Real-time Control Research Articles

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

Active Learning in Feature Extraction for Glass-in-Glass Detection

In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection than RGB cameras, making them suitable for detecting various contaminants. However, acquiring sufficient defective samples for classification is costly and time-consuming. To address this, we propose an anomaly detection system requiring only non-defective samples, automatically classifying anything not recognized as good as defective. Our system, employing active learning on X-ray images, efficiently detects defects like glass fragments in food products. By fine tuning a feature extractor and autoencoder based on non-defective samples, our method improves classification accuracy while minimizing the need for manual intervention over time. The system achieves a 97.4% detection rate for foreign glass bodies in glass jars, offering a fast and effective solution for real-time quality control on production lines.

Dynamic safety information modeling of underground cavern groups in the entire construction process

The construction of underground cavern groups represents a particularly challenging task in current subsurface engineering due to a multitude of variable and often unknown factors, including diverse geological conditions. This study introduces a four-dimensional spatiotemporal model and formulates a dynamic safety information model for these underground systems. Developed using C# and Python, the model integrates the finite element analysis software ABAQUS and Microsoft SQL Server database. The framework allows for real-time visual management of monitoring data, dynamic coupling of construction phases with safety metrics, and continual updates correlating with construction progress. The theoretical findings offer valuable insights for enhancing the safety and efficiency of underground cavern group construction while also supplying methods for real-time safety feedback and control throughout the construction process.

Real-time generation of circular patterns in electron beam lithography

Electron beam lithography (EBL) involves the transfer of a pattern onto the surface of a substrate by first scanning a thin layer of organic film (called resist) on the surface by a tightly focused and precisely controlled electron beam (exposure) and then selectively removing the exposed or nonexposed regions of the resist in a solvent (developing). It is widely used for fabrication of integrated circuits, mask manufacturing, photoelectric device processing, and other fields. The key to drawing circular patterns by EBL is the graphics production and control. In an EBL system, an embedded processor calculates and generates the trajectory coordinates for movement of the electron beam, and outputs the corresponding voltage signal through a digital-to-analog converter (DAC) to control a deflector that changes the position of the electron beam. Through this procedure, it is possible to guarantee the accuracy and real-time control of electron beam scanning deflection. Existing EBL systems mostly use the method of polygonal approximation to expose circles. A circle is divided into several polygons, and the smaller the segmentation, the higher is the precision of the splicing circle. However, owing to the need to generate and scan each polygon separately, an increase in the number of segments will lead to a decrease in the overall lithography speed. In this paper, based on Bresenham’s circle algorithm and exploiting the capabilities of a field-programmable gate array and DAC, an improved real-time circle-producing algorithm is designed for EBL. The algorithm can directly generate circular graphics coordinates such as those for a single circle, solid circle, solid ring, or concentric ring, and is able to effectively realizes deflection and scanning of the electron beam for circular graphics lithography. Compared with the polygonal approximation method, the improved algorithm exhibits improved precision and speed. At the same time, the point generation strategy is optimized to solve the blank pixel and pseudo-pixel problems that arise with Bresenham’s circle algorithm. A complete electron beam deflection system is established to carry out lithography experiments, the results of which show that the error between the exposure results and the preset patterns is at the nanometer level, indicating that the improved algorithm meets the requirements for real-time control and high precision of EBL.

Real-time optimization control of variable rotor speed based on Helicopter/ turboshaft engine on-board composite system

In order to adequately leverage the advantages of coaxial compound helicopters (CCH) to enhance overall performance and efficiency through variable rotor speed, and to improve the fuel economy of the composite propulsion system, a real-time optimization control method of variable rotor speed based on helicopter/turboshaft engine on-board composite system is proposed. Firstly, a steady-state model of the CCH required power and an on-board inverse model of the turboshaft engine based on an improved deep neural network propulsion system model (AdamDNN-PSM) are established. On this basis, an on-board adaptive model based on adaptive square root Kalman filter (ASRUKF) estimator is employed to correct the precision of the on-board inverse model and to track the real engine state. Finally, by adopting the feasible sequence quadratic programming (FSQP) method, an optimal speed rotor (OSR) is obtained through real-time optimization control, which reduces fuel consumption during cruising while considering both the geometric and aerodynamic constraints of the rotor. The simulation results demonstrate that the accuracy and real-time performance of the on-board inverse model have been significantly improved. The real-time optimization control method for OSR effectively reduces fuel consumption during cruising and enhances the CCH performance of the entire flight envelope.

