Tracing of large‐scale actor systems

Driving simulators are very suitable test beds for the evaluation and development of intelligent transportation systems (ITSs). However, the impact of such systems on the behavior of individual drivers can properly be analyzed through driving simulators only if autonomous vehicles in the driving scenario move according to the system under evaluation. This condition means that the simulation of the traffic surrounding the interactive vehicle should already take into account the driver's behavior as affected by the system under analysis. Currently, this loop is not properly tackled, because the effects on individuals and traffic are, in general, separately and, often, independently evaluated. The integration of traffic and driving simulations, instead, may provide a more consistent solution to this challenging evaluation problem. It also opens up new scenarios for enhancing the credibility of both traffic modeling and driving simulation and for their combined development. For instance, because drivers directly interact with driver/traffic models in a driving simulation environment, such models may also be tested against nonnormative behavior, and this case seems the only way to test driver/traffic models for safety applications. Based on this idea, this paper describes the integration of a driving simulation engine known as SCANeR and a traffic-flow microsimulation model known as AIMSUN. Methodological and technical issues of such integration are first presented, and future enhancements for higher consistency of the simulation environments are finally envisaged. © 2006 IEEE.

The integration of Jenkins and Amazon Web Services (AWS) into Siemens Opcenter MES (Manufacturing Execution System) represents a transformative approach to modernizing industrial software deployment. As manufacturing systems evolve toward Industry 4.0, the need for agile, scalable, and automated deployment pipelines becomes critical. This paper explores how Continuous Integration and Continuous Deployment (CI/CD) methodologies, facilitated by Jenkins automation and AWS cloud infrastructure, can bridge the digital divide in traditional MES environments. We present a structured framework for automating build, test, and deployment processes in Siemens Opcenter, leveraging AWS services (EC2, ECS, Lambda, CloudFormation) for scalable and secure execution. Key benefits include reduced deployment times, enhanced reliability, and cost-efficient scalability, while addressing challenges such as security, compliance, and system dependencies. A real-world case study demonstrates measurable improvements in deployment speed and operational efficiency, validating the proposed approach. Finally, we discuss emerging trends, including AI-driven deployment optimization, serverless architectures, and edge computing, highlighting future directions for MES DevOps. This research provides actionable insights for manufacturing IT teams seeking to adopt modern CI/CD pipelines while maintaining robustness in industrial environments.

This fast development of cloud-native computing and Generative Artificial Intelligence (GenAI) has facilitated the creation of scalable and intelligent applications in various fields. But current literature and practice are full of individual studies on cloud-native machine learning pipelines and Generative AI systems, creating a fragmented architecture, haphazard governance, and more complicated operation in enterprise. Specifically, no combined architectural advice exists that combines principles of cloud-native computing with foundation model-driven GenAI processes into an integrated and consistent framework. The given paper provides a systematic literature review of cloud-native AI architectures, Generative AI models, and AWS-managed AI services to reveal major patterns of design, challenges, and gaps of the current practices. It was on this basis that we have developed a consistent cloud-native reference architecture to deploy and operate Generative AI workloads on Amazon Web Services (AWS). This work has the contribution of introducing a design-oriented AWSspecific reference architecture that applies standard cloud-native principles to unify traditional machine learning pipelines and Generative AI systems. Instead of concentrating on empirical performance measurement, the study suggests architectural guidance and practical design advice that are supposed to guide the organization in designing scalable, secure, and governance-conscious GenAI deployments on AWS. Future research will involve implementation-level verification and empirical evaluation of the proposed architecture in the real-life application contexts.

The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.

Real-time embedded systems are typically constrained in terms of three system performance criteria: space, time, and energy. The performance requirements are directly translated into constraints imposed on the system's resources, such as code size, execution time, and energy consumption. These resource constraints often interact or even conflict with each other in a complex manner, making it difficult for a system developer to apply a well-defined design methodology in developing a real-time embedded system. Motivated by this observation, we propose a design framework that can flexibly balance the tradeoff involving the system's code size, execution time, and energy consumption. Given a system specification and an optimization criteria, the proposed technique generates a set of design parameters in such a way that a system cost function is minimized while the given resource constraints are satisfied. Specifically, the technique derives code generation decision for each task so that a specific version of code is selected among a number of different ones that have distinct characteristics in terms of code size and execution time. In addition, the design framework determines the voltage/frequency setting for a variable voltage processor whose supply voltage can be adjusted at runtime in order to minimize the energy consumption while execution performance is degraded accordingly. The proposed technique formulates this design process as a constrained optimization problem. We show that this optimization problem is NP-hard and then provide a heuristic solution to it. We show that these seemingly conflicting design goals can be pursued by using a simple optimization algorithm that works with a single optimization criteria. Moreover, the optimization is driven by an abstract system specification given by the system developer, so that the system development process can be automated. The results from our simulation show that the proposed algorithm finds a solution that is close to the optimal one with the average error smaller than 1.0%.

Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.

A new tool for integrating formal methods, particularly model checking, in the development process of component-based real-time systems specified in UML is proposed. The described tool, TANGRAM (Tool for Analysis of Diagrams), performs automatic translation from UML diagrams into timed automata, which can be verified by the UPPAAL model checker. We focus on the CORBA Component Model. We demonstrate the overall process of our approach, from system design to verification, using a simple but real application, used in train control systems. Also, a more complex case study regarding train control systems is described.

This paper proposes a novel method of verifying comprehensive driver model used for the evaluation of driving safety systems, which is achieved by coupling the traffic simulation and the driving simulator (DS). The method consists of three-step procedure. In the first step, an actual driver operates a DS vehicle in the traffic flow controlled by the traffic simulation. Then in the next step, the actual driver is replaced by a driver model and the surrounding vehicle maneuvers are replayed using the recorded data from the first step. Then, the maneuver by the driver model is compared directly with the actual driver's maneuver along the simulation time steps. For example, while turning right (in front of the oncoming traffic), the proposed verification method was applied to our driver-agent model of traffic simulation system ASSTREET (The Advanced Safety System & Traffic REaltime Evaluation Tool) to demonstrate that the proposed method provides a straightforward way of verifying the driver-agent model capability to reproduce the driving behavior including the subtle driving behaviors, such as safety confirmation, that can have significant impact on driving safety. The scheme of interactive connection of ASSTREET and a driving simulator is also described. Language: en

Presents a new technique called 'dynamic instrumentation' that provides efficient, scalable, yet detailed data collection for large-scale parallel applications. Our approach is unique because it defers inserting any instrumentation until the application is in execution. We can insert or change instrumentation at any time during execution by modifying the application's binary image. Only the instrumentation required for the currently selected analysis or visualization is inserted. As a result, our technique collects several orders of magnitude less data than traditional data collection approaches. We have implemented a prototype of our dynamic instrumentation on the CM-5, and present results for several real applications. In addition, we include recommendations to operating system designers, compiler writers, and computer architects about the features necessary to permit efficient monitoring of large-scale parallel systems. >

