A Sequential Processing Method for Converted Measurement Kalman Filters Based on Orthogonal Transform

The needs for satellite formation flying are gradually increasing to perform the advanced space missions in remote sensing and observation of the space or Earth. Formation flying in low Earth orbit can perform the scientific missions that cannot be realized with a single spacecraft. One of the various techniques of satellite formation flying is the determination of the precise baselines between the satellites within the formation, which has to be in company with the precision validation. In this paper, the baseline of Gravity Recovery and Climate Experiment (GRACE) A and B was determined with the real global positioning system (GPS) measurements of GRACE satellites. And baseline precision was validated with the batch and sequential processing methods using K/Ka-band ranging system (KBR) biased range measurements. Because the proposed sequential method validate the baseline precision, removing the KBR bias with the epoch difference instead of its estimation, the validating data (KBR biased range) are independent of the data validated (GPS-baseline) and this method can be applied to the real-time precision validation. The result of sequential precision validation was 1.5~3.0 mm which is similar to the batch precision validation.

State estimation with a destination constraint using pseudo-measurements

Summary Simultaneous source shooting or blended acquisition, which allows a temporal overlap between shot records, has been proposed as a method for substantially reducing the acquisition cost and improving data quality. Deblending followed by traditional processing steps is still the dominant way of dealing with blended data. As we know, the strong reflectivity of the sea surface results in ghost wavefields at the source and the receiver side. The removal of these ghost wavefields is a well-known data preprocessing step to improve the image resolution. In the standard processing workflow, deblending and source deghosting approach are applied in sequence. Thus, each step is likely to generate its own artifacts. Therefore, We proposed an integrated method---joint deblending and source deghosting method with focal transformation using sparse inversion. Synthetic data example demonstrates the joint approach is better than the sequential processing method.

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

BackgroundIn recent years, deep learning methods have been applied to many natural language processing tasks to achieve state-of-the-art performance. However, in the biomedical domain, they have not out-performed supervised word sense disambiguation (WSD) methods based on support vector machines or random forests, possibly due to inherent similarities of medical word senses.ResultsIn this paper, we propose two deep-learning-based models for supervised WSD: a model based on bi-directional long short-term memory (BiLSTM) network, and an attention model based on self-attention architecture. Our result shows that the BiLSTM neural network model with a suitable upper layer structure performs even better than the existing state-of-the-art models on the MSH WSD dataset, while our attention model was 3 or 4 times faster than our BiLSTM model with good accuracy. In addition, we trained “universal” models in order to disambiguate all ambiguous words together. That is, we concatenate the embedding of the target ambiguous word to the max-pooled vector in the universal models, acting as a “hint”. The result shows that our universal BiLSTM neural network model yielded about 90 percent accuracy.ConclusionDeep contextual models based on sequential information processing methods are able to capture the relative contextual information from pre-trained input word embeddings, in order to provide state-of-the-art results for supervised biomedical WSD tasks.

This paper proposes a sequential loading method, which finds the hourly changes of frequency and optimal load distribution between power stations. The method solves to minimize the aggregate cost in the electric power system (EPS). The method also calculates the surplus or deficit of an active power in EPS, hourly power flows of each transmission lines, active power losses in the networks. Computed and analyzed the optimal regimes of Mongolian EPS by the proposed method. The result confirmed that the method can potentially benefit to supply of frequency regulation. Developed software allows doing day-ahead market planning and intraday market preplanning in EPS, which improves electricity market model.

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.

A new method for measuring the position and posture (pose) and recognizing the shape using ultrasonic wave sound pressure signals and neural networks is proposed. In most of the past methods of this kind, the characteristic quantities have been based on either time of flight methods or acoustic holographic methods. In these methods, measuring and recognizing the width and depth directions simultaneously with a high resolution has been difficult in principle. In this paper, the problems of the past methods are resolved by changing the viewpoints by using criteria such as the peak values of the penetrating and reflecting waveforms, their positions, their deflection points, and their intervals when ultrasonic waves are directed onto a measured object, as characteristic quantities given to neural networks. In addition, a prototype measuring and recognizing system implementing the proposed scheme is constructed by introducing a circuit for detecting the peak positions of the sound pressure signal distribution waveforms with high resolution. The experimental results are satisfactory in position measurements and shape recognition, and confirm that the method is applicable to rectangular prisms and the like. It is shown that the position and pose measurements and shape recognition can be realized with high resolution in both the width and depth directions by this scheme, and that the proposed method is very effective when applied to position and pose measurements and shape recognition and the like ultrasonically. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(11): 27–38, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10103

