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Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections. [ABSTRACT FROM AUTHOR]

This paper aims to propose a more efficient control algorithm for improving the control performance of uncertain nonlinear systems. A novel improved Takagi–Sugeno–Kang (TSK) wavelet type-2 fuzzy brain emotional learning controller (ITSKWT2FBC) is proposed. The proposed ITSKWT2FBC contains a brain emotional learning controller (BELC) combined with an improved TSK wavelet type-2 fuzzy system. By taking the advantages of BELC and the improved wavelet type-2 TSK fuzzy system, the control performance of this controller can be improved. The adaptive laws of controller parameters are derived from the defined Lyapunov function and gradient descent method. Moreover, the system stability can be guaranteed based on the Lyapunov stability theory. Finally, three nonlinear systems, a double inverted pendulum system, a Mackey–Glass time series prediction (TSP) and a four dimensional (4D) chaotic system synchronization are simulated to illustrate the effectiveness of the proposed method. The simulation results have demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data‐driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human‐driven vehicles (HVs). The CACC leverages online‐collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data‐driven control design is formulated as a semidefinite program that can be solved efficiently using off‐the‐shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method. [ABSTRACT FROM AUTHOR]

Autonomous and Remotely Operated Vehicle (ARV) are widely used for underwater detection and typically require ARVs to track predefined trajectories. Manual operation is not very accurate and stable, and intelligent control methods are popularly devised. This paper firstly introduces the line of sight (LOS) method for desired heading guidance. Later, a cascaded model predictive control (MPC) is proposed. In the cascaded MPC, a quantum-behaved particle swarm optimisation algorithm based on a non-linear decreasing contraction-expansion coefficient (QPSO-CE-nonlinear) is applied to optimise the controller's output. Several validation and comparative simulations are carried out. The results show that the controller has advantages in accuracy, convergence, and stability. The LOS is applicable to holonomic ARV and accelerates convergence. The QPSO-CE-nonlinear algorithm can improve the rate of convergence when solving optimisation problems. In addition, compared with MPC-SMC (sliding mode control), the cascaded MPC is more robust. [ABSTRACT FROM AUTHOR]

To improve energy conservation and reduce emissions of an engine, this study applies intelligent thermal management to a cooling system. With a marine high-speed diesel engine as the research object, an intelligent cooling system based on controllable cooling components was developed. A variable-universe fuzzy control algorithm is designed to control an electronic thermostat, and a 'T–S fuzzy control + BP-PID' control algorithm is proposed to control standard electric drive pumps. The experimental results indicate that the intelligent cooling system considerably reduces the warm-up time, achieves accurate cooling, and effectively inhibits the hot immersion phenomenon. The intelligent control algorithm has control characteristics such as rapid response, strong adaptive ability, small overshoot, smooth transition, and high precision. The intelligent cooling system reduced the cooling-system power consumption by 5.52%–57.93%, fuel consumption rate by 1.43%–5.15%, smoke emission by 1.40%–4.69%, CO emission by 2.14%–6.03%, and HC emission by 7.41%–12.26%. [ABSTRACT FROM AUTHOR]

In order to achieve intelligent production and quality control, improve production efficiency and accuracy, the author proposes an application method of artificial intelligence technology in mechanical manufacturing and automation. The author aims to explore the specific application of artificial intelligence technology in the mechanical manufacturing industry and summarize the main advantages of automation technology. By analyzing these advantages, the aim is to provide targeted recommendations for future technological development. Meanwhile, the author aims to provide theoretical support for promoting the application of modern artificial intelligence technology and automation technology in the field of mechanical manufacturing. In the manufacturing process, intelligent production and quality control can be achieved, improving production efficiency and accuracy; In the field of automation, artificial intelligence technology can achieve intelligent control systems and autonomous decision-making, improving the flexibility and adaptability of production lines. Overall, the application of artificial intelligence technology has brought revolutionary changes to mechanical manufacturing, promoting the upgrading and development of the industry. [ABSTRACT FROM AUTHOR]

From the perspective of dynamic lighting throughout the year, reflector and louvre are proposed to add to the south-facing window. The optimal parameter is 2.0 m height and 1.0 m width reflector and a louvre angle of 75°. However, when further analysing the indoor illumination and uniformity, the classroom still had high or low illumination during certain periods solely relying on natural lighting, indicating that a single strategy alone cannot meet the overall lighting environment comfort needs of the space. The study suggests that different lighting environments should be used in different seasons, periods and areas of the classroom. The proportion of natural light, combined light and artificial light environments should be 30%, 45% and 25%, respectively. At the same time, considering the transformation and optimisation of the lamps, the improvement measures for windows facing other directions in the classroom is recommended, the lamps be symmetrically distributed in the horizontal direction, with a hanging height of 1.9 m in the vertical direction, which is 0.4 m higher than the original. The lighting power is 34 W and 26 W, and corresponding optimisation strategies for the intelligent control system are proposed. [ABSTRACT FROM AUTHOR]

