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The smart car collects the signal through the infrared sensor, and obtains the gray map in front of the car, according to the gray map, the tracking is judged.Due to the complexity of the environment, many gray-scale maps are difficult to trace, and even cannot be judged, which brings great uncertainty to the operation of the smart car.The paper designs a method to improve the infrared tracking accuracy of smart cars.First, determine the level of contrast in this graph.By improving the brightness in the grayscale image, the gray signal around the tracking line is eliminated, and then the tracking line is highlighted by increasing the contrast.Therefore, it effectively overcomes the shortcomings of poor practicality of smart cars and improves the infrared tracking accuracy of smart cars. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) algorithms in a corner reflector located in Lisbon, Portugal. The reflector was monitored using daily observations from the Global Navigation Satellite System (GNSS) technique with submillimeter precision. The study focuses on comparing the performance and accuracy of different MT-InSAR approaches i) the Persistent Scatterer Interferometry (PSI) method using the open-source software StaMPS, ii) the PSI method using the commercial software SARPROZ, iii) the Quasi-PS method implemented with the SARPROZ software, as well as iv) a hybrid method Persistent scatterer – Distributed Scatterer (PS-DS) obtained from the European Ground Motion Service (EGMS) with SqueeSAR algorithm. The study period assumed was from October 2017 to January 2019, by considering the same initial information (ascending orbit Sentinel images). Statistical analysis of the time series was also performed, and the density of points in the vicinity was evaluated. To provide a comprehensive evaluation of accuracy, the uncertainties associated with both the GNSS and InSAR techniques were assessed. The findings of this comparative analysis offer valuable insights into the strengths and limitations of various MT-InSAR algorithms by using a more precise technique as a benchmark. The results improve the understanding of deformation monitoring in geodetic applications and highlight the potential for enhanced accuracy in such assessments. [ABSTRACT FROM AUTHOR]

One of the key tools for many fields of applied mathematics is the integral equations. Integral equations are widely utilized in many models, atmosphere–ocean dynamics, fluid mechanics, mathematical physics, and many other physical and engineering disciplines. A new numerical strategy based on the Bernoulli wavelet is introduced to solve system of Fredholm integral equations of second kind. In this paper, the Bernoulli wavelets are first built. Second, the system of Fredholm integral equations has been reduced into an algebraic system. In order to demonstrate the viability, and accuracy of the suggested Bernoulli wavelet approach, some numerical examples are offered at the end. The derived numerical results are examined with those from other numerical techniques and with exact solutions, demonstrating the superiority of the proposed method over those techniques. The novelty of proposed technique is that it can be extended for the numerical solution of two dimensional integral equations and differential equations appearing in engineering models, however some modifications will be required. [ABSTRACT FROM AUTHOR]

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the accuracy and the stability of the CN-WSGD when it is applied to the initial–boundary value problem for a space fractional diffusion equation. In the CN-WSGD scheme, the weighted and shifted Grünwald difference (WSGD) scheme is used to discretize the space fractional derivative and the Crank–Nicolson (CN) scheme is used to discretize the temporal derivative. There is a free parameter in a CN-WSGD scheme that affects the stability and the accuracy of the scheme. In general, a CN-WSGD scheme has temporally and spatially second order accuracy, and there exists a scheme that has spatially third order accuracy. However, the third order accuracy scheme is not always stable. In this paper, we present and prove some theoretical results (old and new) on the stability of several CN-WSGD schemes, and consider choosing a value for the parameter such that the corresponding CN-WSGD scheme is unconditioned stable and has optimal upper bound for the accuracy. Numerical results are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

There are lots of research papers for ASC, and in recent years it is rapidly increasing. DCASE also provides different types of competition for the submission of several papers to solve the various tasks of ASC and it is the opportunity for the research scholars either to participate in those competitions or to provide the enhanced model for ASC. This paper provides details about the various recent approaches along with the block diagram used for pre-processing required before model development for ASC. It also includes a description of different recent techniques used for the classification of different sounds for ASC tasks. The comparative analysis for different recent available techniques both for pre-processing and classification has been done and summarized in this paper. It also describes the contributions towards the survey on ASC by comparing this paper with some existing survey papers based on several parameters like functionality described separately, results from description with quantifiable value, a dataset with proper quantifiable analysis, and pictorial representation of model discussed, etc. Finally, considering the benefits for eminent research scholars, this paper has also focused on the details for future directions for both pre-processing and classification for ASC. [Display omitted] • A systematic review on pre-processing and classification techniques for acoustic scenes. • Comparative analysis of pre-processing and classification techniques. • Discussion on different processing techniques for classification. • Identifies the future directions for further research in the area. [ABSTRACT FROM AUTHOR]

The performance of machine tools depends on their working accuracy. The paper presents a new approach for in-process monitoring the geometric and kinematic accuracy of a machine tools. This approach utilizes a virtual sensor for capturing displacements at the tool center point (TCP). The virtual sensor is set up by using a data-driven approach based on extended transmissibility functions. The input data for monitoring are the NC internal data. The transmissibility functions are established via standard experimental procedures such as double ball bar (DBB) tests tests and estimation of frequency response functions (FRF). The data from the virtual sensor can be used for process monitoring and troubleshooting when launching a new process. [ABSTRACT FROM AUTHOR]

