Model-based Programming Research Articles

The impact of a large language model-based programming learning environment on students’ motivation and programming ability

The impact of a large language model-based programming learning environment on students’ motivation and programming ability

Model-based development of software for network control of automotive vehicles’ equipment

BACKGROUND: Various devices and systems connected via onboard network with electronic control unit are the part of automotive vehicles’ equipment. Such devices and systems, which ensure the operability of a vehicle or are the elements of technological units, having microprocessor control, are based on various physical principles. Designers of such devices and systems may not have sufficient knowledge and experience to develop the software independently, which applies to the development of software for the network control.
 AIM: The development of software for information subsystem of a technical device that interacts with an electronic control unit via onboard network as part of automotive vehicles’ equipment, as well as the demonstration of application of model-based programming tools in this development.
 METHODS: A comprehensive description of technical solutions developed to achieve the listed aims using methods of system analysis and methods for developing and debugging of software is given. According to these methods, model-based programming tools are used as handlers for built-in interface modules of microcontroller and elements of software layout. Software elements for processing received messages, performing actions with the data received in them, as well as generation of response messages have been developed in the C language.
 RESULTS: Software that processes messages received by a device subordinated to an electronic control unit via the CAN network, generates response messages addressed to this unit, and sends them has been developed. The method of access to the receiving buffer of the network interface and priority of software execution are taken into account.
 CONCLUSION: It is shown that model-based programming in combination with programming tools based on structured text is an effective software development technology that is convenient for designers of technical devices and systems based on various physical principles and requiring microprocessor control. In particular, the abovementioned should be attributed to specialists in the field of electrical engineering who develop equipment for automotive vehicles.

Разработка контроллера цифрового датчика положения и его программного обеспечения

For the operation of electric drives control systems, closed on the target coordinate of control – speed or position of actuator, an informational and measuring subsystem, formed by hardware and software to detect, capture and process signals from sensors, is required. The functionally completed and independent elements of such a subsystem are the controllers of each of the sensors, including digital position sensors, also used to measure the speed of motion. When developing such a controller, it is necessary to take into account the requirements for control system, performances of sensors and their signals, the form of position measurement result, the technique of the sensor interfacing with the control system, the features of hardware and software computing resources, including execution time. Thus, the development of a digital position sensor controller and its software is an actual problem. When developing the controller, methods of computational mathematics and system analysis, methods of model-based programming, methods of software developing and debugging, as well as methods of experimental research were applied. An algorithmic description of the digital position sensor controller is proposed for converting the angular position of the electric drive shaft, which changes cyclically within one revolution during rotation, into its rotation angle, which has wide limits of change. Also, the measuring of rotation speed of the electric drive shaft is algorithmically described by calculating the increment of its angular position during the observation interval. The software for this controller has been developed, which can serve as a typical element for microprocessor control systems for electric drives. When developing a controller for a digital position sensor, as well as other elements of the informational and measuring subsystem of closed-loop electric drives, it is necessary to take into account the performances of various technical means and requirements for the control system. Successful development is contributed by the use of modern microcontrollers with built-in specialized modules for detecting, capturing and processing signals, as well as the use of detailed computational algorithms and mathematical methods that are the least expensive in terms of execution time.

Модельно-ориентированное программирование как инструмент инженера-электромеханика

Modern electric drives with microprocessor control are complex technical systems with software that ensures the coordination of various physical processes for automatic motion control. At the same time, electromechanical engineers have a little knowledge about modern microprocessor technology, expressive and convenient programming languages, as well as modern software development and debugging technologies. Thus, it is difficult for them to develop electric drives independently. In addition, there is a professional gap between electromechanical engineers and programmers, which keeps them from cooperation on the software of electric drives. Nowadays, there are model-based programming tools designed to develop software in graphical form with a description of the built-in microcontroller modules in the form of configurable model blocks. However, the theoretical and practical issues of this technology are still limited and require development. Thus, the aim to develop model-based programming as a full-fledged technology for control systems development is relevant. Electromechanical engineers, on the one hand, and software developers, on the other hand, are the target of the research using the method of system analysis of professional competencies. The importance of the software for the electric drive microprocessor control systems is considered. The author assesses the capabilities of an electromechanical engineer who has been trained as a bachelor and master’s degree student in the field of “Electric Power and Electrical Engineering” as a developer of microprocessor-controlled electric drives. The author describes the model-based programming technique as an effective technology to develop software for microprocessor control systems intended for engineers of different spheres, including electromechanical engineers, whose activities are related to the development of electric drives. The author suggests an approach based on the application of model-based programming technology to develop microprocessor control systems for electric drives. The approach makes it possible to improve the competence of electromechanical engineers in the field of software development of control systems. Model-based programming technique contributes to the development of modern microprocessor technology and development technologies as an effective tool for independent development of software of electric drive systems by electromechanical engineers without assistance of programmers.

