1- Introduction

2- Method

3- Case study area and data

4- Results

5- Conclusions

References

Abstract

Flash floods evolve rapidly in time, which poses particular challenges to emergency managers. One way to support decision-making is to complement models that estimate the flash flood hazard (e.g. discharge or return period) with tools that directly translate the hazard into the expected socio-economic impacts. This paper presents a method named ReAFFIRM that uses gridded rainfall estimates to assess in real time the flash flood hazard and translate it into the corresponding impacts. In contrast to other studies that mainly focus on individual river catchments, the approach allows for monitoring entire regions at high resolution. The method consists of the following three components: (i) an already existing hazard module that processes the rainfall into values of exceeded return period in the drainage network, (ii) a flood map module that employs the flood maps created within the EU Floods Directive to convert the return periods into the expected flooded areas and flood depths, and (iii) an impact assessment module that combines the flood depths with several layers of socio-economic exposure and vulnerability. Impacts are estimated in three quantitative categories: population in the flooded area, economic losses, and affected critical infrastructures. The performance of ReAFFIRM is shown by applying it in the region of Catalonia (NE Spain) for three significant flash flood events. The results show that the method is capable of identifying areas where the flash floods caused the highest impacts, while some locations affected by less significant impacts were missed. In the locations where the flood extent corresponded to flood observations, the assessments of the population in the flooded area and affected critical infrastructures seemed to perform reasonably well, whereas the economic losses were systematically overestimated. The effects of different sources of uncertainty have been discussed: from the estimation of the hazard to its translation into impacts, which highly depends on the quality of the employed datasets, and in particular on the quality of the rainfall inputs and the comprehensiveness of the flood maps.

Introduction

Numerous flash floods (FF hereafter) occur across the globe every year. In Europe, a yearly average of around 50 FF-related fatalities is recorded (Sene, 2013). Apart from posing a high danger to humans, economic losses in the range of several billion euros can be caused by individual events (CRED, 2016; EEA, 2010; Gaume et al., 2009). Typically, FFs occur in small to medium-sized catchments with steep slopes and prone to convective storms. This setting results in very short lag times between the causative rainfall and the discharge response in the stream channel – usually not more than a few hours (Georgakakos, 1986; Hapuarachchi et al., 2011). This extremely sudden onset is one of the main drivers for the devastating potential of FFs (EEA, 2010; Sene, 2013; Spitalar et al., 2014) since it leaves only little time to coordinate flood response measures (e.g. evacuations or installations of nonpermanent flood protection systems). Early warning systems (EWS) can help to extend the anticipation horizon of FFs; the European Environmental Agency (EEA, 2010) argues that the improvement of EWSs is the most effective measure to reduce FF impacts. One way to improve EWSs is to increase the forecast skill (e.g. by improved rainfall estimation and forecasting). Another way is to include additional information into the EWS to enhance the decision support for the emergency responders (e.g. by simulating the response to the rainfall in the stream network). The latter was the motivation of several methods that forecast the FF hazard (e.g. the return period or discharge at predefined river sections; see Alfieri et al., 2019, 2012; Corral et al., 2019; Gourley et al., 2014; Hapuarachchi et al., 2011 for reviews).

1. Introduction

2. Material and methods

3. Results

4. Discussion

5. Conclusions

Acknowledgements

References

Abstract

Marek’s disease (MD) is one of the most devastating diseases of poultry. It’s caused by the highly infectious alphaherpesvirus MD virus serotype 1 (MDV-1). In this study, a rapid and easy-to-use assay based on recombinase polymerase amplification (RPA) was developed for MDV detection. Primer-probe sets targeting the highly conserved region of Meq gene were designed and applied to the RPA assay. The assay was carried out on a real-time thermostatic fluorescence detector at 39 °C for 20 min. As revealed by the results, no cross-reactions were found with the Newcastle disease virus (NDV), chicken infectious anemia virus (CAV), infectious bursal disease virus (IBDV), avian infectious bronchitis virus (IBV), infectious laryngotracheitis virus (ILTV), avain influenza virus (AIV), avian leucosis virus (ALV), avian reovirus (ARV), Marek’s disease virus serotype 2 (MDV-2) and turkey herpes virus (HVT), indicating appropriate specificity of the assay. Plasmid DNA standards were used to determine the sensitivity of the assay and the detection limit was 102 copies/μL. To further evaluate the clinical performance, 94 clinical samples were subjected to the RPA assay and 28 samples were tested MDV positive, suggesting that the real-time RPA assay was sufficient enough for clinical sample detection. Thus, a highly specific and sensitive real-time RPA assay was established and validated as a candidate for MDV diagnosis. Additionally, the portability of real-time RPA assay makes it suitable to be potentially applied in clinical diagnosis in the field, especially in resource-limited settings.

