Control Process Research Articles

An Integrated Software-Defined Networking–Network Function Virtualization Architecture for 5G RAN–Multi-Access Edge Computing Slice Management in the Internet of Industrial Things

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Gaussian-inverse gamma mixture distribution–based extended Kalman observer for dynamic positioning control system of offshore platform

Under different working conditions and control objectives, fuzzy model predictive control is used to optimize the control effect by adjusting the weight to achieve stable and accurate control for the dynamic positioning control of offshore platforms. Also, aiming at the situation where the measurement noise of dynamic positioning control may be time-varying and non-Gaussian and the statistics of the noise are not exact, this paper proposes a variational Bayesian extended Kalman filter observer. In order to model non-Gaussian noise more reasonably, the joint posterior distribution of non-Gaussian noise with state and time-varying covariance is described as the product of Gaussian and independent inverse gamma distributions. Then, the variational Bayesian (VB) method was used to simultaneously estimate the system state, the intermediate variable values of the new distribution, and the noise parameters through fixed-point iteration. The fuzzy algorithm is applied to the model predictive control, and the weight parameters of the model predictive control are adaptively changed to better adapt to the changing working conditions in the control process. The simulation results show that the variational Bayesian extended Kalman filter observer has better performance than the existing algorithms when dealing with the time-varying non-Gaussian observation noise, whose statistics are not exact. The variational Bayesian extended Kalman filter observer can significantly improve the accuracy of the control when it is used in fuzzy model predictive control.

Визуальная интерпретация парадигмы управления организационной системой c разнородными производственными ресурсами

The object of the study is developing a formalized mechanism for managing an organizational system that implements complex production activities, including different types of industrial production, and applies the corresponding heterogeneous resources. The subject of the article is an important intermediate stage of this development, which is an abstract interpretation of the optimal management paradigm of limited resources of such an organizational system, proposed earlier by the authors. The interpretation will allow reasonably forming a strict mathematical model of objects and control processes. The management mechanism in any subject area, providing for adopting management decisions by people authorized to do so, must have an ergonomic development and appropriate information support for these decisions. The so-called result information plays the role of such support, that is the information on the characteristics of the current and planned states of the management object in the problem being solved which is the productivity of the organizational system resources. It seems advisable to carry out such ergonomic and information development of the designed problem-oriented management mechanism in the form of a visual interpretation of its substantive concept. The prototype of such interpretation tools is graphic images used in modelling and designing technical systems, developed in this work in the field of management in organizational systems with heterogeneous resources. The article provides examples of their application, demonstrating the possibility of visual representation of the current state of the organizational system and the set of its possible controlled states.

Structure and properties of gallate and germanate langasite infrared optical glass materials

Glasses in the system BaO – La2O3 – Ga2O3 – GeO2 and Langasite crystalline phases with the same stoichiometry have been studied considering the possible use of these glasses for laser-induced space-selective growth of crystalline architectures in the glass compositions. For better understanding of mechanisms involved in such controlled crystallization process, crystalline and glass compounds with same stoichiometry and different La/Ba ratios have been synthesized to compare their local structure. Solid-state Nuclear Magnetic Resonance evidence 4- and 5-coordinated gallium units in the glass matrix, denying unambiguously the formation of 6-coordinated gallium sites observed in crystalline phases. Moreover vibrational spectroscopies suggest a link between a 3D network formed by 4- and 5-coordinated gallium, connected to each other and to 4- coordinated Q4 and Q3 germanium units in the glass and their congruent crystallization. The transition from 4- and 5-coordinated gallium to 6-coordinated gallium sites observed in Langasite crystalline phases remains not still elucidated.

Visual feedback and motor memory contributions to sustained motor control deficits in autism spectrum disorder across childhood and into adulthood.

