This study has developed adaptive synergetic control (ASC) algorithm to control the angular position of moving plate in the electronic throttle valve (ETV) system. This control approach is inspired by synergetic control theory. The adaptive controller has addressed the problem of variation in systems parameters. The control design includes two elements: the control law and adaptive law. The adaptive law is developed based on Lyupunov stability analysis of the controlled system, and it is responsible for estimating the potential uncertainties in the system. The effectiveness of the proposed adaptive synergetic control has been verified by numerical simulation using MATLAB/Simulink. The results showed that the ASC algorithm could give good tracking performance in the presence of uncertainty perturbations. In addition, a comparison study has been made to compare the tracking performance of ASC and that based on conventional synergetic control (CSC) for the ETV system. The simulated results showed that the performance of ASC outperforms that based on CSC. Moreover, the results showed that the estimation errors between the actual and estimated uncertainties are bounded and there is no drift in the developed adaptive law of ASC.
The Cyber-Physical and Vehicle Manufacturing Laboratory, a model Industry 4.0 laboratory, is applying new innovative solutions to improve the quality of education. As part of this, a digital twin of the lab was designed and built, where users can practice. In the virtual space, it is possible to apply the known robot motion types, and the tool centre and wrist speed have been measured virtually. Robot control tasks can be performed “offline” using parameters. This information can then be transferred to the actual physical robot unit. The stable diffusion 1.5 deep learning model generates 2D geometric shapes for trajectory, allowing users to perform unique tasks during education. The Google Colab cloud-based service was used to teach our rendered-type dataset. For the 3D simulation frame, we used V-REP, which was developed on a desktop PC equipped with an Intel Core i5 7600K processor, Nvidia GTX1070 VGA with 8 GB of DDR5 VRAM, and 64 GB of DDR4 memory modules. The following material describes an existing industrial six-axis robot arm and its implementation, which can be controlled and programmed while performing virtual measurements after integrating into a Cyber-Physical system and using deep learning techniques.
With the escalating density of vehicles converging at road intersections, the surge in road accidents, traffic conflicts, and traffic congestion has emerged as a pressing concern. This research paper addresses these challenges by employing MC (manual control) techniques to mitigate encroachment issues at three selected intersections. These intersections were identified through a comprehensive analysis of the Ranking-based Instance Selection (RIS), enabling the design of suitable measures to facilitate smooth traffic flow and minimize the occurrence of crashes. In order to gather pertinent data, the study incorporates various parameters such as traffic volume, peak flow rate (PFR), traffic conflicts, accidents, and intersection inventory. Through the implementation of our proposed approaches, which involve both MC techniques and signalized operation, a supreme level of service (LOS) is attainable. Notably, our findings demonstrate a remarkable reduction in the volume-to-capacity ratio (v/c ratio) of up to 0.62. This paper thus serves as a significant contribution to the field of traffic management, offering practical insights for optimizing intersection design and effectively addressing the challenges posed by increased traffic density.
Worldwide, precast and hybrid construction methods are becoming increasingly popular in the construction industry. But many problems occur during the fabrication, such as segregation, bleeding, scaling, plastic shrinkage, dust formation, honeycombing, sintering, high sorptivity, and high permeability and transportation. This problem may be caused by an ineffective curing process that affects the quality of concrete and construction. In addition, it provides inadequate and incomplete cement hydration that has a 20% negative effect on the desired properties of the concrete. Various researchers have demonstrated the components of self-curing lightweight concrete that can enhance strength and physicochemical properties, and address the above-mentioned issues. In this review, the role of the self-curing mechanism in lightweight concrete based on the various self-curing chemical admixtures such as polyethylene glycol (PEG), superabsorbent polymer (SAP), polyvinyl alcohol (PVA), sodium lignosulfonate and calcium lignosulfonate as self-curing agents are discussed in detail. Also, this paper briefly reports on the scope, significance, mechanisms, and tests for self-curing lightweight concrete. Overall, this review analyzes the possibilities of future research perspectives on self-curing lightweight concrete with sustainable materials and fibres with comparative technical information.
Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment.
This study examines the economic optimisation of existing district heating systems. A new approach has been taken to solving a long-standing problem. The authors describe the input-output model of the system, the balance equations for the thermal equilibrium of the system, and the heat transfer system. From the balance equations of the series-connected system elements, the resultant heat transfer balance equation and the resultant power transmission equation are derived. In an example, the authors detailed how perturbations in some input variables can be corrected with other variables. The equations presented and the concepts introduced form absolutely new scientific results.
Selecting the construction delivery method during the contracting period is one of the most important decisions determining the quality of large-scale infrastructure projects. Infrastructure projects have the most complex production processes in civil engineering. Infrastructure projects are among the most complex and resource-intensive endeavours in civil engineering due to their size, scope, multidisciplinary nature, regulatory requirements, financing challenges, environmental considerations, and the need for long-term planning and maintenance. Effective project management, collaboration, and a deep understanding of these challenges are crucial for the successful execution of infrastructure projects. Implementing such projects inevitably demands proper quality management throughout the project lifecycle. Two primary types of construction contracts are under implementation worldwide: Design-Bid-Build (DBB) and Design–and–Build (DB) contracts. In the Western Balkans region, both types of contracts are utilized for infrastructure projects, A noticeable trend is emerging toward transitioning from DBB to DB contracts. This paper provides a comprehensive analysis of quality management within the context of construction contracts with a focus on the roles and responsibilities of key stakeholders and how these factors affect the achievement of quality objectives while managing constraints related to cost and time. This research aims to improve construction practices by selecting an adequate type of contract for construction practices and ensuring successful project outcomes.
In this study, nonlinear control design is presented for trajectory tracking of Tricopter system. A Fractional Order Proportional Derivative (FOPD) controller has been developed. The performance of controlled Tri-copter system can be enhanced by suggesting modern optimization technique to optimally tune the design parameters of FOPD controller. The Spotted Hyena Optimizer (SHO) is proposed as an optimization method for optimal tuning of FOPD's parameters. To verify the performance of controlled Tricopter system based on optimal SHO-based FOPD controller, computer simulation is implemented via MATLAB codes. Moreover, a comparison study between SHO and Particle Swarm Optimization (PSO) has been made in terms of robustness and transient behavior characteristics of FOPD controller.
This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.