Traditional Squares Research Articles

Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (Tectona grandis Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R2) and stability (variations in training and testing samples).

Small architectural forms in open public spaces of the city

The relevance of the problems of the development of public spaces and their design today is one of the most popular global trends in urban planning. The uniqueness of this or that city is formed precisely by public spaces, they also create a single and harmonious image of a metropolis. The so-called "alternative" elements: gazebos, verandas, terraces, pergolas, awnings, arches, recreation areas, stage platforms and others, are also used to form public spaces, like traditional squares, parks, boulevards. This article examines the role of small architectural structures in the context of creating open public spaces. The reasons for their appearance, the history of their existence in different time periods, functionality and rationality of use are also studied. Particular attention is paid to the basic principles of sustainable development of territories, which ensure the high quality of the urban environment. An example of one of these principles is the principle of comfortable mobility — those residents have access to the park zone comparable in speed and level of comfort. The nature-similarity of the forms of buildings is given as an example of one of the most effective and interesting methods of mutual integration of nature and architectural structures. The authors of the article made and presented sketches of various types of arbors: gazebos, barbecues, barbecue gazebos and decorative gazebos of free purpose, analyzed their functionality and considered the criteria for their location in a forest park area. The article contains information on the main types of materials used in modern construction. Also presented are data on measures for the improvement of the territory. On the example of one of the Moscow parks, it is proposed to consider several types of gazebos that meet the requirements of a given theme, the peculiarities of climatic conditions, the nature and specifics of use.

A new class of efficient and debiased two-step shrinkage estimators: method and application

This paper introduces a new class of efficient and debiased two-step shrinkage estimators for a linear regression model in the presence of multicollinearity. We derive the proposed estimators’ mean square error and define the necessary and sufficient conditions for superiority over the existing estimators. In addition, we develop an algorithm for selecting the shrinkage parameters for the proposed estimators. The comparison of the new estimators versus the traditional ordinary least squares, ridge regression, Liu, and the two-parameter estimators is done by a matrix mean square error criterion. The Monte Carlo simulation results show the superiority of the proposed estimators under certain conditions. In the presence of high but imperfect multicollinearity, the two-step shrinkage estimators’ performance is relatively better. Finally, two real-world chemical data are analyzed to demonstrate the advantages and the empirical relevance of our newly proposed estimators. It is shown that the standard errors and the estimated mean square error decrease substantially for the proposed estimator. Hence, the precision of the estimated parameters is increased, which of course is one of the main objectives of the practitioners.

A Comparison of Priors When Using Bayesian Regression to Estimate Oral Reading Fluency Slopes

Bayesian regression has emerged as a viable alternative for the estimation of curriculum-based measurement (CBM) growth slopes. Preliminary findings suggest such methods may yield improved efficiency relative to other linear estimators and can be embedded into data management programs for high-frequency use. However, additional research is needed, as Bayesian estimators require multiple specifications of the prior distributions. The current study evaluates the accuracy of several combinations of prior values, including three distributions of the residuals, two values of the expected growth rate, and three possible values for the precision of slope when using Bayesian simple linear regression to estimate fluency growth slopes for reading CBM. We also included traditional ordinary least squares (OLS) as a baseline contrast. Findings suggest that the prior specification for the residual distribution had, on average, a trivial effect on the accuracy of the slope. However, specifications for growth rate and precision of slope were influential, and virtually all variants of Bayesian regression evaluated were superior to OLS. Converging evidence from both simulated and observed data now suggests Bayesian methods outperform OLS for estimating CBM growth slopes and should be strongly considered in research and practice.