Background Autistic individuals show deficits in sustained fine motor control which are associated with an over-reliance on visual feedback. Motor memory deficits also have been reported during sustained fine motor control in autism spectrum disorders (ASD). The development of motor memory and visuomotor feedback processes contributing to sustained motor control issues in ASD are not known. The present study aimed to characterize age-related changes in visual feedback and motor memory processes contributing to sustained fine motor control issues in ASD. Methods Fifty-four autistic participants and 31 neurotypical (NT) controls ages 10-25 years completed visually guided and memory guided sustained precision gripping tests by pressing on force sensors with their dominant hand index finger and thumb. For visually guided trials, participants viewed a stationary target bar and a force bar that moved upwards with increased force for 15s. During memory guided trials, the force bar was visible for 3s, after which participants attempted to maintain their force output without visual feedback for another 12s. To assess visual feedback processing, force accuracy, variability (standard deviation), and regularity (sample entropy) were examined. To assess motor memory, force decay latency, slope, and magnitude were examined during epochs without visual feedback. Results Relative to NT controls, autistic individuals showed a greater magnitude and steeper slope of force decay during memory guided trials. Across conditions, the ASD group showed reduced force accuracy (β = .41, R 2 = 0.043, t 79.3 =2.36, p = 0.021) and greater force variability (β=-2.16, R 2 = .143, t 77.1 =-4.04, p = 0.0001) and regularity (β=-.52, R 2 = .021, t 77.4 =-2.21, p = 0.030) relative to controls at younger ages, but these differences normalized by adolescence (age x group interactions). Lower force accuracy and greater force variability during visually guided trials and steeper decay slope during memory guided trials were associated with overall autism severity. Conclusions Our findings that autistic individuals show a greater rate and magnitude of force decay than NT individuals following the removal of visual feedback indicate that motor memory deficits contribute to fine motor control issues in ASD. Findings that sensorimotor differences in ASD were specific to younger ages suggest delayed development across multiple motor control processes.

TSG Extends the Longevity of Caenorhabditis elegans by Targeting the DAF-16/SKN-1/SIR-2.1-Mediated Mitochondrial Quality Control Process.

The improvement of mitochondrial function is described as a strategy for alleviating oxidative stress and intervening in the aging process. 2,3,5,4'-Tetrahydroxystilbene-2-O-β-D-glucoside (TSG) is one of the major bioactive components isolated from Polygonum multiflorum Thunb, and it exhibits multiple activities, including antioxidant and anti-inflammatory effects. In this study, we found that 200 μM TSG significantly extended the mean lifespan of Caenorhabditis elegans by 16.48% and improved health status by delaying age-associated physiological decline in worms. The longevity prolongation effect of TSG depended on the regulation of the mitochondrial quality control process mediated by DAF-16/FOXO, SKN-1/Nrf2 and SIR-2.1/SIRT1 to improve mitochondrial function. Moreover, TSG treatment obviously alleviated the proteotoxicity of β-amyloid and tau proteins in worms. Our findings indicated that TSG is a promising natural product for preventing aging and treating aging-associated neurodegenerative diseases by regulating the mitochondrial quality control process to improve mitochondrial function.

EASY: Energy-Efficient Analysis and Control Processes in the Dynamic Edge-Cloud Continuum for Industrial Manufacturing

AbstractAccording to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable Edge-Cloud Continuum with a runtime environment and services for the execution of AI-based Analysis and Control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.

Controllable Crystallization of Perovskite Films during the Blade-Coating Fabrication Process for Efficient and Stable Solar Cells

