Function Block Research Articles

Adaptive Industrial Control Systems via IEC 61499 and Runtime Enforcement

This work envisions industrial control systems that can reliably adapt to requirements. We rely on the international standard IEC 61499 to achieve this goal. The standard allows downtimeless system evolution such that an application can be modified at runtime to satisfy the requirements. However, an IEC 61499 application consisting of multiple Function Blocks (FBs) can be modified in many different ways, such as inserting or deleting FBs, creating new FBs with their respective internal behaviours and adjusting the connections between FBs. These changes require considerable effort and cost, and there is no guarantee to satisfy the requirements. This article applies runtime enforcement techniques for supporting adaptive IEC 61499 applications. This set of techniques can modify the runtime behaviour of a system according to specific requirements. Our approach begins with specifying the requirements as a state machine-based notation called contract automaton. This automaton is then used to synthesise an enforcer as an FB. Finally, the new FB is integrated into the application to execute according to the requirements. A tool support is developed to automate the approach. Experiments were performed to evaluate the performance of enforcers by measuring the execution time of several applications before and after the integration of enforcers.

Integrated Artificial Neural Network with Trainable Activation Function Enabled by Topological Insulator-Based Spin-Orbit Torque Devices.

Nonvolatile memristors offer a salient platform for artificial neural network (ANN), yet the integration of different function and algorithm blocks into one hardware system remains challenging. Here we demonstrate the brain-like synaptic (SOT-S) and neuronal (SOT-N) functions in the Bi2Te3/CrTe2 heterostructure-based spin-orbit torque (SOT) device. The SOT-S unit exhibits highly linear and symmetrical long-term potentiation/depression process, resulting in a fast-training of the MNIST data set with the classification accuracy above 90%. Meanwhile, the Sigmoid-shape transition curve inherited in the SOT-N cell replaces the software-based activation function block, hence reducing the system complexity. On this basis, we employ a serial-connected, voltage-mode sensing ANN architecture to enhance the vector-matrix multiplication signal strength with low reading error of 0.61% while simplifying the peripheral circuitry. Furthermore, the trainable activation function of SOT-N enables the implementation of the Batch Normalization algorithm and activation operation within one clock cycle, which bring about improved on/off-chip training performance close to the ideal baseline.

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.

Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks.

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover's Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times.

A systematic review of upper extremity outcome measures assessed in randomized controlled trials of post stroke upper extremity rehabilitation over time

ABSTRACT Background The heterogeneity in outcome measures of post stroke rehabilitation trials suggests the need for consensus approach in stroke recovery measurement. To reach this aim, it is important to understand the past and current use of outcome measures in randomized control trials (RCTs) of stroke rehabilitation. Objective To systematically review RCTs of post stroke UE rehabilitation interventions to understand the use of UE outcome measures in research and their changes over time. Methods CINAHL, Embase, PubMed, Scopus and Web of Science were searched from 1960 to 1 April 2021. Studies were eligible for inclusion if they (1) were RCTs or crossovers published in English (2) ≥50% of participants were affected by stroke, 3) included adults ≥ 18 years old, and (4) applied an intervention to the hemiparetic UE as the primary objective of the study. Results 1,276 RCTs met inclusion criteria, and 112 different outcome measures were identified. Outcome measures were classified according to the International Classification of Functioning, Disability and Health (ICF) framework. Outcome measures most frequently assessed body function and structure (n = 1,692), followed by activities (n = 1,572) and participation (n = 162). The most used outcome measures were the Fugl-Meyer Assessment (n = 619), the modified Ashworth Scale (n = 255), Action Research Arm Test (n = 211), Wolf Motor Function Test (n = 184), and Box and Block Test (n = 178). Conclusions Understanding the breadth of outcome measures that have been used over time emphasizes the need for proposed standardization of outcome measures but also the need to adjust and expand consensus recommendations based on past and ongoing research trends.

Development of motion control function library based on PLCopen specification

The motion control library is a standard set of functional blocks integrated in a programmable integration environment, which is of great significance for the rapid development of reconfigurable Programmable Automation Controllers (PAC). However, due to the encapsulation and concealment of function blocks and the incompatibility between software and hardware, the development efficiency of function blocks is low, which is not conducive to the development of autonomous, controllable PAC. Aiming at the above problems, the methods of function block opening and key technology are studied, and a kind of motion control function library architecture and rapid development method based on the PLCopen specification are proposed. A series of motion control function libraries are developed on domestic PLC software. An experimental platform based on domestic hardware and software was designed to verify the typical motion control function library. Experimental results show that this method can effectively shorten the development cycle and that the developed motion control module can meet the actual control needs.

