Hardware System Research Articles

Design and Implementation of a Hardware-in-the-Loop Air Load Simulation System for Testing Aerospace Actuators

This paper presents the design and implementation of the hardware and control strategies of an electrohydraulic air load simulation system for testing aerospace actuators. The system is part of an Iron Bird, which is an energy management research platform developed in collaboration between Saab AB and Linköping University. The purpose of the air load system is to provide realistic forces on the test object through the integration of a flight simulator for full mission evaluation. The challenge with electrohydraulic force control is tackled by increasing the hydraulic capacitance from increased load cylinder dead volumes, together with a feed-forward link based on accurate modelling of the test object and load system by adopting an optimisation routine to find model parameters. The system is implemented for both an electromechanical and servohydraulic actuator as test objects with different performance requirements. The control design is based on nonlinear and linear modelling of the system, and experimental test data are used to tune the models. Finally, test results of the air load system prove its force-tracking performance.

Self-Healing of Complex Hardware and Software Systems

Self-Healing of Complex Hardware and Software Systems

MPPT constraint conditions based on four-parameter cell model for some usual photovoltaic systems

Hitherto there is no work to specially analyze in detail the question when the maximum power point (MPP) of the photovoltaic (PV) system exists and when it disappears. It has caused the difficulty in the hardware and software designs of the PV system when the maximum power point tracking (MPPT) control must be implemented all the time. To solve this question, in this paper, the MPPT constraint conditions (MPPTCC) of the 12 usual PV systems are proposed under ideal conditions and expressed by the model parameters of the four-parameter cell model in the engineering application. They are the necessary and sufficient conditions to guarantee the existence of the MPPs of these 12 PV systems. Meanwhile, they also disclose the inherent relationships between load (or bus voltage) and cell parameters when the MPP of the PV system always exists. In addition, to apply them in practice, their expressions in practical application are also presented. Finally, some simulation experiments verify the reasonableness and accuracy of these MPPTCC in practical application, and test the influence of the varying weather to the MPPT range. By this work, the possible failure of the MPPT control arising from the occasional disappearance of the MPP of the PV system can be completely avoided. Therefore, the findings in this work can be used as the theory foundation for the hardware design, theoretical research and product installation of the PV system.

3D position and pose measurement based on coded light field

AbstractHigh‐precision relative position and attitude measurement technology has a wide range of applications in aerospace and industrial production. Currently, the commonly used method for measurement is based on visual cooperative signs. However, its accuracy significantly decreases as the distance increases. Therefore, a relative positioning system is designed based on light field spatial coding and visual recognition. The projector emits spatially encoded structured light within the coverage of the light field, while the receiving end captures, identifies, measures the code, and calculates its pose in the light field coordinate system. Compared with the traditional measurement method, the measurement accuracy of this system does not decrease greatly with the increase in distance, the measurement distance can be adjusted in real‐time and does not depend on an external light source. By changing the projection pattern with different resolutions without changing hardware systems, it can adjust effective measurement distance accordingly. Theoretical and experimental results show that the proposed method can maintain measurement accuracy with the increase in distance.

Solar-heat pump combined drying with phase change heat storage: Multi-energy self-adaptive control

Due to the various uncertainties in multi-energy drying systems, traditional condition control struggles with the rational matching of multiple heat sources and precise temperature regulation. In order to automatically switch energy utilization modes based on current parameters to reduce energy consumption, this paper developed a fuzzy control system for a kelp multi-energy drying device. An automatic switching strategy between different operating modes and the action processes of various actuators were established for matching multiple energy sources. The system hardware and software were designed and installed, including a remote monitoring devices and human-machine interface. Drying experiments of kelp were conducted in solar, solar-heat pump, heat pump and Heat storage-heat pump mode using both the fuzzy control system and the original. The fuzzy control system showed a temperature overshoot of 2.57 %–3.95 %, temperature deviation within ±1.5 °C, which were superior to the original control system. The energy consumption in solar-heat pump mode was 0.398 kW h/kg, only 43 % of the original control system. BP neural networks prediction model of kelp moisture content under different drying modes was established to prevent over-drying. The model can accurately describe the pattern of moisture content variation with RMSE of 2.36–4.27.