The passive sensing and active control of electromagnetic (EM) waves have always been attractive in electronic and information areas, especially during the intelligent era. Here a new method is presented to achieve the angle sensing of incident wave and adaptive control of backward scattering using the intelligent metasurface. The proposed unit cells have the ability to dynamically manipulate the receiving and reflection of the EM energy respectively. The angle sensing of incident waves can be actualized using the method of compressive sensing based on multiple receiving patterns, which are generated by randomly switching the receiving and reflection states of the unit cells. Afterward, the customized performances of backward scattering waves according to the cognitive incident angle can be realized by controlling the programmable reflective phases of unit cells correspondingly. One prototype composed of the metasurface and the module for sensing and adaptive feedback control is fabricated. The whole intelligent metasurface with customizing the function of retro‐reflection or low scattering is measured without human intervention and the good results acquired can verify the validity of the proposed concept and design. [ABSTRACT FROM AUTHOR]

By applying vision sensing to realize online intelligent control on the pipeline welding process, the welding efficiency and qualification rate of pipeline girth butt joint during on-site construction of long-distance oil and gas pipelines can be effectively improved. During the pipeline external welding process, based on the designed combined line structured lights vision sensor (C-LSLVS), a partitioned time–frequency-space (PTFS) image processing algorithm is proposed to extract the laser centerline for the deformed laser lines image of double-V composite groove. The algorithm first utilizes information in the time domain to remove random interference from the image, and then, based on Radon transform (RT) and discrete Fourier transform (DFT), other stationary interferences can be eliminated and laser line characteristics can be enhanced by back-projection reconstruction in the frequency domain and space domain, so the stable and accurate extraction of laser centerline can be achieved under different situations. Subsequently, the theoretical analysis and error calculation of the pipeline groove sizes and related parameters solution model based on the local plane fitting method are carried out, and high-precision calculations of the parameters are completed. Finally, an intelligent pipeline welding system based on the C-LSLVS is constructed. In the intelligent welding experiment, the maximum detection error of the pipeline welding groove sizes did not exceed 0.20 mm, and the weld seam tracking deviation did not exceed 0.25 mm. The welding results show that the system has important application value for the development of pipeline intelligent welding. [ABSTRACT FROM AUTHOR]

Zero-refractive index metamaterials have a wide range of applications in directional transmission and wave-front shaping due to their unusual acoustic properties. However, for most given acoustic topological metamaterials, the operating frequency is relatively fixed and the effect of temperature on their topological properties is rarely considered. Therefore, temperature-controlled tunable topological zero-refraction acoustic metamaterials are proposed in this paper. Firstly, a metamaterial with quadruple degenerate Dirac-like points at the center of the Brillouin zone is constructed, and the influence of temperature on the Dirac-like points is analyzed. The results show that the topological bandgap frequency range is more sensitive to temperature. The existence of pseudospin-polarized edge state is demonstrated by analysing the band structure of supercells with different topological phase phonon crystal. The topological zero-refraction property of the edge states outcoupled into free space is numerically demonstrated, and the non-contact active control of their operating frequencies can be realized by temperature. This study can provide a corresponding reference for the intelligent control of near-zero refractive index acoustic topological materials in elastic wave collimation and acoustic communication. [ABSTRACT FROM AUTHOR]

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Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: To explore the feasibility and safety of aflexible ureteroscopy with intelligent control of renal pelvic pressure(FUS-ICP) without a post-operative indwelling urinary catheter. Methods: In this retrospective study, we assessed patients with upper urinary tract stones who were treated with FUS-ICP at the Ganzhou People's Hospital from February 2022 to December 2023. Patients were divided into the non-urinary catheter (non-UC) and urinary catheter (UC) groups according to whether an indwelling catheter was used after surgery. Results: In total, 142 patients were included in the study. There was no significant difference in the preoperative general data between the two groups. Patients in the non-UC group performed better than those in the UC group in terms of catheter-related bladder irritation (P = 0.001), the Sedation-Agitation Scale score (P = 0.012), and the numerical rating scale (P = 0.003). The incidences of urinary retention (P = 0.620), urinary tract infection (P = 0.529), and replacement of urethral catheter s (P = 0.438) in the UC group were inferior to those in the non-UC group, but there was no statistical significance. Conclusions: It is feasible and safe to perform FUS-ICP without a post-procedure indwelling urinary catheter. [ABSTRACT FROM AUTHOR]

