Automatic crossbow control in industrial hot-dip galvanizing lines

Add-on Harmonic Disturbance Cancellation Control in Continuous Hot-Dip Galvanizing Lines

In this paper, the immunity of a Gigabit Ethernet Switch (GES) embedded in an industrial Programmable Logic Controller (PLC) is analyzed. For this matter, two configurations are tested. In the first, the GES is mounted on a dedicated test board, which isolates the integrated circuit (IC) with minimum interface components. In the second, the GES is embedded in a commercial PLC product. A Near Field Scan of Immunity setup, based on a local magnetic loop antenna injection is exploited to highlight potential weaknesses of the GES and indentify its sensitive functions. This study showed two susceptible functions in the GES: the oscillator pins and a data bus called Gigabit Media Independent Interface (GMII). These two functions were revealed to be susceptible in two generations of the same integrated circuit. This article also demonstrates the repeatability of the NFS results when the architecture embedding the IC is modified. The modeling of the NFSI and its coupling to the package and clock circuits is also discussed.

This paper presents a novel control scheme using an industrial programmable logic controller (PLC) with a new fuzzy module to enhance the power factor and voltage of a hybrid wind/PV power generation system. The proposed scheme can automatically regulate both the power factor and voltage of a wind induction generator under various wind speeds. From the experimental results, it shows that the proposed control scheme can effectively provide better power factor and voltage profile for the studied renewable energy system

The significance of digital twins in mechanical and plant engineering companies is becoming increasingly important in the course of Industry 4.0 as a cyber-physical system. Digital twins represent a real object in virtual space. The properties of the real object are simulated using structural and behavior models, so that the digital twin represents a spitting image of the real object in virtual space. The structural models and behavioral models are created by modeling the digital twin in different modeling and simulation domains. A common form of the digital twin in mechanical and plant engineering is a digital twin, which is based on a kinematic multibody simulation. The kinematic digital twin can be coupled with industrial controllers and is often used for the virtual commissioning of plants/machines. These digital twins usually represent the kinematic behavior of the plan/machine in an idealized way and neglect the physically determined system dynamics. By extending the digital twin by the generation of state variables of a multiphysical dynamic simulation and the use of these state variables by industrial programmable logic controller (PLCs), advantages arise in the use of the digital twin with respect to virtual commissioning, fault analysis, condition monitoring and simulative optimization of machines and plants. Thus, it is necessary to complement the kinematic digital twin by a multiphysical dynamic simulation and to connect this extended model of the digital twin to PLCs.

An implementation of a class of advanced process control algorithms is considered. The class is related to adaptive control systems and it is characterized by nonlinearities of a controlled process and singularities of an actuation channel. The implementation is suitable to be easily integrated into a modern industrial environment because it is based on industrial programmable logic controllers. The implementation is verified in hardware simulator that is connected to a numerical model of a nonlinear process.

We present a real-time hardware-in-the-loop simulation environment for the validation of a new hierarchical path planning and control algorithm for a small fixed-wing unmanned aerial vehicle (UAV). The complete control algorithm is validated through on-board, real-time implementation on a small autopilot having limited computational resources. We present two distinct real-time software frameworks for implementing the overall control architecture, including path planning, path smoothing, and path following. We emphasize, in particular, the use of a real-time kernel, which is shown to be an effective and robust way to accomplish real-time operation of small UAVs under non-trivial scenarios. By seamless integration of the whole control hierarchy using the real-time kernel, we demonstrate the soundness of the approach. The UAV equipped with a small autopilot, despite its limited computational resources, manages to accomplish sophisticated unsupervised navigation to the target, while autonomously avoiding obstacles.

We present a real-time hardware-in-the-loop simulation environment for the validation of a new hierarchical path planning and control algorithm for a small fixed-wing unmanned aerial vehicle (UAV). The complete control algorithm is validated through on-board, real-time implementation on a small autopilot having limited computational resources. We present two distinct real-time software frameworks for implementing the overall control architecture, including path planning, path smoothing, and path following. We emphasize, in particular, the use of a real-time kernel, which is shown to be an effective and robust way to accomplish real-time operation of small UAVs under non-trivial scenarios. By seamless integration of the whole control hierarchy using the real-time kernel, we demonstrate the soundness of the approach. The UAV equipped with a small autopilot, despite its limited computational resources, manages to accomplish sophisticated unsupervised navigation to the target, while autonomously avoiding obstacles.