The paper proposes new second-order accuracy metrics for scoring/rating models, which show the target preference of the model - it is better to diagnose "good" objects or better to diagnose "bad" ones for a constant generally accepted predictive power determined by the first-order metric - the Gini index. Two metrics proposed, they have both an integral representation and a numerical one. The numerical representation of metrics is of two types, the first of which is based on binary events to evaluate the model, the second on the default probability calibration given by the model. Comparison of the results of calculating the metrics allows you to validate the calibration settings of the scoring/rating model and reveals its distortions. The article provides examples of calculating second-order accuracy metrics for ratings of several rating agencies, as well as for the well-known approach to calibration based on van der Burg's ROC curves. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a widely used technology to accomplish the essential task of indoor localization. Hundreds of studies, with many techniques, applications, and metrics, are presented to use, analyze, and improve the performance of BLE-based localization systems. This paper presents a state-of-the-art survey of these studies, taking the accuracy metric into main consideration. Using a majority analysis of reported values in these studies, the presented survey has four key contributions. First, it quantifies the level of accuracy reached by previous localization techniques. The presented survey shows that sub meter accuracy is currently achievable, whereas sub decimeter counterpart is still an open research area. Second, different domains of BLE-based localization applications and techniques are both classified into three groups of high, moderate, and low accuracy. This helps future researchers find the suitable technique for each application. Third, categories of techniques that realize sub meter accuracy are enumerated and thoroughly investigated. These are the ones that primarily use Machine Learning (ML), filtration, fusion, or the Angle of Arrival (AoA) feature of BLE v5.1 and above. Fourth, targeted evaluation metrics by these categories of techniques are reviewed. This reflects that some important metrics, e.g., security, still require much attention and contributions. [ABSTRACT FROM AUTHOR]

• The new algorithm is explicit for both displacement and velocity. • The new algorithm is unconditionally stable for linear and nonlinear softening stiffness systems. • The new algorithm has extremely small period error. • The new algorithm has no energy dissipation. • There still exist overshoot of the new algorithm. Several model-based or structure-dependent integration algorithms with explicit displacement formulation and unconditional stability are recently developed. According to the difference equations, these algorithms can be divided into dual-explicit and semi-explicit, where the first category algorithms are explicit for both displacement and velocity, while the second category are only explicit for displacement. The well-known Chen-Ricles (CR) and Chang algorithms are two representatives of dual-explicit and semi-explicit model-based algorithms, respectively. The model-based integration algorithms are promising with excellent computing efficiency, but most of them are second-order accurate. In this paper, a new dual-explicit (NDE) algorithm with higher-order accuracy is developed. The integration parameters of the NDE algorithm are obtained by using the pole mapping and second-order Pade approximation. Numerous important numerical characteristics including the consistency and local truncation error, stability, numerical dispersion and energy dissipation and overshoot are systematically analyzed for the new algorithm. It is found that the NDE algorithm is fourth-order accurate for free vibration without damping. As for forced vibration with/without damping and free vibration with damping, the NDE algorithm is no longer fourth-order accurate, but it is still more accurate than the CR and Chang algorithms. In addition, the period error of the algorithm is extremely small. Three representative numerical examples are finally provided to demonstrate the high accuracy of the NDE algorithm. Several classical and latest model-based integration algorithms and three fourth-order accurate algorithms are also adopted for comparisons. It has been proved that the NDE algorithm has much better performance than other selected model-based algorithms. [ABSTRACT FROM AUTHOR]

Abstract Industrial sensors efficiency is commonly determined by the ability to provide accurate indications during a mission. In addition, several techniques and methods are incorporated to increase sensors measurement accuracy. These examinations are classified into experimental and theoretical classes. Experimental examinations focus on determining the flow coefficient variation, which results from the installation effect and flow characteristics. Theoretical proposals deal principally with formulation of the instrument reliability function. These formulations integrate different parameters which affect the sensor measurement accuracy during a given mission. This paper discusses the failure risk and effect analysis for the Pitot sensors system measurement accuracy during a given mission. In addition, a set of factors is used to determine the causal diagram and to model the corresponding hazard function. These factors are subject to statistical analysis in the second part of this paper. The current study is also driven by a practical need to well manage aeronautical problems linked to Pitot sensors system malfunctioning during a flight. [ABSTRACT FROM AUTHOR]

Highlights • This paper presents a novel algorithm to classify complex-rendered text images. • The proposed algorithm uses the combined characteristics of RGB sub-channels. • The classification accuracy is improved by 15.9% for complex-rendered texts. • The computational complexity is reduced without compression efficiency degradation. Abstract This paper presents a novel algorithm to classify complex-rendered text images by examining the color levels of the adjacent sub-pixels. The proposed algorithm is different from the previous approaches in that it uses the combined characteristics of RGB sub-channels, whereas the previous approaches use independent characteristics of RGB sub-channels. Experimental results demonstrate that the proposed algorithm improves the classification accuracy over the previous best algorithm by 15.9% for complex-rendered texts. Furthermore, the compression method optimized for text sub-images significantly reduces the computational complexity without degradation in compression efficiency. [ABSTRACT FROM AUTHOR]

Highlights • The Energy Justice Metric (EJM) quantifies energy justice. • The EJM balances competing aims of the energy trilemma. • Presents energy justice performance of US, UK, Germany, Denmark and Ireland. • The EJM addresses inequality in the energy sector and society. Abstract Energy justice is a fast emerging research and policy tool which captures the injustices across the energy life-cycle, i.e., from 'cradle-to-grave'. The Energy Justice Metric (EJM) quantifies energy justice through analyzing the energy justice performance of different countries utilising data from international institutions and national governments. This paper identifies why there is a need for a modeling tool such as the EJM which focuses on the full energy life-cycle and also has a distributive (inequality-correcting) oriented approach. The EJM demonstrates how a country can achieve an improved balance between the three competing aims of the energy trilemma, i.e. economics, politics and the environment. A key feature of the EJM is modeling energy justice using a ternary plot where the energy justice performance of a country can be transferred directly onto the energy trilemma. In this paper, five countries are analysed, the US, UK, Germany, Denmark and Ireland. The EJM presents a research and policy decision-making tool that can contribute to the growing literature that tackles the issue of inequality in society, and informs on society's decision on which energy source would be more just for a society to build. [ABSTRACT FROM AUTHOR]