Research on human-robot interaction for robotic spatial 3D printing based on real-time hand gesture control

With the rapid advancements in three-dimensional (3D) printing, researchers have shifted their focus towards the mechanical systems and methods used in this field. While Fused Deposition Modelling (FDM) remains the dominant method, alternative printing methods such as Spatial 3DP (S-3DP) have emerged. However, the majority of existing research on 3D printing technology has been emphasizing offline control, which lacks the capability to dynamically adjust the printing path in real time. Such an limitation has resulted in a decrease in printing efficiency. Therefore, this paper proposes a human-robot interaction (HRI) method based on real-time gesture control for Robotic Spatial 3DP (RS-3DP). This method incorporates utilization of YOLOv5 and Mediapipe algorithms to recognize gestures and convert the gesture information into real-time robot operations. Results show that this approach offers a feasible solution to address the issue of discontinuous S-3DP nodes because it achieves a gesture-controlled robot movement accuracy of 91 % and an average system response time of approximately 0.54 s. The proposed HRI method represents a pioneering advancement in real-time control for RS-3DP, thereby paving the way for further exploration and development in this field.

Low hysteresis, water retention, anti-freeze multifunctional hydrogel strain sensor for human-machine interfacing and real-time sign language translation.

Hydrogel strain sensors have received increasing attention due to their potential applications in human-machine interfaces and flexible electronics. However, they usually suffer from both mechanical and electrical hysteresis and poor water retention, which limit their practical applications. To address this challenge, a poly(acrylic acid-co-acrylamide) hydrogel crosslinked by silica nanoparticles is fabricated via photo polymerization and salting-out of hydrophilic ions for the strain sensor. The resulting hydrogel strain sensor possessed low electrical hysteresis (1.6%), low mechanical hysteresis (<7%), high cycle stability (>10 000 cycles), high durability, water retention and anti-freezing ability. Moreover, this strain sensor can be used as a wearable sensor for real-time control of robotic hands and hand gesture recognition. Finally, a sign language translation system has been demonstrated with the aid of machine learning, achieving recognition rates of over 98% for 15 different sign languages. This work offers a promising prospect for human-machine interfaces, smart wearable devices, and the Internet of Things.

Closed-loop optogenetic neuromodulation enables high-fidelity fatigue-resistant muscle control.

Closed-loop neuroprostheses show promise in restoring motion in individuals with neurological conditions. However, conventional activation strategies based on functional electrical stimulation (FES) fail to accurately modulate muscle force and exhibit rapid fatigue because of their unphysiological recruitment mechanism. Here, we present a closed-loop control framework that leverages physiological force modulation under functional optogenetic stimulation (FOS) to enable high-fidelity muscle control for extended periods of time (>60 minutes) in vivo. We first uncovered the force modulation characteristic of FOS, showing more physiological recruitment and significantly higher modulation ranges (>320%) compared with FES. Second, we developed a neuromuscular model that accurately describes the highly nonlinear dynamics of optogenetically stimulated muscle. Third, on the basis of the optogenetic model, we demonstrated real-time control of muscle force with improved performance and fatigue resistance compared with FES. This work lays the foundation for fatigue-resistant neuroprostheses and optogenetically controlled biohybrid robots with high-fidelity force modulation.

Toward a water-sensitive precinct with stormwater harvesting: a case study in South Africa

ABSTRACT The study assessed static management, i.e., without planned periodic maintenance of the storage (no sediment dredging) denoted as SC1, or with maintenance denoted as SC2. The second approach was dynamic management based on applying Real-Time Control (RTC) techniques to scenario SC1 and scenario SC2 denoted as RTC-1 and RTC-2, respectively. The dynamic management of the UCT dam with RTC-1 and RTC-2 approaches increase yield by 2.1 and 1.1 ml, respectively. Additionally, RTC-1 and RTC-2 approaches increase volumetric reliability by 5.3 and 2.5%, respectively, while maintaining the required level of service of a stormwater harvesting system. SC1 and SC2 results in water savings of up to 21.15 and 21.45 ZAR/kl, respectively, while RTC-1 and RTC-2 could save up to 19.35 and 19.45 ZAR/kl. Thus, static configurations results in water savings approximately 9% in comparison to RTC. In addition, static configurations harvested stormwater at a relatively lowest unit cost in comparison to RTC configurations. Notwithstanding this finding, the RTC system exhibits a great potential in reshaping the stormwater harvesting system to simultaneously deliver water conservation and stormwater management.

