Central Control Research Articles

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.

Collaborative Multi-Agent Video Fast-Forwarding

Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collaboratively to perceive the environment for efficient and accurate situation awareness. However, these agents often have limited computation, communication, and storage resources. Thus, reducing resource consumption while still providing an accurate perception of the environment becomes an important goal when deploying multi-agent systems. To achieve this goal, we identify and leverage the overlap among different camera views in multi-agent systems for reducing the processing, transmission and storage of redundant/unimportant video frames. Specifically, we have developed two collaborative multi-agent video fast-forwarding frameworks in distributed and centralized settings, respectively. In these frameworks, each individual agent can selectively process or skip video frames at adjustable paces based on multiple strategies via reinforcement learning. Multiple agents then collaboratively sense the environment via either 1) a consensus-based distributed framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMVF</b> that periodically updates the fast-forwarding strategies of agents by establishing communication and consensus among connected neighbors, or 2) a centralized framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MFFNet</b> that utilizes a central controller to decide the fast-forwarding strategies for agents based on collected data. We demonstrate the efficacy and efficiency of our proposed frameworks on a real-world surveillance video dataset VideoWeb and a new simulated driving dataset CarlaSim, through extensive simulations and deployment on an embedded platform with TCP communication. We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.

Design of Hybrid Sprinkler: The IOT-Powered Robot for Watering Plants

Innovative methods for watering plants have been developed as a result of the growing demand for efficient and automated systems in agriculture. The design of a hybrid sprinkler, an IoT-powered robot created specifically for effective and precise plant irrigation, is presented in this study. The suggested system enables autonomous operation and intelligent control by fusing the benefits of conventional sprinklers with those of IoT technology. To continuously monitor the environmental conditions, the hybrid sprinkler integrates a variety of sensors, including soil moisture sensors, temperature sensors, and humidity sensors. A central control unit receives this real-time data and uses sophisticated algorithms to establish the best watering schedule for each plant depending on its individual needs. The hybrid sprinkler's design prioritizes sustainability and energy economy. The robot has a rechargeable battery system, and to increase battery life, it uses energy-saving features like sleep modes and sophisticated power management. In a variety of agricultural and horticultural applications, this system assures optimal watering practises, conserves resources, and fosters healthy plant growth by fusing IoT technology, cutting-edge sensors, precision spraying mechanisms, and remote control capabilities.

Multi-Agent Safe Graph Reinforcement Learning for PV Inverters-Based Real-Time Decentralized Volt/Var Control in Zoned Distribution Networks

To realize real-time voltage/var control (VVC) in active distribution networks (ADNs), this paper proposes a new multi-agent safe graph reinforcement learning method to optimize reactive power output from PV inverters. The network is divided into several zones, and a decentralized framework is proposed for coordinated control of reactive power output in each zone to regulate voltage profiles and minimize network energy loss. The VVC problem is formulated as a multi-agent decentralized partially observable constrained Markov decision process. Each zone has a central control agent that embeds graph convolution networks (GCNs) in the policy network to improve the decision-making capability. The GCN extracts graph-structured features from the ADN topology, reflecting the relationship between VVC and grid topology, and can filter noise and impute missing data. The training process includes primal-dual policy optimization to rigorously satisfy voltage safety constraints. Simulations on a 141-bus distribution system demonstrate that the proposed method can effectively minimize network energy loss and reduce voltage deviations, even in the presence of noisy or incomplete input measurements.

IoT Based Public Transportation Accident Prevention with Driver Alcohol Detection and Smoke System

In Nigeria, 1,834 deaths were recorded from 3,345 road accidents between January and March 2022, according to the National Bureau of Statistics (NBS), underscoring the critical need for improved road safety measures. This study proposes an IoT-based system designed to prevent accidents in public transportation by addressing two major risk factors: alcohol-impaired driving and smoking while driving. The system utilizes MQ3 and MQ2 sensors for alcohol and smoke detection, respectively, which are installed within the vehicle interior. These sensors are connected to a central control unit that processes data in real-time. Upon detection of alcohol or smoke, the system triggers visual and auditory alerts, deactivates the vehicle’s engine, and sends notifications to the relevant transportation authority. The architecture of the system ensures seamless data communication, allowing for remote monitoring and control. The findings demonstrate the potential of IoT-driven solutions in reducing road accidents and underscore the importance of integrating sensor technology, real-time data processing, and wireless communication for next-generation accident prevention.