Introduction

Marek’s Disease Virus (MDV) is the causative agent of Marek’s disease (MD) in chickens [1]. It is a highly contagious oncogenic herpesvirus of poultry that can infect chickens, turkeys and pheasant. The virus is shed from feather follicles of infected chicken or birds and contaminates the living environment. Chickens will be infected by inhalation of contaminated air [2,3]. The prevalence of MD in all poultryrearing countries causes huge economic losses every year [4,5]. Isolates of MDV can be classified into three serotypes, only serotype 1 (MDV1) or Gallid herpesvirus 2 (GaHV-2) is pathogenic and oncogenic. The severity of MD varies depending on virus stains, host genotypes and the vaccination status of the infected hosts [6]. Despite the wide use of vaccines, the economic burden of the disease will substantially increase. Therefore, timely and accurate detection of MDV in poultry farms is extremely important.

Conventional methods such as virus isolation and serum neutralization tests are labor-intensive and time-consuming [7]. To date, real-time PCR, GeXP-multiplex PCR assays and xTAG assay have been developed for MDV detection [8–10]. These methods are specific, sensitive and even enable high-throughput detection, which make them perfect choices for laboratory diagnosis. However, the need of expensive equipment such as Luminex-200 reader or GeXP analyser and well-trained technicians limits their applications in poultry farms [11,12].

I- Introduction

II- Voltage Distortion With Different IGBT Models

III- IGBT Transient Model

IV- Circuit Solver With FPGA

V- High-Power Traction System Simulation Study

VI- Conclusions

References

Abstract

The hardware-in-the-loop (HiL) simulation plays a vital role in the test of high-power electronic system. Although the application of field programmable gate array (FPGA) embedded system has enabled the real-time system simulating below 500ns, the transient characteristic of high voltage insulated-gate bipolar transistor (HVIGBT) is largely compromised. In this paper, a new piece-wise HVIGBT model, considering its driver circuit effect and parasite parameter, is proposed for FPGA-based real-time simulation applications. With the attempt to reduce the simulation latency, we propose a FPGA solver with a parallel structural to divide the system into several layers. The model could not only provide accurate system-level performance of the power electronic converter but also give an insight into the transient behavior effect of high-power electronic system. At last, a case study about emulation of traction system of high-speed train is also presented. Implementations are made on a FPGA Kintex-7 embedded in National Instruments FlexRIO PXIe-7975. The obtained results show that the proposed modeling algorithm can achieve both accuracy and efficiency within a fixed real-time simulation time step of 25 nanoseconds.

Introduction

The high voltage insulated-gate bipolar transistor (HVIGBT) is widely used in the electrified transportation where high voltage and current are required. Advancements in power electronics applications demand IGBT operating in high switching speed as well as high switching frequency [1]. Due to dead-time, switching time, delay time and voltage drops, the output voltage and current are distorted with respect to an ideal switch performance [2]. Its model precision is crucial for model-based motor control strategy [3]. Researching HVIGBT model is an important stream for theses megawatt power electronic system simulation. In recent years, there are three types of IGBT model used in real-time simulation. The most accurate model is numerical model, which solves the physical equations in analytical expression describing carriers and electrical behavior. The analytical model utilizes the parameter extracted from testing waveform and solves high-order physical equations. Despite these physical or device level models are assumed to be too complex to be implemented in FPGA, [4][5][6] have reported using physical or device level semiconductor simulation in FPGA. They are impressive for an FPGA implementation seeking low steps within acceptable on-chip resource utilization. However, these implementations are not suitable for a HVIGBT with high switch frequency and high switch speed. The relative slow calculation speed in their execution results a high latency between simulator and controller, which could largely affect the simulator’s response to the controller. As for the behavioral model [7][8][9], it treats semiconductor devices as ideal or nearly ideal switches when the semiconductors are in either completely on or completely off states. This idealization is suitable for the fast computation speed required by the real-time.

The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit, consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate, recall rate, and the F-measure. Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.

Introduction

Nowadays, it is widely accepted that the quality of software is not only related to the product, but to the organization and to the production process that is carried out [1]–[3]. Software process modeling helps to create process descriptions that correspond to processes actually performed during software development or maintenance. Process models can be used to visualize tacit knowledge, roles, and information flows in the processes, identifying points for improvement and optimization [4], [5]. However, with the deepening of research on software development, problems associated with traditional subjective modeling methods have become apparent. These arise because the task of designing a software process model is complex and error prone, and because the life cycle of the model is short, individuals are not sensitive to differences between actual processes and the process model, and there are increasing requirements for process engineers, all while the software process is still evolving [6]–[8]. As software systems become more and more complex, the establishment of a sound process model is becoming more like ‘‘an art rather than a science’’ [9].

Twitter is one of the most popular social platforms. It has changed the way of communication and information dissemination through its real-time messaging mechanism. Recently, it has been used by researchers and industries as a new source of data for various intelligent systems, such as tweet sentiment analysis and recommendation systems, which require high data quality. However, due to its flexibility and popularity, Twitter has become the main target for spamming activities such as phishing legitimate users or spreading malicious software, which introduces new security issues and waste resources. Therefore, researchers have developed various machine-learning algorithms to reveal Twitter spam. However, as spammers have become smarter and more crafty, the characteristics of the spam tweets are varying over time making these methods inefficient to detect new spammers tricks and strategies. In addition, some of the employed methods (e.g. blacklisting) or spammer features (e.g. graph-based features) are extremely time-consuming, which hinders the ability to detect spammer activities in real-time. In this paper, we introduce a framework to deal with the volatility of the spam contents and new spamming patterns, called the spam drift. The framework combines the strength of unsupervised machine learning approach, which learns from unlabeled tweets, to retrain a real-time supervised tweet-level spam detection model in a batch mode. A set of experiments on a large-scale data set show the effectiveness of the proposed online unsupervised method in adaptively discovers and learns the patterns of new spam activities and achieve stable recall values reaching more than 95%. Although the average spam precision of our method is around 60%, the high spam recall values show the ability of our proposed method in reducing spam drift problems compared to traditional machine learning algorithms.

Introduction

The new great characteristics of Social Web that involve users as information producers have exposed different information quality (IQ) problems (Agarwal & Yiliyasi, 2010). For example, Twitter, which is the most popular microblogging sites, has a real-time messaging mechanism that makes it more popular and suitable for handling real-time public events and updates. In addition, due to its popularity, social-based researchers adopt it as a main source of information in performing their experiments on related research areas (Abascal-Mena, Lema, & Sèdes, 2015; Hoang & Mothe, 2016; Mezghani et al., 2015; Mezghani, Zayani, Amous, Péninou, & Sèdes, 2014; Zubiaga, Spina, Amigó, & Gonzalo, 2012; Zubiaga, Spina, Fresno, & Martínez-Unanue, 2011). However, the simplicity and flexibility of using these sites, and the absence of any effective restrictions on content posting action might be viewed as additional challenges for having IQ issues. Indeed, social spam content, which is published by a well-known kind of ill-intentioned users, so-called social spammers, is one of the most common noises appearing on online social media (OSM) sites and is categorized under IQ problems. Social spammers intensively post nonsensical contents such as advertisements, porn materials, viruses, malware, and phishing websites in different contexts (e.g., topics) and in an automated and systematic way (Benevenuto, Magno, Rodrigues, & Almeida, 2010; Washha, Qaroush, & Sèdes, 2016). Moreover, Social spammers exploit trending topics and available services or APIs to lunch their spammy content in short periods (e.g., one day) to maximize their monetary profits and speed up their spamming behavior.

