Mitigation Of Disturbances Research Articles

Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview

Exploiting preview information of disturbances within robot control is an effective method of handling state perturbations. For underwater vehicles operating in wave-dominated environments, disturbances significantly influence vehicle response and pose a threat to operational safety. In consideration of this, a complete end-to-end control architecture is developed in this work for disturbance rejection during station keeping tasks under wave perturbations, encompassing a nonlinear model predictive controller (NMPC) combined with a deterministic sea wave predictor (DSWP). The wave predictor exploits a continuous measurement of the wave elevation at a location upstream of the vehicle to form a forecast of the temporal evolution of the wave elevation at the vehicle location. The predicted wave parameters are then used to estimate the impending wave-induced hydrodynamic loading, enabling explicit consideration of accurate short-term disturbance forecasts within the controller, ensuring this preview is incorporated in the optimised control sequence. Experimental testing confirms the validity of the wave predictor, producing RMSE as low as 0.017 m. The proposed strategy is then simulated under various wave conditions, optimising control actions inclusive of the preview information. The NMPC outperforms a similar disturbance mitigating feed-forward controller, with average improvements of up to 52% and is found to perform best even with noisy, lower accuracy wave predictions, as well as with communication time delays, providing a high degree of robustness. This demonstrates the potential of the proposed control framework to effectively tackle disturbance mitigation of underwater vehicles subject to large magnitude wave disturbances, expanding the operational envelop of autonomous systems towards forbidding ocean environments.

Plant miR6262 Modulates the Expression of Metabolic and Thermogenic Genes in Human Hepatocytes and Adipocytes.

Edible plants have been linked to the mitigation of metabolic disturbances in liver and adipose tissue, including the decrease of lipogenesis and the enhancement of lipolysis and adipocyte browning. In this context, plant microRNAs could be key bioactive molecules underlying the cross-kingdom beneficial effects of plants. This study sought to explore the impact of plant-derived microRNAs on the modulation of adipocyte and hepatocyte genes involved in metabolism and thermogenesis. Plant miR6262 was selected as a candidate from miRBase for the predicted effect on the regulation of human metabolic genes. Functional validation was conducted after transfection with plant miRNA mimics in HepG2 hepatocytes exposed to free fatty acids to mimic liver steatosis and hMADs cells differentiated into brown-like adipocytes. miR6262 decreases the expression of the predicted target RXRA in the fatty acids-treated hepatocytes and in brown-like adipocytes and affects the expression profile of critical genes involved in metabolism and thermogenesis, including PPARA, G6PC, SREBF1 (hepatocytes) and CIDEA, CPT1M and PLIN1 (adipocytes). Nevertheless, plant miR6262 mimic transfections did not decrease hepatocyte lipid accumulation or stimulate adipocyte browning. these findings suggest that plant miR6262 could have a cross-kingdom regulation relevance through the modulation of human genes involved in lipid and glucose metabolism and thermogenesis in adipocytes and hepatocytes.

3D Correlation Imaging for Localized Phase Disturbance Mitigation

Correlation plenoptic imaging is a procedure to perform light-field imaging without spatial resolution loss, by measuring the second-order spatiotemporal correlations of light. We investigate the possibility of using correlation plenoptic imaging to mitigate the effect of a phase disturbance in the propagation from the object to the main lens. We assume that this detrimental effect, which can be due to a turbulent medium, is localized at a specific distance from the lens, and is slowly varying in time. The mitigation of turbulence effects has already fostered the development of both light-field imaging and correlation imaging procedures. Here, we aim to merge these aspects, proposing a correlation light-field imaging method to overcome the effects of slowly varying turbulence, without the loss of lateral resolution, typical of traditional plenoptic imaging devices.

Threats and Hazards of Grey Rhino Paradigm

Grey Rhino is a metaphor for security threat, which is unkept irrational for large extent also high probability of harmful effect on a subject of risk owner. Business continuity management system is assessed in a diagram as controlling instrument aimed at operational hazard mitigation of continuity disturbance of affected subject. A ´from Operations´ Value Added – O´VA is established as qualitative dynamic information about productivity of activities. The measures are designed in crisis scenario for quality improvement of subject´s crisis readiness. An indicator of business risk continuity slip is defined. In a connection with war conflict on Ukraine is assessed if it is Grey Rhino manifestation.

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

Social Media Data Mining and Mental Health Status Assessment Based on Sentiment Analysis

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Computer Modeling Analysis of Electric Vehicle's Choice Behavior Considering Latent Variables

At present, there are few researches on the influence of psychological latent variables on the choice behavior of electric vehicles, and lack of consideration of individual heterogeneity. In this study, computer modeling was used to construct not only a traditional discrete choice model that takes into account individual heterogeneity, but also a hybrid choice model,which takes into account psychological latent variables are constructed to explore the mutual effects of observable personal attributes, vehicle attributes and difficult to directly observe psychological latent variables on consumers’ choice behavior of electric vehicles. The estimation results of hybrid choice model with psychological latent variables and the traditional discrete choice model without considering psychological latent variables are compared. The result show that perceived usefulness, perceived risk and purchasing attitude have a significant impact on consumers’ choice behavior of electric vehicles. Compared with traditional discrete choice model, the hybrid choice model, which takes into account psychological latent variables has significantly higher prediction accuracy and has better interpretation ability and fitting effect. The research results can provide theoretical support for further expanding the electric vehicle market and formulating relevant policieshe usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Development and Application of Control Concepts for Twin-Screw Wet Granulation in the ConsiGmaTM-25: Part 1 Granule Composition

Continuous manufacturing of pharmaceuticals offers several benefits, such as increased production efficiency, enhanced product quality control, and lower environmental footprint. To fully exploit these benefits, standard operation mode (production processes with no or minimal disturbance mitigation measures) should be supported by adopting novel quality-by-control (QbC) methodologies. The paper at hand is the first part of a study focused on developing QbC algorithms for optimizing twin-screw wet granulation in the industrial manufacturing line ConsiGmaTM-25, specifically addressing granule composition.This work relies on previously established process-analytical-technology (PAT) equipment for real-time monitoring of the granule composition, i.e., the active pharmaceutical ingredient (API) and liquid content in wet granules. The developed control platform integrates model-based process control algorithms that aim to keep the API- and liquid content at target values through real-time adjustments of the process parameters. Furthermore, the platform integrates a digital operator assistant, which aims to detect and classify granulation disturbances and provides messages and instructions for the plant operator.The present manuscript systematically outlines all design steps from the development phase in the simulation environment to the final real system application and validation. The control platform’s performance is demonstrated through selected test scenarios on the ConsiGmaTM-25 manufacturing line. The obtained results indicate improved disturbance robustness and an increase in intermediate/final product quality (compared to conventional operating modes): The process control algorithms successfully maintained the API- and liquid content at target values despite process disturbances. Furthermore, realistic disturbances (feeder, pump, and material) were accurately detected and classified by the digital assistant algorithm. The information was provided through a user interface, offering real-time support for plant personnel.

Linear parameter-varying output-feedback for active vibration control of an elastic kinetic roof structure with experimental validation

Elastic kinetics are an approach to design transformable lightweight structures with a stable transformation process. The transformation is realized through elastic bending of structural members by exploiting the compliant material behavior. This lightweight and flexible design comes at the cost of increased sensitivity to static and dynamic disturbances. However, most of the current research focuses on the principles of elastic kinetic transformation instead of effective disturbance mitigation. This work focuses on dynamic disturbance mitigation for such transformable lightweight structures using active control. Modeling and controller synthesis are performed in the linear parameter-varying (LPV) framework, since the dynamics of elastic kinetic structures are transformation-state dependent due to geometric nonlinearities. Based on an LPV model in a grid-based representation, an LPV output-feedback control can be designed and synthesized via a gridding approach. This methodology is experimentally tested and validated for the example of an active hybrid roof structure prototype.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

Chronic anthropogenic disturbance mediates the biodiversity‐productivity relationship across stand ages in a large temperate forest region

