Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

ReinforcementBased Fuzzy Neural Network Control with Automatic Rule Generation

A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and simulation result shows significant improvements on the rule generation.

Monetary policy rules：Forward-looking and backward-looking in an overlapping generations model

In the framework of an overlapping generations model, forward-looking monetary policy roles and backward-looking monetary policy rules were investigated. It is shown that the monetary steady state is more likely to be indeterminate under an active forwardlooking rule than under the corresponding backward-looking rule. It is also shown that backward-looking roles can render the monetary steady state unstable.

Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document content.Also,this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)respectively.Here,DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches.

Generation Rules in POMA Architecture

Another component in Pattern-Oriented and Model-Driven Architecture (POMA) is the concept of model generation. The generation code of models is the process of creating a source code from a model using generation rules. In this paper, we present the generation rules that are used to support the automated code generator of POMA architecture to generate the source code of the entire interactive system. These Platform-Specific Model (PSM) models are based on patterns which illustrate how several individual models of patterns can be generated at different levels of abstraction such as PSM models to source code in the development of interactive systems.

Comparative Study of the Performance of M5-Rules Algorithm with Different Algorithms

The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when initiating software projects allows us to plan adequately any forthcoming activities. As far as estimation and prediction is concerned there is still a number of unsolved problems and errors. To obtain good results it is essential to take into consideration any previous projects. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. In this study, performance of M5-Rules Algorithm, single conjunctive rule learner and decision table majority classifier are experimented for modeling of Effort Estimation of Software Projects and performance of developed models is compared with the existing algorithms namely Halstead, Walston-Felix, Bailey-Basili, Doty in terms of MAE and RMSE. The proposed techniques are run in the WEKA environment for building the model structure for software effort and the formulae of existing models are calculated in the MATLAB environment. The performance evaluation criteria are based on MAE and RMSE. The result shows that the M5-Rules have the best performance and can be used for the effort estimation of all types of software projects.

An Effective and Customized Itinerary Planning System Using Association Rule Mining Technique with Personalized Points of Interest

Mining the data from the huge collection that are present in the database and uncovering the relationships between the item set are one of the key aspects of data mining technologies. Itinerary planning system with personalization in selecting the places to the users is one of the demanding features in most of the travel plan. In this work, the system is designed in such a way to provide the customized journey plan to the users and also the effective one to the back pack travelers. Here the Points of Interests are the places to visit in each destination for the number of days chosen by the travelers. In this system, the users are allowed to specify the desired POIs to visit for the selected destination and can make their customized travel plan effectively. This proposed system is designed to choose the customized places to visit and to plan travel for K-day itineraries. The most visited itineraries are saved and updated in the database. Association rules are used to find out the frequent places visited in each destination and to provide the reputed places to the users to plan the journey. Here the Weka tool is used to evaluate the performance of the algorithm and the rules that are generated for the given travel dataset. Data set is designed by considering several attributes that can take part during travel such as source, destination, travel cost, budget, etc. Statistical analysis is done to evaluate the performance of the proposed system and the list of features that are present in the system. During the analysis part, registered users, number of logins, frequent visits, and attributes are analyzed. Thus the system can be redefined further with the help of this statistical analysis. It is mostly used at the organization end to evaluate their performance and improve the features. Report is generated once the user has chosen their customized places to visit and all detailed description of journey is presented to the user. Report could be saved at the user end and they can use it for the future reference. Thus the goal of the system is to provide the customized travel with personalization in choosing POIs and to find the frequent places visited with desired amenities.

Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine

To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.

Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill

Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real- life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The perform- ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy C-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

Kinematic generation of ruled surface based on rational motion of point-line

This paper studies representation of rigid combination of a directed line and a reference point on it （here referred to as a ＂point-line＂） using dual quatemions. The geometric problem of rational ruled surface design is viewed as the kinematic prob- lem of rational point-line motion design. By using the screw theory in kinematics, mappings from the spaces of lines and point-lines in Euclidean three-dimensional space into the hyperplanes in dual quaternion space are constructed, respectively. The problem of rational point-line motion design is then converted to that of projective Bezier or B-spline image curve design in hyperplane of dual quatemions. This kinematic method can unify the geometric design of ruled surfaces and tool path generation for five-axis numerical control （NC） machining.

