Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Uncertainty Set Based Robust Operation of Integrated Electricity and Heating Systems with Variable Mass Flow

With the rapid growth of renewable energy, system operators are placing increasing emphasis on operational flexibility and robustness. In this paper, we propose a robust operation of integrated electricity and heating system (IEHS) with variable mass flow, where heat operation with variable mass flow increases flexibility and robust operation increases safety. To reduce the conservativeness of robust optimization and capture the long-term temporal correlation of renewable energy generation, we use the multivariate uncertainty set derived from the GARCH model. Based on the duality theory, we transform the robust operation problem with the multivariate uncertainty set into a tractable deterministic convex programming by using mathematical tricks and equivalent transformation. Case studies show that: (1) The proposed robust operation has less conservativeness since the multivariate uncertainty set captures the long-term temporal correlation of renewable energy forecast errors; (2) The corresponding solution technique is effective and has greatly reduced the computational burden.
Short-Term Offshore Wind Speed Prediction Based on OWT-LSTM-ARIMA Combined Model

The randomness and volatility of offshore wind speed make a single prediction model less accurate. In order to improve the prediction accuracy, this paper proposes a combined offshore wind speed prediction model based on the combination model of orthogonal wavelet transform (OWT) -Long -Short Term Memory Network (LSTM) -Autoregressive Integrated Moving Average (ARIMA) model. The OWT is first used to decompose the offshore wind speed series into low-frequency sub-series and high-frequency sub-series to reduce the impact of volatility. The LSTM model is used to predict the high frequency subseries, and the ARIMA model is used to predict the low frequency subseries. Then the prediction results are fused to form a complete prediction of the offshore wind speed series. In addition, K-Medoids algorithm is improved to simulate the extreme weather at sea represented by lightning to further prove the superiority of the combined model. The results show that the combined model has higher prediction accuracy and stronger stability than the single model.
Architecture Design of Enterprise Information System Based on Docker and DevOps Technology

Under the background of large and medium-sized platform strategy, many excellent platform as a service architecture designs have emerged. The focus of the technical platform is to build the PaaS layer. The PaaS layer is designed to form a cluster environment with relatively distributed virtual machines provided by the IaaS layer, the main technologies to be used include Docker container and Kubernetes orchestration engine. In addition, software project development is a process of comprehensive practice, and it is far from enough to rely on Docker technology alone. In view of the lack of standardized project management, code management, testing and quality assurance means in software project development, the introduction of DevOps software development model and the study of enterprise information system architecture design are of great significance for guiding the design of large-scale distributed architecture.
A mean score method for missing and auxiliary covariate data in regression models

SUMMARY We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and missing data, allows missingness to depend on available response and covariate values, and is applicable to both cohort and case-control study designs. The method previously proposed by Flanders & Greenland (1991) and by Zhao & Lipsitz (1992) is generalised and asymptotic theory is derived. Our expression for the asymptotic variance of the estimator provides intuition regarding performance of the method. Optimal sampling strategies for the validation set are also suggested by the asymptotic results.
Modeling and Implementation of Real-Time Power Market Based on Autonomous Controllable Optimization Solver

The clearing model of real-time power market is a typical linear programming problem. In the current spot market pilot areas, relevant technical support systems use foreign commercial optimization solvers such as CPLEX and GUROBI as tools for real-time power market modeling and calculation. In recent years, domestic optimization solvers have developed rapidly in linear programming. Excellent products such as Cardinal Operations COPT and Alibaba MindOpt have emerged, realizing the independent control of optimization solvers. This paper analyzes and compares the differences between the autonomous controllable optimization solver and foreign mainstream products in parameter setting, algorithm function, function interface function and use mode, and establishes a unified modeling interface suitable for multiple solvers, so as to achieve the effect that different solvers can be called at one modeling. Secondly, according to the mathematical model of real-time power market, the modeling and calculation of real-time power market clearing model are realized based on the unified modeling interface. Finally, the application of real-time solver and controllability in foreign power market is verified by the independent test of real-time solver.
Improved Intrusion Detection System (IDS) performance using Machine Learning: A Comparative Study of Single Classifier and Ensemble Learning