Robust Reward-Free Actor-Critic for Cooperative Multiagent Reinforcement Learning.

In this article, we consider centralized training and decentralized execution (CTDE) with diverse and private reward functions in cooperative multiagent reinforcement learning (MARL). The main challenge is that an unknown number of agents, whose identities are also unknown, can deliberately generate malicious messages and transmit them to the central controller. We term these malicious actions as Byzantine attacks. First, without Byzantine attacks, we propose a reward-free deep deterministic policy gradient (RF-DDPG) algorithm, in which gradients of agents' critics rather than rewards are sent to the central controller for preserving privacy. Second, to cope with Byzantine attacks, we develop a robust extension of RF-DDPG termed R2F-DDPG, which replaces the vulnerable average aggregation rule with robust ones. We propose a novel class of RL-specific Byzantine attacks that fail conventional robust aggregation rules, motivating the projection-boosted robust aggregation rules for R2F-DDPG. Numerical experiments show that RF-DDPG successfully trains agents to work cooperatively and that R2F-DDPG demonstrates robustness to Byzantine attacks.

Retracted: A Novel Distributed Denial of Service (DDoS) Detection Method in Software Defined Networks

The software Defined Networks (SDN) are the most promising solutions in solving challenges regarding complex network control. The SDNs are responsible for separating network control functions from the network switch. By applying SDNs, communication companies and operators can control and manage networks through one central controller unit therein with no need by applying hardware or the manufacture's guideline. Lack of complex DDoS attack detection, lack of rapid detection sensitivity on packet type attack, lack of methods in promoting detection attack to specific unknown protocols, lack of ability in detecting at switch level, lack of assessing attacks packet types, constitute drawbacks. Although is made in this article to overcome these drawbacks, consequently the GainRatioAttributeEval algorithm and Ranker method are applied. The datasets involved here are the ISCX-SlowDDoS2016, ISCX-IDS2012, CTU-10 and CTU-11.This proposed method is compared with similar methods and it is found that this method with 99.95% accuracy and 0.04 FPR for UNB-ISCX dataset and 99.84% accuracy and 0.1 FPR for CTU-13 dataset is better than others.

Enhancing Gas Leak Detection with IoT Technology: An Innovative Approach

The presence of a natural gas leak within a household carries the potential for fires and poses a risk of natural gas poisoning. Similar to how we approach other hazardous energy sources such as electricity and gasoline, it is crucial to exercise caution when dealing with natural gas exposure. To prevent potential hazards and dangerous situation, identified a smart gas detection system for rapid and accurate detection of gas leaks. The proposed gas leakage detection system combines advanced sensor technology, real-time monitoring, and automated alert mechanisms to ensure timely identification and response to gas leaks. The MQ2 sensor helps in detection of gas leakage. The MQ2 sensor possesses the capability to detect a wide range of gases, including methane, propane, carbon monoxide, and hydrogen. This versatility makes it an invaluable tool in ensuring safety and protecting against potential hazards and early identification of gas leaks. The collected data is analyzed using sophisticated algorithms to distinguish between normal background gas levels and potential leaks. NodeMCU, equipped with its Wi-Fi capabilities, functions as the central control unit of the system. The NodeMCU gathers real-time data from the gas sensors, constantly monitoring gas levels. It processes this data and sends it to the cloud or a central server using its internet connection. The system's ability to monitor in real-time ensures that any gas leaks detected are quickly reported to the relevant personnel or authorities through automated alerts. The fast notification system allows for quick actions, reducing the risks of accidents, saving lives, preventing property damage, and mitigating harm to the environment.