Abstract Temperate forests, especially those in the densely populated regions of the world, are experiencing increasing levels of habitat degradation and biological impoverishment due to subtle but pervasive chronic anthropogenic disturbances including frequent and continuous grazing and extraction of non‐timber forest products. However, the effects of these subtle, chronic disturbances on the biodiversity‐productivity relationship have rarely been examined especially in forests at different development stages. Accordingly, this study explores how chronic anthropogenic disturbance affects the relationship between tree species diversity and forest productivity at different stand development stages in a large temperate forest region. We used the human footprint index as a proxy for chronic human disturbance. Hierarchical Bayesian models were employed to assess the effects of chronic human disturbance on the relationship between tree diversity and forest productivity across different stand age. Several measures of diversity were employed, including taxonomic, functional and phylogenetic diversity. Forest productivity consistently increased with taxonomic, functional and phylogenetic biodiversity; these biodiversity facets were the main drivers of forest productivity compared to stand age, chronic human disturbance and climate. However, the magnitude at which productivity increases with the increments of taxonomic and functional diversity diminishes with the increasing chronic disturbance, especially in younger stands. The effects of phylogenetic diversity on productivity did not vary with chronic disturbance, regardless of stand age. Synthesis and applications: Chronic human disturbance in a large temperate forest region reduces the increase in community productivity due to different facets of biodiversity, especially in young forests. The evidence suggests that the mitigation of chronic human disturbance and the conservation of biodiversity will be effective in sustaining essential ecosystem functions.

Mitigation of wildlife disturbance at oil and gas industry electrical facilities in Riau Province, Indonesia

Abstract. Suwondo, Darmadi, Roza D, Murdiya F, Oktarianda R, Risaundi DD. 2023. Mitigation of wildlife disturbance at oil and gas industry electrical facilities in Riau Province, Indonesia. Biodiversitas 24: 6914-6922. Electrical facilities in the oil and gas industry often experience interference from wildlife, disrupting oil and gas operational activities. Solutions are required to mitigate these disturbances. This research analyzes the level of wildlife disturbance in electrical facilities in the oil and gas industry and their mitigation. This research used a survey method with purposive random sampling; the observation was carried out in the work area of Pertamina Hulu Rokan Company, Riau Province, Indonesia. The data obtained are primary and secondary, and the parameters measured consist of population, behavior, disturbance, and mitigation of disturbance to wildlife. A total of 38 species of wild animals were found, consisting of the mammal group (5 species), the reptile group (3 species), and the aves group (30 species). The population of long-tailed monkey (Macaca fascicularis) is the largest species of mammal found (1,500 individuals), and the most common daily behaviors of macaques are climbing (22.5%), eating (17.5%), and playing (15%). There were 169 incidents of wildlife disturbance at the WK Rokan electrical facility, with the most disturbance caused by macaques at 111 incidents. Disturbance from wildlife has a linear positive interaction with wildlife mortality around oil and gas industry electrical facilities. Mitigation is conducted as follows: isolating/eliminating the presence of wild animals in affected facilities/locations, building barriers against wild animals around electrical facilities, and providing repellents.

Unified power quality conditioner (UPQC) with voltage dips and over-voltages compensation capability

Unified power quality conditioners (UPQCs) allow the mitigation of voltage and current disturbances that could affect sensitive electrical loads while compensating the load reactive power. Diverse control techniques have been proposed to evaluate the instantaneous output voltage of the series active power filter of the UPQC but, in most cases, these controllers only can compensate a kind of voltage disturbance. This paper proposes a new digital controller for UPQCs which allows the load current harmonics and reactive power to be compensated at the grid side while avoiding the effect of voltage dips, over-voltages and voltage harmonics on the local loads. The controller characteristics can be established on-line without changing its basic structure. The performance of the proposed controller has been evaluated through simulation tests.

Power Quality Improvement based on VSHDE Algorithm Incorporating Shunt Active Power Filter

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Retracted: Assessment and mitigation of sleep disturbances in Egyptian acute care settings

BackgroundSleep disturbances in acute care settings pose challenges to patient recovery and well-being. Acute care facilities provide a unique context to explore these issues due to their diverse patient populations. Understanding the prevalence and nature of sleep disturbances is crucial for developing effective interventions tailored to the acute care setting. AimAssess and mitigate of sleep disturbances in Egyptian Acute Care Settings among patients. MethodsA cross-sectional design was utilized, involving 158 adult patients aged 18 and above with various medical conditions. Sleep assessments utilized the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Berlin Questionnaire, and Insomnia Severity Index (ISI). Statistical analyses, including t-tests and ANOVA, were conducted using SPSS 26. ResultsThe study, encompassed 158 patients, revealed several key findings: heightened sleep latency (p = 0.042) and a substantial increase in the Global PSQI Score (p = 0.018), indicated compromised overall sleep quality. Daytime sleepiness escalated across activities (p < 0.01), hinted at potential sleep-related issues. Additionally, occurrences of snoring, witnessed apnoea's, and daytime sleepiness significantly rose (p < 0.05), suggested the possibility of sleep-disordered breathing. Results from the Insomnia Severity Index (ISI) underscored challenges across various sleep aspects (p < 0.01), highlighted the overall severity of insomnia symptoms (p = 0.001). These findings emphasized the necessity for targeted interventions to address these sleep disturbances ConclusionThe study underscored the significant impact of sleep disturbances on patients within Egyptian Acute Care Settings. With heightened sleep latency, compromised overall sleep quality, and increased daytime sleepiness observed among the patient cohort, there is a pressing need for tailored interventions to mitigate these issues. RecommendationsDevelop interventions tailored to address sleep disturbances, enhance healthcare provider awareness, and incorporate regular sleep assessments into patient care routines. Additionally, future research should evaluate the efficacy of these interventions within acute care environments.

Observer-based Attack and Disturbance Mitigation for Heavy-Duty Vehicles in an Autonomous Docking Application*

Observer-based Attack and Disturbance Mitigation for Heavy-Duty Vehicles in an Autonomous Docking Application*

Analysis and mitigation of PQ disturbances in grid connected system using fuzzy logic based IUPQC

Renewable energy integration introduces grid instability due to variable and intermittent sources like solar and wind, impacting reliability. This paper provides a thorough discussion of recent advancements and emerging trends in grid-integrated wind energy systems (GIWES) and grid-integrated solar energy systems (GISES). More than 70 research articles have been rigorously assessed and listed the technological and economic challenges. The increase in installations of grid-Integrating systems gives rise to challenges like as grid strain, peak shaving impacts, unpredictability of renewable energy sources (RES), and power quality disturbances. A variety of custom power devices, such as dynamic voltage restorers (DVR), static synchronous compensators (STATCOM), active power filters (APF), and unified power quality conditioners (UPQC), have gained popularity in response to these challenges. Among the various challenges, power quality disturbances, including voltage sag, swell, current and harmonics pose significant issues. To address these disturbances this work present a novel approach utilizing fuzzy logic (FL) to develop multi-feeder interline unified power-quality conditioners (MF-IUPQCs). The MF-IUPQC has three legs and three levels, each of which has four diode-clamped inverters. Switching is carried out through the use of space vector pulse width/duration modulation (SVPWM). Total harmonic distortion (THD) induced by nonlinear loads is reduced by the FLC-based MF-IUPQC, which also improves dynamic performance and offers a smooth DC-link voltage. The proposed control mechanism is implemented using MATLAB/Simulink. The fuzzy-based controller is compared to the industry-standard proportional-integral (PI) controller to determine its efficacy. Among them, the MF-IUPQC based on FLC delivers the smoothest voltage profile and the lowest THD.

Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards.

Speed-Sensorless Control of Induction Motor Drives With a STA-FLL Speed Estimation Scheme

Speed-sensorless control (SSC) techniques enhance the overall reliability while reducing the cost, and thus become attractive in induction motors (IMs). Simplicity and flexibility are the main features that make phase-locked loop (PLL)- and frequency-locked loop (FLL)-based speed estimation schemes stand out among considerable estimation methods. However, many PLL and FLL schemes exhibit a poor estimation accuracy in the cases of acceleration and deceleration (AaD). Accordingly, an adaptive second-order generalized integrator-FLL (SOGI-FLL)-based scheme is introduced. While, the issue of the estimated speed feedback (ESF) in the adaptive SOGI-FLL scheme remains an obstacle. With the above, a speed estimation scheme that combines the super-twisting algorithm with an FLL (STA-FLL) is proposed for the SSC of IM drives in this paper. In the proposed STA-FLL scheme, an STA-based quadrature signal generator (STA-QSG) is properly designed, in which the ESF is cancelled to further ensure speed estimation. Moreover, a closed-loop flux observer is adopted in the implementation to enhance the proposed STA-FLL scheme in terms of disturbance mitigation. Experimental tests are performed to validate the proposed STA-FLL scheme.

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller