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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]

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]

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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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]

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]

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]

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]

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]

Expanding waste-to-energy (WtE) facilities is difficult, and with tightening incineration regulations, improvements in WtE facility operations are required to dispose of waste that is increasing by an average of 4.8% annually. To achieve this, an intelligent combustion control (ICC) system was studied using digital technologies such as the Internet of Things and artificial intelligence to improve the operation of WtE facilities. The ICC system in this study is composed of three modules: perception, decision, and control. Perception: collecting and visualizing digital data on the operating status of WtE facilities; Decision: using AI to propose optimal operation methods; Control: automatically controlling the WtE facility according to the AI-suggested optimization methods. The ICC system was applied to the "G" WtE facility, a solid waste WtE facility operating in Gyeonggi province, Republic of Korea, and the digital data collected over six months showed high quality, with low delay and a data loss rate of only 0.12%. Additionally, in January 2024, the ICC system was used to automatically control the second forced draft fan and induced draft fan over a four-day period. As a result, the incinerator flue gas temperature decreased by 0.66%, steam flow rate improved by 2.41%, power generation increased by 3.09%, CO emissions were reduced by 60.72%, and NOx emissions decreased by 7.33%. Future research will expand the ICC system to include the automatic control of the first forced draft fan and the operation time of the stoker. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

Energy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack. [ABSTRACT FROM AUTHOR]

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. An intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials. [Display omitted] [ABSTRACT FROM AUTHOR]

This study is aimed at developing an intelligent factory control system for improving environment quality and reducing electricity consumption. By automating intelligent equipment and leveraging internet networks, the system enables the remote monitoring and management of environmental conditions. We combine the fuzzy method and deep reinforcement learning (DRL) to handle complex factory data and optimize decision-making. The fuzzy method uses fuzzy sets and rules to generate accurate outputs from the data. On the other hand, the DRL system learns optimal policies by interacting with the environment using environment quality, central air conditioner (AC), and alarm data. Hardware implementation uses an ESP32-S microcontroller to send data to Google's Firebase cloud for seamless management and monitoring through a mobile app or website. The study involves developing 36 fuzzy rules and creating 10 models with different combinations of hidden layers, epochs, and learning rate values. Among the fuzzy inference system (FIS)-DRL modeling results, the fourth model stands out as the preferred option to proceed with the experiment, as it achieves the highest accuracy of 91.16%. Note that this model also exhibits a loss value of 1.64% and an incredibly short inference time of only 3 ms. The proposed system offers benefits such as enhanced energy efficiency and reduced costs, making it ideal for intelligent factories. By optimizing resource usage, it will contribute to sustainable development in various industries. [ABSTRACT FROM AUTHOR]

Emerging technological advances are reshaping the casting sector in latest decades. Casting technology is evolving towards intelligent casting paradigm that involves automation, greenization and intelligentization, which attracts more and more attention from the academic and industry communities. In this paper, the main features of casting technology were briefly summarized and forecasted, and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed. Moreover, the technical visions of intelligent casting process were also put forward. The key technologies for intelligent casting process comprise 3D printing technologies, intelligent mold technologies and intelligent process control technologies. In future, the intelligent mold that derived from mold with sensors, control devices and actuators will probably incorporate the Internet of Things, online inspection, embedded simulation, decision-making and control system, and other technologies to form intelligent cyber-physical casting system, which may pave the way to realize intelligent casting. It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control, with the defects, microstructure, performance, and service life of the fabricated castings can be accurately predicted and tailored. [ABSTRACT FROM AUTHOR]

Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects. [ABSTRACT FROM AUTHOR]

In the current study, which focuses on the operational safety problem in intelligent three-dimensional garages, an obstacle avoidance measurement and control scheme for the AGV parking robot is proposed. Under the premise of high-precision distance detection using Kalman filtering, a mathematical model of a brushless DC (BLDC) motor with full-speed range hybrid control is established. MATLAB/Simulink (R2022a) is used to build the control model, which has dual closed-loop vector-controlled motors in the low- to medium-speed range, with photoelectric encoders for speed feedback. The simulation results show that, at lower to medium speeds, the maximum overshoot of the output response curve is 1.5%, and the response time is 0.01 s. However, at higher speeds, there is significant jitter in the speed output waveform. Therefore, the speed feedback is switched to a sliding mode observer (SMO) instead of the original speed sensor at high speeds. Experiments show that, based on the SMO, the problem of speed waveform jitter at high motor speeds can be significantly improved, and the BLDC motor system has strong robustness. The above shows that the motor speed under the full-speed range hybrid control system can meet the AGV control and safety requirements. [ABSTRACT FROM AUTHOR]

In optical data center networks, the utilization of several software-defined network controllers has been implemented to enhance scalability and reliability. However, during dynamic variations in network traffic, static controller deployment often leads to load imbalance among the controllers. Previous studies have proposed dynamic switch migration as a solution to realize controller load balancing. However, the effectiveness of existing switch migration methods remains insufficient. The load balancing performance of controllers demonstrates limited enhancement following migration, and the migration operations themselves result in supplementary costs. As a result, the issue of switch migration efficiency remains unresolved. To tackle controller load balancing, this research introduces the intelligent controller load balancing (ICLB). ICLB incorporates multiple load metrics to evaluate controller load and selects the ideal target controller based on available resources, leading to enhanced load balancing effectiveness. Moreover, it selects switches while the minimizing migration process, effectively reducing supplementary expenses. Additionally, ICLB can simultaneously manage the workload of multiple controllers during a switch migration process, resulting in enhanced migration efficiency. The outcomes of simulations provide evidence that ICLB offers substantial improvements in controller load balancing performance, along with reduced migration expenses compared to existing solutions. [ABSTRACT FROM AUTHOR]

• Designed affordable 3DOF attitude simulator. • In–house built 3-axis Helmholtz coil cage. • Identified CubeSat demonstrator parameters. • Demonstrated CubeSat self-actuation & detumbling. Spacecraft attitude determination and control systems (ADCS) stand as a pivotal factor for ensuring the success of missions. As satellite sizes continue to decrease, the need for more intelligent and efficient control algorithms becomes increasingly pronounced. In response to this imperative, we propose the development of a scaled-down testbed specifically designed for evaluating satellites (ACDS). This testbed will demonstrate effective control of a CubeSat through the use of Helmholtz coils, magnetic torquer solenoids, reaction wheels and inexpensive student-made components and available components off the shelf (COTS) in the San Diego State University SPACE Lab. Referred to as the Spacecraft Attitude Testbed (SAT), this custom-designed 3-DoF experimental attitude platform replicates the conditions experienced by a weightless satellite in space. The objective of this research paper is to improve the understanding of how to manufacture a testbed and components like those within this paper, expose some of the issues encountered and improve the outcomes for future academic testbeds. Within this study, the testbed employs torque application to magnetic torquers and reaction wheels to regulate attitude. In summary, this research endeavors to establish a comprehensive and cost-effective testbed platform dedicated to the assessment of spacecraft attitude control and determination systems. It not only seeks to enhance our understanding of advanced control algorithms but also aims to contribute valuable insights into fault detection and adaptive control strategies, ultimately bolstering the efficiency and robustness of satellite operations. [ABSTRACT FROM AUTHOR]

This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Momentum LSTM (MLSTM), on a dataset of 21,644 operational records. These models were trained to predict a critical operational parameter, FlowRate, which is essential for operational integrity and efficiency. Our results demonstrate substantial improvements in predictive accuracy: the LSTM model achieved an R2 score of 0.9720, the BiLSTM model reached 0.9725, and the MLSTM model topped at 0.9726, all with exceptionally low Mean Absolute Errors (MAEs) around 0.0090 for LSTM and 0.0089 for BiLSTM and MLSTM. These high R2 values indicate that our models can explain over 97% of the variance in the dataset, reflecting significant predictive accuracy. Such performance underscores the potential of integrating ML with SCADA systems for real-time applications in the oil and gas industry. This study quantifies ML's integration benefits and sets the stage for further advancements in autonomous well-monitoring systems. [ABSTRACT FROM AUTHOR]

The vibroflotation construction is a significant measure for foundation improvement in hydropower engineering. Currently, vibro-stone columns are still constructed manually. The construction process relies mainly on the experience and responsibility of workers, which has the shortcomings of low precision in pile formation control and low degree of automation, leading to problems such as uneven pile formation, high labor intensity, and poor pile quality. This study proposes an intelligent vibroflotation control model (iVCM) based on the analysis of the pile formation mechanism and former experiences. Intelligent control of vibroflotation construction is realized through intelligent control of the vibrator current, rope pull, filler amount, and vibrator depth. A crane-type intelligent vibroflotation control system was developed to realize one-button intelligent control of pile formation. Its application in Yingliangbao Hydropower Station shows that the intelligent vibroflotation control system can regulate the construction operation of the pile formation process, improve the vibroflotation quality, reduce labor intensity, and have prospects for applicability in similar projects. [ABSTRACT FROM AUTHOR]

Dynamic load forecasting is essential for effective energy management and grid operation. The use of GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) networks for precise load prediction is investigated in this paper. This research examines dynamic load patterns by innovatively integrating heterogeneous information from several datasets. The results show that the LSTM and GRU models are equally good at making predictions and that this holds true across a variety of datasets. Furthermore, the models' ability to accurately capture the temporal relationships in the load data is demonstrated by their low Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. Additionally, the comparative analysis results, which highlight flexibility in model selection, can aid energy sector decision makers. The significance of precise load projections for maintaining grid dependability and optimizing resources is further highlighted by this work, which also elucidates the effects of forecast inaccuracies on decision-making procedures. Our research study provides important information for power system management strategy planning, which in turn promotes the continuous innovation of smart grids in dynamic load forecasting to keep up with changing energy consumption patterns. [ABSTRACT FROM AUTHOR]

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