Evaluation of induced polarization measurements using a new inversion method

In this paper, a new inversion method is proposed to process laboratory-measured induced polarization (IP) data. In the new procedure, the concept of the series expansion-based inversion is combined with a more general definition of the objective function. The time constant spectrum of the IP effect is assumed a line spectrum approximated by a series of Dirac’s delta function resulting in a square-integrable forward problem formula. This gives the applicability of the generalized objective function. The expansion coefficients as unknowns represent the model parameters of the inversion procedure. We use the new inversion procedure on an apparent polarizability dataset measured on a rock sample originated from the Recsk ore complex, northeast Hungary. The inversion results was compared to those of three additional laboratory datasets, which were measured on samples rich in ore minerals collected from the same area. The results are compared to those given by the traditional series expansion-based least squares method. It is shown that the newly proposed method gives more accurate and stable parameter estimation.

Comparing the performance of Bayesian and least-squares approaches for inverse kinematics problems

Bayesian inference has recently been identified as an approach for estimating a subjects’ pose from noisy marker position data. Previous research suggests that Bayesian inference markedly reduces error for inverse kinematic problems relative to traditional least-squares approaches with estimators having reduced variance despite both least-squares and Bayesian estimators being unbiased. This result is surprising as Bayesian estimators are typically similar to least-squares approaches unless highly informative prior distributions are used. As a result the purpose of this work was to examine the sensitivity of Bayesian inverse kinematics solutions to the prior distribution. Our results highlight that Bayesian solutions to inverse kinematics are sensitive to the choice of prior and that the previously reported superior performance of Bayesian inference is likely due to an overly informative prior distribution which unrealistically uses knowledge of the true kinematic pose. When more realistic, ‘weakly-informative’ priors, which do not use the known kinematic pose are used then any improvements in estimator accuracy are minimal when compared to the traditional least squares approach. However, with appropriate priors, Bayesian inference can propagate uncertainties related to marker position to uncertainty in joint angles, a valuable contribution for kinematic analyses. When using Bayesian methods, we recommend researchers use weakly-informative priors and conduct a sensitivity analysis to highlight the effects of prior choice on analysis outcomes.

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors.

Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm.

Novel robust time series analysis for long-term and short-term prediction

Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

A New Approach to Calculate Earthquake Slip Distributions

A transdimensional, probabilistic approach is more flexible than traditional least squares fits and provides better handling of sharply varying slip distributions.

Sparse Bayesian learning based channel estimation in FBMC/OQAM industrial IoT networks

The next generation of communication technology is accelerating the transformation of industrial internet of things (IIoT). Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), as a candidate wireless transmission technology for beyond fifth generation (5G), has been widely concerned by researchers. However, effective channel estimation (CE) in IIoT communication should be solved. In practice, wireless channels have block–sparse structures. For the conventional sparse channel model, the general sparse channel estimation methods do not take the potential block–sparse structure information into account. In this paper, we have investigated the sparse Bayesian learning (SBL) framework for sparse multipath CE in FBMC/OQAM communications. Block SBL (BSBL) algorithm is proposed to estimate the channel performance by exploiting the block–sparse structure of sparse multipath channel model. The BSBL method can improve the estimation performance by using the block correlation of the training matrix. Computer simulation results demonstrate the robustness of the BSBL CE approach in FBMC/OQAM systems, which can achieve lower mean square error (MSE) and bit error rate (BER) than traditional least squares (LS) method and classical compressive sensing methods. The state of art compressive sampling matching pursuit (CoSaMP) greedy algorithm with a prior knowledge of sparse degree can provide slightly better CE performance than BSBL algorithm, but the proposed method maintains robustness in practical channel scenario without the prior knowledge of sparse degree.

Study on creep models and parameter inversion of columnar jointed basalt rock masses

Taking the Baihetan hydropower station in Southwest China as an example, creep models and parameter inversion of the columnar jointed basalt rock mass were investigated in this study. Based on the large in situ flexible double pillow bearing plate compression creep tests, the applicability of empirical creep models, such as the power function model, the logarithmic model and the exponential model, for the creep characteristics of columnar jointed basalt rock masses in the dam site area was analyzed first. Then, the compression creep formula of the measuring points in the middle of the flexible double pillow pressure plate was derived based on the generalized Kelvin model. Finally, three optimization algorithms, including the traditional least squares method (LSM), particle swarm optimization algorithm with a compression factor (CFPSO), and particle swarm optimization algorithm with a compression factor based on the simulated annealing algorithm (SA-CFPSO), were used to identify the creep parameters of the generalized Kelvin model. The results confirmed that the generalized Kelvin model based on the flexible double pillow pressure plate outperforms the traditional empirical models, and the performance of the proposed SA-CFPSO algorithm is superior to that of the LSM and CFPSO algorithms in identifying the creep parameters of the generalized Kelvin model.