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

Метод визначення вібраційних характеристик промислового обладнання в частотній та часовій областях

This paper proposes hardware and software solutions and a data processing method for vibration diagnostics of industrial equipment, which uses discrete Fourier transform and Allan dispersion to increase the accuracy and stability of measurement processes and result processing. The object of this study is the use of vibration diagnostic methods to implement the concept of maintenance of industrial equipment based on monitoring its current and future condition. The subject of this research is the hardware and software solutions for vibration diagnostics systems and methods for processing measurement results. The purpose of this work is to develop a new resource-saving IoT-oriented wireless solution for vibration diagnostics, where the contact method and MEMS accelerometers are used to measure vibration parameters and to evaluate the effectiveness of new methods and algorithms for processing experimental data. The task: justify the need to find new hardware and software solutions and methods of processing the obtained results for the implementation of the service concept based on the tracking of vibration indicators of technical equipment; provide basic hardware and software solutions for the implementation of the cloud platform of vibration diagnostics; develop methods of processing results; check the developed methods and algorithms using mathematical modeling methods and in an on-site experiment; compare the effectiveness of own and competitive solutions; draw conclusions and formulate a plan for further research. Conclusions. It has been proven that the combination of known analysis methods in the time and frequency domains with multi-level processing gives better results than analogous methods. The developed hardware and software tools and the method of processing measurement results effectively implement the contact method of vibration measurement, which provides the possibility of tracking the state of technical equipment. The developed equipment for the calibration of vibration acceleration sensors can reduce accelerometer errors. Further areas of research are the search for the optimal distribution of calculations on IoT levels, reducing the computational complexity of algorithms, increasing the time of continuous autonomous operation of the lower-level microcontroller, creating micro services for time-series analysis, and researching the dependence of the technical state of the equipment on the calculated Allan deviation.

Enhanced energy extraction from wind driven PMSG using digital twin model of battery charging system

The pressing need to meet sustainability and climate objectives has prompted a call for expanding the use of renewable energy sources, improving the intelligence and flexibility of electricity grids, and increasing the adoption of electric vehicles and other electricity-powered products. In light of their numerous benefits, widespread electrification and digital technologies can form the basis of energy and climate policy. This research presents an innovative biased transformer and digital twin battery model to support the wind energy based electric vehicle battery charging system. During periods of low and moderate wind, the biased transformer is utilized to increase the voltage for charging electric vehicles. A digital twin of the battery system creates a window into battery charging and aging levels by evaluating the data gathered based on internal resistance. Furthermore, the State of Charge estimation model and different internal resistance estimation algorithms are also exploited. A novel method for precise State of Charge (SOC) estimation in third-gen battery models is presented, integrating Collaborative Gradient Boosting and Adaptive Extended Kalman Filter (AEKF). This approach, implemented in MATLAB Simulink, combines machine learning and adaptive filtering, enhancing accuracy and adaptability for efficient battery management. The functionalities and resilience of both hardware and software of the proposed system are validated under field operation.

Data and Energy Impacts of Intelligent Transportation—A Review

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles.

A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology

The author has designed an automated monitoring system for settlement and deformation of high-speed railway subgrade. Firstly, an automated monitoring system is designed based on sensors, data collection and transmission, client tracking and querying, monitoring result processing, automated warning, manual monitoring data analysis, monitoring data analysis and evaluation. Secondly, the system software and hardware are designed, the author calculated and analyzed engineering examples from the perspective of practical applications using artificial neural network methods, obtained corresponding deformation analysis models, and predicted deformation. Finally, the practical application of the system was analyzed for its effectiveness. The experimental results indicate that, the BP artificial neural network method is used to model and predict the Deformation monitoring data. On the premise of 20 learning samples and 4 prediction samples, the Root-mean-square deviation of the prediction is 0.32mm, which shows that the deformation prediction using BP model is feasible and effective in a certain precision range. It has been proven that the application of artificial neural network methods in monitoring and prediction of practical engineering has certain practical significance.