Lateral cerebellothalamic tract activation underlies DBS therapy for Essential Tremor

BackgroundWhile deep brain stimulation (DBS) therapy can be effective at suppressing tremor in individuals with medication-refractory Essential Tremor, patient outcome variability remains a significant challenge across centers. Proximity of active electrodes to the cerebellothalamic tract (CTT) is likely important in suppressing tremor, but how tremor control and side effects relate to targeting parcellations within the CTT and other pathways in and around the ventral intermediate (VIM) nucleus of thalamus remain unclear. MethodsUsing ultra-high field (7T) MRI, we developed high-dimensional, subject-specific pathway activation models for 23 directional DBS leads. Modeled pathway activations were compared with post-hoc analysis of clinician-optimized DBS settings, paresthesia thresholds, and dysarthria thresholds. Mixed-effect models were utilized to determine how the six parcellated regions of the CTT and how six other pathways in and around the VIM contributed to tremor suppression and induction of side effects. ResultsThe lateral portion of the CTT had the highest activation at clinical settings (p < 0.05) and a significant effect on tremor suppression (p < 0.001). Activation of the medial lemniscus and posterior-medial CTT was significantly associated with severity of paresthesias (p < 0.001). Activation of the anterior-medial CTT had a significant association with dysarthria (p < 0.05). ConclusionsThis study provides a detailed understanding of the fiber pathways responsible for therapy and side effects of DBS for Essential Tremor, and suggests a model-based programming approach will enable more selective activation of lateral fibers within the CTT.

Application of Model-Based Programming for Transistor Power Converters of Electric Drive Control

Application of Model-Based Programming for Transistor Power Converters of Electric Drive Control

Application of model-based programming for transistor power converters of electric drives control

Application of model-based programming for transistor power converters of electric drives control

A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model

The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection.

Vine copula and cloud model-based programming approach for agricultural water allocation under uncertainty

In the existing agricultural water management models under uncertainty, the mutual-correlation and their self-correlation of random variables (like precipitation (P), runoff (R), reference evapotranspiration (ET0), etc.) are often ignored. When expressing the fuzziness of socio-economic factors, fuzzy membership function is usually determined by the experience of decision-makers, which often brings some confusions. To solve the above questions, first, C-vine copula is introduced in this study to depict the multiple interdependence structures. Two kinds of three-dimensional copulas is constructed: $${CV}_{1}({R}_{t}, {P}_{t}, {R}_{t-1})$$ and $${CV}_{2}({ET}_{0t}, {P}_{t}, {ET}_{0(t-1)})$$ , where t is at t-th month. Second, the cloud model, as a novel qualitative and quantitative transformation model, is chosen to describe the uncertainty of crop prices. Combining these two uncertainty-expressing methods, an agricultural water resources optimization model is built to gain maximum net benefit by allocating limited surface water and groundwater. Then this model was applied to a case study in northwestern China. Results show that the developed model could provide the decision-makers with not only the best or the optimum range of system net benefits but also the probability of obtaining a given benefit under complex uncertainties. For comparison, the ordinary models without consideration of dependence of variables as an independent were also built. When overlooking the mutual-correlation and self-correlation, the optimal water allocation and system net benefits would be higher in dry years with total water allocation higher by 4.5%. This unreasonable allocation results may cause excessive agricultural irrigation to squeeze water for other industries in dry years, which would exacerbate water shortages. The discussion and comparison results prove the necessity and effectiveness of this research.

Mobile Application for Parkinson's Disease Deep Brain Stimulation (MAP DBS): An Open-Label, Multicenter, Randomized, Controlled Clinical Trial

Mobile Application for Parkinson's Disease Deep Brain Stimulation (MAP DBS): An Open-Label, Multicenter, Randomized, Controlled Clinical Trial

Multiobjective Optimization for Scaffolding Space Planning in Industrial Piping Construction Using Model-Based Simulation Programming

AbstractAs the main type of temporary structures, scaffolding systems have a significant impact on labor productivity and project cost in construction, but the ability to provide space planning ass...

Online programming technique for flexible assembly of fuselage

In order to meet the deep flexible demands of "one fixture with more models"and "one fixture with more states"for digital assembly of fuselage,enable the "bridge-frame"flexible tooling to have better capacity for independent adaptability,its kinematic model must be set up and its movement controls strategy must be planned properly by online programming technique. For the locating styles of fuselage and the characteristics of flexible tooling,the kinematic model of flexible tooling was set up,then simulation model-based online programming disciplines,the way of building reference points for assembly and the technology of movement controls strategy planning were studied. The way of building a process-driven simulation environment based on structure function was proposed. In order to generate the codes quickly to control motion,the online programming system for flexible assembly of fuselage was developed. With the assembly of rear fuselage,for example,the accuracy,high efficiency and strong independent adaptability of this system were verified.

Model-based programming environments for spreadsheets

Spreadsheets can be seen as a flexible programming environment. However, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data.We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way.We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus.Finally, we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved.

A monitoring and control framework for lost foam casting manufacturing processes using genetic programming

Monitoring and control of manufacturing processes is an essential part of any industry. Being able to collect sensor measurements, analyse the measurements in an intelligent way, select appropriate actions and validate the desired results of these actions is a tremendous goal to be achieved. In this paper, we propose a monitoring and control framework of a multi-tier closed-loop controlling lost foam casting (LFC) system. The proposed system consists of several subsystems like production activity control (PAC), enterprise resource planning (ERP), and business intelligence (BI). Another essential part of the system is the electrical capacitance tomography (ECT) subsystem. This subsystem is in charge of collecting measurements from the LFC process, develops an evolutionary model-based genetic programming (GP) of the process and reconstructs an image of the casting process. The proposed framework can be used to improve the quality of manufacturing processes and to enhance process reliability which, as a result, will increase companies' profit. The proposed framework can be extended to a variety of applications.

Reactive, model-based monitoring in RFID-enabled manufacturing

Radio frequency identification (RFID) technology is widely applied throughout the manufacturing lifecycle to track important objects. It is imperative to establish a reactive monitoring system to convert large volumes of RFID data into meaningful information. Two major challenges must therefore be addressed: modeling complexity and monitoring efficiency. When the relationship between high-level “untagged” elements (business processes, indicators) and low-level “tagged” elements (persons, objects) becomes intricate, it will become difficult to define, maintain and reason by only using traditional rule-based event processing approaches. In order to resolve this problem, the paper proposes a reactive, model-based approach to monitor important elements by estimating their most likely states according to RFID information and a constraint-based model. The approach contains two phases. (1) The model-based programming phase formalizes RFID information and flexibly embeds them into the model that specifies the monitoring rules. (2) The model-based monitoring phase interprets the model and estimates desired states efficiently. Therefore, the RFID-enabled manufacturing units are capable of responding in a timely manner to unexpected disturbances or other important issues.

Learning from demonstration in robots: Experimental comparison of neural architectures

Robots have played an important role in the automation of computer aided manufacturing. The classical robot control implementation involves an expensive key step of model-based programming. An intuitive way to reduce this expensive exercise is to replace programming with machine learning of robot actions from demonstration where a (learner) robot learns an action by observing a demonstrator robot performing the same. To achieve this learning from demonstration (LFD) different machine learning techniques such as Artificial Neural Networks (ANN), Genetic Algorithms, Hidden Markov Models, Support Vector Machines, etc. can be used. This piece of work focuses exclusively on ANNs. Since ANNs have many standard architectural variations divided into two basic computational categories namely the recurrent networks and feed-forward networks, representative networks from each have been selected for study, i.e. Feed Forward Multilayer Perceptron (FF) network for feed-forward networks category and Elman (EL), and Nonlinear Autoregressive Exogenous Model (NARX) networks for the recurrent networks category. The main objective of this work is to identify the most suitable neural architecture for application of LFD in learning different robot actions. The sensor and actuator streams of demonstrated action are used as training data for ANN learning. Consequently, the learning capability is measured by comparing the error between demonstrator and corresponding learner streams. To achieve fairness in comparison three steps have been taken. First, Dynamic Time Warping is used to measure the error between demonstrator and learner streams, which gives resilience against translation in time. Second, comparison statistics are drawn between the best, instead of weight-equal, configurations of competing architectures so that learning capability of any architecture is not forced handicap. Third, each configuration's error is calculated as the average of ten trials of all possible learning sequences with random weight initialization so that the error value is independent of a particular sequence of learning or a particular set of initial weights. Six experiments are conducted to get a performance pattern of each architecture. In each experiment, a total of nine different robot actions were tested. Error statistics thus obtained have shown that NARX architecture is most suitable for this learning problem whereas Elman architecture has shown the worst suitability. Interestingly the computationally lesser MLP gives much lower and slightly higher error statistics compared to the computationally superior Elman and NARX neural architectures, respectively.

Selected Papers from SLA++P 07 and 08 Model-Driven High-Level Programming of Embedded Systems

Model-based high-level programming of embedded systems has become a reality in the automotive and avionics industries. These industries place high demands on the efficiency and maintainability of the design process as well as on the performance and functional correctness of embedded components. These goals are hard to reconcile in the face of the increasing complexity of embedded applications and target architectures. Research efforts towards meeting these goals have brought about a variety of high-level engineering design languages, tools, and methodologies. Their strength resides in clean behavioral models with strong semantical foundations providing a rigorous way to go from a highlevel description to mathematically certifiable executable code. The most successful representatives of this trend of putting logic and mathematics behind design automation in embedded systems are synchronous languages; they have been receiving increasing attention in industry ever since they emerged in the 80s. Lustre, Esterel, and Signal are now widely and successfully used to program real-time and safety critical applications, from nuclear power plant management layer to Airbus air flight control systems. Their recent successes in the automatic control industry highlight the benefits of formal verification and automatic code generation from high-level models. Model-based programming is making its way in other fields of software engineering too. Strong interest is emerging in component programming for large-scale embedded systems, in the link between simulation tools and compiler tools, in languages for describing the system and its environment, integrated tools for both compilation and simulation of more general models of communication and coordination, and so forth. The impact of such unifying methodologies will depend on the extent to which it will be possible to maintain the high degree of predictability and verifiability of system behavior that is the strength of the classic synchronous world.

Conflict-directed A * and its role in model-based embedded systems

Artificial Intelligence has traditionally used constraint satisfaction and logic to frame a wide range of problems, including planning, diagnosis, cognitive robotics and embedded systems control. However, many decision making problems are now being re-framed as optimization problems, involving a search over a discrete space for the best solution that satisfies a set of constraints. The best methods for finding optimal solutions, such as A *, explore the space of solutions one state at a time. This paper introduces conflict-directed A * , a method for solving optimal constraint satisfaction problems. Conflict-directed A * searches the state space in best first order, but accelerates the search process by eliminating subspaces around each state that are inconsistent. This elimination process builds upon the concepts of conflict and kernel diagnosis used in model-based diagnosis [J. de Kleer, B.C. Williams, Diagnosing multiple faults, Artif. Intell. 32(1) (1987) 97–130; J. de Kleer, A. Mackworth, R. Reiter, Characterizing diagnoses and systems, Artif. Intell. 56 (1992) 197–222] and in dependency-directed search [R. Stallman, G.J. Sussman, Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis, Artif. Intell. 9 (1977) 135–196; J. Gaschnig, Performance measurement and analysis of certain search algorithms, Technical Report CMU-CS-79-124, Carnegie-Mellon University, Pittsburgh, PA, 1979; J. de Kleer, B.C. Williams, Back to backtracking: controlling the ATMS, in: Proceedings of AAAI-86, 1986, pp. 910–917; M. Ginsberg, Dynamic backtracking, J. Artif. Intell. Res. 1 (1993) 25–46]. Conflict-directed A * is a fundamental tool for building model-based embedded systems, and has been used to solve a range of problems, including fault isolation [J. de Kleer, B.C. Williams, Diagnosing multiple faults, Artif. Intell. 32(1) (1987) 97–130], diagnosis [J. de Kleer, B.C. Williams, Diagnosis with behavioral modes, in: Proceedings of IJCAI-89, 1989, pp. 1324–1330], mode estimation and repair [B.C. Williams, P. Nayak, A model-based approach to reactive self-configuring systems, in: Proceedings of AAAI-96, 1996, pp. 971–978], model-compilation [B.C. Williams, P. Nayak, A reactive planner for a model-based executive, in: Proceedings of IJCAI-97, 1997] and model-based programming [M. Ingham, R. Ragno, B.C. Williams, A reactive model-based programming language for robotic space explorers, in: Proceedings of ISAIRAS-01, 2001].

Model-based programming of fault-aware systems

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. These systems are being composed, for example, into vast networks of space, air, ground, and underwater vehicles. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. A model-based program is state and fault aware; it elevates the programming task to specifying intended state evolutions of a system. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. At the executive's core is a method, called CONFLICT-DIRECTED A*, which quickly prunes promising but infeasible solutions, using a form of one-shot learning. This approach has been demonstrated on a range of systems, including the National Aeronautics and Space Administration's Deep Space One probe. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.

Model-based programming of intelligent embedded systems and robotic space explorers

Programming complex embedded systems involves reasoning through intricate system interactions along lengthy paths between sensors, actuators, and control processors. This is a challenging, time-consuming, and error-prone process requiring significant interaction between engineers and software programmers. Furthermore, the resulting code generally lacks modularity and robustness in the presence of failure. Model-based programming addresses these limitations, allowing engineers to program reactive systems by specifying high-level control strategies and by assembling commonsense models of the system hardware and software. In executing a control strategy, model-based executives reason about the models on the fly, to track system state, diagnose faults, and perform reconfigurations. This paper develops the reactive model-based programming language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan executes an RMPL program using extensive component-based declarative models of the plant to track states, analyze anomalous situations, and generate novel control sequences. Within its reactive control loop, Titan employs propositional inference to deduce the system's current and desired states, and it employs model-based reactive planning to move the plant from the current to the desired state.