FORMATION OF THE HUMAN AND STREET ANIMALS INTERACTION ENVIRONMENT

The paper is devoted to the problem of arrangement of the city's ecosystem by the architectural and design tools. The contradictions and conditions of joint habitation of people and street animals (cats) in the urban environment are revealed. The initial potential capacity of different types of urban environment (beaches, a traditional Odessa courtyard, public parks and squares, modern multi-storey buildings) in the formation of a harmonized, conflict-free interaction between people and animals have been investigated. The sketches of the search for the artistic appearance of cat settlements (students’ works) are presented.

Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

The changing aspects of function and meaning of space in Alun-Alun Kidul Keraton Surakarta

As one of the traditional squares on the island of Java, Alun-alun Kidul Keraton Surakarta faces the challenge of changing its function. The physical condition of the South Square of the Keraton Surakarta is covered by a wall called beteng. In the Javanese spiritual concept, Alun-alun Kidul is connected in an imaginary line to the south coast. In terms of structure, the Alun-alun Kidul has a characteristic Javanese town square in general which is influenced by the cosmological concept of Hindu society. In the past, the function of the Alun-alun Kidul was closed and had a high degree of privatization and became a place for rituals of silence and contemplation of the king. On the other hand, the current physical condition and function of Alun-Alun Kidul have changed. This change is an effort to support the needs of the city today and in the future. The method used in this research is a phenomenological descriptive approach, refers to a grounded theory that does not use the term population but emphasizes a social situation consisting of four elements, namely place, activity, person (actor), and time. The results showed a pattern of spatial use for sacred and profane activities in spatial and temporal characteristics. Physical structure patterns in permanent form do not occur much, but semi-permanent and non-permanent physical patterns show changes in the meaning and behavior of space. These patterns show non-physical influences, namely the influence of cultural aspects, social aspects, economic aspects, and political aspects, as well as the behavior and mindset of the community, especially Surakarta City.

Does Foreign Direct Investment Affect SO2 Emissions in the Yangtze River Delta? A Spatial Econometric Analysis

As the major source of air pollution, sulfur dioxide (SO2) emissions have become the focus of global attention. However, existing studies rarely consider spatial effects when discussing the relationship between foreign direct investment (FDI) and SO2 emissions. This study took the Yangtze River Delta as the research area and used the spatial panel data of 26 cities in this region for 2004–2017. The study investigated the spatial agglomeration effects and dynamics at work in FDI and SO2 emissions by using global and local measures of spatial autocorrelation. Then, based on regression analysis using a results of traditional ordinary least squares (OLS) model and a spatial econometric model, the spatial Durbin model (SDM) with spatial-time effects was adopted to quantify the impact of FDI on SO2 emissions, so as to avoid the regression results bias caused by ignoring the spatial effects. The results revealed a significant spatial autocorrelation between FDI and SO2 emissions, both of which displayed obvious path dependence characteristics in their geographical distribution. A series of agglomeration regions were observed on the spatial scale. The estimation results of the SDM showed that FDI inflow promoted SO2 emissions, which supports the pollution haven hypothesis. The findings of this study are significant in the prevention and control of air pollution in the Yangtze River Delta.