This paper summarizes interesting results on systematic backward and forward error analyses performed for corner cutting algorithms providing evaluation of univariate and multivariate functions defined in terms of Bernstein and Bernstein related bases. Relevant results on the conditioning of the bases are also recalled. Finally, the paper surveys important advances, lately obtained, for the design of algorithms adapted to the structure of totally positive matrices, allowing the resolution of interpolation and approximation problems with Bernstein-type bases achieving computations to high relative accuracy. • De Casteljau-type algorithms are numerically stable under imprecise computer arithmetic or perturbed input data. • Bernstein-type bases are optimally stable for evaluation among all bases of the generated spaces formed by nonnegative functions. • Interpolation and approximation to high relative accuracy with generalized Bernstein bases can be achieved. [ABSTRACT FROM AUTHOR]

3D hand pose estimation methods have made significant progress recently. However, estimation accuracy is often far from sufficient for specific real-world applications, and thus there is significant room for improvement. This paper proposes TriHorn-Net, a novel model that uses specific innovations to improve hand pose estimation accuracy on depth images. The first innovation is decomposition of the 3D hand pose estimation into the estimation of 2D joint locations in the depth image space (UV), and the estimation of their corresponding depths aided by two complementary attention maps. This decomposition prevents depth estimation, which is a more difficult task, from interfering with the UV estimations at both the prediction and feature levels. The second innovation is PixDropout, which is, to the best of our knowledge, the first appearance-based data augmentation method for hand depth images. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on three public benchmark datasets, achieving 5.73 mm, 7.13 mm, and 7.68 mm on the ICVL, MSRA, and NYU datasets, respectively. The proposed model achieves this performance with a relatively fast average inference speed of 9.25 ms per frame on an NVIDIA 1080Ti GPU, which, as discussed in the paper, places the proposed model among the fastest-performing 3D hand pose estimation models. Our implementation is available at https://github.com/mrezaei92/TriHorn-Net. • 3D hand pose estimation has made significant progress recently. • We propose a model, TriHorn-Net, for accurate depth-based 3D hand pose estimation. • Our model, decomposes the 3D hand pose into the 2D joint locations. • The proposed model uses the Pix-Dropout for appearance-based data augmentation. • The proposed model outperforms the state-of-the-art methods on three public datasets. [ABSTRACT FROM AUTHOR]

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality. [ABSTRACT FROM AUTHOR]

Abstract In designing a new drug, inhibitor compound is usually used to control the enzyme work to recover a particular disease. In the drug design technique, the classification of inhibitor is carry out by docking software to simulate the bounding of mixing (new inhibitor candidate) with the targeted enzyme. DUD-E is a database to simulate docking with high dimensional data characteristic, which lead to the feasibility of machine learning approach as the analytical tool. A compound with specific characterictics can be classified into ligand or decoy by using many characterictics leading to a problem in the machine learning algorithm. This paper discusses feature selection analysis to obtain the compound characteristics which are effectively determine ligand or decoy. This paper examined Mutual Information-based Feature Selection (MIFS), Correlation-based Feature Selection (CFS) as well as Fast Correlation-Based Filter (FCBF), and the results show that the FCBF always selects less number of features with fastest runtime of classification. The highest classification accuracy is obtained when all features are used in the classification by k-NN. However, the accuracy is slightly different with classification using selected features. The CFS method performs well for Data-A with accuracy of 89,55%, while the MIFS outperforms the others for Data-B and Data-C with the classification accuracy of 92,34% and 95,20% consecutively. [ABSTRACT FROM AUTHOR]

Abstract District heating and cooling (DHC) networks play a fundamental role in the transition towards a sustainable supply of heating and cooling, due to their ability to integrate any available source of thermal energy and to distribute it to the buildings. However, the use of renewable non-constant sources together with the variable heat demand of the buildings creates instable and pronounced transient operating conditions. In order to analyse the hydraulic and thermal behaviour and the dynamics occurring within these networks, several physical models based on different methods were proposed by previous researchers. Numerical thermal models based on finite difference methods (FDM) were pointed out to suffer from artificial diffusion when simulating the propagation of heat through the network. However, due to a wide and well-known literature on these methods, they are still used by many researchers and are therefore worth being investigated. The present paper analyses the effects of artificial diffusion using two models based on two different first-order approximation schemes. An ideal temperature wave and a dataset from a real DH network were used to evaluate the accuracy of the models using different discretization levels in time and space. As a result, the paper provides a framework to set a proper discretization when simulating a DHC network with FDM-based models considering both the expected accuracy and the computation time as criteria. [ABSTRACT FROM AUTHOR]

As one of the most important components of satellites, the thruster must maintain high reliability, but the assessment is difficult when lacking test failure data. When conducting hot test of the satellite thruster, we encountered the problem of reliability assessment on type-I censored data with only one failure, which is common for small sample tests. This paper proposes a novel interval statistic based reliability analysis method, which can fulfill the life information from the failure time to the censored time ignored by conventional methods, and improve the assessment accuracy. In this paper, a life distribution model is established by proper failure mechanism analysis and prior test information exploitation. As thrust chamber burning-through has been considered as the main failure mode, life test of the chamber coating was conducted under different thermal conditions to obtain the model parameter. Based on the interval statistic theory, a detailed derivation is illustrated. Then, reliability assessment and life prediction for the satellite thruster in both transfer orbit phase and synchronous orbit phase have been achieved, and the results show that our method performs very well, which provides an important way for dealing with test data with only one failure. [ABSTRACT FROM AUTHOR]