Ubiquitous noise pollution has been associated with significant negative impacts on human health. However, current porous sound‐absorbing materials encounter considerable obstacles such as thick density, narrow absorbing band, and limited function. Here, a facile‐producing method for lightweight and efficiently sound‐absorbing aerogels made from bacterial cellulose (BC) and poly(vinyl alcohol) (PVA) is presented. The fabricated anisotropic aerogels with directional pores exhibit a minimum density as low as 11.3 mg cm−3. Meanwhile, the lamellar aerogels with low areal density (20.89 mg cm−2) exhibit remarkable noise attenuation performance with the noise reduction coefficient of 0.51.Furthermore, the BC‐PVA‐Ba0.85Ca0.15Zr0.9Ti0.1O3 (BCZT) aerogels show enhanced sound absorption performance, and these aerogels are self‐powered sensors to monitor vehicle collisions and human gestures. The algorithm yields high accuracy in human gesture recognition (100%) based on the deep‐learning model. These aerogels offer an encouraging application prospect in the automobile field to realize car weight reduction and vehicle's intelligent control system. [ABSTRACT FROM AUTHOR]

This article presents the implementation of different compensators like PD, PID, PID with Integral ANTI-WINDUP, Neuro-fuzzy controller (Ni-F) and Neuro-fuzzy with PID (NiF-PID) controller with different inputs over the 2-DOF ball balancer system. The modeling of different controllers is done using MATLAB/Simulink. Along with simulation, the designed controllers are applied to an experimental setup of a real-time Quanser ball balancer system. Both transient and steady-state response analyses are done to evaluate the performance of these compensators. The comparisons for variation in ball position, applied input voltage to servo motor and plate angle are done for the proposed controllers for both simulation and real-time experimentation results. Position and plate angle control with load variation are also executed in real time with NiF-PID compensator. The assessment of simulation outcomes and real-time experimental response implies that the NiF-PID controller provides overall better control performances, the best adaptability and relevancy among all designed controllers for the ball balancer system. NiF-PID compensator provides 18.55% better result in case of steady-state error and 10 times less overshoot in case of real-time experimentation as compared to the PID controller. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the accuracy and effectiveness of renal pelvic pressure (RPP) control during flexible ureteroscopic perfusion at various sites. Methods: Flexible ureteroscopy for the intelligent control of RPP was based on a ureteral access sheath (UAS) that integrated pressure measurement and suction functions (with automatic adjustment). Eleven consecutive patients with indurating nephrostomy tubes were enrolled. The nephrostomy tube was connected to a disposable invasive blood pressure sensor. The RPP was zeroed after the pressure stabilized. The flow rate and control value were set at 100 ml/min and − 5 mmHg, respectively. An 8.5 Fr flexible ureteroscope was irrigated at the renal pelvis, as well as the upper, middle, and lower calyces of the kidney for 1 min. Results: All 11 patients with upper urinary tract calculi underwent successful UAS placement. Pressures measured by the sheath and fistula during perfusion were − 5.07 ± 1.41 and − 4.89 ± 1.07 mmHg at the renal pelvis, -5.16 ± 1.36 and − 5.12 ± 1.32 mmHg at the upper calyces, -4.98 ± 0.87 and − 5.39 ± 1.01 mmHg at the middle calyces, as well as -4.95 ± 1.56 and − 5.64 ± 1.24 mmHg at the lower calyces, respectively. There were no significant differences in pressure between the sheath and fistula groups or in sheath and fistula pressures among parts. The RPP fluctuated; however, all values were within the safe limit of 20 mmHg. Conclusion: The UAS-based pressure monitoring technology can accurately and reliably monitor and control RPP within a set range. [ABSTRACT FROM AUTHOR]

