Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Self-Learning of Multivariate Time Series Using Perceptually Important Points

In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Mathematical model for cooling process and its self-learning applied in hot rolling mill

Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Neuron self-learning PSD control for backside width of weld pool in pulsed GTAW with wire filler

In this paper, the weld pool shape control by intelligent strategy was studied. A neuron self-learning PSD controller for backside width of weld pool in pulsed GTAW with wire filler was designed. The PSD control arithmetic was analyzed, simulating experiment by MATLAB software was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were successfully implemented. The study results show that the neuron self-learning PSD control method can attain a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

The Self-Learning Gate for Quantum Computing

Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.

A novel self-learning approach to overcome incompatibility on TripAdvisor reviews

Among social media networks,TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations.Sentiment analysis is amethod that can be used to analyze people's behaviors and opinions onpublic and socialmedia platforms.In this study,hotel reviews are extracted fromthe five most attractive Sri Lankan cities,and user-written reviews are compared over user bubble ratings,which define overall travelers'experiences as a numerical scale that ranks from 1 to 5.We find that the compatibility between userwritten reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users'genuine opinions expressed in their reviews.To address this problem,a two-phase approach is proposed:(1)the ensemblemethod to improve the performance of lexicon-based outputs and identify the correctlymatching user review and bubble rating;(2)the self-learning approach to finding the sentiment of a review that does not properly label by the user.The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposedmodel.For example,regardless of bigram“not good”,the average percentages of the word“good”for each negatively identified review from the proposed model and bubble rating are 25.63%and 38.85%,respectively.Thereby,it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.

Grid System Analysis of Urban Flora of Bukhara City (Uzbekistan)

This article presents information on the study of the flora of Uzbekistan based on grid system mapping. The urban flora of the city of Bukhara was researched in it. As a result of research, the territory of Bukhara city was divided into 85 indexes based on 1 × 1 km2 grid mapping system. The diversity and density of species in the indexes are determined. The influence of anthropogenic factors on the diversity of species in the indexes is determined.

A Self-Learning Diagnosis Algorithm Based on Data Clustering

The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.