Wireless Smart Lighting System: A Cost-Effective Approach to Energy Saving with Centralized Control for Large-Scale Campus Infrastructure

In order to improve lighting management throughout a campus infrastructure, operational flexibility, and energy efficiency, this study suggests a wireless smart lighting system with centralized control. The system combines wireless sensors, LED illumination, and a central control unit to manage lighting according to occupancy, the time of day, and environmental factors. Significant energy savings are possible since the system may be remotely monitored and optimized through the use of a wireless communication protocol. Phases of the implementation included installing sensors and LEDs, setting up a centralized control system, and evaluating the campus lighting infrastructure. The outcomes demonstrate a significant reduction in energy use, easier upkeep, and better illumination around the campus. Energy-efficient and environmentally friendly lighting solutions are in high demand, particularly in huge infrastructures like college campuses. Conventional lighting systems frequently need human operation and are inefficient, which increases operational complexity and wasteful energy use. A wireless smart lighting system with centralized control that is specifically designed for campus infrastructures is suggested in this research. Using wireless sensors, LED lights, and a centralized control unit, the system dynamically modifies illumination according to occupancy, ambient light levels, and time of day in real time.

Integrating corporate governance and forensic accounting: A sustainable corporate strategy against fraud

In the realm of financial oversight and corporate management, forensic accounting (FA) holds a critical position, serving as a central control mechanism and being widely recognized as an essential component of corporate governance. FA plays a crucial role as a central control mechanism and is acknowledged as a pivotal element of corporate governance. Consequently, it needs to continuously adapt in response to shifts in corporate governance practices, while the role of internal auditors transforms to actively support corporate sustainability. The aim of this research is to assess the effectiveness of FA and explore its relationship with corporate governance, based on the relevant literature. Thus, the main objectives of the present study are to identify the internal control attributes that influence the quality of its performance and to evaluate how corporate governance contributes to enhancing the quality of FA. To achieve the aim and the objectives of the paper, a literature analysis was carried out. The main contribution of the present paper is to refresh the existing body of knowledge on contemporary FA and its interplay with corporate governance.

Precision Agriculture Model for Farm Irrigation using Machine Learning to Optimize Water Usage

The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.

Smart Home Automation using IoT: Prototyping and Integration of Home Devices

Smart homes are becoming a reality thanks to the Internet of Things (IoT) technology's quick progress, providing homeowners with never-before-seen levels of efficiency, convenience, and security. This abstract gives a summary of a project that combines prototyping and integration to turn a standard house into a modern smart home. The paper focuses on creating an extensive IoT ecosystem that seamlessly connects diverse household equipment, such as lighting controls, thermostats, and security cameras, to a single, intelligent network. We use cutting-edge IoT communication and protocol technologies to create strong connectivity between these devices, enabling homeowners to remotely monitor and control them from a central hub or their smartphones. Our smart home system's central controller, which includes cutting-edge sensors and AI algorithms, is its brain. To reduce energy usage, improve security, and accommodate resident preferences and routines, this controller gathers and examines data from the linked devices. It learns user patterns through machine learning and suggests automation procedures to make life easier. A smart home system prioritises security and privacy in addition to increasing convenience. We also place a strong emphasis on flexibility and scalability, which enable the incorporation of new hardware and features as the IoT landscape changes. For homeowners, tech enthusiasts, and developers interested in building their own smart houses, our project on prototyping and integration serves as a model. It highlights how IoT technology has the ability to upgrade conventional living quarters into intelligent, networked areas that improve our quality of life while fostering energy efficiency and security. In the end, this project advances the continued development of smart home automation by increasing everyone's access to and ability to customise it.

Peculiarities of ship power plant diagnostics using the ERS-500 TechSim TRANSAS MIP LTD simulator complex in the process of ship engineers' training

This paper presents an analysis of the results obtained using the ERS 5000 TechSim Engine Room Simulator software. The software simulates remote control from the ship's central control station using virtual equipment panels, which are dedicated hardware combined into a separate console. The simulator covers the main areas of training in accordance with the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers with the Manila Amendments of 2010 and meets the requirements of IMO Conventions and Resolutions and IEC standards. The simulator is certified by the Norwegian classification society DNV/GL. Main engine diagnostics are performed using the Cylinder Combustion Quality diagnostic panel and the Indicator Diagram Comparison panel. Analysis of changes in gas turbine engine parameters and the indicator diagrams themselves at different levels of piston ring wear showed a decrease in compression pressure, mean indicated pressure and maximum combustion pressure, as well as an increase in the return oil temperature of the piston cooling system and the consumption of lubricating oil in the cylinder. The advantages of using simulators in the training process include the ability to perform laboratory work in a frontal method, which significantly improves the learning experience