To achieve effective temperature-regulated modification of spray-bonded cotton for superior thermal management capabilities, a dual-network phase transition microspheres (PCP) was developed through a one-step process using emulsion polymerization. This innovative approach incorporated eutectic polyethylene glycol (PEG) as the phase change component, chemically cross-linked polyacrylamide (PAM) as the first network, and ionically cross-linked calcium alginate (CA) as the second network. The PCP, which contains PEG solid-loaded in the dual network, was utilized as a modifier for the post-treatment of spray-bonded cotton using a coating technique. Key findings reveal that the effectively immobilization of PEG has been, without any abserved chemical reactions between PEG and PAM/CA. Additionally, PEG maintained its crystalline properties when combined with PAM/CA. The PCP exhibited a distinct morphology with smooth, spherical particles averaging round 5 μm in diameter. The optimized PCP sample exhibits a melting onset temperature of 37.20 °C, a melting enthalpy of 92.59 J·g−1, and impressive heat resistance. The resulting temperature-regulated spray-bonded cotton (TRSBC) displayed intelligent temperature control characteristics with a melting onset at 37.65 °C and a latent heat of 32.08 J·g−1. Compared to untreated spray-bonded cotton (USBC), TRSBC exhibited a notable delay effect in both heating and cooling processes, resulting in a temperature differential of 23.5 °C during heating and 16.3 °C during cooling, underscoring its superior temperature control capabilities. Research indicates that TRSBC possesses significant potential in thermal management applications, encompassing clothing, high-tech textiles, and temperature-regulating sandwich panels, thereby presenting considerable opportunities for commercial ventures. [ABSTRACT FROM AUTHOR]

This article studies and develops an intelligent control emergency response system for power cable tunnels based on image recognition and ultra-wideband technology. The system aims to improve the safety monitoring and emergency response capabilities of cable tunnels by integrating real-time data transmission, multi-sensor data fusion, UWB (ultra-wideband) precise positioning, and AI (Artificial Intelligence) image recognition technology. The system has the ability to monitor data transmission and processing in real time and can quickly respond to emergency situations so as to effectively control sudden, hidden, and temporary security risks. In addition, the application of UWB technology provides precise positioning capability, which is crucial for ensuring personnel positioning in emergency response. AI image recognition technology can analyze surveillance video content, detect and handle potential construction hazards in a timely manner, and effectively prevent accidents from occurring. The application of this technology not only improves the efficiency of safety supervision but also enhances the safety guarantee ability of on-site operators. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are anticipated to dominate passenger car transportation, playing a pivotal role in advancing sustainable mobility. However, with the increasing enthusiasm for EVs, impediments endure within the realm of power transmission. This is especially evident in addressing challenges related to minimizing torque ripple and implementing advanced control techniques in traction for high-performance and efficient operation of EVs. Numerous control algorithms for motor drives have been developed in the recent past but face challenges in attaining effective control under varying drive cycles of EVs. To tackle these challenges, motor drive control algorithms integrate various control techniques, including field orientation control, model predictive control, intelligent control, etc. This paper proposes an innovative online-tuned MPCC algorithm based on the adaptive neuro-fuzzy inference system (ANFIS). The traditional proportional–integral (PI) controller is replaced with an adapted ANFIS algorithm, and the tuning of ANFIS parameters is achieved by leveraging the error between the reference and adjustable models through a hybrid training algorithm. The proposed novel control technique improves the dynamic speed response of permanent magnet synchronous motor drives EVs. This improvement is realized by replacing the PI-HCC controller with an ANFIS controller coupled with MPCC. A laboratory prototype of the proposed control technique for EVs has been developed, and a comparative analysis of ANFIS-MPCC techniques with other known control techniques has been presented. This paper also demonstrates the importance of choosing optimal motor control techniques for torque ripple minimization and improving the overall performance of EVs. [ABSTRACT FROM AUTHOR]

Spontaneous coal combustion adversely affects coal mine safety and restricts safe, efficient, and green coal mining. Inert gas fire prevention and extinguishing technology is a widely used fire prevention and extinguishing method in coal mines. CO2 is often used as the primary raw material for inert gas fire prevention and extinguishing, owing to its good inerting and cooling characteristics. However, the lack of data on the physical and chemical properties and fire extinguishing mechanism of CO2 by field personnel has limited the efficient application of CO2 in coal mine fire prevention and extinguishing. To explore the practical application effects of CO2 fire prevention and extinguishing technology on coal mine sites, this paper summarised and analysed the research and development status of CO2 fire prevention and extinguishing technology and expounded the physical and chemical properties, phase characteristics, and fire prevention and extinguishing mechanisms of CO2. The CO2 pipeline, CO2 pipeline intelligent monitoring and control system, CO2 inerting mechanism, and comprehensive gas fire prevention and extinguishing technology are summarised and discussed. This study provides a systematic theoretical basis for the field application of CO2 fire prevention and extinguishing technology. [ABSTRACT FROM AUTHOR]

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system's productivity and usefulness compared to the older manual monitoring and operating methods. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations. [ABSTRACT FROM AUTHOR]

