The bootstrap technique exhibited better efficacy than the linear regression, therefore, it may be a useful tool to conduct real-time calibration of differential drive WMRs.

DESIGN AND CONSTRUCTION OF A LOW-COST 30KV VARIABLE DC POWER SUPPLY UNIT

A high voltage power supply is a key component in the advancement of science and technology. Application of high voltage power supply requires careful attention to critical variables such as voltage ripple, long and shortterm stability, repeatability and accuracy. These are important factors in the consideration of reliable scientific data. This paper presents the design of a low-cost high voltage power supply from the off-the-shelf electronics components to meet the high-end requirement of high voltage power supply. A 30kV, 63.8mA maximum power supply was constructed at the Fountain University electronics workshop. This high voltage directs current (HVDC) power supply was built around three basic compartments that include an adjustable low voltage power supply (LVPS), a high frequency oscillator, and a line output transformer (LOPT) using flyback transformer, NE555timer, BU508D BJT, and other off-the-shelf components. The current-voltage relationship at the output of the constructed High Voltage Direct Current was found to be linear. This power source will serve any high DC voltage applications such as electrospinning. The constructed 30kV power supply has been tested in the electrospinning laboratory of the Center for Energy Research and Development (CERD) Obafemi Awolowo University (OAU) Ile-Ife. The unit successfully electrospun Zinc-Titaninm polymeric solution into fibers at about 8 kV. The importance of this fabricated device is its high reliability despite its low cost and capability to produce different magnitude of high voltage DC.

Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries

Due to the inevitable degradation for lithium-ion batteries, the accuracy of state of charge (SOC) estimation decreases along with battery aging. How to ensure the accuracy of battery SOC estimation during a battery’s lifetime at different operating conditions leaves a big challenge. This work develops a parameter updating method of a simplified first principles-thermal coupling model that has good parameter identifiability and applicability for different operating conditions to ensure the accuracy of the model-based SOC estimation. Because updating all the model parameters offline is time-consuming, this work first conducts a model parameter sensitivity analysis and determines which parameters need to be accurately updated according to their sensitivities. Offline prediction methods are then developed to update the sensitive parameters according to their degradation laws. SOC validations show that the offline prediction methods can ensure the short-term accuracy, but the accuracy will decrease gradually along with the increase of the offline prediction errors of the capacity-related parameters. To address this problem, a multi-time-scale updating method combining the offline prediction and the capacity parameter online estimation is developed. Essential validations are provided to assess the SOC estimation accuracy using different parameter updating methods.

Discovering Market Prices: Which Price Formation Model Best Predicts the Next Trade?

For most purposes of technical analysis, valuation metrics and many other relevant financial methods, the price of the last transaction is considered representative of the market price. The straightforward argument is that at this price, supply and demand have last met. However, on closer examination, the question arises as to why a past event should be relevant to the future, and why other, potentially more recent information should not be used to discover a future price. Building on this question, we apply a range of new price formation models to current data available on crypto currency exchanges that depict level II market data, and compare their short-term forecast accuracy against the common-used ticker price and mid-price. Data on crypto currencies is used as the closest example to free markets, since crypto currency trading is continuous, markets never close, and interferences through oversight is extremely rare. We find that two of the five price formation models investigated outperform the widely used ticker as a price indicator for the next trade. We conclude that the volume-limited clearing price best predicts the price of subsequent trades. Its usage can thus enhance the explanatory power of various financial analyses.

Short-Term Traffic Forecast of Urban Bus Stations Based on Long Short-Term Memory

Smart Transportation is an important part of Smart City. Buses, as one of the most important modes of travel in urban public transport, not only facilitate the work and life of urban residents but also provide effective solutions to urban energy conservation and environmental protection. Improving the accuracy of passenger traffic forecast at bus stations has been a focus of research in smart public transport. To compensate for the limitations of single-station forecasting and short-term memory in traditional time series models (such as ARMA and SVR) and improve the short-term prediction accuracy of urban bus station traffic flow, we propose a long short-term memory (LSTM)-based neural network model than can learn long-term series data at multiple sites and forecast the traffic flow of multiple sites simultaneously. Experimental results show that learning multi-site traffic simultaneously can improve the accuracy of prediction results and minimize mean squared error and mean absolute error to 3.18 and 1.43, respectively. Aside from its long-term memory, the neural network model based on LSTM can also show the potential correlation between multiple sites, suggesting obvious advantages in short-term traffic forecast. This study proves the feasibility of using LSTM for multi-site bus station traffic forecast. In addition, the performance of the proposed model is considerably better than that of single-site traffic forecast. Traffic monitoring data analysis indicates that multi-site bus station traffic flow is correlated. This paper provides a timely and accurate data reference for the rapid decision-making and integrated management of public transport operations.