1. Introduction

2. Methodology

3. Experimental Outcome

4. Conclusion

5. Acknowledgement

References

In this paper, a real time application to replicate nine arm movements is proposed. The two important joints that are controlled are wrist and elbow. Electromyogram signals are recorded for four wrist positions and five elbow positions. These signals are enhanced and features pertaining to muscle movements are extracted. Dimension of these feature sets is reduced to obtain the optimal set of features. These feature sets are given as input to the classifier. Performance evaluation of Support Vector Machine (SVM), K-Nearest Neighbors, Random Forest and Relevant Vector Machine (RVM) classifiers, in recognizing different wrist and elbow positions, is discussed. As per the results, the best overall accuracy of 93.3% was obtained from SVM with radial basis function (RBF) kernel, in classifying both the wrist and elbow positions. Although, RVM as a classifier yielded the same accuracy in recognizing wrist positions, it resulted in the lowest accuracy of 88.67% in recognizing elbow positions. Therefore, SVM-RBF fared better in identifying the arm movements. Furthermore, these arm movements are used to control the actuators.

Introduction

The muscle attached to the bone is called the skeletal muscle. The contraction of this muscle is responsible for the movement of the limbs. This contraction leads to the generation of an electrical signal called electromyography (EMG) signal. Multiple strands called myofibrils present in the muscle are chemically activated by neurons which leads to the generation of charge whose motion in turn produces electromagnetic field. In a motor unit all the action potentials generated by myofibrils are combined together and hence this is termed as motor unit action potential (MUAP). Surface EMG electrodes collect several such MUAPs for each skeletal muscle. The physiology of the signal depends on the arm movement that led to its generation. This unique identity of each signal can be used to replicate specific arm movements in an orthotic arm using actuators.

Abstract

Green cloud computing attracts significant attention from both academia and industry. One of the major challenges involved, is to provide a high level of Quality of Service (QoS) in a cost-effective way for the end users and in an energy-efficient manner for the cloud providers. Towards this direction, this paper presents an energy-efficient, QoS-aware and cost-effective scheduling strategy for real-time workflow applications in cloud computing systems. The proposed approach utilizes per-core Dynamic Voltage and Frequency Scaling (DVFS) on the underlying heterogeneous multi-core processors, as well as approximate computations, in order to fill in schedule gaps. At the same time, it takes into account the effects of input error on the processing time of the component tasks. Our goal is to provide timeliness and energy efficiency by trading off result precision, while keeping the result quality of the completed jobs at an acceptable standard and the monetary cost required for the execution of the jobs at a reasonable level. The proposed scheduling heuristic is compared to two other baseline policies, under the impact of various QoS requirements. The simulation experiments reveal that our approach outperforms the other examined policies, providing promising results.

Introduction

As cloud computing continues to gain momentum, providing a high level of Quality of Service (QoS) in a cost-effective way for the end users and in an energy-efficient manner for the cloud providers, is an ongoing challenge that gathers significant attention from both academia and industry. A Service Level Agreement (SLA) is commonly required between the end users and a cloud provider. It is a contract between the two parties that defines the type of the provided service, the QoS requirements, as well as the adopted pricing scheme [1]. Compared to other data center infrastructure components, processors typically consume the greatest amount of energy [2–4]. A widely used power management method is the Dynamic Voltage and Frequency Scaling (DVFS) technique. DVFS allows the dynamic adjustment of the supply voltage and operating frequency (i.e., speed) of a processor, based on the workload conditions [5,6]. Modern multi-core processor architectures incorporate voltage regulators for each integrated core, allowing per-core DVFS, so that each core can operate at a different voltage and frequency level from the other cores of the same processor [7]. While this provides flexibility and better energy efficiency, it involves higher control complexity, especially in the case of clouds where the heterogeneous physical resources are usually virtualized and managed by the hypervisor (Virtual Machine Monitor — VMM) [8]. With the rapid growth of cloud computing, there is a dramatic increase in the number and variety of applications processed on such platforms. They encompass a wide spectrum of sectors and activities, ranging from social media and big data analytics, to healthcare monitoring and financial applications [9,10]. The workload generated by such applications usually comprises jobs with multiple component tasks that have precedence constraints among them.