To address the variable initial state and trail length this paper first presents a robust PD‐type open‐closed‐loop iterative learning control (ILC) law for a multiple‐input‐multiple‐output (MIMO) linear discrete‐time system. It is demonstrated that the convergence condition is dependent on the PD‐type feed‐forward learning gains, while an appropriate feedback learning gain can improve the ILC convergence performance. As a special case of PD‐type open‐closed‐loop ILC law, P‐type and D‐type open‐closed‐loop ILC laws are deduced. The three developed ILC laws ensure that as the number of iterations approaches infinity, the expectation of ILC tracking error will be constrained within a limited range, where the boundary is proportional to the initial state variation. Through a numerical simulation, the effectiveness of the proposed ILC laws is illustrated. [ABSTRACT FROM AUTHOR]

As an emerging multi‐energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES‐DC). The mathematical model of IES‐DC is first established to reveal the energy conversion process of the electricity‐heat‐gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi‐energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES‐DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin‐delayed deep deterministic policy gradient (TD3), which is a model‐free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES‐DC. [ABSTRACT FROM AUTHOR]

A self-powered bimodal tactile imaging device (TID) is developed for ultrasensitive pressure sensing, real-time visualization recognition, and precise touch positioning. Furthermore, based on optical-electrical bimodal tactile imaging of the TID, an intelligent control has been achieved through synergetic effect of visualization recognition and precise touch positioning. [Display omitted] • A self-powered bimodal tactile imaging device is developed for visible ultralow-pressure sensing and touch positioning. • By embedding high dielectric BTO:La NPs into PVDF-HFP matrix, the TIEL properties are greatly enhanced. • The TIEL is developed for visible tactile imaging and real-time visualization recognition under the aid of machine learning. • An intelligent control system based on the bimodal optical and electrical signals of the TID is established. In the domain of smart robotics, the refinement of tactile imaging constitutes a seminal element for enhancement of human–machine interaction (HMI) and enrichment of artificial intelligence (AI). This field is confronted with dual challenges of achieving high-sensitive pressure detection and precise localization of tactile stimuli. In response, the current research introduces a groundbreaking self-powered bimodal tactile imaging device (TID), featuring a configuration of high-dielectric thin film superimposed on laser-induced graphene (LIG) electrodes. This pioneering design is conceived to facilitate not only the detection of subtle pressure but also to support real-time visual recognition and intelligent control functionalities. The bimodal nature of the TID allows for the transformation of slight tactile inputs into both luminous triboelectrification-induced electroluminescence (TIEL) and measurable electrical signals, thereby seamlessly merging the realms of tactile perception and optical display. Leveraging the luminosity of TIEL, the TID adeptly achieves tactile imaging and immediate visual recognition, with its capabilities further enhanced through the integration of machine learning algorithms. Additionally, the TID exhibits a remarkable proficiency in precise tactile localization, through the analysis of voltage outputs initiated by delicate touching and sliding motions. Moreover, an advanced intelligent control system, predicated on the optical-electrical dual-modal sensing provided by the TID, has been developed. This system illustrates the synergistic fusion of visual recognition with accurate tactile localization, underscoring the substantial utility of the bimodal TID across diverse applications in HMI, AI, and intelligent robotic platforms and heralding new avenues for interactive and responsive robotic systems. [ABSTRACT FROM AUTHOR]

A parametric Adaptive Model Predictive Controller (AMPC) based on Particle Swarm Optimization-Back Propagation (PSO-BP) neural network has been developed in this paper, the primary focus is on improving the trajectory tracking performance of autonomous vehicles under varying road conditions. The PSO-BP neural network is employed for real-time adjustment of the controller's predictive horizon and sampling time. A vehicle dynamics model is established and an improved tracking control algorithm involving road curvature feedforward is proposed. In the design of AMPC, the real-time update of tire lateral stiffness is achieved through the adoption of the Recursive Least Squares (RLS) method, ensuring the precision of trajectory tracking for the vehicle under varying operating conditions. The simulation platform, which combines Carsim and Simulink, was employed for validating the proposed approach. The findings reveal that the proposed controller can promptly adjust the predictive horizon and sampling time according to the vehicle's state. Through the employed estimation strategy, real-time adjustments of tire lateral stiffness are achieved, allowing for dynamic alterations of vehicle speed and front wheel angle in response to road curvature. As a result, this approach significantly enhances control accuracy and lateral steering stability, especially in large curvature conditions. [ABSTRACT FROM AUTHOR]