Water quality prediction analysis of Qingyi River based on time series

According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality.

The revision and transfer effects of direct and indirect comprehensive corrective feedback on ESL students’ writing

This study investigated the short-term and delayed effects of comprehensive written corrective feedback (WCF) on L2 learners’ revision accuracy and new pieces of writing (i.e., the transfer effect of feedback). Three types of feedback were compared: direct feedback and two types of indirect feedback that differed in their degree of explicitness (i.e., underlining only and underlining+metalinguistic cues). Fifty-three intermediate level learners of English as a second language (ESL) were divided randomly into four groups: One direct, two indirect, and a control group. Students produced three pieces of writing from different picture prompts and revised them over a three-week period. Each group also produced a new piece of writing two weeks later. The study included seven sessions: Writing 1, revision of Writing 1, Writing 2, revision of Writing 2, Writing 3, revision of Writing 3, and Writing 4 (delayed writing). The results showed that all the three feedback groups significantly outperformed the control group in revision tasks. Some short-term accuracy improvements were also found on new pieces of writing for direct and underlining+metalinguistic feedback, but the effects were largely non-significant.

Improvement on Azimuth in Rotary Inertial Navigation System Using Photoelectric Encoder and Fiber Optic Gyro

For its obvious high accuracy in a long term, rotary inertial navigation system becomes popular. However, as errors increasing with time are changed into periodic errors with zero mean, the accuracy of its azimuth angle decreases in view of short term and can’t fully satisfy the need of users such as flight control system. The precision of azimuth angle is studied by analyzing its influence factor and establishing the scale factor error model and drift models of fiber optic gyro and the measurement error model of photoelectric encoder. Correction for fiber optic gyro and photoelectric encoder based on their mutual assistance is designed to improve the short-term accuracy of rotary inertial navigation system’s azimuth angle. Experimental results in a fiber optic gyro based rotary inertial navigation system verify the proposed method.

Improving the Accuracy of the Martian Ephemeris Short-Term Prediction

The Chinese Mars exploration mission is planned to be launched in 2020, which includes an orbiter, a lander, and a rover. High precision Martian ephemeris is very important in Mars exploration, especially for the Martian orbit insertion and the Martian lander/rover landing. In this paper, we used simulation data to analyze the short-term prediction accuracy of the Martian ephemeris. The simulation results show that the accuracy of Mars position is expected to be better than 50 m for 180-day prediction, when 90-150 days’ range measurements are used to estimate the orbit of the Mars. Range bias affects the prediction accuracy and the arc length for estimation is limited. The prediction accuracy will improve with higher orbit, and the orbit error of probes has an obvious effect on the prediction accuracy of the Martian ephemeris.

Does Crowdsourced Research Discipline Sell-Side Analysts?

We examine whether increased competition stemming from technological innovation disciplines sell-side analysts. We document a decline in short-term forecast bias for firms added to Estimize, an open platform that crowdsources short-term earnings forecasts, relative to matched control firms; this decline is greater when (1) existing sell-side competition is smaller, (2) earnings uncertainty is higher, and (3) Estimize coverage is less biased and more accurate. We also document an increase in short-term forecast accuracy and representativeness. Finally, we find no change in bias for longer-horizon forecasts or recommendations, suggesting competition from Estimize rather than broad economic forces drives our results.