论孟子的德政思想

文章指出孟子以唐虞三代之盛世图景为理论参照,以性善论为心性凭借,以制产裕民为先决条件,建构起一套极具民本色彩的德政思想体系。同时,孟子还对德政模式下的社会阶层结构、人才选拔原则及德政理想的现实切入路径作了极富智慧的探讨。

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Integrated knowledge-based modeling and its application for classification problems

Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.

Research on Innovation of Landscape Culture

After the illustration of the concept of landscape culture, the generation rule and innovation of landscape culture have been analyzed creatively in the perspective of landscape cultural innovation. It considers that the innovation of landscape culture should center on "virtual landscape culture", and emphasizes its ideological form. Based on characteristics of materialistic dialectics of Marxism, it has pointed out the generation rule of landscape culture. Then, it has analyzed factors obstructing innovation of landscape culture, and highlighted that landscape cultural innovation is of practical significance to urban planning and the construction of landscape cultural network with regional feature.

浅谈1500kW风电机组运行规程的编制

针对不同厂家设备的技术要求差异,对1500kW风电机组运行规程进行规范化编制,达到提高安全生产监控能力和机组运行可靠性的目的。

Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network

This paper proposes machine learning techniques to discover knowledge in a dataset in the form of if-then rules for the purpose of formulating queries for validation of a Bayesian belief network model of the same data. Although do-main expertise is often available, the query formulation task is tedious and laborious, and hence automation of query formulation is desirable. In an effort to automate the query formulation process, a machine learning algorithm is lev-eraged to discover knowledge in the form of if-then rules in the data from which the Bayesian belief network model under validation was also induced. The set of if-then rules are processed and filtered through domain expertise to identify a subset that consists of “interesting” and “significant” rules. The subset of interesting and significant rules is formulated into corresponding queries to be posed, for validation purposes, to the Bayesian belief network induced from the same dataset. The promise of the proposed methodology was assessed through an empirical study performed on a real-life dataset, the National Crime Victimization Survey, which has over 250 attributes and well over 200,000 data points. The study demonstrated that the proposed approach is feasible and provides automation, in part, of the query formulation process for validation of a complex probabilistic model, which culminates in substantial savings for the need for human expert involvement and investment.

Analysis of (1/2)^+ baryon states containing fourth-family quarks from QCD sum rules

When the fourth generation of quarks have sufficiently small mixing with ordinary standard-model 1＋ quarks, the hadrons made up from these quarks can be long-lived enough. We analyze the 1/2 baryon states containing fourth-generation quarks and standard-model quarks, i.e. the charm or bottom quarks, in the QCD sum rules approach. Considering the perturbative and two gluon condensate contributions in the calculation, we give the numerical results of the masses and pole residues.

Background-free detection of molecular chirality using a single-color beam [Invited]

We propose and numerically demonstrate a simple and background-free all-optical chiral spectroscopy technique for gas molecules.Our approach is based on high harmonic generation driven by a new type of laser beam that is produced by one linearly polarized single-color beam passing through a lens and a prism.It is shown that chiral and achiral signals are completely separated in frequency,indicating strong background-free and highly sensitive chirality detection.We believe this all-optical method can open new opportunities for ultrafast detection for chiral dynamics in the femtoseond to attosecond time scale.

A Creative Computing Approach to Film-story Creation:A Proposed Theoretical Framework

The film industry is currently witnessing a severe shortage of good stories and a decline in storytelling art.Meanwhile,creative computing has been employed successfully in the humanities,especially in the fields of art.Seeing the similarities between the process of film-story creation and that of creative computing,we propose a theoretical framework across the two domains,where the exploratory,the combinational and the transformational rules are jointly utilized to generate new ideas and provide potential options in film-story creation.The framework consists of a film knowledge library,a creative computing system,an evaluation model and an output module.The combination of creative computing and film story creation not only helps to produce novel storylines,shorten the creation cycle,and speed up film industry,but also contributes to the novelty and specificity of interdisciplinary studies.