Control Issues Research Articles

Driverless Planning and Decision-Making based on GRU

In the wake of the swift advancement of artificial intelligence, driverless technology has progressively emerged as a revolutionary technology. The existing driverless technology primarily relies on advanced sensors, artificial intelligence and machine learning algorithms, with a view to perceiving and analyzing the surrounding environment in real time, and making driving decisions accordingly. At the present time, despite the fact that driverless technology has introduced a lot of convenience to human travel, it still encompasses a significant number of challenges, such as safety, perception technology, vehicle control, road planning, cost and other issues. In a similar vein, the safety issue is an unavoidable part of the discussion on the driverless. Driverless vehicles require robust system safety and protection mechanisms. This paper is designed to establish a data model by analyzing the NGSIM dataset, and to probe the driverless perception capability and decision-making on intelligence in behavior-aware motion perspective with GRU approach. Eventually, it was concluded that neither too many nor too few neighboring vehicles should be taken into account when forecasting the trajectory of each vehicle in the coming years, while the appropriate neighboring vehicles should be selected to achieve the best prediction results.

SPATIAL PARTITION FOR HETEROGENEOUS CITY NETWORKS COMPOSED OF FACTORS THAT INFLUENCE THE DISTRIBUTION OF THE MACROSCOPIC FUNDAMENTAL DIAGRAM

Using a Macroscopic Fundamental Diagram (MFD) to implement partition control is effective in improving mobility in heterogeneous city networks. As one of the most complex issues in partition control, accurate sub-region partition is critical for control effectiveness. Current partition methods focus on the link density and precondition of existing MFD and disregard the factors that influence MFD distribution. To overcome this drawback, this study uses the characteristic value of the link and the intersection connected to the link as the analysis object and proposes an MFD sub-region partitioning method for large-scale networks. Firstly, the influences of road state parameters on MFD distribution are classified into traffic flow parameters, network physical properties, network operation mechanisms and emergencies. Simulation experiments are conducted to determine the degree to which these classifications affect MFD distribution. Secondly, a partition method combined with the link density and influence parameters of MFD is developed. The method is used for a preliminarily division of a road network through Minimum Spanning Tree (MST) and depth partition by the Normalised Cut (Ncut) algorithm. Finally, a case study is conducted in an actual city centre network, and results show that the developed method is superior to the single method based simply on link density.

Sequential Model Predictive Control for Grid Connection in Offshore Wind Farms Based on Active Disturbance Rejection

In order to harness a greater share of wind energy resources, offshore wind energy projects are venturing into deep-sea locations. In this progression, the issue of grid integration control becomes increasingly challenging. Traditional Model Predictive Control (MPC) has been introduced in offshore wind energy grid integration control due to its merits, such as not requiring modulators, dispensing with decoupling, incorporating constraint handling, and facilitating online optimization. However, it heavily relies on a model and consequently experiences a substantial loss of control effectiveness in the face of system parameter variations. In light of this, this study presents an active-disturbance-rejection-based three-vector sequence model predictive control approach. This method effectively mitigates the influence caused by changes in system parameters, endowing the system with self-disturbance rejection capabilities and enhancing its fault recovery abilities. The method employs self-disturbance control to track voltage reference values and employs the concept of sequence control to eliminate weighting factors in the cost function. Furthermore, it employs three-vector control to achieve error-free operation. The simulation results confirmed that the proposed method effectively minimizes voltage and power transients. It demonstrated superior control effectiveness and shorter response times, enabling the system to rapidly restore to a stable operational state following disturbances.

Smart h‐Chain: A blockchain based healthcare framework with insurance fraud detection

AbstractIn today's smart applications, very frequently used data like electronic health records (EHR's) are highly sensitive and soft target to the unauthorized agencies. Data management, integrity, and security issues plague EHR systems. Insurance firms are prohibited by law and corporate privacy from sharing patient data, but because the data are not linked and synchronized among insurance providers, there has been a rise in healthcare fraud. A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. Building a system to securely manage and track insurance operations by combining data from all sources is required to combat health insurance fraud. Over time, blockchain grew in popularity, and the healthcare sector's interest in it led to the development of design concepts that addressed security issues. In this study, a blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, their caregivers, physicians, and insurance agents to access the clinical data of the patients. By using off‐chain storage and cryptographic key exchange through blockchain our approach also covers the scalability, integrity and access control issue that a healthcare system in general is facing.A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. A blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, caregivers, physicians, and insurance agents to access the clinical data of the patients.

Direct Impact of the Air on Mutant Cells for Mutagenicity Assessments in Urban Environments.