Correct ambiguity resolution (AR) is the key for high-precision positioning. In order to reliably resolve ambiguity, an improved single-epoch segmented ambiguity algorithm is proposed in this paper. First, the double-difference wide-lane ambiguities resolved by the Melbourne-Wubbena (MW) method are divided into two categories based on signal-to-noise ratio (SNR). One is the primary ambiguity group that can be resolved easily, and the other is the secondary ambiguity group that is difficult to resolve. Second, the primary ambiguity group is divided into different levels in a (4,1,1…1) pattern based on SNR, and the different levels’ ambiguities are resolved successively, and the method being that the integer ambiguity is selected from two candidate values built by rounding up and down. Third, the resolved primary ambiguity group is used as a constraint to solve the secondary ambiguity group. Finally, the wide-lane observation equation and the basic observation equation are combined to solve the ambiguity of the L1 carrier phase, and the resolved ambiguity can be obtained by directly rounding. The candidate values of the primary ambiguity group is limited to two cycles through this step, and the (4,1,1…1) pattern can reduce ambiguity search times. Since an appropriate selection enables the correct resolution of primary wide-lane ambiguities, the selection of the threshold for SNR is the key to group wide-lane ambiguity, and we use the data from Novatel receiver as an example to discuss how to confirm the SNR threshold in this paper. Static short baseline data and kinematic short baseline that were less than 20 km were collected to validate the proposed algorithm. These results were compared with the commercial software IE8.60. These experiments show that the successful rate reaches 100%, 99.78% and 99.42%, respectively, for the new single-epoch segmented ambiguity method in the three data sets, and the accuracy is similar to that of IE8.60 for the combined double difference. [ABSTRACT FROM AUTHOR]

To construct a parametric polynomial curve for interpolating a set of data points, the interpolation accuracy and shape of the constructed curve are influenced by two principal factors: parameterization of the data points (computing a node for each data point) and interpolation method. A new method of computing nodes for a set of data points was proposed. In this paper, the functional relationship between data points and corresponding nodes in cubic polynomials was established. Using this functional relationship, a functional cubic polynomial with one degree of freedom can pass through four adjacent data points. The degree of the freedom can be represented by two adjacent node intervals can be obtained by minimizing the cubic terms of the cubic polynomial. Since each node is computed in different node spaces, a method for constructing a quadratic curve is presented, which transforms all the quadratic curves into a unified form to compute nodes. Nodes computed using the new method exhibit quadratic polynomial precision, i.e., if the set of data point is taken from a quadratic polynomial F (t) , the nodes by the new method are used to construct a interpolation curve, an interpolation method reproducing quadratic polynomial gives quadratic polynomial F (t). The primary advantage of the proposed method is that the constructed curve has a shape described by data points. Another advantage of the new method is that the nodes computed by it have affine invariance. The experimental results indicate that the curve constructed by the nodes using the new method has a better interpolation accuracy and shape compared to that constructed using other methods. • If points are from a quadratic polynomial, the nodes by new method are accurate. • The constructed curve by the new method has a shape described by the data points. • The nodes computed by the new method have affine invariance. [ABSTRACT FROM AUTHOR]

This paper presents an innovative deployment strategy for a power-aware and delay-aware TinyML model optimized for agricultural soil quality monitoring and management. The proposed method employs an enhanced fusion of Dynamic Voltage and Frequency Scaling (DVFS), Sleep/Wake Strategies based on Genetic Algorithms, Energy Harvesting, and Task Partitioning. Through this method, the model reduces energy consumption by 8.5% and delay by 10.4%, while maintaining accuracy comparable to state-of-the-art techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Q Learning techniques. The proposed model also uses a Proof-of-Energy Efficiency (PoEE) based consensus for creation of new blocks. These blocks are added to different sidechains for strengthening the security levels. While blockchains require higher energy during mining, the proposed PoEE Model uses a light weight consensus, which is backed by Q Learning based sidechain formation process. This lightweighted combination allows the model to reduce delay, maintain lower energy consumption, with better communication throughput even under multiple attack scenarios. This model is necessary due to the growing demand for sustainable and accurate agricultural practices. The quality of the soil is crucial to soil yield and productivity. Monitoring and managing soil conditions enables farmers to optimize irrigation, fertilization, and overall resource allocation, leading to an increase in soil yield and a decrease in environmental impacts. However, the deployment of energy-efficient and low-latency TinyML models is essential to enable real-time decision-making in agricultural settings with limited resources. This power-aware and delay-aware TinyML model has applications in various agricultural real-time scenarios. It can be used to continuously monitor soil quality parameters including moisture content, pH level, and nutrient levels for various farm types. Incorporating sensors and low-power microcontrollers, the model enables on-site analysis and timely farmer feedbacks. The decreased energy consumption extends the battery life of deployed devices, whereas the decreased delay ensures that soil management interventions and treatments are responsive and timely for different scenarios. [ABSTRACT FROM AUTHOR]

Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states. • We reviewed 39 empirical papers on cardiac correlates of fatigue in-lab and on-road. • Significant pooled effect sizes and consistency were found both in-lab and on-road. • Highest fatigue detection was in-lab but still fairly accurate and fast on-road. [ABSTRACT FROM AUTHOR]

For ITER or the future DEMO remote maintenance system (WPRM), several types of special tailored automatic manipulators are needed for vacuum vessel (VV) component transportation, inspection, and removal from and replacement to the VV wall. Due to the extreme work conditions such as big size, big payload and high environment temperature, both the static and dynamic error sources should be considered when design a controller. In this paper, the accuracy improvement issues regarding the static and dynamic error sources are taken into account. The paper proposed a plane constraint calibration method for static error calibration, whilst the dynamic errors are calibrated using a linear inverse dynamic error model. Differential Evolution global optimization method was used to generate optimal excitation trajectories, and to identify the static and dynamic parameter errors. A 6-DOF (degrees of freedom) commercial industrial serial robot was employed to carry out the corresponding simulation and experimental studies. This paper mainly focuses on the general case studies, the results found in this research would be extrapolated to support the ITER or the future DEMO remote handling systems. [ABSTRACT FROM AUTHOR]

The half-space assumption has been employed in many solution methods for non-conforming contact problems in elasticity such as the Hertz theory and the Kalker's variational theory. It is generally believed that to guarantee acceptable accuracy in these half-space-based methods, the characteristic size (twice length of one semi-axis) of the contact patch should be much smaller than the significant dimensions (i.e. the height, width, length and the principal radii of curvature) of each body in contact. In engineering practice, the 3x rule is often employed, which requires that the significant dimensions be at least three times as large as the characteristic size. However, this requirement has not been justified. In this paper, the applicability of half-space-based methods is investigated by comparing the solutions obtained using the Hertz theory and the Kalker's theory with those of the Finite Element (FE) method which is not limited to the half-space assumption. Different combinations of significant dimensions in terms of height, width and length are studied. Various contact patch eccentricities and contact body shapes are considered. It is found that the half-space-based methods yield high-accuracy calculation for non-conforming contact problems. Even when the significant dimensions are as small as 1.1x the characteristic size, the differences between the solutions of the half-space-based methods and the FE method are within 9%. The findings of this paper indicate that the typically assumed 3x restriction can be greatly relaxed. Since a clear estimation of the deviation of the results of half-space-based methods from those of the FE method is provided, the applicability of half-space-based methods in mechanical engineering can be much better understood. [ABSTRACT FROM AUTHOR]

Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The increasing availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantify savings, offers the potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer these ‘M&V 2.0′ capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline the M&V process. In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. For the data sets studied we evaluate the fraction of buildings that are well suited to automated baseline characterization, the uncertainty in gross savings that is due to M&V 2.0 tools’ model error, and indications of labor time savings, and how the automated savings results compare to prior, traditionally determined savings results. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14. In addition the findings suggest that M&V 2.0 methods may also offer time-savings relative to traditional approaches. Finally we discuss the implications of these findings relative to the potential evolution of M&V, and pilots currently being launched to test how M&V automation can be integrated into ratepayer-funded programs and professional implementation and evaluation practice. [ABSTRACT FROM AUTHOR]

This paper sets out the method of improving the accuracy of mechatronic systems used for measurements of complex geometry. At the core of this method is a programmed compensation of systematic errors (inaccuracies) of mechatronic systems. The difference of the proposed method from the existing solutions is that this method employs three relatively simple reference gauges: cylindrical, flat, and spherical. The methodology suggests measuring the reference gauges, calculating the compensation parameters and their subsequent accounting when deriving the results of measurements. The parameters of compensation of systemic errors are defined on the basis of the results of measurements of the said reference gauges of surfaces in a mechatronic system. This paper describes a model which enables to optimize the choice of parameters for measurement of reference gauges. The method may be applied for enhancement of the accuracy of measurements of mechatronic systems used in process-specific coordinate measurement machines. [ABSTRACT FROM AUTHOR]

There are many cases in engineering practice when it is necessary to know values close to exact for natural modes of beam and framing vibrations. This paper devoted to enhance the accuracy of calculations of natural frequencies of a beam, which is realized on the basis of an extrapolation of numerical results obtained by spline functions of 5th degree. This paper shows that proposed spline-method has the second order of convergence This method has the minimum relative error less than 1·10 –8 for the first five natural frequencies at number of elements M = 512, and for the following five frequencies the method has an error less than 1·10 –6 . Therefore, the realized method of extrapolation shown itself as very effective instrument to improve the accuracy of the spline-method for determining the natural frequencies for the beam under various fixing conditions, and in future it may allow to achieve the calculation results for many practical problems with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, proved that the Perpendicularity Error in as a translation error element modeling and as the angle error element modeling on the error precision of the model and modeling of complex different influence degree. For multi axis machine tools, from the complexity of angle error modeling process, as modeling complexity is less than the translational error as the angle error the modeling, error of perpendicularity accumulate, distinguish the complexity is more obvious, mainly the additional angle error matrix in the modeling the matrix. Compensation from the point of view, this study shows that, for only need displacement compensation of the machine tool, the compensation effect of two kinds of the modeling results are the same, but for multi axis machine tool needs angle compensation, compensation matrix as the angle error the modeling accuracy obtained is higher than that of as translational displacement. Comprehensive analysis, this paper puts forward in multi axis machine tool error synthesis the modeling priority when the verticality error as a translation error the modeling. [ABSTRACT FROM AUTHOR]

The determination of the nonlinear response of structural members subjected to a close-in explosive threat is challenging. There are various ways to quantify the blast resistance and mode of failure of a structural member, e.g. experimental testing which is expensive and time-consuming, or high-fidelity finite element analysis which is also time-consuming. The familiar analytical approach using an equivalent single-degree-of-freedom (SDOF) system is much less costly, however, as will be shown in this paper, may lack accuracy. Based on an extensive study of the nonlinear response of a ground-floor column of a high-rise building subjected to close-in explosive threat, using nonlinear explicit finite element analysis as well as analytical single-degree-of-freedom (SDOF) system approach, the source of inaccuracy of the analytical approach is identified, and an empirical approach using Multivariable Polynomial Regression (MPR) is established. The paper presents a head-to-head comparison of the results produced by finite element analysis, the SDOF system analysis, the empirical approach, and experimental test data to emphasize the accuracy and time-efficiency of the proposed approach. The approach has the potential to establish itself as a time-efficient preliminary vulnerability assessment methodology of structural members subjected to close-in blast loading. [ABSTRACT FROM AUTHOR]

Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work is done in the Anaconda environment based on python programming language and Scikit-learn library. [ABSTRACT FROM AUTHOR]

• A new method based on optimal deep belief network (DBN) for breast tumor diagnosis. • The DBN is optimized by a developed artificial rabbits optimizer (ARO). • Experimented with the proposed system and compared it to existing methods. • Greater efficiency than existing methods. • Offering a promising solution for breast cancer detection. Artificial intelligence can be extremely useful for identifying the location and size of a tumor by analyzing medical breast cancer images. This can help doctors to make more accurate diagnoses and plan more effective treatment strategies. It can also be used to analyze the texture and shape of tumors, which can provide important information about their malignancy and potential for metastasis. This information can be used to guide treatment decisions and monitor the progress of therapy. This paper presents a study designed to leverage an optimized deep belief network (DBN) based on a developed version of artificial Rabbits optimization (ARO) to diagnose breast tumors. The proposed model was designed and evaluated using data from the MIAS breast cancer database in reference. The performance of the proposed model was compared with other existing models for accuracy, speed, and sensitivity metrics. Results show that the proposed model can achieve better accuracy and faster performance than existing deep-learning techniques when applied to the classification of breast tumors. Furthermore, the proposed model exhibited improved sensitivity in detecting malignant tumors compared to existing models. [ABSTRACT FROM AUTHOR]

Present-day Electric Power Systems are driven under much stressed circumstances when compared to the past and creating a developing need for accuracy, flexibility, and reliability in the areas of Transmission, Distribution and Electric Power Generation. In all stages of power system, voltage stability problems are increasing more and more. So, the only alternate solution for these problems is proper placement and sizing of UPFC. The paper presents the Placement and Tuning of UPFC for a multi-objective function consisting of minimization of transmission losses, load voltage deviation. Here L-index is used to place the UPFC in a specified location i.e., weakest bus, critical line and the weak area of the system. In this paper, a newly developed meta-heuristic algorithm named Krill Herd (KH) is introduced to solve multi-objective problem of optimization. Simulation is carried on IEEE 14-bus system and the results have been compared with the Genetic algorithm with and without UPFC. [ABSTRACT FROM AUTHOR]

This paper represents the second part of the work described in the paper with the title “Decision-making tool for moving from 3-axes to 5-axes CNC machine-tool”. The problem of using either 3 axes CNC machine-tools or 5 axes CNC machine tools was presented in the first part, together with a fuzzy logic based decision support tool. This time, an AHP approach is used in order to evaluate the decision of moving to a manufacturing process based upon 5 axes machine tools. Three variants were taken into consideration and analysed. The consistency of the proposed approach was evaluated and a sensitivity analysis was also introduced. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) have been very popular as a large margin classifier due its robust mathematical theory. It has many practical applications in a number of fields such as in bioinformatics, in medical science for diagnosis of diseases, in various engineering applications for prediction of model, in finance for forecasting etc. It is widely used in medical science because of its powerful learning ability in classification. It can classify highly nonlinear data using kernel function. This paper proposes and analyses diagnostic model to classify the most common skin illnesses and also provide a useful insight into the SVM algorithm. In rural areas where people are generally treated by paramedical staff, skin patients are not subject to proper diagnosis resulting in mistreatment. We think SVM is a good tool for proper diagnosis. This paper uses various kernels for classification and achieving the best accuracy of 95.39%. [ABSTRACT FROM AUTHOR]

This paper deals with the production of a geometrically specified borehole diameter using EDM drilling. To produce a specified borehole different electrode diameters can be used. For each electrode diameter the discharge energy has to be adjusted. This influences the amount of removed material of a single discharge and therefore the productivity of the EDM drilling process and the geometrical accuracy of the borehole are affected. For the industrial use of EDM drilling there are no recommendations concerning the electrode diameter and machining settings for the discharge energy to be used to achieve a specific diameter. To study the influence of the electrode diameter and discharge energy on the productivity and geometrical accuracy a specified borehole diameter of 2.0 mm was produced using four different electrode diameters. The electrode diameter ranged from 1.6 mm to 1.9 mm. By varying the discharge current and discharge time different machining settings were developed to achieve the desired borehole diameter. The influence of the parameter settings on the feed rate, the electrode wear ratio and the geometrical accuracy of the borehole were studied. Furthermore, the influence of the servo of the feed axis on the formation of a bulging at the borehole entry was investigated. The experiments show an opposing trend in maximizing the productivity and the accuracy of the produced borehole respectively. The results presented in this paper help to access the potential of EDM drilling to produce a specified borehole diameter and to provide basic technological information for industrial users. [ABSTRACT FROM AUTHOR]

Fast and precise propagation of satellite orbits is required for mission design, orbit determination and payload data analysis. We present a method to improve the computational performance of numerical propagators and simultaneously maintain the accuracy level required by any particular application. This is achieved by determining the positional accuracy needed and the corresponding acceptable error in acceleration on the basis of the mission requirements, removing those perturbation forces whose effect is negligible compared to the accuracy requirement, implementing an efficient and precise algorithm for the harmonic synthesis of the geopotential gradient (i.e., the gravitational acceleration) and adjusting the tolerance of the numerical propagator to achieve the prescribed accuracy level with minimum cost. In particular, to achieve the optimum balance between accuracy and computational performance, the number of geopotential spherical harmonics to retain is adjusted during the integration on the basis of the accuracy requirement. The contribution of high-order harmonics decays rapidly with altitude, so the minimum expansion degree meeting the target accuracy decreases with height. The optimum degree for each altitude is determined by making the truncation error of the harmonic synthesis equal to the admissible acceleration error. This paper presents a detailed description of the technique and test cases highlighting its accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The coordinated five-axis machine tools have been used to machine aerospace large thin-walled parts. Synchronization motion accuracy is important during mirror motion process. This paper develops a method for detecting the synchronous motion accuracy of this kind of system. By measuring the trajectory errors of two tool center positions corresponding to different tool lengths on both sides, the synchronization motion tool center point error and tool axis direction error can be calculated. Simulation and experiments are conducted to verify its effectiveness. [ABSTRACT FROM AUTHOR]

