Hasil untuk "Control engineering systems. Automatic machinery (General)"

Yan Liu, Yaxin Liu, Ruiqing Han et al.

Grasp generation methods based on force‐closure analysis can calculate the optimal grasps for objects through their appearances. However, the limited visual perception ability makes robots difficult to directly detect the complete appearance of objects. Building predefined models is also a costly procedure. These reasons constrain the application of force‐closure analysis in the real world. To solve it, this article proposes an interactive robotic grasping method based on promptable segment anything model and force‐closure analysis. A human operator can mark a prompt on any object using a laser pointer. Then, the robot extracts the edge of the marked object and calculates the optimal grasp through the edge. To validate feasibility and generalizability, the grasping generation method is tested on the Cornell and Jacquard datasets and a novel benchmark test set of 36 diverse objects is constructed to conduct real‐world experiments. Furthermore, the contributions of each step are demonstrated through ablation experiments and the proposed method is tested in the occlusion scenarios. Project code and data are available at https://github.com/TonyYounger‐Eg/Anything_Grasping.

María del Mar Castilla Nieto, Ángeles Hoyo Sánchez

En esta nueva edición de Mujeres en Automática se presenta a la matemática e ingeniera de control australiana Ruth Curtain. Su investigación supuso un avance significativo en la teoría de control al ampliar la teoría desarrollada para sistemas de dimensiónfinita a sistemas de dimensión infinita. Asimismo, fue la coordinadora de la beca Rosalind Franklin, una exitosa iniciativa para aumentar el número de mujeres en puestos científicos permanentes en la Universidad de Groninga.

Xue-Chen Dong, Qiu-Yu Ou, Yi-Han Wu et al.

This paper introduces a generalized integral transform that encompasses the Laplace, Fourier, and numerous contemporary integral transforms as particular instances, while preserving their defining characteristics. Secondly, we propose the application of the generalized integral transform in the variational iteration method for the straightforward identification of the Lagrange multiplier, thus enabling the resolution of nonlinear oscillator problems. Finally, we present a series of illustrative examples to demonstrate the efficacy of this approach.

Shubham Joshi, B. Partibane, W. Hatamleh et al.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

Qianqian Wang, Nan Xiang, Ji Lang et al.

Soft miniature machines demonstrate multimodal actuation and morphology change capabilities in narrow spaces smaller than their dimension. The wirelessly controlled soft‐bodied features make them promising candidates for microrobotic manipulation and targeted operation in a noninvasive manner. Liquid‐bodied machine offers an ultrasoft body with extreme deformability owing to its fluid nature, enabling adaptive navigation with smooth contact with objects and environmental restrictions. Over the last decade of development, significant research progress has been achieved in wirelessly controlling liquid‐bodied machines for diverse manipulation applications. Herein, an overview of the recent research results in magnetic control methods and diverse microrobotic applications of liquid‐bodied machines is provided. Considering the control mechanisms and application challenges, ferrofluid‐based, liquid metal‐based, and liquid marble‐based machines are mainly discussed with a brief discussion on droplet‐based machines. The connection between control methods and applications is highlighted with a detailed analysis of machine–object and machine–environment interactions. The current challenges and research opportunities on liquid‐bodied miniature machines are outlined, aiming at designing intelligent liquid‐bodied machine‐based microrobotic systems and promoting the development of small‐scale robotics.

Liang Yang, Xiaoli Jia, Xiangning Liao et al.

Exhaust sound quality is an important part of vehicle performance. In this paper, the sporty exhaust sound quality of an economical vehicle equipped with a 4-cylinder and 4-stroke engine is evaluated, analyzed, and improved under acceleration. Firstly, a sporty feeling evaluation method with engine speed divided is proposed, and the influence of exhaust sound order components on sporty exhaust sound is analyzed. The results show that while the A-weighted sound pressure level (ASPL) of Order 2 is lower and the ASPLs of Orders 4 and 6 are higher, the exhaust sound is sportier. Then, a hybrid predicted model of vehicle sporty exhaust sound under acceleration is established based on convolutional neural network (CNN) and support vector regression (SVR) algorithm. The relative errors between the predicted results of CNN-SVR hybrid model and the subjective evaluation results are limited within 2%, which indicates that the CNN-SVR hybrid prediction model achieves a high accuracy in assessing the sporty feeling of exhaust sound. Finally, considering the frequency ranges corresponding with the above order components under the practical accelerating condition, a strategy is proposed to enhance the sporty feeling of exhaust sound by reducing the sound energy within 100 Hz and increasing the sound energy within 100–450 Hz. Based on this strategy, a muffler with different structure is selected and installed on the economical vehicle, and the sporty feeling of exhaust sound is 0.63 points higher than before.