Taking advantage of features incorporated into One-Chipz microcontrollers applicable when special functions of industrial type programmable logic controllers are performed

A key challenge for low-altitude unmanned air transportation is to minimize operational risks by all means. Besides many other measures to be considered, the aircraft’s trajectory must be planned carefully and optimized as there are inevitable remaining risks which should be minimized when flying over sparsely populated areas. The risk may be mitigated by a safe termination of the flight if circumstances permit. Also, the probability of violating any operational constraints that would lead to a flight termination should be reduced as much as possible. Adequate risk models and efficient algorithmic risk assessment techniques are required to perform such optimizations. Furthermore, the aircraft may have to react to certain events such as high-priority traffic by changing its trajectory online during flight. As command and control (C2) links may have limited reliability, it must be possible to perform trajectory re-planning onboard with limited computational resources. This poses high demands on the runtime efficiency of the planning algorithms. In this work, we present conceptual approaches to risk modeling and assessment based on geospatial datasets and aircraft dynamic models. We further present the design and experimental results of a software framework for onboard and online trajectory planning. Our results demonstrate that risk-based motion planning for unmanned aircraft can be performed with limited onboard computational resources allowing for safe autonomous flight.KeywordsMotion planningPath planningSampling-based planningRisk-based planningTrajectory generationFlight termination

Processing data generated at high volume and speed from the Internet of Things, smart cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data processing and analytics services at the Edge to reduce the latency and response time of the applications. The fog computing edge infrastructure consists of devices with limited computing, memory, and bandwidth resources, which challenge the construction of predictive analytics solutions that require resource-intensive tasks for training machine learning models. In this work, we focus on the development of predictive analytics for urban traffic. Our solution is based on deep learning techniques localized in the Edge, where computing devices have very limited computational resources. We present an innovative method for efficiently training the gated recurrent-units (GRUs) across available resource-constrained CPU and GPU Edge devices. Our solution employs distributed GRU model learning and dynamically stops the training process to utilize the low-power and resource-constrained Edge devices while ensuring good estimation accuracy effectively. The proposed solution was extensively evaluated using low-powered ARM-based devices, including Raspberry Pi v3 and the low-powered GPU-enabled device NVIDIA Jetson Nano, and also compared them with Single-CPU Intel Xeon machines. For the evaluation experiments, we used real-world Floating Car Data. The experiments show that the proposed solution delivers excellent prediction accuracy and computational performance on the Edge when compared to the baseline methods.

Recently, with the fast development of sensing and wireless communication technology, wireless sensor networks (WSNs) have been applied to monitor the physical world. A WSN consists of a set of sensor nodes, which are small sensing devices with limited computational resources able to communicate with each other located in their radio range. Network protocols ensure the effectiveness of communication between sensor nodes and provide the foundation for WSN applications. The characteristics of WSNs, including the limited energy supply and computational resources, render the design of WSN algorithms challenging and interesting. Both the Database and Network communities have dedicated considerable efforts to make WSNs more effective and efficient. In this chapter, we survey the problems arisen in practical applications of WSNs, focusing on various query processing techniques over captured sensing data.

Modeling real-life objects although in reach of current technology used to be confined to a niche of sophisticated industrial applications because of the high equipment costs involved. The costs of 3D imaging devices are substantially coming down, furthermore 3D modeling, within certain object complexities, is becoming feasible with limited (and inexpensive) computer resources. This work reports on modeling complex real-life objects with small computer resources. Practical provisions and clear examples of the object complexity in reach of the resources of this league are presented. Our considerations hold general validity but are demonstrated by examples pertaining to the field of cultural heritage, which is the field of our interest. This area is representative both of the typical difficulties encountered in practical 3D modeling and of the great opportunities offered by these techniques once production costs are substantially lowered.

Object detection on hardware platforms plays a very significant role in developing driver assistance systems (DASs) with limited computational resources. Object detection for DAS is a multiclass detection problem that involves detecting various objects like cars, auto, traffic lights, bicycles, pedestrians, etc. DAS also requires accuracy, speed, and sensitivity for detecting these objects in various challenging conditions. The lighting and weather conditions pose a serious challenge for accurate object detection for DAS. This paper proposes a speed-efficient and lightweight fully convolutional neural network (CNN) architecture for object detection in adverse rainy conditions. The proposed architecture uses a CNN-based deraining network with a custom SSIM loss function in the object detection pipeline, which can give an accurate performance using limited computational and memory resources. The object detection architecture contains some architectural modifications to the existing single shot multibox detector (SSD) architecture to make it more hardware efficient and improve accuracy on small objects. It uses a trainable color transformation module using [Formula: see text] convolutions for handling the adverse lighting conditions encountered in DAS. The architecture uses feature fusion and the dilated convolution approach to enhance the accuracy of the proposed architecture on small objects. The datasets available for object detection in DAS are very imbalanced with cars as a predominant object. The class weight penalization technique is used to improve the performance of the architecture on scarcely present objects. The performance of the architecture is evaluated on well-known datasets like Kitti, Udacity, Indian Driving Dataset (IDD), and DAWN. The architecture achieves satisfactory performance in terms of mean average precision (mAP) and detection time on all these datasets. It requires three times fewer hardware resources compared to existing architectures. The lightweight nature of the proposed architecture and modification of CNN architecture with TensorRT allow the efficient implementation on the jetson nanohardware platform for prototyping, which can be integrated with other intelligent transportation systems.

The report is related to the improvement of the automated self-service car wash system by making better the monitoring and preventive control systems, as well as the collection and reporting of data from the operation of the system to achieve more efficient operation and optimization in customer service. For this purpose, modern industrial programmable logic controllers with “Internet of Things” (IoT) capabilities are used. The advantages are low operating costs, the long life-cycle of the equipment, maximum satisfaction of the service customers

ABSTRACTEfficient and accurate segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) is crucial for clinical diagnosis and treatment planning. Traditional methods tend to concentrate solely on feature extraction from individual modalities, overlooking the substantial potential of multimodal feature fusion in enhancing segmentation performance. In this paper, we present a novel method that not only integrates salient features from different modalities strategically but also takes into account the constraints imposed by limited computational resources, ensuring both accuracy and efficiency. Two key modules, the attention‐guided cross‐modality fusion module (ACFM) and the hierarchical asymmetric convolution module (HACM), were designed to leverage the distinct modalities and the varying information focuses found within different dimensions. The ACFM is based on a transformer framework, utilizing self‐attention and cross‐attention mechanisms. These mechanisms enable the capture of both local and global dependencies within and between different MRI modalities. This design allows for the effective fusion of complementary features from multiple modalities, thereby enhancing segmentation performance by leveraging the valuable information contained in each modality. Meanwhile, the HACM reduces computational complexity using a pseudo‐3D convolution approach. This approach breaks down 3D convolutions into components along the transverse and sagittal axes. Unlike traditional 2D convolutions, this method preserves essential spatial information across dimensions. It ensures accurate segmentation while maximizing efficiency by capitalizing on the varying focus of information in different spatial planes. This approach takes advantage of the varying information density in these dimensions, achieving a balance between accuracy and efficiency. Through extensive experiments on the BraTS2021 dataset, our proposed modality fusion‐based network under limited resources (LFBTS) achieves dice scores of 0.925, 0.911, and 0.886 for whole tumor (WT), tumor core (TC), and enhanced tumor (ET), respectively. These results outperform state‐of‐the‐art (SOTA) models and consistently demonstrate superiority over models developed in the preceding 2 years. This highlights the potential of our approach in advancing brain tumor segmentation and improving clinical decision‐making, particularly in settings with limited resources.