New pacing strategies for heart failure

In patients with areduced left ventricular (LV) systolic function (ejection fraction < 35%) and aleft bundle branch block with a QRS duration > 130 ms, cardiac resynchronization therapy (CRT) can contribute to an improvement in the quality of life and areduction in mortality. The resynchronization is mostly achieved by pacing via an epicardial LV lead in the coronary sinus; however, this approach is often limited by the patient's venous anatomy and an increase in the stimulation threshold over time. In addition, up to 30% of patients do not respond to the intervention. New treatment approaches involve direct stimulation of the conduction system by pacing of the bundle of His or left bundle branch. This enables a more physiological propagation of the stimulus. Pacing of the left bundle branch is achieved by advancing the lead into the right ventricle and screwing it deep into the interventricular septum. Due to the relatively large target area of the left bundle branch the success rate is very high (currently > 90%). Observational studies have shown agreater reduction in the QRS duration, amore pronounced improvement in systolic function and alower hospitalization rate for heart failure associated with conduction system pacing compared to CRT using acoronary sinus lead. These findings have been confirmed in small randomized trials. Therefore, the use of left bundle branch pacing should be considered not only as abail out in the case of failed resynchronization using coronary sinus lead placement but increasingly also as an initial pacing strategy. The results of the first large randomized trials are expected to be released in late 2024.

METHOD OF PROTOTYPING CONTROL UNITS BASED ON THE OPENPLC APPLICATION

The problem of designing control devices based on programmable logic controllers (PLCs) is their high cost and, as a rule, unavailability at the initial design stage. The purpose of the research is to reduce the time and cost of designing the system by creating prototypes of control units with the software implementation of control algorithms in the languages of the MEK 61131-3 standard and the execution of programs in the Arduino board. The research method consists in the decomposition of project models of operating and control automata of the control device and their implementation in the software environment of the OpenPLC application in the form of program organization components (POU) in Ladder Diagram, Function Block Diagram, Sequential Function Chart and Structured Text languages. The result of the study is a method of creating typical POU operating and control automata of the control system, which are executed in the Arduino board. An example of the application of the proposed methodology for the design of a prototype of the facility's temperature control system is given. The developed prototype was tested using a logical PLC and a physical prototype, which confirmed their functional compliance with the original and a reduction in the cost of the equipment by at least an order of magnitude.

Real-Time Detection of Drill Pipe Joints Using Improved YOLOv5x Model Applied to Drilling Operation Images

It is widely known that pipe assembly and disassembly still lacks automation. To enhance drilling efficiency, we propose an autonomous detection and positioning model based on improved YOLOv5x for drill pipe joints. Firstly, we use Activate or Not (ACON) activation function and Convolution Block Attention Module (CBAM) to enhance feature extraction and representation ability. Then, the loss function is changed from Complete IoU (CIOU) loss to Scale-Invariant IoU (SIOU) loss, which increases the accuracy of the bounding box regression. Finally, the predict heads are tailored to be more effective in detecting tiny targets. Following network training, the final drill pipe joint detection model, based on the improved YOLOv5x, achieved an average accuracy of 99.10%, a 3.5% improvement over the base YOLOv5x algorithm. The proposed method effectively promotes the intelligence of oil drilling equipment by quickly and accurately detecting drill pipe joints, consequently enhancing drilling operation efficiency.

A Flexible MATLAB/Simulink Simulator for Robotic Floating-base Systems in Contact with the Ground: Theoretical Background and Implementation Details

This paper presents an open-source MATLAB/Simulink physics simulator for rigid-body articulated systems, including manipulators and floating-base robots. Thanks to MATLAB/Simulink features like MATLAB system classes and Simulink Function blocks, the presented simulator combines a programmatic and block-based approach, resulting in a flexible design in the sense that different parts, including its physics engine, robot-ground interaction model, and state evolution algorithm are simply accessible and editable. Moreover, through the use of Simulink dynamic mask blocks, the proposed simulator supports robot models integrating open-chain and closed-chain kinematics with any desired number of links interacting with the ground. This simulator can also integrate second-order actuator dynamics. Furthermore, the simulator benefits from a one-line installation and an easy-to-use Simulink interface.

Investigating the graphical IEC 61131-3 language impact on test case design and evaluation of mechatronic apprentices

Abstract Mechatronic on-site technicians are responsible for maintenance and thus adequate testing of automated production systems. Hence, they must derive test cases from the IEC 61131-3 control code, which requires systematic testing skills. But despite the industry’s high demand for such skills, testing is not established in mechatronic apprentices’ educational curriculum. For the design of a teaching strategy for mechatronic apprentices on testing, this paper investigates the impact of the IEC 61131-3 language on test case design and evaluation. Comparing Sequential Function Chart (SFC) and Function Block Diagram, a trend towards SFC in apprentices’ self-perceived and real competence is shown.