1. Main text

2. Related Works

3. The Project Definition and its Impact in the Processing Architecture

4. The New Structural Coefficient based on the Definitions of the Attributes

5. Implementation of the Text Similarity-Driven Structural Coefficient

6. Conclusions

Acknowledgement

References

The real-time data processing constitutes a critical area when talking about real-time decision making. Strong decisions are based on recommendations for describing the associated course of actions, but the real-time processing gives a very short time for searching them. The Processing Architecture based on Measurement Metadata is a data stream engine oriented to measurement projects, which supports the decision making through an organizational memory. The search space related to the organizational memory is initially in-memory limited using the structure of the measurement projects. Given a project, the related projects are ordered based on a given scoring from its structural definition. Here, a new structural coefficient based on the text similarity, which is computed from the textual definition of each descriptive attribute of a project is introduced. This allows better scoring of the related projects, even when its definitions could be affected by human errors or multiple definitions. The scoring is critical when in a given situation, a project has not specific experience for recommending, in such context, the recommendations from the near projects are served. The pabmm_sh library is outlined and a simulation on its associated processing times for the similarity computing are introduced based on the token definition for a measurement project. The library adds a new alternative perspective in the processing architecture for driving the searches into the organizational memory. It can update 2000 projects less than 1 second, keeping the individual processing time of each project under 1 millisecond.

Main text

The data processing is cheaper every day thanks to the technology evolution jointly with the scale economy, even the big data repositories and the data streams are fed allowing the data-driven decision making as a natural aspect in the different organizations (e.g. the governments) [1]. The measurement and evaluation constitute a logic step for determining the current state of a concept under monitoring and its posterior evaluation. One of the applications of the data-driven decision making is the monitoring of entities, which is especially useful for keeping track of an entity (e.g. a person), jointly with the characterization of its behavior [2].

Traditionally, structural optimization is a numerical process; candidate designs are created and evaluated through numerical simulation (e.g., finite element analysis). However, when dealing with complex structures that are difficult to model numerically, large errors could exist between the numerical model and the physical structure. In this case, the optimization is less meaningful because the optimal results are associated with the numerical model instead of the physical structure. Experiments can be included in the optimization algorithm to represent complex structures or components. However, the time and cost limitations are prohibitive when iteratively constructing and evaluating complete structural systems. Real-time hybrid simulation (RTHS) is an efficient and cost-effective experimental tool that combines numerical simulation with experimental testing to capture the total structural performance. This paper proposes a framework for real-time hybrid optimization (RTHO); RTHS is used to evaluate the performance of candidate designs within the optimization process. The framework creates a cyber-physical optimization environment using RTHS, a modern experimental technique with roots in earthquake engineering. This paper outlines the framework for RTHO with accompanying proof-ofconcept studies. In a preliminary study, the base isolation design of a two-story building was optimized for seismic protection. RTHO was further validated for the optimal selection of multiple semi-active control law parameters for an MR damper installed in the isolation layer of a five-story base-isolated building. Both cases used RTHS to evaluate the candidate designs and particle swarm optimization (PSO) to drive the optimization. RTHO is well-suited to evaluate nonlinear experimental substructures, in particular those that do not undergo permanent damage such as structural control devices. Structural damage, if of interest, can be modeled through the numerical component. This paper proposes and demonstrates the integration of state-of-the-art optimization algorithms with state-of-the-art experimental methods – a cyber-physical approach to structural optimization.

Introduction

In the last two decades, design trends for civil infrastructure have shifted from prescriptive code procedures to performance-based methods. The structural engineers of tomorrow will be asked to produce lighter, taller, and more cost-effective designs to meet performance demands under natural and human-made hazards. Sustainable solutions that consider long-term costs and benefits will require new approaches to design and optimization. Structural optimization enables engineers to minimize user-specified objectives (e.g., material use) while ensuring strength and serviceability requirements constraints are met (e.g., requirements for drift and acceleration). Structural optimization includes size optimization, shape optimization, and topology optimization [1]. Size optimization focuses on optimizing the cross-section of the discrete structural members such as beams and columns, or thickness of continuous material such as panels and slabs. Shape optimization allows the location of nodes and connections to vary. Topology optimization uses the distribution of material and structural connectivity to find the optimal layout of the structure for a given shape.