The Digital Media Art and Design System under the Integration of Computer Big Data ChatGPT Technology

Traditional digital media art and design systems take a long time to run and cannot handle a large number of objects simultaneously. Therefore, the author proposes a digital media art and design method based on ChatGPT technology. ChatGPT technology is an important part of networking.Prepare to provide conference support for various applications such as customer service, personal assistant, robotic interview, and so on. ChatGPT can not only answer the question that is set, but also solve the question openly.The comprehensiveness and intelligence of ChatGPT have attracted widespread attention since its launch. The hardware system adopts dual core TMS320C6657 and DDR3Thel1333 series processors as the core circuit, together with high speed external storage, to expand the image transmission line of multi-data; System software uses module architecture to design image modules, video modules, and voice modules, all of which are integrated into the core of the system as modules. In this experiment, 10 images were transferred and the running time was tested. Experimental results showed that, the traditional digital media art and design system had three errors, which took more time to run.

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Due to the limitations of the hardware system, analysis of retail stores has caused problems such as excessive workload, incomplete analysis, slow analysis speed, difficult data collection, non-real-time data collection, passenger flow statistics, and density analysis. However, heatmaps are a viable solution to these problems and provide adaptable and effective analysis. In this paper, we propose to use the deep sequence tracking algorithm together with the YOLO object recognition algorithm to create heatmap visualizations. We will present key innovations of our customized YOLO-Deep SORT system to solve some fundamental problems in in-store customer behavior analysis. These innovations include our use of footpad targeting to make bounding boxes more precise and less noisy. Finally, we made a comprehensive evaluation and comparison to determine the success rate of our system and found that the success rate was higher than the systems we compared in the literature. The results show that our heatmap visualization enables accurate, timely, and detailed analysis.

Hardware and Software of the Booster Nuclotron Channel Control System of the NIKA Complex

Hardware and Software of the Booster Nuclotron Channel Control System of the NIKA Complex

Codesign of reactor-oriented hardware and software for cyber-physical systems

Modern cyber-physical systems often make use of heterogeneous systems-on-chip with reconfigurable logic to provide adequate computing power and flexible I/O. However, modeling, verifying, and implementing the computations spanning CPUs and reconfigurable logic is still challenging. The hardware and software components are often designed by different teams and at different levels of abstraction, making it hard to reason about the resulting computation. We propose to lift both hardware and software design to the same level of abstraction by using the Lingua Franca coordination language. Lingua Franca is based on a sparse synchronous model that allows modeling concurrency and timing while keeping a sequential model for the actual computation. We define hardware reactors as a subset of the reactor model of computation underlying Lingua Franca. We also present and evaluate reactor-chisel, a hardware runtime implementing the semantics of hardware reactors, and an extension to the Lingua Franca compiler enabling reactor-oriented hardware-software codesign.

Complications and Learning Curve Associated with an Imageless Burr-Based (CORI) Robotic-Assisted Total Knee Arthroplasty System: Results from First 500 Cases.

The use of robotic-assisted total knee arthroplasty (RA-TKA) is gaining traction. There is evidence to suggest that RA-TKA can help to optimize the precision and accuracy of implant positioning and that there may be protective effects on surrounding bony and soft tissues. Yet, there are important differences between the various RA-TKA systems currently on the market. One such newly introduced RA-TKA system uses imageless technology and performs bony cuts with the use of a burr-based device. The learning curve and complications unique to this system have yet to be assessed. We evaluated 500 consecutive RA-TKA cases using a newly developed burr-based and imageless system which were done by a single surgeon between the months of October 2021 and February 2023. Operative times were recorded and compared to the previous 150 conventional TKA cases allowing for the learning curve to be calculated using the CUSUM method. Intraoperative and postoperative complications were categorically profiled. The learning curve of this RA-TKA system was found to be 6 cases. Intraoperative complications included unintended bony over resection (n = 3), soft tissue injury (n = 2), and robotic system hardware (n = 2) or software (n = 2) malfunction. Postoperative complications consisted of superficial pin site infection (n = 1) and periprosthetic fracture near the pin sites (n = 1). There were no identified cases of prosthetic joint infection, instability events, or wound complications. The learning curve and the complication profile of a newly introduced imageless and burr-based RA-TKA system were described. This information serves to guide surgeons in adopting this technology and can counsel them regarding the potential pitfalls and challenges associated with its integration into practice. The work sheds light on the complexity and learning curve of the recently released imageless burr-based RA-TKA system. This important information is intended to help surgeons accept this cutting-edge technology by providing advice on any errors and difficulties that can occur when integrating it into clinical practice. This information can help surgeons navigate the complexities of integrating this new burr-based robotic technology into knee replacement procedures, enabling them to make well-informed decisions and receive guidance.