Most safety analysis is conducted using crash data. Surrogate safety measures, such as various time-based measures of time-to-collision can be related to crash potential and used to gain insight into the frequency and severity of crashes at a specific location. One of the most common and acknowledged measures is post-encroachment time (PET) which defines the time between vehicles occupying a conflicting space. While commonly used in studies of motor vehicle interactions, studies of PET for bicycle-vehicle interactions are few. In this research, the PET of bicycle-vehicle interactions measured in the field, a driving simulator, and in a micro-simulation are compared. A total of 52 right-hook conflicts were identified in 135 hours of video footage over 14 days at a signalized intersection in Portland, OR (SW Taylor and SW Naito Pkwy). The results showed that 4 of 17 high-risk conflicts could not be identified by the conventional definition of PET and PET values of some conflicts did not reflect true risk of collision. Therefore, right-hook conflicts were categorized into two types and a modified measure of PET was proposed so that their frequency and severity were properly measured. PETs from the field were then compared to those measures in the Oregon State University driving simulator during research conducted by Dr. Hurwitz et al. (2015) studying the right-hook conflicts. Statistical and graphical methods were used to compare field PETs to those in the simulator. The results suggest that the relative validity of the OSU driving simulator was good but not conclusive due to differences in traffic conditions and intersections. To further explore the field-observed PET values, traffic simulation models of the field intersection were developed and calibrated. Right-hook conflicts were extracted from the simulation files and conflicts observed in PM-peak hours over 6 days in the field were compared to those obtained from 24 traffic simulation runs. The field-observed PET values did not match the values from the simulation values very well. However, the approach does show promise. Further calibration of driving and bicycling behaviors would likely improve the result.

In this paper, an intelligent method for image contrast enhancement in real time digital video applications is proposed. This new technique is based on the generation of adaptive transfer functions by using Neural Network. The method is designed in order to be implemented in digital television systems, based on Thin Film Transistor Liquid Cristal Displays (TFT-LCD), that has become the dominant technology in consumer electronics. The method provides the required amount of contrast enhancement for every image according to real time analysis of brightness and contrast in the video frame information, then a different contrast enhancement transfer curve for each image is generated. The statistical information is extracted by histogram analysis from current or previous image frames. Technological factors are considered where it is applied, as well as aspects of the visual perception within a non-controlled environment, which is the case of the consumer environment. The method is based on the design of a general transfer function model that compensates the decrement in gray scale dynamic range in images representation which is a consequence of the TFT-LCD technology limits; in particular the light leakage from the panel backlight sources of the Cold Cathode Fluorescent Light (CCFL) or the LED type, as well as an unsuitable dynamic control of backlight intensity. Using subjective methods for image quality, the contrast is enhanced in a set of representative images for different degrees of brightness and contrast, a unique transfer function is obtained for each representative image. This set of transfer functions, as well as the statistic of the pixel values distribution from each image frame are used as an input - output pattern during the training of a neural network. On the basis of the experimentation, an evaluation and comparative analysis takes place between the functions of transformation obtained by subjective method of image quality and the curves obtained through the trained neural network.

Experimental and computational fluid dynamics investigations of tracking CPC solar collectors

Recently, distributed computing infrastructures such as grid computing, have witnessed huge developments which have a very important impact on computationally intensive scientific, industrial and engineering problems. Collaborative computing, resources access and sharing facilitated by grid computing systems amplifies concerns about cyber attacks and misbehaviors of grid resources (resources provider). In many cases, an organization may send out jobs for remote execution on untrustworthy machines. Since resource sharing and cooperation among different administrative domains are among the central goals of grid computing, it is necessary to guarantee that, among the shared resources, there are no malicious resources interested in invalidating or corrupting the job results. In order to guarantee reliable jobs execution in grid systems, we have developed a new approach for malicious grid resources tolerance using reputation to improve the efficiency of majority voting mechanisms. The grid broker service investigates the trustworthiness of the grid resources used in the voting procedure; this investigation is based on distributed and hierarchical reputation management system which maintains a reputation value for each grid resource based on its historical behavior. The validation of the result is then decided based on the grid resources reputation level.

DEGAS provides discrete-event scheduling capability to a GNAT Ada program without requiring extra calls to a simulation library. We accomplish this by intercepting all calls destined for the pthread library and then rerouting them to the dynamically linked DEGAS library; this allows a developer to switch between real-time and discrete-event modes at runtime in a non-intrusive manner. DEGAS narrows the separation between simulation and real time applications, and has significant implications for software that includes elements of concurrency, synchronization, and time. We foresee applications that go beyond simulations, including executable specifications, algorithm development, and system verification.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkSteiner TreeSensor ReadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.