There has been a discernible rise in the growth and progress of the Internet, networking, and mobile communication. The complexity of networking systems has increased due to the advancements in devices, resources, and infrastructure. A novel and developing network technology called software-defined networking (SDN), gets beyond the drawbacks of conventional networks and gives networking systems intelligent control. While SDN is the most adaptable and promising network management control solution, its implementation also introduces a number of new security risks. There is a need to deploy networking systems intelligently to manage, optimize, and organize these complex systems. Machine learning (ML) approaches have been extensively utilized to detect many attacks, and an ML technique may assist the network administrator in taking the necessary precautions to avoid intrusions. In SDN, ML techniques are used to manage the network and make Network Intrusion Detection Systems (NIDS) detect network attacks like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. This paper comprehensively surveys ML and Deep Learning (DL) algorithms and techniques used for intrusion detection in SDN. Different studies are categorized accordingly, such as supervised, unsupervised, and deep learning models. Research studies are compared in the form of a table, and learned lessons are also leveraged for each category. Finally, this survey presents the key challenges faced in implementing different intrusion detection techniques in SDN before comprehensively highlighting future research directions. [ABSTRACT FROM AUTHOR]

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions. • A new approach based on WADC is proposed for damping inter-area oscillations. • A new scheme based on individual WADC for each generator is proposed. • CART technique is used for estimating optimal gains of individual WADC controller. • A combination index is proposed to find the most effective individual WADCs. [ABSTRACT FROM AUTHOR]

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy. • An adaptive intelligent formation control method is proposed based on SCMAC and SBELC. • A Mexican hat wavelet function is developed to enhance the ability of the formation control system to process signals. • The GVF and flexible constraints strategy are introduced to avoid static obstacles and ensure control performance. • A self-structuring algorithm is embedded to optimize the structure of SCMAC and SBELC. [ABSTRACT FROM AUTHOR]

In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor's wheels are computed with a proposed kinematic control model with an adaptive term. As the system's dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat's lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented. • A hybrid controller is proposed for tractor-trailer systems with wheel slippage. • By putting forward a kinematic control law, the desired velocities are calculated. • At the dynamical level, RBFNN is used to approximate the uncertainties of the system. • The stability of the system is analyzed with Lyapunov theory and Barbalat's Lemma. • The proposed control scheme is verified through simulation studies. [ABSTRACT FROM AUTHOR]

Redundant degree-of-freedom (DOF) manipulators offer increased flexibility and are better suited for obstacle avoidance, yet precise control of these systems remains a significant challenge. This paper addresses the issues of slow training convergence and suboptimal stability that plague current deep reinforcement learning (DRL)-based control strategies for redundant DOF manipulators. We propose a novel DRL-based intelligent control strategy, FK-DRL, which integrates the manipulator's forward kinematics (FK) model into the control framework. Initially, we conceptualize the control task as a Markov decision process (MDP) and construct the FK model for the manipulator. Subsequently, we expound on the integration principles and training procedures for amalgamating the FK model with existing DRL algorithms. Our experimental analysis, applied to 7-DOF and 4-DOF manipulators in simulated and real-world environments, evaluates the FK-DRL strategy's performance. The results indicate that compared to classical DRL algorithms, the FK-DDPG, FK-TD3, and FK-SAC algorithms improved the success rates of intelligent control tasks for the 7-DOF manipulator by 21%, 87%, and 64%, respectively, and the training convergence speeds increased by 21%, 18%, and 68%, respectively. These outcomes validate the proposed algorithm's effectiveness and advantages in redundant manipulator control using DRL and FK models. [ABSTRACT FROM AUTHOR]

Aiming at the problems of traditional ship cold chain monitoring systems being easily affected by environmental factors, difficult to achieve real‐time monitoring in the open sea without network signals, and low efficiency in transmitting Beidou short message data, a multi‐link compressed transmission ship cold chain monitoring system was designed by combining 5G technology, Beidou short message transmission technology, and multi‐protocol transmission technology. The system can adaptively switch the strength of wireless signals to ensure that information transmission is not lost. At the same time, a Beidou short message compressed transmission method was proposed to improve transmission efficiency. Test results show that the system can accurately complete data collection and processing, with small system errors, effectively improving the reliability and efficiency of the monitoring system, and has high application value. [ABSTRACT FROM AUTHOR]

The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors' contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real‐world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM‐ANN) self‐organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100‐m range can be achieved. With this result, the authors conclude that we can use LM‐ANN for the wireless sensor network SON model in the jungle environment. [ABSTRACT FROM AUTHOR]

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields. [ABSTRACT FROM AUTHOR]

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems. [ABSTRACT FROM AUTHOR]

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination ( R 2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error ( M A E ) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature. [ABSTRACT FROM AUTHOR]