İbrahim Mucuk, Ayhan Özdemir

Abstract This paper presents the design of a complex‐step extended Kalman filter (CS‐EKF) to estimate the states of the twin‐rotor MIMO system (TRMS) which is a non‐linear system. Since the model of TRMS is quite complex and contains discontinuous functions, it is very difficult to calculate the Jacobian matrix in the TRMS analytically by hand. This makes it difficult to implement control methods that require Jacobian matrix calculation for TRMS. Herein, to calculate the Jacobian matrix, the CS‐EKF uses the complex‐step derivative approach, which is a numerical technique and offers near‐analytical accuracy in a single function evaluation. The effectiveness of the CS‐EKF is demonstrated through simulation and real‐time experiments. Also, The CS‐EKF is compared to the finite‐difference extended Kalman filter (FD‐EKF) and the unscented Kalman filter (UKF) in terms of estimation accuracy, computational load, and ease of implementation.

Shakib Sadat Shanto, Mushfiqur Rahman, Jaber Md. Oasik et al.

Aims: A smart greenhouse system that utilizes the Blynk IoT app is an innovative technology designed to automate greenhouse operations, thereby reducing labor and resource costs and optimizing plant growth. Study Design: In this paper we propose a real-time mobile app-based monitoring system to automate greenhouse agriculture. The system involves the usage of Blynk mobile application that allows users to monitor, control both manually and remotely as well as automation of various aspects of the greenhouse environment in real-time. Place and Duration of Study: Department of Computer Science and Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh between February 2023 to April 2023. Methodology: Multiple environmental sensors, temperature, humidity, soil moisture, light intensity, and Light Dependent Resistor (LDR) sensors continuously collect data. Farmers can remotely monitor and control the greenhouse using the Blynk IoT application. A fire sensor alarms it. Farmers can tailor alerts for environmental changes like irrigation or heating. They can manually and remotely control fans, lights, and humidifiers. If temperature, light intensity, or humidity drop below a specified threshold, the system turns on fans, lights, and humidifiers. A water pump that kicks on automatically based on soil moisture and can be regulated remotely ensures the plants get enough water. Results: The proposed system allows for real-time updates of sensor data and enables remote control of connected devices via the mobile app from any location, with notable fast processing subject to the quality of the internet connection. Conclusion: Upon successful implementation of this research, we anticipate the emergence of an advanced automated smart greenhouse monitoring system that will greatly benefit farmers in Bangladesh. This new method could change greenhouse and farm management, improving efficiency, cost, and crop yields.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Per Sebastian Skardal, Alex Arenas

Robust coordination and organization in large ensembles of nonlinear oscillatory units play a vital role in a wide range of natural and engineered system. The control of self-organizing network-coupled systems has recently seen significant attention, but largely in the context of modifying or augmenting existing structures. This leaves a gap in our understanding of reactive control, where and how to design direct interventions, and what we may learn about structure and dynamics from such control strategies. Here we study reactive control of coupled oscillator networks and demonstrate dual control strategies, i.e., two different mechanisms for control, that may each be implemented on their own and interchangeably to achieve synchronization. These diverse strategies exploit different network properties, with the first directly targeting oscillators that are challenging to entrain, and the second focusing on oscillators with a strong influence on others. Thus, in addition to presenting alternative strategies for network control, the distinct control sets illuminate the oscillators' dynamical and structural roles within the system. The applicability of dual control is demonstrated using both synthetic and real networks.