The scalable production of high-quality perovskite thin films is pivotal for the industrialization of perovskite thin film solar cells. Consequently, the solvent system employed for the fabrication of large-area perovskite films via coating processes has attracted significant attention. In this study, a solvent system utilizing a volatile solvent as the primary reagent has been developed to facilitate the rapid nucleation of volatile compounds. While adding the liquid Lewis base dimethylformamide (DMF) can help to improve the microstructure of perovskite films, its slow volatilization renders the crystal growth process uncontrollable. Based on the solvent system containing DMF and ethanol (EtOH), introducing a small amount of NH4Cl increases the proportion of the intermediate phase in the precursor films. This not only results in a controllable growth process for the perovskite crystals but also contributes to the improvement of the film microstructure. Under the simulated illumination (AM1.5, 1000 W/m2), the photoelectric conversion efficiency (PCE) of the inverted solar cells has been improved to 20.12%. Furthermore, after 500 hours of continuous illumination, the photovoltaic device can retain 95.6 % of the initial, indicating that the solvent system is suitable for the scalable fabrication of high-quality FAPbI3 thin films.

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization–Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is less than 3.5%. Second, RBF neural network is introduced to identify the stack temperature and provide the accurate ∂y(k)∂u(k) for BP-PID, which solves the problem of using sign function sgn(∂y(k)∂u(k)) to approximate the ∂y(k)∂u(k) in BP-PID. Regarding the temperature control of air-cooled PEMFC, several controllers are compared, including PID, Fuzzy-PID, BP-PID and RBF-BP-PID. The proposed RBF-BP-PID achieves the best control effect, which reduces the integrated time and absolute error (ITAE) by 3.4% and 15.8% based on BP-PID in the startup and steady phases, respectively. Since the ∂y(k)∂u(k) provided by RBF changes softly and continuously during the control process, the parameters self-tuning ability of RBF-BP-PID is better than BP-PID. Third, to improve the control effect of RBF-BP-PID further, TLBO-DE is adopted to optimize the parameters of RBF neural network and BP neural network.

Drying of avocado peels using carbonation-ultrasonication as pretreatment: energy consumption, antioxidant capacity and rheological properties

Fruit processing by-products, especially peels are underused and mostly discarded as waste. The aim of this study was to assess the effect of ultrasonication (US) combined with carbonation (CUS) on the convective drying process of avocado peels at different temperatures, adding value to this waste. For this, the avocado peels were treated with dry ice for 15 min and subjected to US (400W/10 min, 30°C) before convective air drying (50-70°C). The energy consumption, antioxidant capacity, water adsorption isotherms, rehydration rate, and dynamic rheology of dried avocado peels pretreated by CUS were compared with a control process (without pretreatment) and only US pretreatment. The combined treatment (CUS70) resulted in a 2.6-fold reduction in drying time and a 2.3-fold decrease in energy consumption, with CO2 preserving bioactive compounds and antioxidant capacity after the drying process. Differences were also observed in the rehydration capacity of the samples (CUS), with an increased water absorption capacity, along with a reduction in the viscoelastic properties of the peels. The results present new perspectives for the application of CUS in the food industry, paving the way for the development of creative, innovative, and simple approaches for the management and recycling of agri-food waste, with the potential to extend this knowledge to new food matrices.

Investigating the benefits of viewing nature for components of working memory capacity

Prior work regarding nature's benefits to different working memory capacity processes is mixed within the existing literature. These mixed results may be due to an emphasis on tasks rather than focusing on construct validity and the underlying mental processes they are intended to measure. When considering underlying process, all might be sensitive to the benefits of nature or perhaps only specific processes of working memory capacity will receive these benefits. Attention Restoration Theory (Kaplan, 1995) would specifically predict that attentional control is the most likely process to benefit from interacting with nature. To address this possibility, three studies investigated whether working memory capacity and its component processes of attentional control, primary memory, and secondary memory benefit from viewing nature images. Montana State University students completed two tasks with a nature or urban image viewed before a block of trials that measured either working memory capacity (Experiment 1), attentional control (Experiment 2), or primary/secondary memory (Experiment 3). Results revealed higher performance after viewing nature images compared to urban images for attentional control but not for working memory capacity or either of its underlying memory components. These results are discussed with respect to the importance of current psychometric standards of measuring behavior when investigating the potential influence of nature on cognition.

