Miracle-dz/UniFAR-FTUs huggingface dataset:
The Height of Water-conducting fractured zone prediction using principal component analysis and relevance vector machine

This study proposes a model to predict the height for water-conducting fractured zone using easily obtained parameters which is Relevance Vector Machine (RVM) based on Principal Component Analysis (PCA). PCA was used to reduce the several factors affecting the water conducted fractured zone height to a few principal components. A nonlinear mapping relationship between the water conducted fractured zone height and principal components was established by using RVM model, and corresponding results of water conducted fractured zone height prediction is inferred. Applying this method to specific examples, comparison and analysis the predicted results of Back Propagation (BP), Partial Least Squares (PLS), Partial Least Squares-Back Propagation (PLS-BP) and Particle swarm optimization-Radial Basis Function (PSO-RBF) methods under the same learning sample conditions, the results reflect that the PCA-RVM prediction method reduces several influencing factors into a few principal components reasonably by analyzing the correlation and contribution rate of various factors, which is significantly better than the remaining 4 methods in information screening. The prediction results of each method show that the PCA-RVM method has the advantages of high precision, small discreteness and high reliability, providing a new method for the prediction of the water conducted fractured zone height.
An Investigation on the Relationship between Cybersickness and Heart Rate Variability When Navigating a Virtual Environment

In this study, we explore the physiological effect of cybersickness using heart rate variability (HRV) and examine the relationship between the physiological and subjective measurement of cybersickness when navigating a virtual environment (VE). To achieve these objectives, we conducted two experiments. In experiment 1, four types of forwarding translation method (teleportation + embodied control [EC], steering + EC, teleportation + instrumental control [IC], and steering + IC) were compared, while six types of rotation (yaw, pitch, roll, yaw + pitch, yaw + roll, and pitch + roll) were examined in experiment 2. The HRV analysis for experiment 1 revealed that the steering conditions yielded significantly higher low-frequency (LF) power (ms2) than did the teleportation condition. In addition, the simulator sickness questionnaire (SSQ) results showed that the steering conditions produced stronger symptoms of cybersickness than did the teleportation conditions. For experiment 2, the yaw + roll and pitch + roll conditions had greater LF power (ms2) than did the other four conditions. However, the SSQ indicated that the level of cybersickness was higher for the roll, yaw + roll, and pitch + roll conditions than for the other rotation conditions. The participants completed the four and six conditions over a limited period across two experimental days. However, the overall HRV and SSQ results for both experiments indicated no explicit additive effects of sickness symptoms. Based on these outcomes, this study verified the relationship between cybersickness, HRV, and SSQ during navigation in a VE.
Neural Correlates of Cognitive Load While Playing an Emergency Simulation Game: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Functional near-infrared spectroscopy (fNIRS) provides reliable results for determining cognitive load based on averaged cortical blood flow during multiple repetitions of short cognitive tasks. At the same time, it remains unclear how to use this technique for assessing cognitive load during prolonged single-trial activity. In this study, we used a computer-based emergency simulation game for inducing different levels of cognitive load. We propose a novel approach to measure cognitive load using specific time slots, determined based on simulation log-data interpreted in light of Barrouillet's time-based resource-sharing model. To validate this approach, we compared cortical activity in dorsolateral prefrontal cortex (DLPFC) and left inferior frontal gyrus (IFG) regions measured at four specific time slots during a simulation. We found significant associations between cognitive load and neuronal activity within the DLPFC depending on the chosen time slot, whereas no such dependencies were found for the IFG. These results illustrate how knowledge of task structure could be used advantageously for the identification of cognitive load. Although requiring further investigation in terms of reliability and generalizability, the presented approach can be considered promising evidence that fNIRS might be suitable for more general reliable assessments of cognitive load during prolonged single-trial activities and for real-time adaptations in simulation-based learning environments.
Intelligent Information System for Optimizing the Production Planning Process

The article presents an intelligent information system that allows, on the basis of various problem-oriented optimization methods, to automate the planning process and form optimal production schedules for flexible multi-assortment productions. The core of the proposed system are intelligent interfaces that allow ergonomically configuring the system by filling in expert tables of reconfiguration of units and production lines from one type of product to another, formalizing the representation of knowledge about the characteristics of production involved in the formation of the formulation of the optimization problem and the objective function. Information support is described, including updated and augmented databases developed on the basis of intelligence maps that allow graphically structuring information about the parameters of products, orders and equipment involved in planning. Visualization of planning results is presented in the form of a calendar plan - a Gantt chart, as well as in the form of an explanatory tree for solving the optimization problem, which allows you to track the improvement of the value of the objective function at all stages of optimization. The proposed architecture and functionality ensure the variability of the application of the system and its effective use for various flexible multi-assortment productions. The implementation of the system makes it possible to significantly increase the efficiency of the planning process, reduce the cost of production and simplify the process of making production control decisions.
FBR: Dynamic Memory-Aware Fast Rerouting

Modern internet communication networks, such as MPLS networks, provide fast rerouting mechanisms in the data plane in order to quickly react to link failures and hence become more dependable. However, fast rerouting requires additional memory for the conditional failover rules—a scarce and expensive resource. We initiate the study of memory-aware fast rerouting mechanisms and present Forward-Backward Routing (FBR), a dynamic rerouting approach with a provably high resilience to multiple link failures, which accounts for memory constraints on the routers. FBR relies on backtracking search along the routing paths via packet header modifications, and in our experimential evaluation on a wide range of ISP topologies, it outperforms state-of-the-art solutions while using less memory.
Discussion on the feasibility of news sentiment analysis to predict stock prices

Since its birth, China's A-share market has been regarded as a “policy city”, which means that the introduction of policies will cause earth-shaking changes in the overall market, and it will be less affected by the overall public opinion. In order to verify the influence of public opinion on the overall market index and the overall trend of individual stocks, it is proposed to use a linear regression (Linear Regression) and polynomial regression model (Polynomial Regression) based on the widely agreed academic community to fit the overall market news sentiment tendency and the magnitude of the corresponding individual stock and market index changes. This model first collects the corresponding news data of Resset database, and uses the natural language processing pre-training model to quantify the corresponding news headline data into sentiment analysis values, and then enters the sentiment analysis values together with the stock price, stock price change range and stock index and stock index change range of the corresponding date into Excel to fit the corresponding data. Comparing the R2 of the corresponding regression models of different stocks and stock indices, the regression results are verified and compared to verify the feasibility of relevant news sentiment analysis for stock price prediction. The experimental results show that the correlation coefficient R2 between the sentiment analysis value and the corresponding stock price or index fluctuation value of the polynomial regression model is less than 0.2, and the correlation is not enough to reach a significant level, and other indicators need to be introduced to achieve feasibility.
Damage Detection of an Aluminum Plate by Using Nonlinear Ultrasound with a Frequency-swept Excitation

In the traditional nonlinear ultrasound methods, one or dual excitation pulses with fixed center frequencies are usually simultaneously sent to the structures to generate nonlinear interaction near the damage, and some nonlinear parameters are applied to identify the structural damages in early age. However, when the damage evolves, the nonlinear effect due to damages usually changes, and the best matching frequency also shifts. Therefore, a new method based on nonlinear ultrasound with a frequency-swept excitation is proposed. In this new method, a frequency-swept signal is proposed to take place of the pulse with a fixed frequency, the wavelet packet decomposition is introduced to process the nonlinear response signal, and a nonlinear damage index is used for damage size estimation. To validate the method, some experiments are conducted on an aluminum plate. The experimental results of three specimens show that the nonlinear damage index is influenced by both the frequency band of the excitation signal and the diameter of the simulated damage. Moreover, when the best frequency band (260 kHz~280 kHz) is selected, the proposed damage index is linearly increases as the size of the simulated damage changes from 0 mm to 0.7 mm, which indicates that this index can be applied to identify damages with a small size on plates. This study puts forward a new avenue to detect structural damages with a small size in an early age on plate-like structures.
Researching Sensitivity of Complicated Systems based on Analysis of Finite Fluctuations

Sensitivity Analysis being a tool for mathematical model researching is aimed at estimating influences of model inputs (arguments, factors) on model output. Existing techniques deliver results dependent on model's structure or its parameters. The paper introduces the approach based on Analysis of Finite Fluctuations, which is technique consisting of replacing studied model by the model linking the finite fluctuations of the output and finite fluctuations of inputs. Conducted numerical experiments showed the consistency of the approach. There are given examples of applying introduced technique to two different problems.
Technique for Data Analysis and Modeling in Economics, Finance and Business Using Machine Learning Methods

Complex economic, financial and business systems have some features as dynamism, uncertainty, stochasticity and heterogeneity of their parameters, variety of parameters scales, deviation of parameters (variables) from the normal distribution, multicollinearity and false correlations that complicate of modeling and prediction of these systems. An approach is required that, under conditions of described data features, can adequately identify, model and predict processes in such systems, ensuring a high level of accuracy and efficiency. In the paper we suggest the technique which provides organizational and methodological support for the data analysis and modeling using machine learning methods. The proposed technique allow, firstly, to carry out a system analysis of an controlled object taking into account the uncertainty of the internal and external environment on the basis of heterogeneous tools for qualitative and quantitative analysis. Secondly, it forms an adequate model of the controlled object and develops maching learning algorithms corresponding to the nature of the object under study. Thirdly, it can be the basis for decision support system for object management.
MU-Net: Modified U-Net Architecture for Automatic Ocean Eddy Detection

Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Although deep learning algorithms for detecting eddies are a relatively new trend, it is still in their infancy. In this letter, a deep learning method for ocean eddy identification based on semantic segmentation is proposed. In semantic segmentation, understanding the context efficiently for pixel-level recognition is crucial. Two attention modules are proposed to tackle this problem. The proposed work consists of VGG16-based U-Net architecture with two attention modules to show a contextual correlation in the channel and spatial dimensions. Every pixel or channel adapts to include context from every other pixel or channel based on their correlations. Furthermore, a new residual path is proposed to replace the conventional skip connection between encoder and decoder modules. The findings of the experiments show that adopting an attention-based deep framework and new residual path improves the model performance over the existing state-of-the-art techniques.
Impact Analysis of Low-Voltage Ride-Through on the VSG-Based Power Flow Control Applied to Grid-forming DG Systems

This paper presents a case study to evaluate the impact of the low-voltage ride-through (LVRT) on the virtual synchronous generator (VSG) strategy performance applied to power flow control in grid-forming distributed generation (DG) systems. For this purpose, the DG system was subject to asymmetrical faults applied to the point of common coupling (PCC) to characterize the LVRT condition. The VSG strategy performance is evaluated through simulation results addressing the grid-connected DG system operation with different scenarios of asymmetrical voltage sags. The analysis presents the main impacts on the system control variables, which can guide the improvement of standard VSG strategies to minimize the impacts caused on the DG power flow control.
Power Electronics Laboratory Projects Enhance Interdisciplinary Engineering Education

Delivering modern and interdisciplinary education that provides students with a wide set of engineering skills is essential for addressing the current needs of multiple industrial sectors. With this aim, three power electronics laboratory group projects for higher education students are presented in this paper. The projects focus on i) the development of an Intelligent Power Module-based inverter, ii) the development of a measurement board and iii) their integration with a rapid control prototyping platform. The intended learning outcomes are presented together with the work methodology for each project. Furthermore, it is highlighted that although the projects are discrete in nature, they can be combined to form a complete power converter system which can serve as a platform for further experimentation and research into modern applications, such as renewable energy sources integration and motor drives. The latter is demonstrated through the description and results of tests rigs built using the developed hardware components.
Results of a Comparative Analysis of the Actual Electrical Loads of Multi-Apartment Residential Buildings in Moscow

One of the main directions for the “Roselectromontazh” Association is research work on the development of specific design electrical loads. The need to update the specific design electrical loads has been repeatedly highlighted by scientists, the essence of the problem lies in the inefficient use of resources in the construction of external electrical networks of residential and public buildings, as well as increased losses in power transformers. The evolution of electrical appliances towards energy efficiency and the digital transformation of the power grid complex contribute to the monitoring of the electrical loads of residential buildings in order to reduce the cost of building and operating electrical networks by reducing the gap between real and calculated loads. The scientific work is devoted to the analysis of the electrical loads of a set of apartments in residential buildings in Moscow for the period from April 2021 to September 2022 for a cluster of 11–18 floors. The article presents a comparison of the electrical load of a set of apartments with the load on the input-distribution device of an apartment building (including apartments, general house needs and commercial load). The size of the studied sample for the cluster of 11–18 floors in Moscow was 3721 apartments. For sample sets of apartment buildings in the considered cluster, dependences of the maximum specific electrical load of apartments on their number in the group were obtained.
Comparison of Dimension Reduction Algorithms on the Example of Hard Disk State Classifiers

This article discusses data dimension reduction algorithms in the framework of machine learning problems. Dimension reduction algorithms such as PCA, t-sne, UMAP are considered. The efficiency of these algorithms has been tested within the framework of the problem of classifying the states of hard disks. The classifiers are built on the basis of such machine learning tools as: SVM, RF, LR, KNN, LSTM. Dimension reduction algorithms are compared based on the results of the models trained on the data transformed by the selected algorithms.
Wind Power Interval Prediction Based on External Attention LSTM and Quality-Driven Loss Function

The uncertainties of the wind power forecasting should be qualified effectively, a higher quality prediction interval (PI) is able to provide more valuable forecasting information. In this paper, a new model based on external attention long short-term memory network (EA-LSTM) is proposed, and a simplified quality-driven loss function is used to train the models. A new evaluation index is designed to obtain the optimal parameters of the loss function. Two 15 minutes time resolution datasets are used and two benchmark models are compared in case studies. The results demonstrate that the PI coverage probabilities (PICPs) generated by the proposed model can always reach the PI nominal coverages (PINCs), and the high quality PIs can be obtained. The EA-LSTM is also proved to be effective.
An Investigation on Multi-UAVs Cooperative Control Algorithm for Target Chasing

Aiming at the problem of collective pursuit of Multiple Unmanned Aerial Vehicles (multi-UAVs) against non-cooperative UAVs, a directional chase strategy based on the Multi-Agent Deep Deterministic Policy Gradient(MADDPG) algorithm is designed by using deep reinforcement learning theory. By designing the algorithm model, state variables, actoin variables and reward function, the UAVs are trained to learn the directional chase strategy on a distributed Actor, centralized Critic structure. It is verified by simulation that the proposed chase strategy has higher learning efficiency while ensuring accuracy than the strategy with distributed training using the Deep Deterministic Policy Gradient (DDPG) algorithm. It also has a higher capture efficiency than the conventional pursuit-only strategy, provides a new research idea for multi- UAV confrontation.
A NEW FRACTAL INTERPOLATION ALGORITHM AND ITS APPLICATIONS TO SELF-AFFINE SIGNAL RECONSTRUCTION

A new fractal interpolation method called PPA (Pointed Point Algorithm) based on IFS is proposed to interpolate the self-affine signals with the expected interpolation error, solving the problem that the ordinary fractal interpolation can't get the value of any arbitrary point directly, which has not been found in the existing literatures. At the same time, a new method to calculate the vertical scaling factors is proposed based on the genetic algorithm, which works together with the PPA algorithm to get the better interpolation performance. Experiments on the theoretical data and real field seismic data show that the proposed interpolation schemes can not only get the expected point's value, but also get a great accuracy in reconstruction of the seismic profile, leading to a significant improvement over other trace interpolation methods.
FORTIFY: Software Defined Data Plane Resilience

Given the scale and mission-critical nature of production networks today, it is essential to solidify their resilience to link failures. Building this resilience in each application separately is not scalable. In order to minimize downtime, at least some degree of resilience should be built directly into the data plane. Fast Failover groups in OpenFlow offer a mechanism to achieve this, but programming them introduces additional complexity to the existing arduous task of developing an SDN controller application. In this paper, we discuss how this complexity can be decoupled from the controller implementation. We introduce FORTIFY, a transparent resiliency layer that incorporates data plane fault tolerance into any existing controller application without any modification to it. FORTIFY operates as a shim layer between the controller and the data plane, and dynamically transforms the data plane rules computed by the controller to use Fast Failover groups. FORTIFY can be used off-the-shelf, or customized programmatically to choose specific types of backup paths. Experimental results collected on a production testbed demonstrate that FORTIFY is a practical, high-performance solution to data plane fault tolerance in SDNs.
Identity Verification based on the RGB and NIR Images of the Palm

In this paper, we propose to extract the intersection points of the palmprint and the palm-vein lines from multi-spectral images and use them as reliable features for identity verification. Essentially, by utilizing a sum of cardinal directional image difference operation, the palmprint and palm-vein line features are respectively extracted from palm images of the Blue channel and the NIR channel of image spectrums based on simple matrix projection. Subsequently, the intersection locations of the two biometric line features are extracted and utilized to compute a set of keypoint descriptors. After calculating the match scores based on the extracted keypoint descriptors, a score level fusion of the matching results obtained from the Blue channel and the NIR channel is adopted to enhance the verification performance. The proposed method has been experimented on a public domain multispectral palm database where encouraging results in terms of verification accuracy have been obtained.
Formation of GaAs∕AlGaAs and InGaAs∕GaAs nanorings by droplet molecular-beam epitaxy

GaAs/AlGaAs lattice-matched nanorings are formed on GaAs (100) substrates by droplet epitaxy. The crucial step in the formation of nanorings is annealing Ga droplets under As flux for proper time. The observed morphologic evolution of Ga droplets during annealing does not support the hypothesis that As atoms preferentially react with Ga around the periphery of the droplets, but somehow relates to a dewetting process similar to that of unstable films. Photoluminescene (PL) test results confirm the quantum-confinement effect of these GaAs nanorings. Using similar methods, we have fabricated InGaAs/GaAs lattice-mismatched rings. (c) 2005 American Institute of Physics.