GNSS is now being speedily expanded to our daily life, but the positioning precision still can hardly meet the demands of many high-precision applications, such as approaching landing system on airports. Due to the development of GNSS, quadruple-frequency signals are now available in China’s BeiDou Navigation Satellite System (BDS) and the European Galileo system, which can contribute to positioning precision. Positioning precision can not be improved obviously by quadruple-frequency carrier phases until cycle slips are detected and repaired. A method using four linear combinations to detect and repair quadruplefrequency cycle slips is proposed in the paper. The choices of the four linear combinations are conducted in cascaded steps in accordance to the cycle slip fixing probability. When the four detection combinations are determined, cycle slips on original carrier phase observations can be uniquely determined. The proposed algorithm has been tested on real 30-second quadruple-frequency static observations of BDS and Galileo and on real 0.05-second quadruple-frequency kinematic observations of BDS and Galileo. Simulated and real cycle slips are tested. The results show that the proposed algorithm can detect and repair cycle slips even for one cycle effectively.

Introduction

With the completion of the experimental and regional phases, China’s BeiDou Navigation Satellite System (BDS) is being speedily expanded to a global and multifunctional satellite navigation system, BDS-3 [1]. To ensure the smooth transition from BDS-2 to BDS-3, B1I and B3I will continue to be broadcast while B2I will not be retained [2]. At the same time, two new OS (open service) signals, i.e. B1C and B2a, will be broadcast by the BDS-3 satellites [3], [4]. Those four signals are broadcast on four frequencies and can be received by BDS users. In addition, the European Galileo system has broadcasted quadruple-frequency signals, i.e. E1, E5a, E5b and E6, and several researches have been done based on Galileo quadruple-frequency signals [5], [6]. Carrier phase measurements are the important observations for highly accurate positioning because of their high accuracy. Multi-frequency signals can form more linear combinations with small combined noise, small ionospheric delay and long wavelength compared with single-frequency signal, and multi-frequency signals are used in many high precise applications, such as approaching landing system and geodetic measurement applications [7]–[18].

In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can efficiently represent and compute the complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays.

Introduction

Accurate prediction of train delays (deviations from timetable) is an important requirement for proactive and anticipative realtime control of railway traffic. Traffic controllers need to predict the arrival times of the trains within (or heading towards) their area in order to control the feasibility of timetable realisation. Similarly, the transport controllers on behalf of train operating companies may use the predictions to estimate the feasibility of planned passenger transfers, as well as rolling-stock and crew circulation plans. Valid estimates of arrival and departure times are therefore important for preventing or reducing delay propagation, managing connections, and providing reliable passenger information. The difficulty for predicting the train event times comes from the uncertainty and unpredictability of process times in railway traffic. The models for real-time traffic control have so far mostly focused on overcoming the great combinatorial complexity of train rescheduling (Corman et al., 2014b; Meng and Zhou, 2014; Törnquist and Persson, 2007), delay management (Dollevoet et al., 2014) and rolling-stock and crew rescheduling (Nielsen et al., 2012; Potthoff et al., 2010). The developed approaches are able to solve complex instances in real-time, however they typically assume perfect deterministic knowledge of the input traffic state and subsequent traffic evolution. In recent years, the uncertainty of train event times has been recognised as one of the major obstacles for computing feasible and implementable solutions for rescheduling problems in railway traffic (Corman and Meng, 2014; Quaglietta et al., 2013). The uncertainty of an event is usually represented by the probability distribution of its realisation. However, most of the existing approaches assume fixed probability distributions for train delays and do not consider the effect that real-time information on train positions and delays may have on (the parameters of) the corresponding distributions. In order to create realistic online tools for real-time traffic management, the dynamics of uncertainty of delays needs to be considered. When new information about train positions and delays becomes available, the uncertainty for predicting subsequent events is typically reduced. The main objective of this paper is to examine the effect that the prediction horizon and incoming information about a running train may have on the predictability of subsequent arrival and departure times of all trains. In other words, we try to give an answer to the question: how does the probability distribution of delay of an event change over time?

1- Introduction

2- Theoretical background

3- Formulation of the problem

4- Development of RL-based RTS using the MDRs mechanism

5- Experiment

6- Conclusion and future work

References

Abstract

Previous studies of the real-time scheduling (RTS) problem domain indicate that using a multiple dispatching rules (MDRs) strategy for the various zones in the system can enhance the production performance to a greater extent than using a single dispatching rule (SDR) over a given scheduling interval for all the machines in the shop floor control system. This approach is feasible but the drawback of the previously proposed MDRs method is its inability to respond to changes in the shop floor environment. The RTS knowledge base (KB) is not static, so it would be useful to establish a procedure that maintains the KB incrementally if important changes occur in the manufacturing system. To address this issue, we propose reinforcement learning (RL)-based RTS using the MDRs mechanism by incorporating two main mechanisms: (1) an off-line learning module and (2) a Q-learning-based RL module. According to various performance criteria over a long period, the proposed approach performs better than the previously proposed MDRs method, the machine learning-based RTS using the SDR approach, and heuristic individual dispatching rules.