Quality assessment of the Ozone_cci Climate Research Data Package (release 2017) – Part 1: Ground-based validation of total ozone column data products

Abstract. The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate–chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC) at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4 % (for SCIAMACHY). Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % and 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ∼ 1 % for GOME and OMI to ∼ 2 % for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade of the differences in the Northern Hemisphere of −0.11 ± 0.10 % decade−1 for Dobson and +0.22 ± 0.08 % decade−1 for Brewer collocations. The exceptional quality of the level-3 GTO-ECV v3 TOC record temporal stability satisfies well the requirements for the total ozone measurement decadal stability of 1–3 % and the short-term and long-term accuracy requirements of 2 and 3 %, respectively, showing a remarkable inter-sensor consistency, both in the level-2 GODFIT v4 and in the level-3 GTO-ECV v3 datasets, and thus can be used for longer-term analysis of the ozone layer, such as decadal trend studies, chemistry–climate model evaluation and data assimilation applications.

Indoor positioning tracking with magnetic field and improved particle filter

The indoor magnetic field is omnipresent and independent from external equipment. Local magnetic field is also relatively stable compared with WiFi signals in the same environment and nonuniform in different locations. However, it has low discernibility, in that there are similar magnetic features in different areas. Pedestrian movement model is a continuous navigation method based on inertial sensors. However, inertial sensors provide only short-term accuracy and suffer from accumulation error. Hence, an indoor positioning tracking that uses the magnetic field and an improved particle filter is proposed in this article. First, adaptive four-threshold step-detection and mixed adaptive step length methods are used to obtain the travel distance in different walking states. Furthermore, an improved particle filter is adopted to calibrate the pedestrian movement model by fusing indoor magnetic field information. Besides, initial locations of particles are restricted in a determined area according to WiFi signals, and the diversity of the particles is increased by a classified heuristic resampling. The proposed system was implemented on an Android phone and extensive experiments were conducted in real indoor environments. The experiments show that the positioning accuracy and system robustness are greatly improved compared with other methods.

ARIMA Forecasting: Variables without a Cause

AbstractARIMA forecast methods offer short term accuracy but have severe limitations in the appraisal context. ARIMA forecasts fail to identify or model causal variables, require more data points than are usually available and are very difficult to explain to non-statisticians. Better forecast alternatives are available to appraisers.

Indoor mobile robot attitude estimation with MEMS gyroscope

A method of attitude estimation with a low-cost, strap-on MEMS sensor was proposed in the article. The method relies on dynamic gyroscope bias change estimation and updates during detected stops of the robot. The algorithm has been tested in laboratory with iRobot Roomba robot and should be improved to be useful in an industrial environment. The measurement of attitude of a mobile platform is necessary for correct navigation, especially in autonomous vehicles, which use dead reckoning between position updates from external sources. Since the system is intended to operate indoors, in industrial halls and shops, it cannot avail of GPS and Earth magnetic field sensors because of anomalies, which are common inside the steel constructions. Therefore, the accuracy of the gyroscope-based attitude estimation is significant. The proposed method aims to address the common problem of gyroscope bias drift, by dynamic update of sensor bias and simultaneous use of all gyroscope axes, to improve the quality of the measurements. A popular 3-axial gyroscope and 3-axial accelerometer sensors were used during the test runs. Obtained results suggest that it is possible to improve short-term accuracy of inertial dead reckoning, to get a system that could be of practical use in industrial AGV systems or intelligent vehicles.

Comparison of the Short-Term Forecasting Accuracy on Battery Electric Vehicle between Modified Bass and Lotka-Volterra Model: A Case Study of China

The potential demand of battery electric vehicle (BEV) is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV) model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE) and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.

Forecasting intraday call arrivals using the seasonal moving average method

Research into time series forecasting for call center management suggests that a forecast based on the simple Seasonal Moving Average (SMA) method outperforms more sophisticated approaches at long horizons where capacity planning decisions are made. However in the short to medium term where decisions concerning the scheduling of agents are required, the SMA method is usually outperformed. This study is the first systematic evaluation of the SMA method across averages of different lengths using call arrival data sampled at different frequencies from 5min to 1h. A hybrid method which combines the strengths of the SMA method and nonlinear data-driven artificial neural networks (ANNs) is proposed to improve short-term accuracy without deteriorating long-term performance. Results of forecasting the intraday call arrivals to banks in the US, UK and Israel indicate that the proposed method outperforms standard benchmarks, and leads to improvements in forecasting accuracy across all horizons.

Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.