In order to accurately describe the influence of the random factors in distribution systems on the probabilistic load flow (PLF), a PLF method of distribution system containing wind power and EVCL based on a stochastic response surface method (SRSM) is put forward. The proposed method can sufficiently consider the randomness of wind speed as well as uncertainty of basic load and EVCL. Furthermore, the orthogonal transformation is introduced to deal with the correlated wind speeds among different wind farms. Comparing with the traditional Monte Carlo simulation method with simple random sampling (MSRS), the proposed method can solve problems that they are facing, like more number of calculation times, long simulation time. Simulation results on IEEE14 and IEEE30 bus system demonstrate the validity, accuracy, rapidity and practicability of proposed method. In contrast to the MSRS, the proposed method is of higher computational efficiency and better simulation accuracy.

A modified sequential gradient-based method for the inverse estimation of transient heat transfer coefficients in non-linear one-dimensional heat conduction problems

In recent times, feature extraction was the focus of many researches due to its usefulness in the machine learning and pattern recognition fields. Feature extraction mainly aims to extract informative representations from the original set of features. This can be carried out using various ways. The proposed method is targeting a hybrid linear feature extraction scheme for supervised multi-class classification problems. Inspired by recent robust sparse LDA and Inter-class sparsity frameworks, we will propose a unifying criterion that is able to retain these two powerful linear discriminant method’s advantages. Thus, the obtained transformation encapsulates two different types of discrimination, the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. We will introduce an iterative alternating minimization scheme in order to estimate the linear transform and the orthogonal matrix. The linear transform is efficiently updated via the steepest descent gradient technique. We will also introduce two initialization schemes for the linear transform. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. According to the experiments which have been carried out on several datasets including faces, objects and digits, the proposed method was able to outperform the competing methods in most cases.

The twisting force arm (TFA) is an important part of the pillar landing gear, anti-fatigue optimization on its structure can improve the reliability of the landing gear. However, the optimization accuracy of the conventional optimization method is limited by the basic topological structure from the views of topological theory. Besides that, the optimization efficiency of the conventional method is also relatively low because of the high computational cost of the fatigue life estimation. In this paper, an anti-fatigue optimization method of the TFA was developed to improve the optimization accuracy and efficiency by using the approximate sequential optimization method after an optimal basic topological structure was obtained. To verify its effectiveness, the proposed method was introduced to the anti-fatigue optimization of a pillar landing gear TFA. The results show that the optimization accuracy of the proposed method is higher than the conventional method, and the computational cost can be reduced 82.35%. This indicates that the proposed method can improve the optimization accuracy and efficiency of the anti-fatigue optimization.

A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces

Metamodel has been widely used to solve computationally expensive engineering problems, and there have been many studies on how to efficiently and accurately generate metamodels with limited number of samples. However, applications of these methods could be limited in high-dimensional problems since it is still challenging due to curse of dimensionality to generate accurate metamodels in high-dimensional design space. In this paper, recursive decomposition coupled with a sequential sampling method is proposed to identify latent decomposability and efficiently generate high-dimensional metamodels. Whenever a new sample is inserted, variable decomposition is repeatedly performed using interaction estimation from a full-dimension Kriging metamodel. The sampling strategy of the proposed method consists of two units: decomposition unit and accuracy improvement unit. Using the proposed method, latent decomposability of a function can be identified using reasonable number of samples, and a high-dimensional metamodel can be generated very efficiently and accurately using the identified decomposability. Numerical examples using both decomposable and indecomposable problems show that the proposed method shows reasonable decomposition results, and thus improves metamodel accuracy using similar number of samples compared with conventional methods.

The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency.