SNG-TE: Sports News Generation with Text-Editing Model

Currently,the amount of sports news is increasing,given the number of sports available.As a result,manually writing sports news requires high labor costs to achieve the intended efficiency.Therefore,it is necessary to develop the automatic generation of sports news.Most available news gen-eration methods mainly rely on real-time commentary sentences,which have the following limitations:(1)unable to select suitable commentary sentences for news generation,and(2)the generated sports news could not accurately describe game events.Therefore,this study proposes a sports news generation with text-editing model(SNG-TE)is proposed to generate sports news,which includes selector and rewriter modules.Within the study context,a weight adjustment mechanism in the selector module is designed to improve the hit rate of important sentences.Furthermore,the text-editing model is introduced in the rewriter module to ensure that the generated news sentences can cor-rectly describe the game events.The annotation and generation experiments are designed to evaluate the developed model.The study results have shown that in the annotation experiment,the accuracy of the sentence annotated by the selector increased by about 8%compared with other methods.Moreover,in the generation experiment,the sports news generated by the rewriter achieved a 49.66 ROUGE-1 score and 21.47 ROUGE-2,both of which are better than the available models.Additionally,the proposed model saved about 15 times the consumption of time.Hence,the proposed model provides better performance in both accuracy and efficiency,which is very suitable for the automatic generation of sports news.

MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System

The microgrid is a typical cyber-physical microgrid system(CPMS). The physical unconventional distributed generators(DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components,such as the embedded computer and communication network,are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid,the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information(such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system(MAS). Based on the global information from cyber components of CPMS, automatic generation control(AGC) and automatic voltage control(AVC)are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

AI-infused Semantic Model to Enrich and Expand Programming Question Generation

Creating practice questions for programming learning is not easy.It requires the instructor to diligently organize heterogeneous learning resources,that is,conceptual programming concepts and procedural programming rules.Today’s programming question generation(PQG)is still largely relying on the demanding creation task performed by the instructors without advanced technological support.In this work,we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items.The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph(LKG)and Abstract Syntax Tree(AST).For any given question,the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures.We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions.The results show that the instructormade questions had much simpler complexity than the textbook ones.The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relation to the student performances.Finally,we report a user study results on the proposed Artificial Intelligent-infused semantic PQG model in examining the machine-generated questions’quality.

Simulation-based automatic generation of risk scenarios

A methodology for automatically generating risk scenarios is presented.Its main idea is to let the system model ＂express itself＂ through simulation.This is achieved by having the simulation model driven by an elaborated simulation engine,which：（i） manipulates the generation of branch points,i.e.event occurrence times;（ii） employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point.In addition,a backtracking technique,as an extension,is implemented to recover some missed risk scenarios.A widely discussed dynamic reliability example（a holdup tank） is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.

Simulation and uniform design-based automatic generation of risk scenarios

This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics(e.g.,the order,timing and magnitude of events,the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system.The main idea of the methodology is:(i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine;(ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics;(iii) for each selected design point,employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point.A highly dynamic example adapted from the literature(a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.

Research of testing method based on UML statecharts

Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. UML-based software development and modeling environments have been widely accepted in industry, including areas in which safety is an important issue such as spaceflight, defense, automobile, etc. To ensure and improve software quality becomes a main concern in the field. As one of the key techniques for software quality, software testing can effectively detect system faults. UML based software testing based is an important research direction in software engineering. The key to software testing is the generation of test cases. This dissertation studies an approach to generating test cases from UML statecharts.

Automatic Generation of Amharic Math Word Problem and Equation

Math word problem uses a real word story to present basic arithmetic operations using textual narration. It is used to develop student’s comprehension skill in conjunction with the ability to generate a solution that agrees with the story given in the problem. To master math word problem solving, students need to be given fresh and enormous amount of problems, which normal textbooks as well as teachers fail to provide most of the time. To fill the gap, a few research works have been proposed on techniques to automatically generate math word problems and equations mainly for English speaking community. Amharic is a Semitic language spoken by more than hundred million Ethiopians and is a language of instruction in elementary schools in Ethiopia. And yet it belongs to one of a less resourced language in the field of linguistics and natural language processing (NLP). Hence, in this paper, a strategy for automatic generation of Amharic Math Word (AMW) problem and equation is proposed, which is a first attempt to introduce the use template based shallow NLP approach to generate math word problem for Amharic language as a step towards enabling comprehension and learning problem solving in mathematics for primary school students. The proposed novel technique accepts a sample AMW problem as user input to form a template. A template provides AMW problem with placeholders, type of problem and equation template. It is used as a pattern to generate semantically equivalent AMW problems with their equations. To validate the reality of the proposed approach, a prototype was developed and used as a testing platform. Experimental results have shown 93.84% overall efficiency on the core task of forming templates from a given corpus containing AMW problems collected from elementary school mathematics textbooks and other school worksheets. Human judges have also found generated AMW problem and equation as solvable as the textbook problems.

Automatic generation of context-aware workflow documents for ubiquitous robot services

Intelligent robots in ubiquitous computing environment should be able to receive a variety of surrounding information and provide users with appropriate services.A developer can describe the robot services that are proper to users' environments by using his or her various environments,and process them through the execution engine.However,it is difficult for a developer to describe and develop robot services,who knows all surrounding information which is called context information.If there is a method for describing and documenting robot services in intuitive expressions,that is to use graphical user interfaces(GUIs),it would be very helpful.This paper suggests that robot service developers describe robot services using intuitive GUIs with context-awareness.And the services can be automatically generated into workflow documents.Robot services that robot service developers have made with intuitive GUIs can be automatically generated into workflow documents by using the object modeling technique(OMT).Developers can describe robot services based on context-aware workflow language(CAWL).For testing,scenario-based robot services are described using CAWL-based development tool,and their workflow documents are automatically generated.

Automatic Generation of UML Class Diagrams for Object-oriented Design and Programing Course

UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.

Leveraging Structured Information from a Passage to Generate Questions

Question Generation(QG)is the task of utilizing Artificial Intelligence(AI)technology to generate questions that can be answered by a span of text within a given passage.Existing research on QG in the educational field struggles with two challenges:the mainstream QG models based on seq-to-seq fail to utilize the structured information from the passage;the other is the lack of specialized educational QG datasets.To address the challenges,a specialized QG dataset,reading comprehension dataset from examinations for QG(named RACE4QG),is reconstructed by applying a new answer tagging approach and a data-filtering strategy to the RACE dataset.Further,an end-to-end QG model,which can exploit the intra-and inter-sentence information to generate better questions,is proposed.In our model,the encoder utilizes a Gated Recurrent Units(GRU)network,which takes the concatenation of word embedding,answer tagging,and Graph Attention neTworks(GAT)embedding as input.The hidden states of the GRU are operated with a gated self-attention to obtain the final passage-answer representation,which will be fed to the decoder.Results show that our model outperforms baselines on automatic metrics and human evaluation.Consequently,the model improves the baseline by 0.44,1.32,and 1.34 on BLEU-4,ROUGE-L,and METEOR metrics,respectively,indicating the effectivity and reliability of our model.Its gap with human expectations also reflects the research potential.

Automatic Generation Control with Virtual Synchronous Renewables

As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do not provide the inertial response,primary frequency response,secondary frequency response,and tertiary frequency regulation.As a result,the remaining SGs may not be sufficient to maintain the power balance and frequency stability.The concept and control strategies of virtual synchronous generators(VSGs)enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems.This paper focuses on the automatic generation control(AGC)with virtual synchronous renewables(VSRs).First,the VSR strategy that enables the RESs to participate in AGC is introduced.Second,based on the interval representation of uncertainty,the output of RES is transformed into two portions,i.e.,the dispatchable portion and the stochastic portion.In the dispatchable portion,the RESs can participate in AGC jointly with SGs.Accordingly,a security-constrained economic dispatch(SCED)model is built considering the RESs operating in VSR mode.Third,the solution strategy that employs the slack variables to acquire deterministic constraints is introduced.Finally,the proposed SCED model is solved based on the 6-bus and 39-bus systems.The results show that,compared with the maximum power point tracking(MPPT)mode,VSRs can participate in the active power and frequency control jointly with SGs,increase the maximum penetration level of RESs,and decrease the operating cost.

Compensating active power imbalances in power system with large-scale wind power penetration

Large-scale wind power penetration can affect the supply continuity in the power system.This is a matter of high priority to investigate,as more regulating reserves and specified control strategies for generation control are required in the future power system with even more high wind power penetration.This paper evaluates the impact of large-scale wind power integration on future power systems.An active power balance control methodology is used for compensating the power imbalances between the demand and the generation in real time,caused by wind power forecast errors.The methodology for the balance power control of future power systems with large-scale wind power integration is described and exemplified considering the generation and power exchange capacities in2020 for Danish power system.

A Two-stage Kalman Filter for Cyber-attack Detection in Automatic Generation Control System

Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

A Deep Reinforcement Learning Algorithm for the Power Order Optimization Allocation of AGC in Interconnected Power Grids

The integration of distributed generations(solar power,wind power),energy storage devices,and electric vehicles,causes unpredictable disturbances in power grids.It has become a top priority to coordinate the distributed generations,loads,and energy storages in order to better facilitate the utilization of new energy.Therefore,a novel algorithm based on deep reinforcement learning,namely the deep PDWoLF-PHC(policy dynamics based win or learn fast-policy hill climbing)network(DPDPN),is proposed to allocate power order among the various generators.The proposed algorithm combines the decision mechanism of reinforcement learning with the prediction mechanism of a deep neural network to obtain the optimal coordinated control for the source-grid-load.Consequently it solves the problem brought by stochastic disturbances and improves the utilization rate of new energy.Simulations are conducted with the case of the improved IEEE two-area and a case in the Guangdong power grid.Results show that the adaptability and control performance of the power system are improved using the proposed algorithm as compared with using other existing strategies.

Real-time AGC dispatch units considering wind power and ramping capacity of thermal units

The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service.To fulfill the automatic generation control(AGC)and compensate the influence of wind power fluctuations simultaneously,ramping capacity should be considered in the dispatch model of thermals.Meanwhile,conventional methods in this area do not take the impact of transmission loss into the dispatch model,or rely on offline network model and parameters,failing to reflect the real relationships between the wind farms and thermal generators.This paper proposes an online approach for AGC dispatch units considering the above issues.Firstly,the power loss sensitivity is online identified using recursive least square method based on the real-time data of phasor measurement units.It sets up power balance constraint and results in a more accurate dispatch model.Then,an improved multi-objective optimization model of dispatch is proposed and a connection is established between the thermal units with fast ramping capacity and the wind farms with rapid fluctuations.Genetic algorithm is used to solve the dispatch model.The proposed method is compared with conventional methods in simulation case in the IEEE 30-bus system.Finally,simulation results verify the validity and the feasibility of identification method and optimization model.

Advanced ECU Software Development Method for Fuel Cell Systems

The electronic control unit （ECU） in electrical powered hybrid and fuel cell vehicles is exceedingly complex. Rapid prototyping control is used to reduce development time and eliminate errors during software development. This paper describes a high-efficiency development method and a flexible tool chain suitable for various applications in automotive engineering. The control algorithm can be deployed directly from a Matlab/Simulink/Stateflow environment into the ECU hardware together with an OSEK real-time operating system （RTOS）. The system has been successfully used to develop a 20-kW fuel cell system ECU based on a Motorola PowerPC 555 （MPC555） microcontroller. The total software development time is greatly reduced and the code quality and reliability are greatly enhanced.

Real-time impact of power balancing on power system operation with large scale integration of wind power

Highly wind power integrated power system requires continuous active power regulation to tackle the power imbalances resulting from the wind power forecast errors. The active power balance is maintained in real-time with the automatic generation control and also from the control room, where regulating power bids are activated manually. In this article, an algorithm is developed to simulate the activation of regulating power bids, as performed in the control room, during power imbalance between generation and load demand. In addition, the active power balance is also controlled through automatic generation control, where coordinated control strategy between combined heat and power plants and wind power plant enhances the secure power system operation. The developed algorithm emulating the control room response,to deal with real-time power imbalance, is applied and investigated on the future Danish power system model. The power system model takes the hour-ahead regulating power plan from power balancing model and the generation and power exchange capacities for the year 2020 into account.The real-time impact of power balancing in a highly wind power integrated power system is assessed and discussed by means of simulations for different possible scenarios.

Automatic Generation Control of Multi-area Power System with Network Constraints and Communication Delays

Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes.

THE COMPACTION OF TIME-DEPENDENT VISCOUS GRANULAR MATERIALS CONSIDERING INERTIAL FORCES

In the present paper, compactions of time-dependent viscous granular materials are simulated step by step using the automatic adaptive mesh generation schemes. Inertial forces of the viscous incompressible aggregates axe taken into account. The corresponding conservation equations, the weighted-integral formulations, and penalty finite element model are investigated. The fully discrete finite element equations for the simulation are derived. Polygonal particles of aggregates are simplified as mixed three-node and four-node elements. The automatic adaptive mesh generation schemes include contact detection algorithms, and mesh upgrade schemes. Solu- tions of the numerical simulation axe in good agreement with some results from literatures. With minor modification, the proposed numerical model can be applied in several industries, including the pharmaceutical, ceramic, food, and household product manufacturing.