基于WF StateMachine的UML状态图动态构建与测试

采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Machine业务流程映射关系,选取UML中典型状态图,依据一定的命名转换规则,实现了从UML状态图分析设计到WF状态机业务过程可视化的构建,完成了动态测试。

基于Spring StateMachine的有限状态机应用研究

状态机是一种表示有限状态以及在这些状态之间的转移和动作等行为的模型。可以通过发送事件,或者请求当前状态来与状态机进行交互。Spring StateMachine是Spring框架提供的一款在Web应用中利用有限状态机轻量级的框架,它可以帮助开发者简化状态机的开发过程,提高代码安全性和开发效率。以电商系统的订单状态管理为例,通过Spring StateMachine框架的使用,对状态机设计模式进行应用研究。

基于WF State Machine的UML Communication Diagram动态构建及测试

在基于UML的业务流程分析与设计过程中,从静态模型分析到动态模型构建,经过一系列抽象转换和代码实现,往往满足不了业务需求,缺少一种所见即所得的业务过程实现。在探索UMLCommunicationDiagram和WF StateMachine业务流程映射关系的基础上,选取UML用户指南中典型案例,研究从CommunicationDiagram到State Machine编程模型之间的静态映射和动态规则转换,基于WF可视化地实现了动态构建与测试,解决了从分析、设计到构建的无缝转换。

High-energy nuclear physics meets machine learning

Although seemingly disparate,high-energy nuclear physics(HENP)and machine learning(ML)have begun to merge in the last few years,yielding interesting results.It is worthy to raise the profile of utilizing this novel mindset from ML in HENP,to help interested readers see the breadth of activities around this intersection.The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP.From different aspects and using examples,we examine how scientific questions involving HENP can be answered using ML.

Audio Signal Generator System Based On State Machines

A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.

Real-time embedded software testing method based on extended finite state machine

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.

Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.

Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population:NHANES database

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice.

Verifying the accuracy of interlocking tables for railway signalling systems using abstract state machines

Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly recommends the utilization of finite state machines during system modelling stage and formal proof methods during the verifi- cation and testing stages of control algorithms. Due to the high importance of interlocking table at the design state of a sig- nalization system, the modelling and verification of inter- locking tables are examined in this work. For this purpose, abstract state machines are used as a modelling tool. The developed models have been performed in a generalized structure such that the model control can be done automatically for the interlocking systems. In this study, NuSMV is used at the verification state. Also, the consistency of the developed models has been supervised through fault injection. The developed models and software components are applied on a real railway station operated by Metro Istanbul Co.

Artificial emotional model based on finite state machine

According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.

Global Control Simulation of Electric Vehicle Based on Finite State Machine Theory

Finite state machine theory (FSM) is introduced and applied to global control of electric vehicle. Theoretical adaptation for application of FSM in control of electric vehicle is analyzed. Global control logic for parts of electric vehicle is analyzed and built based on FSM. Using Matlab/Simulink, BJD6100-HEV global control algorithm is modeled and prove validity by simulation.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

Semantic Sentence Similarity Using Finite State Machine

In this paper, a finite state machine approach is followed in order to find the semantic similarity of two sentences. The approach exploits the concept of bi-directional logic along with a semantic ordering approach. The core part of this approach is bi-directional logic of artificial intelligence. The bi-directional logic is implemented using Finite State Machine algorithm with slight modification. For finding the semantic similarity, keyword has played climactic importance. With the help of the keyword approach, it can be found easily at the sentence level according to this algorithm. The algorithm is proposed especially for Nepali texts. With the polarity of the individual keywords, the finite state machine is made and its final state determines its polarity. If two sentences are negatively polarized, they are said to be coherent, otherwise not. Similarly, if two sentences are of a positive nature, they are said to be coherence. For measuring the coherence (similarity), contextual concept is taken into consideration. The semantic approach, in this research, is a totally contextual based method. Two sentences are said to be semantically similar if they bear the same context. The total accuracy obtained in this algorithm is 90.16%.

EDSM-Based Binary Protocol State Machine Reversing

Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols are emerging in endlessly various network environments.Herein,relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection.Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols,most existing methods have proven to be time-consuming with limited efficiency,especially when applied on unknown protocol state machines.This paper proposes a state merging algorithm based on EDSM(Evidence-Driven State Merging)to infer the transition rules of unknown protocols in form of state machines with high efficiency.Compared with another classical state machine inferring method based on Exbar algorithm,the experiment results demonstrate that our proposed method could run faster,especially when dealing with massive training data sets.In addition,this method can also make the state machines have higher similarities with the reference state machines constructed from public specifications.

Balancing four-state continuous-variable quantum key distribution with linear optics cloning machine

We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution（CV-QKD） by using the linear optics cloning machine（LOCM）. Benefiting from the LOCM operation, the LOCM-tuned noise can be employed by the reference partner of reconciliation to achieve higher secret key generation rates over a long distance. Simulation results show that the LOCM operation can flexibly regulate the secret key generation rate and the maximum secure distance and improve the performance of four-state CV-QKD protocol by dynamically tuning parameters in an appropriate range.

Test Selection on Extended Finite State Machines with Provable Guarantees

Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.