Enhancing Efficiency and Safety in Self-Guided Logistics Vehicles: A Comprehensive Analysis and Integration of Hardware and Control Systems

Self-guided logistics vehicles have become increasingly popular in various industries and warehouses for material lifting and transportation purposes. These automated systems have proven to be valuable in both small-scale production lines and large areas. However, challenges persist in the field of logistics, where industries and factories face difficulties in efficiently transporting materials. In response to these challenges, self-guided vehicles have been employed, but issues such as collisions with obstacles, material falls, and rigid path constraints have been observed. This project aims to address these challenges by implementing hardware safety precautions and incorporating routing and scheduling capabilities to enhance system automation. The article presents a comprehensive analysis of hardware safety precautions and highlights key features including sensors, actuators, and control systems, which contribute to the overall efficiency and smooth operation of the self-guided logistics vehicle system. By integrating these elements, the system can overcome obstacles, ensure material safety, and adapt to changing traffic conditions, thereby optimizing logistics operations in industries and warehouses.

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Abstract In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15 mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.

Fabric4show: real-time vision system for fabric defect detection and post-processing

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry.

Hybrid Beamforming for mm-Wave Massive MIMO Systems with Partially Connected RF Architecture

To satisfy the capacity requirements of future mobile systems, under-utilized millimeter wave frequencies can be efficiently exploited by employing massive multiple input-multiple output (MIMO) technology with highly directive beamforming. Hybrid analog-digital beamforming has been recognised as a promising approach for large-scale MIMO implementations with a reduced number of costly and power-hungry radio frequency (RF) chains. In comparison to fully connected architecture, hybrid beamforming (HBF) with partially connected RF architecture is particularly appealing for the practical implementation due to less complex RF power division and combining networks. In this paper, we first formulate single- and multi-user rate maximization problems as weighted minimum mean square error (WMMSE) and derive solutions for hybrid beamformers using alternating optimization. The algorithms are designed for the full-array- and sub-array-based processing strategies of partially connected HBF architecture. In addition, we propose lower complexity sub-array-based zero-forcing algorithms. The performance of the proposed algorithms is evaluated in two different channel models, a simple geometric model and a realistic statistical model. The performance results of the WMMSE HBF algorithms are meant to reveal the potential of partially connected HBF and serve as upper bounds for lower complexity methods. Numerical results imply that properly designed partially connected HBF has the potential to provide an good compromise between hardware complexity and system performance in comparison to fully digital beamforming.

Home Automation using Android App

Abstract: The rapid expansion of smart technology has changed many aspects of daily life, with home automation taking the lead. This study examines the creation, advancement, and deployment of a Bluetooth-based home automation system. Utilizing a microcontroller, the system enables seamless communication between household appliances and user-controlled devices. By integrating Bluetooth communication protocols, users can manage and monitor their home environment from a distance, enhancing convenience, efficiency, and energy conservation. The document delves into system architecture, hardware elements, software algorithms, and user interface design, providing insights into the technical aspects of the configuration. It also covers practical considerations such as security protocols and scalability to ensure a stable and reliable operation. Through experimental outcomes, the efficacy and usability of the proposed home automation solution are illustrated, demonstrating its potential to transform traditional houses into intelligent, interconnected ecosystems. This research contributes to the existing knowledge in home automation and serves as a valuable resource for researchers, professionals, and enthusiasts keen on exploring the capabilities of Bluetooth technology to enhance everyday living experiences.