Unlike conventional firewalls, intrusion detection systems (IDS) are acknowledged as a very significant and leading strategy for detecting hazardous behaviors on computer networks, and they are rapidly replacing them. Unlike traditional firewalls, IDS employs analytical methodology such as data mining and machine learning techniques to intelligently identify threats, whereas traditional firewalls do not. Ensemble learning has spurred progress in machine learning and pattern classification research in recent decades, with results that surpass single classifiers. Research was carried as part of this study to improve the accuracy rate of an intrusion detection system. To evaluate which technique worked best, a singular classifier was used to identify given data in order to obtain the highly accurate results, which were then evaluated to the findings of ensemble learning and feature selection. Using ensemble learning, the goal is to obtain the most accuracy out of a single classifier. The results are produced from the confusion matrix value, and they will be evaluated through making a comparison acquired using the two different methods stated above. The researchers were able to get an accuracy score of 77.4% for a single classifier and 96.8% for ensemble learning.
Current and Power Quality Coordinated Control for VSC-HVDC Connected to Unbalanced Power Grid

When a serious asymmetrical fault occurs in the power grid, if only the effective transmission of active power is considered, the power devices in the inverter may cause severe heating or even damage to the power equipment due to overcurrent. Accordingly, the coordinated control of power and current is proposed to reduce overload current under grid-connected regulations. The method comprehensively optimizes different control objectives by introducing a coordination factor to establish a coordination function between power flow and load current. First, the VSC-HVDC model under unbalanced grid is constructed, which illustrates the contradiction between different control objectives. Then, the quantitative mathematical derivation of the proposed comprehensive control strategy is carried out, and the comprehensive target control of the inverter is carried out by adjusting the coordination factor. Finally, the simulation results under various operating conditions also show that the proposed strategy can realize the grid-connected control of the comprehensive target under the condition of grid asymmetry.
Techniques to Improve TCDA Bandwidth Beyond 46:1

Low profile high isolation ultra-wideband (UWB) antennas and arrays are key components in several advanced communications and defense systems. Over the last decade, TCDA has become a widely used array that boasts polarization diversity (both linear and circular), wide scanning volumes, large bandwidths, and inexpensive fabrication. Further, the TCDA is scalable to millimeter wave frequencies and fabricated for operation up to 94GHz using printed circuit technology. Even more importantly, it has been adapted to various aerospace and naval projects due to its wideband yet compact design. The premise of the paper is the challenges associated with extending the TCDA bandwidth beyond 20:1 in a low profile.
Plant Control Parameter Identification of Renewable Energy Station Based on Improved Least Square Method

Comprehensive analysis of wind farm performance is highly dependent on the knowledge of plant control. The plant control parameters are diverse and commonly unavailable in practical power stations. In this paper, a discrete mathematical model of the plant control from the western electricity coordinating council (WECC) is developed. Based on this discrete model, a modified least square method is proposed to identify the control parameters of this typical plant control. The modified least square method employs a variable forgetting factor, offering an accurate identification with relatively high calculation efficiency. The data needed for identification is obtained by injecting disturbance signals into controller inputs and measuring controller outputs. The proposed parameter identification method of the plant control is validated in a wind farm simulated in PowerFactory of Digsilent. The proposed method is proven to be effective and accurate.
Research on Load Forecasting of Multi-scenario Building Air-Conditioning Based on TSO-LSTM Algorithm

This paper proposes a short-term air-conditioning load forecasting model of multi-scenario building based on Tuna Swarm Optimization (TSO) algorithm and long short-term memory network (LSTM). The model uses the TSO algorithm to optimize the hyperparameters of LSTM, which solves the problem that the manual selection of hyperparameters of LSTM neural network is difficult to obtain the optimal results. The TSO algorithm has the characteristics of fast global convergence and high searching optimal ability, which can optimize the number of neurons in the hidden layer, learning rate, and training times of the LSTM neural network. Through the analysis of different case data, the forecasting effect of the proposed algorithm is verified. Forecasting results of LSTM, PSO-LSTM, and TSO-LSTM algorithms are compared in different scenario building airconditioning load, which shows that the proposed TSO-LSTM algorithm has better accuracy and rapidity.
Coordinated Planning of Active Distribution Network and V2G Charging Stations Considering the Load Characteristics of V2G Stations