Prediction of cutting forces in finishing conditions is becoming essential for the automotive industry, in order to achieve the desired surface integrity and keep controlled phenomena as chatter, especially in workpieces with small thickness walls manufactured by die casting. For predictions, orthogonal to oblique models are widely used, which require a material database as input data. Databases found in the literature have been proven to predict forces accurately in roughing conditions. However, several geometric parameters of the inserts are not taken into account, which could lead to inaccurate predictions in finishing conditions. In this paper, the effects of the cutting edge radius and clearance angle are analysed over the cutting and feed forces during orthogonal turning. A material database for A-356 aluminium alloy is developed following a methodology in accordance with previous research. Finishing face milling tests are carried out for validation, obtaining good agreement with modelled forces. [ABSTRACT FROM AUTHOR]

Transmission system operators (TSOs) forecast load and renewable energy generation to maintain smooth functioning of the grid by contracting sufficient generation and reserve capacity. These forecasts are also utilized by third parties, such as energy generators and demand aggregators, in their own forecasting and decision-making pipelines e.g. to determine suitable trading strategies. Inaccurate forecasts by the TSOs can therefore lead to increased balancing needs as well as elevated societal and market costs. The situation is further exacerbated by the challenges arising due to rapidly increasing renewable generation and the effects of the post-Covid era. In this paper, we analyse five years of TSO forecasts for load, wind and solar generation for 16 European countries. More concretely, using a comprehensive set of metrics, we explore relevant questions such as whether there are TSO specific differences in forecast accuracy, and how forecast errors have changed over time and if they can be reduced further. Our results show that while errors tend to increase linearly with demand or renewable generation, most TSOs still have considerable room for improvement in terms of accuracy. The paper concludes with a set of recommendations for TSOs to improve their forecasts, as well as the ENTSO-E transparency platform where we obtained the data used in this study. • Load, solar and wind forecasts from 16 European TSOs are analysed. • Forecast errors scale linearly with increasing demand or generation. • Overall renewable forecast errors have nearly doubled in the last five years. • Forecasts outperform naïve baselines but errors remain highly autocorrelated. • Considerable room for improvement remains in most forecasts. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid explicit/implicit time marching procedure is proposed for wave propagation analyses. The new formulation considers local self-adjustable time integration parameters that are defined for each element of the adopted spatial discretization procedure, following the properties of the discretized model. Thus, the explicit/implicit subdomains of the analysis are established at an element level, taking into account automated computations. Two types of explicit approaches are considered here, allowing extended stability limits to be provided and more efficient analyses performed. Controllable numerical dissipation is also enabled following the locally defined adaptive time integrators of the method. In this sense, optimally designed time integration parameters are developed, providing null spectral radii for the highest sampling frequency of the elements, and close to one values for the important low-frequency response domain. Thus, the influence of spurious high-frequency modes is very effectively extinguished and reduced dissipative and dispersive errors are introduced into the analysis. At the end of the paper numerical results are provided, illustrating the excellent performance of the proposed new methodology. • A new hybrid explicit–explicit–implicit time-marching procedure is proposed. • The single-step technique is self-starting, entirely automated and self-adjustable. • The arrangement of its spectral radius is adaptive and locally optimally defined. • It stands as a stable single-solver procedure based on reduced systems of equations. • It provides enhanced efficiency, accuracy and enables advanced numerical dissipation. [ABSTRACT FROM AUTHOR]

The vibration should be suppressed if it happens during the motion of a flexible robot or under the influence of external disturbance caused by its structural features and material properties, because the vibration may affect the positioning accuracy and image quality. In Tokamak environment, we need to get the real-time vibration information on vibration suppression of robotic arm, however, some sensors are not allowed in the extreme Tokamak environment. This paper proposed a vision-based method for online vibration estimation of a flexible manipulator, which is achieved by utilizing the environment image information from the end-effector camera to estimate its vibration. Short-time Fourier Transformation with adaptive window length method is used to estimate vibration parameters of non-stationary vibration signals. Experiments with one-link flexible manipulator equipped with camera are carried out to validate the feasibility of this method in this paper. [ABSTRACT FROM AUTHOR]

An Intelligent Personal Assistant (IPA) is an agent that has the purpose of helping the user with his daily tasks. This paper is focused on IPAs for Internet of Things (IoT) environments. In this sense, a good IPA has the capability of surveying his user behaviour and suggest tasks or make decisions with the intention of simplifying the user interaction with his surroundings. With this in mind, this paper focuses on studying the accuracy of various classifiers, with the objective of finding the one that suits better the needs of an IPA for IoT. The aim is to test each algorithm with a dataset of events, that relate to past behaviours of the user, and find if there is an opportunity to notify the user that he/she may want to take an action or create an automation based on the learned behaviour. [ABSTRACT FROM AUTHOR]

Many radial basis function (RBF) methods contain free shape parameter or parameters that play an important role for the accuracy of the method. In most papers the authors end up choosing free shape parameter by trial and error or some other ad-hoc means. However, using variable shape parameters provides a clear potential for improved accuracy and stability of the RBF method. Already some progress has been reported to select usable variable shape parameters. In this paper, we propose applying the genetic algorithm to determine good variable shape parameters of radial basis functions for the solution of ordinary differential equations. Numerical results show that the proposed algorithm based on the genetic optimization is effective and provides reasonable shape parameters along with acceptable accuracy in linear and nonlinear case compared with other strategies to determine variable shape parameters. [ABSTRACT FROM AUTHOR]

Flood hazard mapping is a topic of increasing interest involving several aspects in which a series of progress steps have occurred in recent years. Among these, a valuable advance has been performed in solving 2-D shallow water equations in complex topographies and in the use of high resolution topographic data. However, reliable predictions of flood-prone areas are not simply related to these two important aspects. A key element is the accurate set up of the river model. This is primarily related to the representation of the topography but also requires particular attention to the insertion of man-made structures and hydrological data within the computational domain. There is the need to use procedures able to 1) obtain a reliable computational domain, characterized by a total number of elements feasible for a common computing machine, starting from the huge amount of data provided by a LIDAR survey, 2) deal with river reach that receives significant lateral inflows, 3) insert bridges, buildings, weirs and all the structures that can interact with the flow dynamics. All these issues have large effects on the modelled water levels and flow velocities but there are very few papers in the literature on these topics in the framework of the 2-D modelling. So, in this work, attention is focused on the techniques to deal with the above-mentioned issues, showing their importance in flood mapping using two actual case studies in Southern Italy. In particular, the simulations showed in this paper highlight the presence of backwater effects, sudden and numerous changes in the flow regime, induced by the detailed river model, that underline the importance of using 2-D fully dynamic unsteady flow equations for flood mapping. [ABSTRACT FROM AUTHOR]

This paper presents the development of a parametric model for the rotational compliance of a cracked right circular flexure hinge. A right circular flexure hinge has been widely used in compliant mechanisms. Particularly in compliant mechanisms, cracks more likely occur in the flexure hinge because it undergoes a periodic deformation. A parametric model has many good uses, as opposed to a numerical model, including fault diagnosis, facility for the real-time control and optimization of design of a compliant mechanism. The rotational compliance of cracked right circular flexure hinge is derived in this paper. This is achieved by the superposition of the rotational compliance of flexure hinge and the compliance change induced by the crack. Without of loss of generality of the result, this paper only considers one type of flexure hinge, namely right circular flexure hinge. The experiment is performed, and the results show that the parametric model is accurate enough for the intended application. [ABSTRACT FROM AUTHOR]

This paper presents a framework for characterizing errors associated with different categories of human observation combined with a method for integrating these into a hard+soft data fusion system. Error characteristics of human observers (often referred to as soft data sensors) have typically been artificially generated and lack contextual considerations that in a real-world application can drastically change the accuracy and precision of these characteristics. The proposed framework and method relies on error values that change based upon known and unknown states of qualifying variables empirically shown to affect observation accuracy under different contexts. This approach allows fusion systems to perform uncertainty alignment on data coming from human observers. The preprocessed data yields a more complete and reliable situation assessment when it is processed by data association and stochastic graph matching algorithms. This paper also provides an approach and results of initial validation testing of the proposed methodology. The testing approach leverages error characterization models for several exemplar categories of observation in combination with simulated synthetic data. Results have shown significant performance improvements with respect to both data association and situation assessment fusion processes with an average F-measure improvement of 0.16 and 0.20 for data association and situation assessment respectively. These F-measure improvements are representative of fewer incorrect and missed associations and fewer graph matching results, which then must be considered by human analysts. These benefits are expected to translate into a reduction of the overall cognitive workload facing human analysts in situations where they are tasked with developing and maintaining situational awareness. [ABSTRACT FROM AUTHOR]

High-precision of Low Earth orbit (LEO) satellite orbit is essential for some applications having real-time requirements, such as LEO navigation enhancement, real time scientific satellite research, and so on. Currently, more mature means for real-time orbit determination of LEO satellites rely on spaceborne GPS data and RINEX/DORIS 3.0 data. However, most researchers conduct experiments using spaceborne GPS data or RINEX/DORIS 3.0 data separately, and the experiment results from combining the two types of data simultaneously have not been obtained. Therefore, we evaluate simulated real-time orbit determination capability of using the RINEX/DORIS 3.0 phase observations and spaceborne GPS pseudo-range observations from the HY2A satellite in this paper. Since the 1-km precision of the RINEX/DORIS 3.0 pseudo-range observations are unfavorable for the initial orbit, the GPS pseudo-range are used for this computation. But, the RINEX/DORIS 3.0 phase observations are used to solve the real-time orbit of HY2A satellite. Our analysis results are: (1) the three-dimensional (3D) position accuracy of HY2A satellite calculated by the spaceborne GPS pseudo-range is 6.803 m, which meets the initial orbit positional accuracy requirement. The initial orbit velocity requirement can be met when the 5–7th-order Lagrangian interpolation method is used to solve the satellite velocity. The 3D-RMS values of the HY2A satellite velocity is smaller than 0.500 m/s, indicating the initial velocity accuracy requirement is satisfied. (2) After Kalman filter convergence, the agreement of the comparison with the CNES precise orbit shows better than 0.100 m in radial position, an RMS of 0.373 m in 3D total position, and an RMS of 0.374 mm/s in 3D total velocity. The results suggest that the radial position accuracy is better than 0.100 m, and could meet some HY2A altimetry applications. In the course of this study, a Fortran-based prototype real-time orbit determination system RTODLEO (Real-Time Orbit Determination Software for Low Earth Orbit Satellite) was developed and used for the analysis. The results from thirteen-day of real-time orbit determination also validate the RTODLEO software, and show it is reliable. [ABSTRACT FROM AUTHOR]