Urban air pollution is recognized as a critical problem for public health and is classified as a carcinogen for humans. A great number of studies have focused on the monitoring of urban air mutagenicity. One of the best-known and applied methods for assessing mutagenicity is the Ames test, a bacterial reverse mutation test. The classic protocol for assessing air mutagenicity involves the concentration of particulate matter (PM) on filters and subsequent extraction using organic solvents. This work aimed to develop a method for the evaluation of air mutagenicity directly impacted by air on microbial plates already containing an Ames' microbial sensor. A specific six-month sampling campaign was carried out in Turin in a period with high air pollution. Samples were tested for mutagenicity on Salmonella typhimurium strains TA98, TA100, and YG1024 with the traditional method and with the new direct method. The new protocol is able to evaluate the mutagenicity of the sampled air and obtain repeatable results. The final sensitivity is similar to the traditional method (≈10 net revertants/m3); however, the mutagenic response is due to the complete air pollution mixture, including volatile and semivolatile pollutants avoiding the concentration of filters and the following laborious extraction procedures. Despite some critical issues in contamination control, the method is easier, faster, and less expensive than traditional methods.

Device concept for organization of monitoring network

State observation networks of the state of environment include a complex of stationary, route and sub torch observation posts. Stationary observation posts are a fairly expensive structure that used equipment of a certain accuracy. Modern technological development allows to create devices of small dimensions with a unified output interface, having a measurement error comparable to the error of equipment used in stationary observation posts or less. Therefore, this paper proposes the concept of creating a device, which, in particular, may be converted into an atmospheric air monitoring device. Currently, environmental and emission control issues remain relevant. The purpose of the study, the facet of which is revealed in this work, is to organize a monitoring network for the analysis of atmospheric air pollution. The work contains a list of priorities for the organization of the device, its main functions. The result is a structural diagram of the central component of the device, called “Heart” and an additional component - “Expansion Board”, that describes the main necessary nodes for the operating of the device. The working element of the instrument is sensors of substances with different operating principle. In addition, there are described a way to ensure nonvolatile device and organization of a unite interface of data transmission within it. A short list of tasks for which the proposed device is directed is presented.

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

Hamiltonian-path based constraint reduction for deployable polyhedral mechanisms

Most of the deployable polyhedral mechanisms (DPMs) are multi-loop overconstrained mechanisms that causes barriers for their applications due to the issues in assembly, operation and control. Yet, constraint reduction for these multi-loop overconstrained mechanisms is extremely challenging in kinematics. In this paper, by introducing the Hamiltonian path to investigate the 3D topological graphs of a group of Sarrus-inspired DPMs, we propose a systematic method for constraint reduction of multi-loop overconstrained DPMs. We demonstrate that through the removal of redundant joints with the assistant of tetrahedral Hamiltonian path, one equivalent simplest topological graph of tetrahedral mechanism is identified as a reduction basic unit. Subsequently, one simplest form of Sarrus-inspired cubic mechanism is obtained by investigating two Hamiltonian paths of its dual octahedron and sequentially arranging basic units. Furthermore, a total of nineteen simplest forms of Sarrus-inspired dodecahedral mechanisms are identified from seventeen Hamiltonian paths of its dual icosahedron. The overconstraints in each simplest Sarrus-inspired DPM are greatly reduced while preserving the original one-degree-of-freedom (DOF) motion behavior. The method proposed in this paper not only lays the groundwork for further research in wider deployable polyhedrons, but also inspires the reduction of other multi-loop overconstrained mechanisms, with potential applications in the fields of manufacturing, architecture and space exploration.

Optimization of Air Handler Controllers for Comfort Level in Smart Buildings Using Nature Inspired Algorithm

This research seeks to improve the temperature control of AHU in building sub-levels using optimization algorithms. Specifically, the study applies the FA and PSO algorithms to optimize the PID control of AHU’s temperature. The study addresses the issue of temperature control in building sub-levels, which is a common challenge in HVAC systems. The study uses optimization algorithms and a nonlinear model to improve temperature control and reduce fluctuations in temperature from the desired setting. Additionally, a NL-ARX algorithm is utilized to create a nonlinear model based on the thermal dynamics and energy behavioral patterns of ACMV cooling systems. The study evaluates the performance of three controllers—PID, FA-PID, and PSO-PID—based on ITSE as a performance index. The study compares the performance of these controllers to achieve the desired temperature setting, and it analyses the influence of temperature regulation on occupant comfort levels. In this study, we compare different controllers using ITSE as a performance indicator. This shows how well different optimization algorithms work at setting the right temperature. The research gap is the lack of efficient temperature control solutions in building sub-levels that can optimize occupant comfort and energy efficiency. The experimental findings confirm that PSO-PID outperforms conventional PID and FA-PID optimization in terms of achieving the goal objective via computational complexity. Overall, this study’s focus is to explore and compare different optimization algorithms to improve temperature control and occupant comfort in building sub-levels.

Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

The control issues in the network caused by the increasing proportion of dispersed production & sources of clean energy need coordinated handling of these assets. Virtual power plants (VPPs) may be thought of as systems that comprise resources like electric vehicles, energy storage, controlled loads, and other distributed generators. Operation, resource unpredictability, energy management, involvement in electrical markets, etc. are just a few of the many obstacles VPPs must overcome. The challenge of the study is to optimize the performance of VPPs in managing dispersed clean energy sources, and the proposed model employs a distributed control approach and particle swarm optimization (PSO)-based learning reinforcement technique to address this challenge by maximizing VPP output and operational efficiency in various energy market scenarios. In this research, we explore the topic of ongoing electrical power in massively distributed systems based on VPPs. To maximize the VPP output & reach an ideal operational point, the authors of this work used a distributed control approach. The purpose of this article is to examine VPP performance under a variety of markets for energy configurations. Further, a PSO-based learning reinforcement technique is used to examine the benefits and drawbacks of VPPs’ power management.

Pixels and resonators with blazed Littrow structures: Passive and non-Hermitian approaches

The confinement of light by Littrow blazed grating structures is explored for targeted device operation principles. For passive devices, these grating structures are explored in one and two-dimensional versions to study resonant pixel with sizes of about ten grating periods (for 4.5 μm-side), which retain a CMOS compatible design. The resonances are found to substantially enhance the weak silicon absorption at 940 nm, a wavelength of interest for, e.g., distance ranging and face recognition, and to achieve a 7.5° angular tolerance. The addition of gain and loss in generic Littrow structures that display an original dispersion made of crossing manifold is next considered, with a view to the issue of broad-area laser modal control.

The Holistic Empowering Methodological Approach (HEMA): Putting Participants in the Driver's Seat

This article presents the Holistic Empowering Methodological Approach (HEMA), which is philosophically informed by the concept of diversity by design; epistemologically and methodologically guided by hermeneutic phenomenology; and supported by the method of qualitative survey combined with the Single Question Aimed at Inducing Narrative (SQUIN) technique. This is a methodological conceptual article whereby the development and application of the HEMA is illustrated through the study that examined the learning and professional development experiences of disabled and neurodiverse PhD students in library and information science (LIS) programs amid the lingering COVID-19 pandemic and explored their academic lifeworlds. The article critically assesses the merits and shortcomings of the HEMA and provides other researchers with a roadmap for replicating the HEMA in every phase of research: from brainstorming to the selection of methods, to data collection, analysis, interpretation, and reporting. In so doing, the article also strives to reaffirm the vitality of rigorous qualitative methodology in studying underresearched, minoritized, and marginalized communities. From the scholarly worldview to specific methodological choices, this framework advocates for the type of research that puts participants in the “driver's seat,” giving them agency and providing the opportunity for self-definition and self-determination. The nuanced presentation of a holistic methodological approach, with particular attention paid to the issues of methodological rigor and quality control, will be of benefit to both beginner and seasoned researchers and can serve as an educational aid in research methods courses and dissertation supervision.

Designing access control security protocol for Industry 4.0 using Blockchain‐as‐a‐Service

AbstractIndustry 4.0 is a revolution of the operations in the industrial manufacturing for increased productivity, trade and commerce. It is heavily reliant on the automation of the processes and equipment along with complex interconnectivity and insightful analysis using machine learning. The interconnectivity of the manufacturing devices from various industrial sites brings with it several security issues related to communication. This article focuses on solving the security issue of access control between such devices and enable seamless secure communication for the proper functioning of the industry. An access control scheme has been proposed that achieves the necessary security features of anonymity, traceability, and forward secrecy. It is also shown that the proposed scheme takes less communication and computational costs, and is strongly resilient against various attacks such as impersonation attack, replay attack, and denial‐of‐service attack as compared to other relevant schemes.

Ethical issues in Nipah virus control and research: addressing a neglected disease

Nipah virus is a priority pathogen that is receiving increasing attention among scientists and in work on epidemic preparedness. Despite this trend, there has been almost no bioethical work examining...