Neural and Behavioral Correlates of Binge Eating in 9- to 10-Year-Old Children

This observational study compared children with and without binge eating (BE) on biobehavioral measures of reward responsiveness, inhibitory control, and emotion processes, while accounting for the impact of weight. Children aged 9 to 10 years completed the baseline wave of the Adolescent Brain Cognitive Development Study (316 with BE; 7,681 without BE [no-BE]). The prevalence of binge-eating disorder in the BE group was 17.0%; clinically significant internalizing and externalizing symptoms were endorsed by 8.5% and 4.5% of the sample, respectively. The monetary incentive delay (MID) task, stop signal task (SST), and emotional N-Back (EN-Back) task were administered during neuroimaging. Analyses assessed effects of group (BE vs no-BE) on task performance and corresponding neural signal in regions of interest (ROIs). Weight status was evaluated as a covariate and as a moderator of effects. Adjusting for weight status, the BE group (vs no-BE) group showed lower activation during anticipation of reward, specifically large reward (vs no reward), in the composite ROI consisting of the dorsal striatum, nucleus accumbens, orbital frontal gyrus, amygdala, and insula. Groups did not differ significantly in other behavioral or neural outcomes. No interactions between group and weight status were observed. Blunted anticipatory responses to monetary reward were associated with binge eating during peri-adolescence and may play a role in binge eating pathophysiology. Results challenge prior findings in BE that may be confounded by weight, and highlight the importance of future prospective research across binge-eating disorder stage of illness.

Polynorepinephrine and polydopamine-bacterial laccase coatings for phenolic amperometric biosensors

The successful fabrication of biosensors is greatly limited by the immobilization of their bioreceptor, thus we propose a facile and reproducible two-step method to modify graphite electrodes with a bacterial laccase, relying on a fast and controllable potentiostatic process to coat graphite surfaces with biomolecule-compatible thin films of polynorepinephrine (ePNE) and polydopamine (ePDA). Both polymers, synthesized with a similar thickness, were functionalized with bacterial laccase, displaying distinct electrochemical transducing behaviours at pH 5.0 and 7.0. ePNE layer enables adequate electron transfer of anionic and cationic species in acidic and neutral media, whereas transduction across ePDA strongly depends on pH and redox probe charge. ePNE stands out by improving the amperometric responses of the biosensing interface towards a phenolic acid (gallic acid) and a flavonoid (catechin), in respect to ePDA. The optimal graphite/ePNE/laccase interface outperforms biosensing interfaces based on fungal laccases at neutral pH, displaying detection sensitivities of 104 and 14.4 µA cm−2 mM−1for gallic acid and catechin, respectively. The fine synthetic control of the ePNE bio-inspired transduction layer and the use of an alkaliphilic bacterial laccase enabled the construction of an amperometric biosensing interface with extended pH range of polyphenols detection present in food products and agro-industrial waste.

Real-time voltage control of centralized and distributed coordination in active distribution network under multiple coupling

Abstract At present, the “source-load interaction” distribution network operation mode has gradually replaced the “source-load operation” mode. The uncertainty and volatility of the distribution system have been enhanced, the controllability has been weakened, the voltage fluctuations have been frequent, and the power quality has been decreased. Voltage regulation is crucial to fully absorbing new energy generation and improving the operation safety of the active distribution network. To solve the problem of real-time control of distribution networks, this paper proposes a distributed voltage control method of active distribution networks with global sensitivity, establishes a distributed communication mechanism, and builds a distributed forward push-back communication rule to transfer power and voltage information between nodes. The global sensitivity of voltage, active power, and reactive power is used for real-time, independent, and effective control of multiple devices at each node, and the distributed control process is simulated according to actual production conditions. Furthermore, the centralized and decentralized real-time voltage control methods are proposed, and the centralized and distributed methods are implemented in medium voltage primary distribution network (MVPN) and low voltage secondary distribution network (LVSN), respectively, to play the advantages of high computing efficiency and fast control response. Based on model predictive control (MPC), intra-day rolling time scale voltage regulation is constructed, and second-level control is used to adapt the MPC results, finally promoting the optimal scheduling and rapid response of various controllable flexible resources in the multi-level active distribution network.