A smart irrigation monitoring service using wireless sensor networks

The present research uses wireless sensor networks (WSN) to create a smart watering system. The system’s ability to perform real-time monitoring and management of irrigation makes sure that crops get the right quantity of water depending on their unique needs. The suggested method boosts agricultural yields, decreases labor costs, and improves water usage efficiency. The system uses a field-deployed network of inexpensive wireless sensors to track the soil moisture levels in real time. The central controller utilizes the wirelessly sent sensor data to decide when and how much water should be applied to the crops. Utilizing wireless protocols like Zigbee, these nodes connect to a central gateway, where the data is processed and examined to establish the ideal watering needs for each crop. The technology is scalable and simple to install in larger agricultural fields. The study’s findings indicate that the system can boost crop yields by up to 30% while boosting water usage efficiency by up to 60%. Farmers may decrease their water use, save time and money, and enhance their profitability by adopting the smart irrigation monitoring service powered by WSN.

СВЯЗЬ ТИПА СЕМЕЙНЫХ ОТНОШЕНИЙ С РАЗВИТИЕМ КОМПЕТЕНЦИИ САМОРЕГУЛЯЦИИ ДЕВИАНТНЫХ ПОДРОСТКОВ

The article presents the results of a study of the development of self- regulation competence and the type of family relationships in adolescents with deviant and normative behavior. 113 families took part in the study, including 55 prosperous families with adolescents of normative behavior and 58 dysfunctional families with adolescents of deviant behavior under the patronage control of the Municipal Budgetary Institution City Prevention Center "Teenager", Izhevsk. It was revealed that the levels of development of self-regulation and its regulatory processes in adolescents with deviant behavior are qualitatively different from their normally developing peers. It is shown that family relationships affect the level of development of self-regulation competence in deviant adolescents and can become a prerequisite for the formation of specific regulatory features of conscious self-regulation, which will act as resources for achieving / not achieving educational, personal goals, self-activity, socialization and success in various activities. A significant multidirectional relationship was found between the type of family relationships and the development of self-regulation competence in deviant and normative adolescents. The data obtained can be used when working with deviant adolescents and their families in educational organizations and social services.

Dynamic dysregulation of transcriptomic networks in brainstem autonomic nuclei during hypertension development in the female spontaneously hypertensive rat.

Neurogenic hypertension stems from an imbalance in autonomic function that shifts the central cardiovascular control circuits toward a state of dysfunction. Using the female spontaneously hypertensive rat and the normotensive Wistar-Kyoto rat model, we compared the transcriptomic changes in three autonomic nuclei in the brainstem, nucleus of the solitary tract (NTS), caudal ventrolateral medulla, and rostral ventrolateral medulla (RVLM) in a time series at 8, 10, 12, 16, and 24 wk of age, spanning the prehypertensive stage through extended chronic hypertension. RNA-sequencing data were analyzed using an unbiased, dynamic pattern-based approach that uncovered dominant and several subtle differential gene regulatory signatures. Our results showed a persistent dysregulation across all three autonomic nuclei regardless of the stage of hypertension development as well as a cascade of transient dysregulation beginning in the RVLM at the prehypertensive stage that shifts toward the NTS at the hypertension onset. Genes that were persistently dysregulated were heavily enriched for immunological processes such as antigen processing and presentation, the adaptive immune response, and the complement system. Genes with transient dysregulation were also largely region-specific and were annotated for processes that influence neuronal excitability such as synaptic vesicle release, neurotransmitter transport, and an array of neuropeptides and ion channels. Our results demonstrate that neurogenic hypertension is characterized by brainstem region-specific transcriptomic changes that are highly dynamic with significant gene regulatory changes occurring at the hypertension onset as a key time window for dysregulation of homeostatic processes across the autonomic control circuits.NEW & NOTEWORTHY Hypertension is a major disease and is the primary risk factor for cardiovascular complications and stroke. The gene expression changes in the central nervous system circuits driving hypertension are understudied. Here, we show that coordinated and region-specific gene expression changes occur in the brainstem autonomic circuits over time during the development of a high blood pressure phenotype in a rat model of human essential hypertension.

Development and application of an integrated system of dewatering and recharge for the deep foundation pit

A large amount of pumping in the deep foundation pit will reduce the groundwater level and lead to surface cracking, building settlement, ground collapse and spring water cut-off. And the groundwater recharge is a good control measure. An integrated system of dewatering and recharge for the deep foundation pit is developed, and it is mainly composed of a pumping system, an assembled water tank, a variable frequency pressure system, a cleaning and filtering system, a recharge pressure tank, a recharge well system and a central control system. This system can realize intelligent automatic control according to the monitoring data. It does not require staff to operate on site and can be remotely adjusted by computer or mobile phone. Taking a subway station under construction in Jinan as the background, the single well recharge test and the multiple wells recharge test were carried out. It can be found that with the increase of recharge pressure, the recharge volume gradually increases, but the increasing trend is smaller and smaller. The relationship between the groundwater level and the recharge volume is a logarithmic function. Blindly increasing recharge wells may not improve the overall reinjection volume, and the relationship between the number of recharge wells, the diameter of reinjection pipes and the maximum recharge capacity of the variable frequency pressure system needs to be comprehensively considered.

Multiple control strategies for smart photovoltaic inverter under network voltage fluctuations and islanded operation

This article proposes a central control system that communicates with both grid-tied and off-grid control systems to offer various control strategies for operating a smart photovoltaic (PV) inverter. The target is to connect two sets of PV panels and one set of battery storage unit to either a 440 V/60 Hz utility grid or to feed local loads at 380 V/50 Hz using a smart inverter. When the smart PV inverter is connected to the grid, on the one hand, it injects fixed and programmed active power into the grid under all operating conditions, both normal and critical conditions, and on the other hand, by reciprocal exchanging reactive power with the grid, it addresses balanced and unbalanced fluctuations of the grid voltage. Also, when the smart PV inverter is disconnected from the grid, it can operate as an islanded mode to feed time-varying three-phase loads. Nonlinear simulations of the time domain in MATLAB-Simulink software show that the set of control strategies offered for the smart PV inverter not only by controlling the active power leads to the sale of electricity to the grid, but also by controlling the reactive power problems on the grid side such as voltage drop as a result of single-line-to-ground (SLG) or line-to-line-to-ground (2LG) faults, balanced grid voltage fluctuations such as voltage sag-swell and unbalanced voltage conditions will be resolved. Also, variable three-phase loads are fed well in off-grid mode.

Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation.

The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.

Central stress pathways in the development of cardiovascular disease.

Mental stress is of essential consideration when assessing cardiovascular pathophysiology in all patient populations. Substantial evidence indicates associations among stress, cardiovascular disease and aberrant brain-body communication. However, our understanding of the flow of stress information in humans, is limited, despite the crucial insights this area may offer into future therapeutic targets for clinical intervention. Key terms including mental stress, cardiovascular disease and central control, were searched in PubMed, ScienceDirect and Scopus databases. Articles indicative of heart rate and blood pressure regulation, or central control of cardiovascular disease through direct neural innervation of the cardiac, splanchnic and vascular regions were included. Focus on human neuroimaging research and the flow of stress information is described, before brain-body connectivity, via pre-motor brainstem intermediates is discussed. Lastly, we review current understandings of pathophysiological stress and cardiovascular disease aetiology. Structural and functional changes to corticolimbic circuitry encode stress information, integrated by the hypothalamus and amygdala. Pre-autonomic brain-body relays to brainstem and spinal cord nuclei establish dysautonomia and lead to alterations in baroreflex functioning, firing of the sympathetic fibres, cellular reuptake of norepinephrine and withdrawal of the parasympathetic reflex. The combined result is profoundly adrenergic and increases the likelihood of cardiac myopathy, arrhythmogenesis, coronary ischaemia, hypertension and the overall risk of future sudden stress-induced heart failure. There is undeniable support that mental stress contributes to the development of cardiovascular disease. The emerging accumulation of large-scale multimodal neuroimaging data analytics to assess this relationship promises exciting novel therapeutic targets for future cardiovascular disease detection and prevention.