Numerical and experimental investigation on deterministic prediction of ocean surface wave and wave excitation force

Floating marine structures implement real-time wave excitation force prediction to address optimal control issues. The accuracy of force prediction relies on adequate wave forecasting. This paper presents a comprehensive analysis of deterministic wave forecasting by considering various wave steepnesses and directional spreads. In addition, we introduce new methods for predicting wave excitation forces acting on the floating body of interest. The methods are based on a set of frequency coefficients of wave excitation forces, which are generated in conjunction with wave amplitude parameters optimized in the data assimilation and frequency response functions obtained from boundary element method tools. These approaches offer the advantage of streamlining the calculation process, eliminating the need for simulating wave surfaces through wave propagation. Moreover, for the first time, we study a prediction zone for wave excitation forces by comparing predicted forces with theoretical forces. Lastly, the force prediction is validated against experiments conducted on a captive platform model in both unidirectional and multidirectional sea states.

Local energy management: A base model for the optimization of virtual economic units

As non-renewable resources are limited and the overall CO2 emissions need to be reduced drastically, there is an increasing need in making the energy supply more sustainable. This leads to a needed change in the traditional energy system, in which decentralized local energy resources must be better integrated. In order to face the challenges for a future energy management, the consideration of the domestic level is gaining more attention. In this paper, we aim to get insights into the potential value of local cooperations and focus on economic and control issues as to whether and how the domestic level can participate in upcoming solutions. We introduce a possible setup for a virtual economic unit representing a hybrid cooperation of domestic energy participants, whereby the objective of this cooperation is to realize the maximum possible savings for the community with specified individual contracts between the participants and assuming that participants continue to have additional contracts with their energy service provider. We propose a deterministic linear optimization model for determining optimal energy load profiles of the participants with the external suppliers and energy exchange between participants in a virtual economic unit. To efficiently solve this model and get an exact assignment of supply and demand, we present a maximum saving flow algorithm taking into account the underlying bipartite structure of this problem. The solution achieved is specified as a peer-to-peer allocation between the participants involved and provides insights into the aspects that determine the concrete assignment. It also has the advantage of leading to a robust solution within the collaboration case studies for a basic set-up demonstrate the impact of this approach on the economic potential of aggregating local generation and demand at the same time.

Communication-Topology-preserving Motion Planning: Enabling Static Routing in UAV Networks

Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this article, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.

Too little, too late – a dynamical systems model for gun-related violence and intervention

In the United States the laws regulating the carrying of firearms in public vary state-to-state. In a highly publicized event, the Governor of New Mexico recently issued an emergency order temporarily banning the carrying of firearms in some areas – and thus rescinding the right-to-carry law in New Mexico – after a spate of gun violence, citing a statistical threshold of general societal violence under which right-to-carry laws should be superseded. In this paper we frame this policy intervention as a dynamical systems model that measures the incidence of gun violence as a function of gun prevalence. We show that the Governor's emergency order – fittingly issued as an emergency health order – is effectively like trying to stop an epidemic after it has become viral in that with this kind of instantaneous stopping condition, under simple assumptions, such a regulation is too little, too late. On the other hand, a graduated response that scales with increasing violence can drive equilibrium gun prevalence to zero. This is a new mathematical model relating gun violence with gun prevalence which we hope continues to spur the modeling community to bring its tools and techniques to bear on this important and challenging social problem. More importantly, our model, despite its simplicity, exhibits complex dynamics and has substantial research and educational value in promoting the application of mathematics in the social sciences, extending beyond gun control issues.

FDM 3D printer temperature control system based on PID control

With the continuous development of manufacturing technology today, the emerging technology of 3D printing is becoming more mature, and the surface accuracy and quality of printed products are receiving increasing attention from users. The stability and appropriateness of the nozzle temperature have a significant impact on the quality of the prints. This project focuses on the fused deposition modeling (FDM) 3D printer as the research subject, introduces the structure types of FDM 3D printers, and addresses the temperature control issue of FDM printers by using PID control for improvement. By applying SIMULINK to model and simulate the nozzle temperature control system, the simulation results of PID control are analyzed and compared. The results show that PID control shortens the stabilization time, improving the stability and control accuracy of the control system. PID control can enhance the temperature control accuracy of FDM printers, thus improving the precision and surface quality of the printed products.

Adaptive Fuzzy Fault-Tolerant Control of Uncertain Fractional-Order Nonlinear Systems with Sensor and Actuator Faults

In this work, an adaptive fuzzy backstepping fault-tolerant control (FTC) issue is tackled for uncertain fractional-order (FO) nonlinear systems with sensor and actuator faults. A fuzzy logic system is exploited to manage unknown nonlinearity. In addition, a novel FO nonlinear filter-based dynamic surface control (DSC) method is constructed, effectively avoiding the inherent complexity explosion problem in the backstepping recursive process, and in the light of the construction of auxiliary functions, compensating the coupling term introduced by faults. On account of certain assumptions, the stability criterion of the FO Lyapunov function is applied to guarantee the stability of the closed-loop system. Finally, the simulation example verifies the validity of the presented control strategy.