With the increase of mining height at the working face, the influence range of roof fractures in the goaf increases, the advanced supporting pressure on the coal wall increases, ground pressure becomes more intense, and roof support becomes more difficult. Based on the analysis of ground pressure behavior in the first mining and caving stage, the normal mining stage, and the final mining breakthrough stage of the fully-mechanized mining face near 12,404, the relationship between conveyor current and coal speed is studied and compared. Based on the intelligent control system of the fully-mechanized mining face with a super-high mining height of 12,404 and the structure of the football team, the "playing football" roof control mode of the fully-mechanized mining face with super-high mining height under the background of 5G+ big data is put forward. The conclusions are as follows: In 12,404, the ground pressure was first mined. During normal mining, when the roof with a buried depth of more than 200 m is broken, the speed of the coal machine is kept within 12 m/min, and the full guard defends and controls the roof, pulling the lead frame through the area with severe ground pressure. When the roof is good, it is necessary to speed up the coal cutting and get rid of the pressure. When it is less than 200 m, it will overcome the local weighting, and show an offensive trend to speed up and increase production. In the final mining breakthrough stage, the speed of the coal machine should be controlled within 8 m/min, with attention to defense, guarding against roof leakage, and reducing waste rock. [ABSTRACT FROM AUTHOR]

High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flows can propagate backwards on road networks when modeling spatial relationships, as well as associations between distant nodes. In addition, more effective model components for modeling temporal relationships remain to be developed. To address the above challenges, we propose a local–global features fusion temporal convolutional network (LGTCN) for spatio-temporal traffic flow prediction, which incorporates a bidirectional graph convolutional network, probabilistic sparse self-attention, and a multichannel temporal convolutional network. To extract the bidirectional propagation relationship of traffic flow on the road network, we improve the traditional graph convolutional network so that information can be propagated in multiple directions. In addition, in spatial global dimensions, we propose probabilistic sparse self-attention to effectively perceive global data correlations and reduce the computational complexity caused by the finite perspective graph. Furthermore, we develop a multichannel temporal convolutional network. It not only retains the temporal learning capability of temporal convolutional networks, but also corresponds each channel to a node, and it realizes the interaction of node features through output interoperation. Extensive experiments on four open access benchmark traffic flow datasets demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

The article shows how to build and identify intelligent automatic control problems for mobile robots in a flat surface environment at the workplace, with known and unknown obstacles. Research and develop programming and control methods as an operating system for mobile robots robot operating system (ROS). Update map data information, in the operating environment, robot position control process, obstacle overcoming process simultaneous positioning and mapping (SLAM). From there, we aim to calculate and determine the robot's motion trajectory to get a smart path. The positioning trajectory calculation system robots. The authors use actor-critic (AC) algorithm to research and develop control. Research results in simulations, in Gazebo environment and test runs on real mobile robots have shown high-quality practical performance of automatic navigation and control while using this algorithm. [ABSTRACT FROM AUTHOR]

Introduction: The increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcement learning (RL), in automatic generation control (AGC). However, traditional RL cannot ensure constraint safety during training and frequently violates the constraints (e.g., frequency limitations), further threatening the safety and stability of grid operation. Methods: To address the safety issue, we propose a novel safe RL framework that combines expert experiences with the RL controller to achieve imitation-based RL. This method allows an initialized safe policy by imitating expert experiences to prevent random explorations at the beginning. Specifically, we first formulate the AGC problem mathematically as a Markov decision process. Then, the imitation mechanism is developed atop a soft actor-critic RL algorithm. Results and discussion: Finally, numerical studies are conducted with an IEEE 39-bus network, which show that the proposed method satisfies the frequency control performance standard better and improves the RL training efficiency. [ABSTRACT FROM AUTHOR]

In air conditioning systems, air curtains play a crucial role in reducing the exchange of hot and cold air between the interior and exterior environments. Nevertheless, the majority of current air curtains suffer from limited airtightness and real-time performance due to their complex jet trajectory, relying on traditional control methods. Thus, this paper introduces an angle control algorithm for air curtains based on a GA-optimized quadratic BP neural network. Initially, the BP neural network is trained using the Hayes dataset to develop the prediction model for temperature-jet angle. Subsequently, the optimization model for jet angles-windshield angle is constructed, and the optimal angles set meeting the fitness function is identified using GA global search. Later, the prediction model and the optimal angles set are once again trained using the BP neural network to generate prediction model for temperature-jet angles and windshield angle. Following CFD simulation, the airtightness indicator demonstrated a 26.5% improvement with the proposed control method compared to traditional ones, highlighting the superior airtightness. In comparison to other algorithms, the proposed algorithm demonstrates a remarkable 89% enhancement in real-time performance and stronger robustness. This study presents a novel approach for the intelligent control of air curtains, holding significant importance in advancing the intelligent development of air curtain technology and facilitating energy efficiency and emission reduction. [ABSTRACT FROM AUTHOR]

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system's extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the "double ten indicators" requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth. [ABSTRACT FROM AUTHOR]

The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control and optimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions. [ABSTRACT FROM AUTHOR]

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the "Simscape Electrical" toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and insufficient control precision when dealing with complex dynamic systems. However, by integrating physical laws directly into the training process of neural networks, PINNs can effectively learn and capture the kinematic characteristics of vehicles, replacing traditional nonlinear ordinary differential equation models and thus significantly enhancing computational efficiency and control performance. During the model-training phase, this study further incorporates the Theory of Functional Connections (TFC) and adaptive loss balancing strategies to efficiently solve ODE problems without relying on numerical integration and optimize the control strategy. This combined approach not only reduces computational complexity, but also improves the robustness and precision of the control strategy in varying environments. Numerical simulations demonstrate that this method offers significant advantages in AGV trajectory-tracking tasks, manifested in higher computational efficiency and precise control performance. The proposal of the PINN-MPC method provides new theoretical support and innovative methods for real-time complex system control, with important research and application potential, and is expected to play a key role in future intelligent control systems. [ABSTRACT FROM AUTHOR]

In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order to reduce unnecessary acceleration. The shown investigations are conducted within a simulated traffic environment of the city center of Darmstadt, Germany, which is carried out in the traffic simulation software "Simulation of Urban Mobility" (SUMO). The longitudinal dynamics of the not automated vehicles are considered with the Extended Intelligent Driver Model, which is an approach to simulate real human driver behavior. The results show that, in addition to the energy saving caused by a predictive longitudinal control of the ego vehicle, this system can also reduce the consumption of surrounding traffic participants significantly. The area of influence can be quantified to four vehicles and up to 250 m behind. [ABSTRACT FROM AUTHOR]

The intermittent and fluctuating nature of active power output from wind power significantly affects the Load Frequency Control (LFC) in a power grid based on active power balance. To address this issue, this paper proposes a cloud-based intelligent PI controller designed to enhance the performance of LFC in smart grids with large-scale wind power integration. By using the error and the rate of change of error as the antecedent inputs of the cloud model-based controller and the tuning values of P and I as the consequent outputs of the cloud model, adaptive online tuning of the PI parameters is achieved. Based on the control rules of LFC in interconnected power grids and considering the uncertainty of wind power's active power output, the membership cloud parameters are designed, which effectively solves the problems of poor parameter robustness in traditional PI control and significant human influence on membership degrees in Fuzzy PI control. A simulation model of a dual-area interconnected power grid with wind power for LFC was built using Matlab/Simulink. Two typical disturbances, namely random fluctuations in wind power and sudden increases/decreases in load, were simulated. The simulation results demonstrate that the cloud model-based intelligent PI controller designed in this paper can effectively track the frequency variations caused by random fluctuations in wind power and exhibits strong robustness. [ABSTRACT FROM AUTHOR]

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control. [ABSTRACT FROM AUTHOR]

With the continuous advancement of science and technology, there is a growing global focus on new energy sources. Despite the rapid progress of offshore wind power generation systems, they are still plagued by issues such as significant transmission loss, limited transmission distance, and low-frequency oscillation, which hinder further development. To address these challenges, the Flexible Direct Current Transmission System (VSC-HVDC) has emerged as a widely studied solution. The integration of energy storage power stations presents new opportunities for enhancing offshore wind power transmission systems. These power stations not only serve as energy buffer pools to reduce transmission loss but also improve transmission efficiency through intelligent regulation and control, effectively mitigating low-frequency oscillation. This article introduces an optimization control parameter design method based on sensitivity analysis to enhance the stability of MTDC based on MMC. It outlines the topology structure of the offshore VSC-HVDC system, covering the main circuit and control system. Additionally, the article delves into the derivation of the small signal stability model of the system and investigates the selection of control parameters based on the eigenvalue objective function. Lastly, it analyzes the impact of the control system on the stability of the wind power flexible direct output converter station, highlighting the significant influence of control system parameters on the small signal stability of MTDC systems based on MMC. The MMC parameter selection strategy proposed in this paper is shown to effectively enhance system stability. [ABSTRACT FROM AUTHOR]