A Real-time P-SFA hardware implementation of Deep Neural Networks using FPGA

Machine Learning (ML) algorithms, specifically Artificial Neural Networks (ANNs), have proved their effectiveness in solving complex problems in many different applications and multiple fields. This paper focuses on optimizing the activation function (AF) block of the NN hardware architecture. The AF block used is based on a probability-based sigmoid function approximation block (P-SFA) combined with a novel real-time probability module (PRT) that calculates the probability of the input data. The proposed NN design aims to use the least amount of hardware resources and area while maintaining a high recognition accuracy. The proposed AF module in this work consists of two P-SFA blocks and the PRT component. The architecture proposed for implementing NNs is evaluated on Field Programmable Gate Arrays (FPGAs). The proposed design has achieved a recognition accuracy of 97.84 % on a 6-layer Deep Neural Network (DNN) for the MNIST dataset and a recognition accuracy of 88.58% on a 6-layer DNN for the FMNIST dataset. The proposed AF module has a total area of 1136 LUTs and 327 FFs, a logical critical path delay of 8.853 ns. The power consumption of the P-SFA block is 6 mW and the PRT block is 5 mW.

Spin-EPR-pair separation by conveyor-mode single electron shuttling in Si/SiGe

Long-ranged coherent qubit coupling is a missing function block for scaling up spin qubit based quantum computing solutions. Spin-coherent conveyor-mode electron-shuttling could enable spin quantum-chips with scalable and sparse qubit-architecture. Its key feature is the operation by only few easily tuneable input terminals and compatibility with industrial gate-fabrication. Single electron shuttling in conveyor-mode in a 420 nm long quantum bus has been demonstrated previously. Here we investigate the spin coherence during conveyor-mode shuttling by separation and rejoining an Einstein-Podolsky-Rosen (EPR) spin-pair. Compared to previous work we boost the shuttle velocity by a factor of 10000. We observe a rising spin-qubit dephasing time with the longer shuttle distances due to motional narrowing and estimate the spin-shuttle infidelity due to dephasing to be 0.7% for a total shuttle distance of nominal 560 nm. Shuttling several loops up to an accumulated distance of 3.36 μm, spin-entanglement of the EPR pair is still detectable, giving good perspective for our approach of a shuttle-based scalable quantum computing architecture in silicon.

Study and evaluation of automatic offloading for function blocks of applications

Systems using graphical processing units (GPUs) and field-programmable gate arrays (FPGAs) have increased due to their advantages over central processing units (CPUs). However, such systems require the understanding of hardware-specific technical specifications such as Hardware Description Language (HDL) and compute unified device architecture (CUDA), which is a high hurdle. Based on this background, we previously proposed environment-adaptive software that enables automatic conversion, configuration and high-performance operation of existing code according to the hardware to be placed. As an element of this concept, we also proposed a method of automatically offloading loop statements of application source code for CPUs to GPUs and FPGAs. In this paper, we propose a method for offloading a function block, which is a larger unit, instead of individual loop statements in an application to achieve higher speed by automatically offloading to GPUs and FPGAs. We implemented the proposed method and evaluated it using current applications offloading to GPUs and FPGAs.

AN INNOVATIVE METHODOLOGY TO DESIGN GYROID HEAT EXCHANGERS FOR METAL ADDITIVE MANUFACTURING

This study demonstrates an innovative approach to the automatic design of compact gyroid heat exchangers using the advanced engineering software nTopology, which is based on implicit modeling technology. The aim is to provide a modern enhancement to traditional 'Shell and Tube' type heat exchangers. Utilizing functions in implicit modeling and parametric design features, complex internal gyroid structures can be used as an alternative heat transfer interface in 'Shell and Tube' type heat exchangers. The most striking aspect of the methodology is its ability to fully automate the design process. By consolidating specific parameters into a single function block and entering scalar values, a fast and flexible workflow is activated, automatically generating the final geometry. Gyroid structures with high thermal performance and fluid dynamics are automatically adapted for various volumes and geometries. These structures can improve the overall efficiency of heat exchangers and offer significant advantages, especially in specialized application areas such as aerospace and space industries. In the design and sizing process of the gyroid heat exchanger, if manufacturing is planned, it is necessary to pay attention to the design principles for metal additive manufacturing. In conclusion, this study demonstrates that the advanced engineering software known as nTopology can create a synergistic effect in the rapid and easy design of gyroid heat exchangers and in establishing the automation of the design process.

Hollow fiber bioreactor with genetically modified hepatic cells as a model of biologically active function block of the bioartificial liver

Chronic liver disease and cirrhosis, that can lead to liver failure, are major public health issues, with liver transplantation as the only effective treatment. However, the limited availability of transplantable organs has spurred research into alternative therapies, including bioartificial livers. To date, liver hybrid support devices, using porcine hepatocytes or hepatoma-derived cell lines, have failed to demonstrate efficacy in clinical trials.Here, for the first time, we report the construction of a model of biologically active function block of bioartificial liver based on a hollow fiber bioreactor populated with genetically modified hepatic cells. For comprehensive comparison the culturing of hepatic cells was carried out in both static and dynamic conditions in a medium that flowed through porous polysulfone capillaries. The most crucial parameters, such as cell viability, glucose consumption, albumin secretion and urea production, were analyzed in static conditions while glucose usage and albumin production were compared in dynamic cell cultures. This model has the potential to improve the development of bioartificial liver devices and contribute to the treatment of patients with impaired liver function.

Accelerating CEST imaging using a model-based deep neural network with synthetic training data.

To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data. Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods. The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used. The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.

FPGA Implementation of IEC 61131-3-Based Hardware-Aided Timers for Programmable Logic Controllers

Designs of timer function blocks (FBs) are presented in the article. The developed modules are IEC 61131-3. An analysis of IEC 61131-3 in terms of timer functionality and implementation options is presented. Three types are presented, timer-on, timer-off, and timer-pulse, with each type designed to be fully hardware or software-like. Both designs, hardware or software-like, can operate as multi-channel timers. Particularly noteworthy is the software-like design, for which a solution without edge detectors was achieved. Such a feature was obtained by reversing the method of time determination by counting the difference between the start and end times and by using specific features of the D flip-flops, that is, clock-enable inputs. The presented timers were written in Verilog language and implemented in an FPGA chip. Thanks to the universal design of the interface, the proposed FBs can be used for the hardware support of existing programmable logic controllers (PLCs) or as an integral part of newly built PLC CPUs. The idea of a CPU architecture with hardware support is proposed. The paper presents the results of the implementation in an FPGA of the Kintex UltraScale+ family from AMD-Xilinx.

Improving personalized medical implant design: An innovative and automated method leveraging implicit modeling

Advancements in manufacturing technologies have made the design and surgical applications of personalized implants increasingly popular and widespread. This study presents a fast, parametric, and automatic workflow for creating customized implants to be used in the surgical treatment of humerus bone traumas. The workflow operates on various humerus fractures, such as “Butterfly Fragment” fractures, taking into account patient-specific bone geometries and areas where screws will be applied. The workflow is carried out using implicit modelling and nTopology software. Initially, a replica of the “Butterfly Fragment” fracture is created for the study, and the damaged bone tissue is removed. Subsequently, a filler structure connecting the two main fragments is obtained using a porous structure. The workflow continues with the identification of areas where screws will be applied around the bone trauma and the selection of the surface where the implant will be created. Using selected points on the surface mesh and single-dimensional beams guided by these points, the external geometry and screw holes of the implant are shaped. The workflow is completed by offsetting certain structures created in intermediate steps and applying surface treatments to obtain the final shape and thickness of the implant, which is compatible with the bone structure. At the end of the process, the workflow is converted into a single function block, which can be shared for use by an implant designer. This enables the widespread application of personalized implant designs. The workflow accelerates the design process of the implant, providing implicit data suitable for production using additive manufacturing. This study offers a preparatory process for the rapid design and production of customized implants for patients. Future research may include comparisons of CAD design and implicit modelling methods, time studies, process modifications, optimizations, and investigations related to additive manufacturing printers. This study contributes to shortening the time until the application of surgical treatment methods by accelerating the design and production process of customized implants.

Cultivating the toddler data desert with the Visual Attention Processing Protocol (VAPP): A novel scalable measure of individual differences in attentional processing from 2 to 6 years old

The current study proposes the Visual Attention Processing Protocol (VAPP), as a solution to the transferability of methods and scalability of singular attention tasks from toddlerhood to middle childhood. The VAPP was administered to 122 children ages 2- to 6-years-old. Consistent with our hypothesis, this task captured differences specific to orienting, alerting, and executive attention abilities across this age range. Furthermore, orienting was shown to have the least difference across age suggesting it is largely already developed by toddlerhood. The executive function block had the greatest changes among all tasks, indicating that it was the skill least developed at toddlerhood but the one with the most robust developmental gains by middle childhood. There were also developmental shifts in how well children regulate attentional control in the face of conflicting information across age groups—with results indicating that an added tone facilitated a faster response time in older children but interfered with accuracy in younger children. In addition, within- and between-group differences demonstrated the nuances of attentional abilities via error making as it relates to broader cognitive processes such as conflict resolution (e.g., specific to rule use), language (i.e., categorical sorting of items), and selective attention (i.e., direction of an object rather than the spatial location of an object). Together, results indicate a single paradigm can be sensitive to developmental gains in visual attention processing associated with efficiency and speed of responding from toddlerhood to middle childhood. Future directions and implications for clinical use are discussed.