Photoinitiated Transient Self-Assembly in a Catalytically Driven Chemical Reaction Cycle.

Chemically-fueled chemical reaction networks (CRNs) are key in controlling dissipative self-assembly. A key challenge in the field is to store chemical fuel-precursors or "pre-fuels" in the system that are catalytically converted into activating or deactivating fuels that drive the CRN. In addition, real-time control over such catalysis by light is highly desirable, but so far not yet achieved. Here we show a catalytically-driven CRN that is photoinitiated with 450 nm light, producing activated monomers that go on to perform transient self-assembly. Monomer activation proceeds via photoredox catalysis, converting the monomer alcohol groups into the corresponding aldehydes that self-assemble into large supramolecular fibers. Monomer deactivation is achieved by organometallic catalysis that relies on pre-fuel hydrolysis to release formate (i.e., the deactivating fuel). Additionally, irradiation with 305 nm light accelerates the release of formate by photo-uncaging the pre-fuel, leading to a factor ~2 faster deactivation of the monomer. Overall, we show transient self-assembly upon visible light photoactivation, and tunable life-times by ultraviolet light.

Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising

In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

Design of real time closed loop control in hybrid energy system using cascaded converters: a dynamic solution for high power applications

ABSTRACT This paper presents a Hybrid Renewable Energy system combining multiple sources of renewable energy, such as PV, Wind, and energy storage devices, to provide a reliable and sustainable power supply. Cascaded converters are used to efficiently transform and manage the energy from these sources. To achieve optimal performance and reliable operation, a real-time closed-loop control algorithm is designed and implemented for the cascaded converter. The control algorithm facilitates the smooth integration of energy from various renewable sources, optimizing the utilization of available resources and ensuring a consistent power supply. The algorithm maximizes the energy conversion efficiency of the cascaded converter, reducing energy losses and minimizing waste. It also optimizes the integration of renewable sources, regulates output parameters, and ensures efficient energy conversion.

Multi-agent system architecture for winter road maintenance: a real Spanish case study

AbstractRoad safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management

ABSTRACT The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management.

Advancements in Battery Cell Finalization: Insights from an Expert Survey and Prospects for Process Optimization

Battery cell finalization is a crucial process chain in battery manufacturing, contributing to a significant share of CAPEX and OPEX. Thus, there is a high cost-saving potential by improving the process chain. This research paper investigates various crucial facets of the cell finalization process in battery cell production through an expert survey. These include investment cost allocation, potential cost savings in sub-processes, reject generation, early detection of faulty cells, quality measurement techniques, and the utilization of inline data for early quality determination and real-time process control during the formation process. A solution approach for the implementation of electrochemical impedance spectroscopy for inline early quality determination is given. The results yield valuable insights for optimizing the formation process and enhancing product quality.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

An Image-Based Interactive Training Method of an Upper Limb Rehabilitation Robot

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.

Interoperability Structure of Smart Water Conservancy Based on Internet of Things

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO’s interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO.

Sand fill cleanout on wireline enables access to additional perforation zones in gas well producer

An Australian operator wanted to explore options for wireline conveyed sand bailing in one of their offshore deviated ‘S’ shape gas producing wells. The objective was to remove approximately 24 m of sand fill inside 9.625″ production casing, with an internal diameter (ID) of 8.681″. To limit the number of cleanout runs, the latest technology in wireline deployed suction tools with a 4.25″ overall diameter (OD) was chosen, which provided increased recovery volume, plus real-time surface control and monitoring. Because of the tool’s overall diameter, the landing nipple also required milling to allow access. Therefore, a specially designed 4.412″ mill bit was manufactured and run on a milling tool. The electric line deployed milling technology successfully milled through the landing nipple and a ball catcher in four runs, thus increasing the ID sufficiently to allow access for the cleanout tool string. A well fluid column of about seven bars is required to enable operation of the suction tool. The cleanout tool was configured with three bailer sections (seven max) for the first run, to determine sand recovery optimisation. Three different sizes of bailer filters are provided; prior knowledge of debris particle size is therefore advantageous. In this case, a slickline bailing sand sample had been recovered, which immediately aided filter choice. Seventeen bailing runs were conducted over 5 days, successfully recovering a total of 918 L of sand, equating to about 2.4 tons. Ultimately, the process enabled perforation of two additional gas zones to increase production.

Influence of usage and model inaccuracies on the performance of smart hot water heaters: lessons learned from a demand response field test

Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization.