Automatic workload generation for system-level exploration based on modified GCC compiler

This paper presents methods for automatic generation of models of Transducer, a highly flexible communication module for interfacing Multiprocessor System-on-Chip (MPSoC) components. We describe the transducer architecture, comprising the bus interface, high-level communication controllers and buffer management blocks. The well-defined architecture of the transducer enables automatic generation of its Transaction-level and Register-transfer level (RTL) models. Moreover, the simple interface of the transducer provides for a well-defined software interface, making it easy to update the software after changes in MPSoC platform. Our experimental results show that MPSoC design for industrial-size applications, such as MP3 decoder and JPEG encoder, greatly benefits from automatic generation of transducer models. We found productivity gains of 9-23× due to significant savings in modeling effort. On the quality axis, we show that MPSoC communication design using automatically generated transducers has very little overhead in communication delay over a fully connected point-to-point communication architecture. Finally, we show that our automatically generated TLMs greatly reduce the system-level modeling time and provide a fast executable model for early functional validation.

Techniques for the automatic generation and adaptive control of finite element models for both two and three-dimensional domains are presented. The algorithmic approaches to automatic mesh generation and the approaches for adaptively improving finite element meshes are discussed. The integration of these procedures with solid modeling is also considered.

The low-code approach is an important area of research being developed to improve the rapid creation and performance of software applications. This approach allows developers and users to easily create software applications through an interactive interface. Existing research shows that the low-code approach accelerates the development process in innovative ways, saves effort and time, and reduces code complexity. This study analyses and compares automatic code generation and transformation techniques in low-code platforms. The study analyses the contributions of automatic code generation and transformation to software engineering processes and evaluates the impact of these techniques on software product quality and development speed. The importance of the Model-Based Development (MBD) approach in automated code generation processes is highlighted. It is stated that MBD provides benefits such as speeding up the software development process and reducing errors by enabling automatic code generation from high-level abstract models. In the study, various literature studies were evaluated to examine the impact of automatic code generation and transformation techniques on the application development process. These studies have shown how automatic code generation and transformation techniques are applied, what results are achieved and what contributions these techniques make to the software development process. The results of the study show that automatic code generation and transformation techniques have a great impact on the application development process. In particular, the acceleration of the software development process, the reduction of errors and the ability to produce more effective and efficient software products are important benefits of these techniques. In addition, it has been found that this approach increases the success of software projects by enabling software developers to produce fast and accurate solutions in complex systems. In this context, the importance of automatic code generation and transformation techniques plays an important role in software development processes.

3D building models are important in several fields, such as urban planning and roof report for insurance industries. However, enormous time and labor has to be consumed to create these 3D models, using a 3D modeling software such as 3ds Max or SketchUp. In order to automate laborious steps, a GIS and CG integrated system is proposed for automatically generating 3D building models, based on building polygons (building footprints) on digital maps. Digital maps show most building polygons' edges meet at right angles (orthogonal polygon). In the digital map, however, not all building polygons are orthogonal. In either orthogonal or non-orthogonal polygons, the new system is proposed for automatically generating 3D building models with general shaped roofs by straight skeleton computation. In this paper, the algorithm for shrinking a polygon and forming a straight skeleton are clarified and, the new methodology is proposed for constructing roof models by assuming ‘the third event’ and, at the end of the shrinking process, the shrinking polygon is converged to ‘a line of convergence’. In our research, extended straight skeleton computation is used for automatic generation of roof models. Based on the monotone polygons which straight skeleton computation forms, roof boards are automatically generated, and a top view of these roof boards can be a roof report for natural disaster damage.

Cross-Component Material and Joining Selection for Functional Lightweight Design based on the Extended Target Weighing Approach - A Detailed Application Example

This paper presents an approach for linking the vehicle concept design in the early phase of development with findings from the forecast of mobility behavior under the boundary conditions of a future scenario for Germany. The focus is on an objective and data-based methodology for the derivation of design premises for vehicle concepts, illustrated by using the example of a commercial fully automated on-demand fleet. Today, vehicle concepts are designed on the basis of technical premises derived from the customer requirements of the relevant target group determined by market research and the competitive comparison. Technological trends (e.g., fully automated driving) and business model innovations (e.g., mobility as a service) have the potential to change the individual mobility behavior of users significantly so that today’s vehicle concepts will no longer meet the future requirements. Thus, the conventional approach of determining design premises for vehicle concepts alone may no longer be appropriate. Therefore, as the starting point of the overall methodology, the future mobility behavior is simulated in an aspatial travel demand model. By converting the simulated mobility behavior into relevant design premises and using a cluster analysis adapted for the specific application case, total vehicle concepts are objectively derived and optimized regarding the coverage of mobility needs. The concepts are scaled taking technical concept leaps into account. This application example shows that almost 85% of urban autonomous car sharing trips can be covered using a vehicle concept with significantly reduced requirements. Based on the scaled vehicle portfolio and the respective relevance of covering mobility needs, a recommendation for an efficient fleet design can be made.

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

The authors discuss the development of a railway simulation system incorporating automatic model specification and automatic model generation techniques. The modeling environment features a GEM-like graphical user interface consisting of a layout editor and an icon editor. The model builder draws only the geometrical layout of a railway network section (e.g., a railway terminal) including the position of the signals, thus constructing a partial railway model. This graphical model consists of arcs representing tracks and nodes that represent junctions. The system assists in completing the specification of the model by automatically inferring the layout of the remaining control installations and the possible train routes through the network. Thus, by the end of this process the modeler has come up with a complete railway network data model stored in a database. The final stage of the project involves the development of a simulation code generator that will transform the stored data model to simulation code. >

Security is a major target for today’s information systems (IS) designers. Security modelling languages exist to reason on security in the early phases of IS development, when the most crucial design decisions are made. Reasoning on security involves analysing risk, and effectively communicating risk-related information. However, we think that current languages can be improved in this respect. In this paper, we discuss this issue for Secure Tropos, the language supporting the eponymous agent-based IS development. We analyse it and suggest improvements in the light of an existing reference model for IS security risk management. This allows for checking Secure Tropos concepts and terminology against those of current risk management standards, thereby improving the conceptual appropriateness of the language. The paper follows a running example, called eSAP, located in the healthcare domain.KeywordsRisk managementinformation systemsecuritySecure Tropos

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

A major obstacle for a broader adoption of the finite element method (FEM) in clinical biomedical applications is the generation of the model, frequently too slow for the times imposed by the clinical practice. The algorithms for automatic mesh generation have greatly improved, but their adoption by the biomedical community is still limited. The aim of this work is to review the principal algorithms for automatic mesh generation and to critically discuss them with particular reference to their applicability in the biomedical field. Specialized literature on numerical methods was reviewed in order to identify the main theoretical approaches currently available for automatic mesh generation. Then, published methods for the automatic generation of finite element models of organs from computed tomography data were reviewed and classified with a proposed taxonomy. Each method was reconnected to a theoretical approach described in the specialized literature whenever possible. Last, each method was critically reviewed with respect to its applicability to the clinical practice. None of the methods described satisfy all the requirements in terms of automation, generality, accuracy, and robustness imposed by a clinical application. However, some of these methods can already be successfully used in various application contexts, and a few guidelines are drawn.

In this article, a recurrent neural network (RNN) method is employed for dynamic time-domain modeling of both linear and nonlinear microwave circuits. An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the offline training process. This technique extends a recent automatic model generation (AMG) algorithm from frequency-domain model generation to dynamic time-domain model generation. Two types of applications of the algorithm are presented, transient electromagnetic (EM) behavior modeling of microwave structures, and time-domain envelope modeling of power amplifiers (PA). For transient EM modeling, we consider EM structures with varying material and geometrical parameters. AMG automatically varies the EM structural parameters during training and drives time-domain EM simulators to generate necessary amount of data for RNN to learn. AMG aims to model the transient behavior with minimum RNN order while satisfying accuracy requirements. In modeling PA behavior, an envelope formulation is used to specifically learn the AM/AM and AM/PM distortions due to third-generation (3G) digital modulation input. The RNN PA model is able to model these time domain distortions after training and can accurately model the amplifier behavior in both time (AM/AM, AM/PM) and frequency (spectral re-growth). © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.

Workload modeling for resource usage analysis and simulation in cloud computing

Industrial control systems (ICS) are defined with hardware and software components dedicated to control and monitoring tasks for factory process. Proper functioning of ICS architectures is mainly linked to the performance they offer. System integrators (SI) must know performance of the architecture they propose to the customers by assessing them. Many methodologies have already proven their capabilities to assess ICS architecture performance. One of them is colored Petri Nets (CPN). To assess the performance of ICS architecture using CPN involve defining manually the model. Pre-sales uncertain context involves problematic making this manual model definition challenging. This paper introduces a concept allowing automatic CPN model generation by instantiation and parameterization. However before introducing the concept, the paper shows the problematic involved by the pre-sales context. Then shows why CPN methodology is a relevant solution for assessing the performance of ICS in this context.

To give the user proper assistance, an interface agent should be able to predict the user's intentions. Usually, they achieve that through the use of a detailed task model for a particular application domain. However, to develop such knowledge representations is a difficult and time-consuming activity. Generally, it is tedious and annoying for the agent developers to express such knowledge manually. Also, the necessity to have an expert that offers information on the application domain is a problematic restriction. Therefore, this paper will focus on the automatic task model generation without previous knowledge on the application domain.