Similar Single-Difference Model and Its Algorithm for Solving GPS Monitoring Deformation Directly at Single Epoch

A new similar single-difference mathematical model (SS-DM) and its corresponding algorithmare advanced to solve the deformationof monitoring point directly in singleepoch. The method for building theSSDM is introduced in detail, and themain error sources affecting the accu-racy of deformation measurement areanalyzed briefly, and the basic algo-rithm and steps of solving the deform-ation are discussed.In order to validate the correctnessand the accuracy of the similar single-difference model, the test with fivedual frequency receivers is carried outon a slideway which moved in plane inFeb. 2001. In the test,five sessions areobserved. The numerical results oftest data show that the advanced mod-el is correct.

Research on Kalman Filtering Algorithmfor Deformation Information Series ofSimilar Single-Difference Model

Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.

Structural similarity of lithospheric velocity models of Chinese mainland

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features,with dissimilarities due to different data and methods used in model generation.The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration.Here we employ the complex wavelet structural similarity index measure(CW-SSIM)active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade.We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling,which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution.Our results show that the CW-SSIM values vary in different model pairs,horizontal locations,and depths.While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation,the difference of tomography methods may significantly impact the similar structural features of models,such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China.We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.

Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

An Approach to Unsupervised Character Classification Based on Similarity Measure in Fuzzy Model

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.

Similarity model tests of movement and deformation of coal-rock mass below stopes

For a study of the movement and deformation of coal-rock mass and low protected seams below a stope,as well as for fracture developments and rules of evolution of permeability,we designed a plane strain model test stand to carry out model tests of similar materials in order to improve the effect of gas drainage from low protected seams and to measure the movement and deformation of coal-rock mass using a method of non-contact close-range photogrammetry.Our results show that 1) using paraffin melting to take the place of coal seam mining can satisfy the mining conditions of a protective seam;2) coal-rock mass under goafs has an upward movement after the protective seam has been mined,causing floor heaving;3) low protected seams become swollen and deformed,providing a good pressure-relief effect and causing the coal-rock mass under both sides of coal pillars to become deformed by compression and 4) the evolution of permeability of low protected seams follows the way of initial values→a slight decrease→a great increase→stability→final decrease.Simultaneously,the coefficient of air permeability increased at a decreasing rate with an increase in interlayer spacing.

Improved similarity criterion for seepage erosion using mesoscopic coupled PFC–CFD model

Conventional model tests and centrifuge tests are frequently used to investigate seepage erosion. However, the centrifugal test method may not be efficient according to the results of hydraulic conductivity tests and piping erosion tests. The reason why seepage deformation in model tests may deviate from similarity was first discussed in this work. Then, the similarity criterion for seepage deformation in porous media was improved based on the extended Darcy-Brinkman-Forchheimer equation. Finally, the coupled particle flow code–computational fluid dynamics(PFC-CFD) model at the mesoscopic level was proposed to verify the derived similarity criterion. The proposed model maximizes its potential to simulate seepage erosion via the discrete element method and satisfy the similarity criterion by adjusting particle size. The numerical simulations achieved identical results with the prototype, thus indicating that the PFC-CFD model that satisfies the improved similarity criterion can accurately reproduce the processes of seepage erosion at the mesoscopic level.

Study and application of monitoring plane displacement of a similarity model based on time-series images

In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.

A Non-geometrically Similar Model for Predicting the Wake Field of Full-scale Ships

The scale effect leads to large discrepancies between the wake fields of model-scale and actual ships, and causes differences in cavitation performance and exciting forces tests in predicting the performance of actual ships. Therefore, when test data from ship models are directly applied to predict the performance of actual ships, test results must be subjected to empirical corrections. This study proposes a method for the reverse design of the hull model. Compared to a geometrically similar hull model, the wake field generated by the modified model is closer to that of an actual ship. A non-geometrically similar model of a Korean Research Institute of Ship and Ocean Engineering （KRISO）’s container ship （KCS） was designed. Numerical simulations were performed using this model, and its results were compared with full-scale calculation results. The deformation method of getting the wake field of full-scale ships by the non-geometrically similar model is applied to the KCS successfully.

Similar financial models： Are they free lunches？

In recent years, the academic and practical circles have paid so much attention to similar financial models because they generated a miracle in GOME （Gome Electrical Appliances Holding Limited） and SUNING （Suning Appliance Co., Ltd.） respectively. But what＇s the influence of such a model on corporate profitability and risk？ Are similar financial models free lunches？ To seek for the answers to the above questions, in this paper, we take Gree （GREE Electric Appliances, Inc. of Zhuhai）, Midea （Guangdong Midea Electric Appliances Co. Ltd. Stores） and GOME for examples to carry out a comprehensive and in depth financial analysis. The conclusions of this paper are： The company with similar financial characteristics has higher profitability and risk level; Only the companies which meet special requirements need or can implement such a model and the identification and control of hidden risk can guarantee its success.

Numerical verification of similar Cam-clay model based on generalized potential theory

From the mathematical principles, the generalized potential theory can be employed to create constitutive model of geomaterial directly. The similar Cam-clay model, which is created based on the generalized potential theory, has less assumptions,clearer mathematical basis, and better computational accuracy. Theoretically, it is more scientific than the traditional Cam-clay models. The particle flow code PFC3 D was used to make numerical tests to verify the rationality and practicality of the similar Cam-clay model. The verification process was as follows: 1) creating the soil sample for numerical test in PFC3 D, and then simulating the conventional triaxial compression test, isotropic compression test, and isotropic unloading test by PFC3D; 2)determining the parameters of the similar Cam-clay model from the results of above tests; 3) predicting the sample's behavior in triaxial tests under different stress paths by the similar Cam-clay model, and comparing the predicting results with predictions by the Cam-clay model and the modified Cam-clay model. The analysis results show that the similar Cam-clay model has relatively high prediction accuracy, as well as good practical value.

Uncovering offline event similarity of online friends by constructing null models

The emergence of Event-based Social Network(EBSN) data that contain both social and event information has cleared the way to study the social interactive relationship between the virtual interactions and physical interactions. In existing studies, it is not really clear which factors affect event similarity between online friends and the influence degree of each factor. In this study, a multi-layer network based on the Plancast service data is constructed. The the user’s events belongingness is shuffled by constructing two null models to detect offline event similarity between online friends. The results indicate that there is a strong correlation between online social proximity and offline event similarity. The micro-scale structures at multi-levels of the Plancast online social network are also maintained by constructing 0 k–3 k null models to study how the micro-scale characteristics of online networks affect the similarity of offline events. It is found that the assortativity pattern is a significant micro-scale characteristic to maintain offline event similarity. Finally, we study how structural diversity of online friends affects the offline event similarity. We find that the subgraph structure of common friends has no positive impact on event similarity while the number of common friends plays a key role, which is different from other studies. In addition, we discuss the randomness of different null models, which can measure the degree of information availability in privacy protection. Our study not only uncovers the factors that affect offline event similarity between friends but also presents a framework for understanding the pattern of human mobility.

Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time periods.The Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified.

Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Modeling of 3D-structure for regular fragments of low similarity unknown structure proteins

Because it is hard to search similar structure for low similarity unknown structure proteins directly from the Protein Data Bank （PDB） database, 3D-structure is modeled in this paper for secondary structure regular fragments （α-Helices, β-Strands） of such proteins by the protein secondary structure prediction software, the Basic Local Alignment Search Tool （BLAST） and the side chain construction software SCWRL3. First, the protein secondary structure prediction software is adopted to extract secondary structure fragments from the unknown structure proteins. Then, regular fragments are regulated by BLAST based on comparative modeling, providing main chain configurations. Finally, SCWRL3 is applied to assemble side chains for regular fragments, so that 3D-structure of regular fragments of low similarity unknown structure protein is obtained. Regular fragments of several neurotoxins are used for test. Simulation results show that the prediction errors are less than 0.06nm for regular fragments less than 10 amino acids, implying the simpleness and effectiveness of the proposed method.

A Study on Similar Financial Models in Manufacturing Sector

In this paper, we subdivide the sample of manufacturing companies listed in two stock exchanges in China from the year 2000 to 2009 into two categories： the firms which adopted the similar financial models and those which did not. Then we use t-test and nonparametric test to explore the differences between the two sub-samples in cash management, assets and liabilities structures, assets turnover ratios, profitability, liquidity and growth rates. The results show that the former has shorter Cash Conversion Cycle （CCC）, quicker assets turnover, higher Debt Ratio （DR）, higher growth rate and thus has better profitability. In addition, the regression analysis results further demonstrate that Return on Invested Capital （ROIC） of similar-financial-model firms are positively related to Net Profit Margin （NPM） （p 〈 0.01）, and negatively to CCC （p 〈 0.01）, Interest Expense Ratio （IER） （p 〈 0.01）, the Ratio of Interest-Bearing Debt to Total Liabilities （IBDR） （p 〈 0.01） and the ratio of Net Receivables to Sales Revenue （NRSR） （p 〈 0.01）. Moreover, negative relationship between ROIC and dummy variable GROUP implies that the former has better profitability than the latter.

Engineering practice of reducing ground subsidence by grouting into overburden bed-separated and similar model experiment

The subsidence prediction theory under the condition of grouting into bedseparated was developed. Reducing ground subsidence by grouting was carried out on eight fully-mechanized top-coal caving faces, by using the continuous grouting in multiple-layer to obtain experiment results of reducing subsidence under fully mining. The similar material model that can be dismantled under the condition of constant temperature and constant humidity was developed. The model was used to simulate the evolution of overburden bed-separated under such constraints of temperature and humidity, at the same time, and to test the hardening process of similar materials.