In this paper, the MILP methodology is utilized to collaboratively plan the active distribution network (ADN) and Vehicle-to-grid (V2G) charging stations. This research considers the load characteristics of V2G charging stations in different areas, including residential areas, office areas, and commercial areas. In addition, the paper discusses the impact of energy storage system (ESS) and capacitor bank (CB) on planning costs. Besides, a demand response model is developed based on the influence of time-of-use tariff. The modeling also takes into account the load attributes of different nodes, including constant impedance, constant current and constant power characteristics. The present methodology was tested in IEEE 33-nodes distribution system. The planning results of three cases were compared, providing reference for planning issues considering V2G charging stations.
Improving the Performance of a Massive Multi-Input Multi-Output System by Developing a Hybrid Beamforming Model

The massive MIMO ‘‘Multiple-Input Multiple-Output (MMIMO)’’ system uses beamforming techniques to transfer power directly. In current scenario, partially connected hybrid beamformers (HBF), rather than fully connected HBF, reduces the cost, power consumption and complexity of the hardware. As a result, an optimization-driven model for hybrid beam forming for massive multiple-input multiple-output is proposed to reduce interference and strengthen robustness. The proposed model optimizes both analog and digital beams. An improved Social SkiDriver Algorithm (ISSDA) is developed to obtain the optimal digital beam, and analog phase alignment-linear searching (APA-LS) is introduced to obtain the optimal analog beam. MATLAB simulation is utilized for evaluating the proposed algorithm. The simulation outcomes show a better performance in terms of signal-to-noise ratio (SNR) along with beam width. The proposed ISDDA-APALS method is compared to these other methods like MU-SIC, PDJ- BPSA, Hy-SBD and I-BAT.
Research on the Sichuan-Chongqing Gas-hydro Bundled Delivery Model Considering the Willingness of Receiving End to Consume

Replacing thermal power with gas power can promote the realization of the "Carbon peaking and carbon neutrality" goal. Firstly, this paper discusses the development thoughts of gas-hydro integration given the seasonal contradictions of Sichuan-Chongqing hydropower and the advantages of natural gas resources. Secondly, the influence of the willingness of end-end consumption is analyzed on the price of electricity sent to the outside world, and a model of gas-hydro bundled delivery is built. Finally, the economics and carbon emission reduction of gas-electricity replacing thermal power are analyzed taking the example of the Sichuan-Chongqing power grid information in 2019. The case verifies the rationality and feasibility of the model.
Stress patterns of disyllabic words terms with identical pronunciation and different morph-syntactic structures in standard Chinese

Perceptual and acoustic analyses were used to investigate the stress patterns of Chinese disyllabic constituents with identical pronunciation and different morph-syntactic structures.Tests demonstrate that the stress patterns in standard Chinese(SC) are "Strong-Weak" when the final syllable is a neutral tone and that this stress pattern can be used as the cue to discriminate meanings of the disyllabic constitutes.However,due to the final lengthening effect,disyllabic constituents without neutral tones were inclined to be the "Weak-Strong" pattern,which exhibits no corresponding relation with the morph-syntactic structures.
Measurement of the Charge Asymmetry in Top Quark Pair Production

We present a measurement of the charge asymmetry in top quark pair production using an integrated luminosity of 1.09 fb 1 collected with the CMS detector. Top quark pairs with a signature of one electron or muon and four or more jets, at least one of them b tagged, are selected. At the LHC a small charge asymmetry in the rapidity distributions of top and antitop quarks is predicted. Therein slightly broader rapidity distributions for top quarks are expected, while antitop quarks are produced more centrally and possess narrower rapidity distributions. We determine the charge asymmetry based on two di erent sensitive variables and the results are compared with the most precise standard model theory predictions using a dedicated unfolding technique.
Study on Stability Margin Trend of Power System Using GHNet