Optimization of short-arc ellipse fitting with prior information for planetary optical navigation

Autonomous navigation technology has played an increasingly important role during modern planetary exploration missions. The image-based optical navigation method is one of the most promising solutions for autonomous navigation. Previous studies have mainly focused on image processing and navigation filtering algorithms. Classic ellipse fitting approaches rely on the least square method, which may fail in the case of short-arc ellipse fitting. This paper proposes an optimization method with a priori shape information to achieve a high-precision short-arc ellipse fitting. First, by querying the ephemeris and using the projection principle, the projected ellipse of the entire planet can be obtained. Second, the shape information of the ellipse that represents the useful priori information can be extracted using the least squares ellipse fitting algorithm. Third, the shape information is introduced into the standard elliptic equation, whose parameters are optimized to fit the planetary edge arc obtained by the navigation camera. Finally, simulations are conducted and the results show that the proposed method performs better in the accuracy of short-arc ellipse fitting compared to the traditional least squares method.

Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms

Multivariate multiple linear regression (MMLR), which occurs in a number of practical applications, generalizes traditional least squares (multivariate linear regression) to multiple right-hand sides. We extend recent MLR analyses to sketched MMLR in general Schatten p-norms by interpreting the sketched problem as a multiplicative perturbation. Our work represents an extension of Maher's results on Schatten p-norms. We derive expressions for the exact and perturbed solutions in terms of projectors for easy geometric interpretation. We also present a geometric interpretation of the action of the sketching matrix in terms of relevant subspaces. We show that a key term in assessing the accuracy of the sketched MMLR solution can be viewed as a tangent of a largest principal angle between subspaces under some assumptions. Our results enable additional interpretation of the difference between an orthogonal and oblique projector with the same range.

TRADO-MODERN COMMUNICATION STRATEGIES FOR EFFECTIVE IMPLEMENTATION OF COMMUNITY DEVELOPMENT PROGRAMMES IN BAYELSA STATE, NIGERIA

This study examined trado-modern communication strategies for effective implementation of community development programmes in Bayelsa State, Nigeria. Two objectives and two research questions guided the study. The study adopted descriptive survey and exploratory research designs. 405 respondents were adopted as sample size through total enumeration sampling. A structured questionnaire titled “Trado-Modern Communication Strategies for Effective Implementation of Community Development Programmes” (QT-MCSIECDP) with r = 0.82 was used for data collection. The instrument was validated by experts from the Department of Adult and Non-Formal Education in the University of Port Harcourt, Nigeria. Data were analysed using frequency, percentage and mean statistics. Results show that traditional rulers, groups, town-criers, face-to-face interaction and village squares are the available trado-modern communication channels for effective implementation of community development programmes in Bayelsa State; trado-modern communication keeps the communities up-to-date and enables individuals to react knowledgeably and intelligently to the environment, communicates feelings, creates acceptable changes in behaviour, boosts information, shows ways to solve problems and further growth for effective implementation of community development programmes in Bayelsa State. The study recommended among others that traditional rulers, town-criers, groups, village square and face-to-face interaction should always be used to inform, convince and motivate the community citizens for effective implementation of community development programmes.

Compressive Sensing-Based 3-D Rain Field Tomographic Reconstruction Using Simulated Satellite Signals

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

Nonlinear Identification for 4-DOF Ship Maneuvering Modeling via Full-Scale Trial Data

This research involves a 4-DOF ship maneuvering modeling with full-scale trial data. In order to avert the inversion of the multi-innovation matrix in the traditional multi-innovation least squares algorithm, a new novel is proposed based on the recognition concept new multi-innovation least squares algorithm to identify the innovation of the stochastic gradient hyperbolic tangent nonlinearity. A lot of work and efforts have been made to ensure the consistency and final convergence. Combined with relevant data and statistical indicators, the author derives a more effective hyperbolic tangent nonlinear innovation identification scheme to identify ship maneuvering motion. Compared with the previous results, this design scheme has significant computational advantages, with higher accuracy, faster identification speed, higher computational efficiency, and requiring fewer parameters. At the same time, the example is given to illustrate the effectiveness of the algorithm, especially for identification applications with full-scale trial data.