Machine learning-based decision-making approach for predicting defects detection: a case study

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.

Does cognitive control mediate the relationship between peer presence and adolescent risk-taking? An ERP study.

Peer presence influences risk-taking behavior, particularly in adolescence. Based on the dual system model, this event-related potential study examined whether and how the presence of a peer displayed a preference for risky behavior would increase adolescents' risk-taking by disrupting their cognitive control processes in either emotional or non-emotional contexts. A sample of 106 adolescents (17-19 years of age) completed two Stoop tasks and a Balloon Analog Risk Task under three peer presence conditions. Results revealed that compared to other conditions, the presence of a risk-averse peer caused adolescents to make safer decisions through improving their conflict monitoring (more negative N200-diff), whereas a risk-preference peer's presence led adolescents to more risky decisions through disrupting their conflict resolution (more positive N450-diff) but they were only observed on the Emotional Stroop task. These findings suggest that different peer presence contexts could increase or decrease adolescents' risk-taking behaviors by influencing their cognitive control under an emotional context rather than in a non-emotional context.

Deciphering contaminants and toxins in fermented food for enhanced human health safeguarding.

Fermented foods have been a component of the human diet since ancient times, including live bacteria employed to restore gut health, contributing to the frontline of functional food progression. Human concern about the harmful consequences of possible contaminants has increased significantly as their toxicity, carcinogenicity, and teratogenicity have become more publicized. In order to take preventive measures, it is essential to correctly identify and define the implications of contaminants and toxins in human health and intestinal microbiota balance for preventing or diagnosing epidemics before they cause damage. The longer food chain that results from urbanization and underreporting of diseases makes it harder to correlate contaminated food to disease, which in turn presents challenges to improving food safety. This research aims to present the potential physical, chemical, and microbiological pollutants and toxins found in fermented products and their effects on human health. The scope tackles various categories of fermented foods, such as dairy products, alcoholic and nonalcoholic beverages, fermented meat products, traditional bakery products, and fermented cereals and vegetables. Furthermore, it examines specific control processes such as rigorous sanitation protocols, advanced packaging technologies, regulatory harmonization, and decontamination methodologies used to prevent the release of contaminants from fermented foods. Future viewpoints and opportunities are briefly mentioned in the conclusion.

Cooperative Adaptive Command Filtered Backstepping Control for EVs to UPS-Microgrid via Virtual Synchronous Generator.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

ANALISIS HUKUM PEMANFAATAN SUMBER DAYA AIR UNTUK KEBUTUHAN RUMAH PENDUDUK DALAM PERSPEKTIF KESEJAHTERAAN

Water is a natural resource that is very important in everyday life. Some of the benefits of water resources in daily life include irrigation in agriculture, electricity generation, and household activities. The problem that arises is whether the regulation and use of water resources is in accordance with the needs of residents' homes, and how are sanctions applied for misuse of water resources that are not in accordance with the intended needs of residents' homes? The aim of this research is to find out and analyze the use of water resources for residents' housing needs, and to provide alternative solutions to the application of sanctions for misuse of water resources that are not in accordance with the intended use of residents' housing needs. The research method used is normative juridical, with a legislative approach as primary legal material combined with observation and observation as secondary legal material, especially a welfare perspective. results: That the use of water resources for daily needs is not yet optimal, even though there have been several statutory regulations, therefore the implementation of water quality protection and management should be carried out through a process of planning, utilization, control and maintenance, so that the need for water resources for people's homes are filled. That the alternative solution is to enforce the law firmly and clearly and strictly supervise perpetrators who commit violations against the use of water resources for the needs of residents' homes.