Introduction

Industry 4.0, also called “Smart Factory,” aims to increase factory productivity and the efficient utilization of resources in real time (Herrmann, Pentek, & Otto, 2015; Wang, Wan, Li, & Zhang, 2016). These objectives are achieved via flexible event-driven reactions to changes in the factory environment, resource allocation, scheduling, optimization, and control in real time. Most of the “Smart Factory” concepts share the attributes of cyber-physical systems (CPS) for monitoring physical processes by creating a virtual copy of the physical world and making decentralized decisions (Lee, Bagheri, & Kao, 2015). CPS is defined as a transformative technology for managing interconnected systems according to their physical assets and computational capabilities, and recent developments have improved the availability and affordability of sensors, data acquisition systems, and computer networks (Lee, 2008; Wolf, 2009). The competitive nature of current industry is forcing more factories to implement high-tech methods. Thus, the increasing use of sensors, RFID, and networked machines has resulted in the continuous generation of high volume data known as Big Data (Lee, Lapira, Bagheri, & Kao, 2013; Lee, Kao, & Yang, 2014). In this environment, CPS can be developed further to manage Big Data and exploit the interconnectivity among machines to fulfill the goal of producing intelligent, resilient, and self-adaptable machines. Furthermore, by integrating CPS with production, logistics, and services in current industrial practices, it will be possible to transform current factories into Industry 4.0 factories with significant economic potential. This is why it is timely and crucial to consider adaptive scheduling and control (i.e., real-time scheduling; RTS) for dynamic manufacturing environments as key research issues in CPS production management (Goryachev et al., 2013; Kück et al., 2016).

Data hiding (DH) may embed secret data, copyright information, and annotation into various media such as image, audio, video, or text. The embedded data should be invisible to a watchdog, and meanwhile the secret data have to stay hidden in a cover signal without detection from steganalysis tools. For this reason, we should retain the good visual quality of an image with the high embedding data. However, trade-offs exist between the embedding capacity and the degree of quality to cover media modification. That is, as one increases, the other must decrease. Thus, according to our need, we choose the best way to implement DH. Reversible DH (RDH) was derived from DH for sensitive applications such as military and hospital since 10 year ago. Recent research on RDH has been focused on encrypted domains. Another topic of this special issue is visual cryptography scheme (VCS). In the last two decades, investigations on VCS were widely conducted. A basic model of VC was firstly proposed by Naor and Shamir. They demonstrated VCS, where an image was divided into a number of shared images so that only participants in the qualified set could decrypt the secret image, while participants in the nonqualified set have no information about the secret. In VCS, the appealing property is that the decoding process can be performed directly by the human eyes without any complicated cryptographic computation. This ‘‘stackingto-see’’ property of VCS provides many intended applications, e.g., information hiding, access control, watermarking, authentication, identification, and transmitting passwords. This special issue (SI) of the Springer Journal of RealTime Image Processing (JRTIP) entitled Real-Time DH and VCS highlights the latest research results on various image, pattern recognition, and signal processing algorithms and technologies for real-time approaches in DH, RDH, and VCS based on various multimedia. This SI addresses both theoretical and practical problems along with the real-time theme of JRTIP. It includes fast schemes and hardware implementations, as well as software/hardware co-design based on DH/RDH and VCS. The call for papers resulted in 29 submissions. For each submission, at least two reviewers examined its quality, together with the guest editors and editor-in-chief. Finally, 19 papers were accepted for this special issue. The selected papers outlined below can be divided broadly into the following themes. The first theme involves VCS and image secret sharing schemes to support real-time algorithms. Six papers fall into this theme. The second theme involves DH, RDH, and steganography for high capacity and good image quality with fast encoding algorithm, and another nine papers fall into this theme. The third theme has two papers and consists of watermarking and reversible watermarking implemented with FPGA for supporting real-time processing techniques. The fourth theme has two papers including steganalysis and authentication.