Under the guidance of peak carbon dioxide emissions and carbon neutrality, the proportion of the new energy generation will continue to rise significantly. The randomness, volatility and intermittency of new energy make it difficult to arrange the operation mode of the power system, which also brings great challenges to the early warning and pre-control of the dispatching operation. For the power grid stability trend assessment facing the uncertainty of new energy, the core problem lies in finding the typical and dangerous changing processes from numerous possibilities with high efficiency. This paper proposes the grid hierarchy net (GHNet) model and the chain rule of neural networks to search for the most stable and unstable processes to define the security range trend. Firstly, the GHNet model is constructed and well trained. Then, the restraint conditions of new energy are determined by the statistical results of the actual power and predictive power. Finally, a sensitivity analysis is performed by using chain rule and the extreme changing process will be found out by adjusting the predictive operation point according to the sensitivity results. A 114-bus system is used to verify the feasibility and effectiveness of this method.
Enhanced Defect Detection in NDE Using Pixel Level Data Fusion

Pipeline systems in service continue to degrade with passage of time due to usage. Magnetic flux leakage (MFL) and Eddy Current (EC) based Non destructive evaluation (NDE) methods are most common for inline inspection (ILI) of the metallic pipelines. Many times, a single measurement technique fails to reveal all the characteristics of the damage due to resolution constraints. This paper presents a novel pixel based fusion of both MFL and EC inspection that can detect real defects like cracks and slits of sub millimeter dimension. An automated data based screening rule is developed to judge whether fusion is needed. Then a transform domain based fusion method called complementary sparse representation (CSR) along with post processing steps is developed for fusion of the source images. The efficacy of the fusion algorithm is studied on both the MFL and EC data generated by simulation means.
Investigating the impact of Personality Tendencies and Gratification Aspects on OTT Short Video Consumption: A case of YouTube Shorts

This study used the Uses and Gratifications viewpoint to evaluate how people view, promote, and produce YouTube shorts. It also investigated personality tendencies and gratification aspects as determinants of this comprehensive usage behavior. Data was collected from 195 YouTube shorts users recruited through an online survey using random and snowball sampling approaches. Our studies demonstrated a partial association between personality tendencies usage behavior and gratification features, namely cataloguing, self-promotion, glancing, escapism, social involvement, and financial gain, which are all significant predictors of shorts usage behaviors, but the degrees and impact vary. This research contributes to the conceptual and empirical comprehension of media usage in a conversational media context.
Analysis of Educational Recommender System Techniques for Enhancing Student's Learning Outcomes

Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future.
Incentive Mechanism Design with Gold Standard Questions Based on Approval Voting in Crowdsourcing

Crowdsourcing has been widely used in many areas such as data tagging in machine learning these years. However, sometimes the quality of the task is not satisfied because workers lack of expertise knowledge or workers do not put all of their effort. In order to better use crowdsourcing system, it is necessary to design incentive mechanism to ensure workers try their best to propose answers in the process of completing tasks. Therefore, in this paper, we study how to design incentive mechanisms with inserting gold standard questions in the crowdsourced tasks, and applying approval voting to utilize workers' partial knowledge as much as possible. In particular, workers would pay cost for every option they selected, and get proper reward only if they select the true answer for gold standard questions. In the paper, it theoretically proves that the proposed mechanism has good properties compared with other mechanisms and meets the constraint of incentivizing workers to select all options with belief higher than the threshold and thus improves the total quality of workers' answers. Finally, experiment results show that the mechanism can improve the quality of workers' answers in completing tasks.
Optimization of Thermal Coal Multimodal Transportation Considering Externalities

China is a major electricity-consuming country which consumes a large amount of electricity resources every year. Currently, thermal power plants are the main electric power supply types in China, and the differences exist between coal production areas and coal demand areas, resulting that a lot of coal transportation activities are needed every year. The transportation of coal will cause serious dust hazards to the surrounding areas, and the transport tool also affect the environment a lot, which has strong negative externalities. It is believed that the reasonable quantification of the negative external costs incurred in thermal coal multimodal transportation and its inclusion in the total cost of thermal coal multimodal transportation is satisfied with the interests of society. Therefore, this paper establishes a multi-objective optimization model considering the external costs of thermal coal multimodal transport, and obtains the Pareto optimal solution set of the constructed model employing a improved Genetic algorithm named NSGA-II. Finally, the influence of external costs and the rationality of the inclusion of external costs are further analyzed.