Oleg Makarov, Hayato Yase, Takashi Harada

Abstract The increased demand for performing crane ship modeling has led to the necessity for fast and accurate numerical experiments. This paper presents an approach for creating a numerical model of a crane vessel with a suspended load that allows for real-time control of crane parts. The model is developed in the MATLAB/Simulink environment, which makes it possible to extend it further to the user's needs. The authors describe the approach to the calculation of wave-induced ship motions, presents the Simulink block model and describes the features encountered during the simulation process. The possibility of real-time control of the position of crane parts is also shown, keeping the calculation speed of the ship hydrodynamics.

Fernando Soto, Annie Tsui, Sushruta Surappa et al.

Smart materials respond to environmental signals by changing their microstructure and physical properties. Programming multiple behaviors and functions into a single material could increase its utility and adaptability to ever‐changing environmental conditions. A swellable and stretchable metamaterial hydrogel or “metasponge” engineered to morph into customized sizes and shapes that dynamically tune its physical properties and functions is reported. Multiple case studies that take advantage of the morphing properties of the metasponge, including robotic actuation, light guidance, optical and sonic invisibility (“cloaking”), adaptation of propulsion mode, sampling, and multiple biomedical applications, are illustrated. Developing multifunctional smart materials in which logic is programmed into the materials rather than electronic components could pave a new path to autonomy and dynamic responses in soft robots, sensors, and actuators.

Peiyao Wang, Mingxin Yu, Guang Yan et al.

The purpose of this paper is to propose a novel aircraft wing loads calculation model, called long short-term memory residual network (LSTM-ResNet), which can evaluate the loads based on the strain distribution. To achieve this goal, firstly, the data acquisition experiment is designed and performed with a real aircraft wing. In this experiment, we used the Fiber Bragg Grating (FBG) technology as the measurement method to collect strain-load data from the aircraft wing. Then, we propose the LSTM-ResNet model with the one-dimensional convolutional(1D-CNN) architecture. This model is capable of extracting the temporal and spatial representational information from the strain-load data of the aircraft wing. Experimental results demonstrate that the proposed method effectively evaluate the loads of the aircraft wing. To prove the superiority of LSTM-ResNet model, we compared the proposed model with existing loads calculation methods on our experimental dataset. The results show it has a competitive average relative error (0.08%). Moreover, those promising results may pave the way to use the deep learning algorithm in aircraft wing loads calculation.

Song Chun-sheng, Xiong Xue-chun, Yang Qi et al.

The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.

Teng Ji, Ji-wei Liu, Qingfu Li

With the increase in construction scale and difficulty of large and complex bridges in China, it has become increasingly difficult to assess the safety risks of bridges during the construction period. Therefore, how to reasonably assess the safety risk of large, complex bridges during construction has become particularly important. Existing assessment methods are subjective in assigning weights, and it is difficult to select representative important factors to focus on for the prevention and control of numerous risk sources; they do not comprehensively consider the correlation of various risk sources during the construction period. To address the above shortcomings, a safety risk assessment of large and complex bridges during the construction period based on the Delphi-improved fuzzy analytic hierarchy process (FAHP) factor analysis method is proposed in this paper. First, the Delphi method was used to conduct a general survey of safety risk factors during the bridge construction period, and then the work breakdown structure-risk substructure (WBS-RBS) was used to establish the evaluation index system. Second, the improved FAHP was combined with it to calculate the weight of each risk factor. Finally, the factor analysis method was used to determine the correlation degree of each risk factor, and representative factors were selected to express the risk degree of the object to be evaluated to screen out major risk factors in the construction process. Finally, the feasibility and practicality of the method are verified by combining an actual engineering case with AHP (analytic hierarchy process) to perform a comparative study, which provides a reference basis for subsequent bridge construction risk prevention.

Sven Brüggemann, Robert H. Moroto, R. Bitmead

Pipe flow models are developed with a focus on their eventual use for feedback control design at the process control level, as opposed to the unit level, in gas processing facilities. Accordingly, linearized facility-scale models are generated to describe pressures, mass flows, and temperatures based on sets of nonlinear partial differential equations from fluid dynamics and thermodynamics together with constraints associated with their interconnection. As part of the treatment, the divergence of these simplified models from physics is assessed since robustness to these errors will be an objective for the eventual control system. The approach commences with a thorough analysis of pipe flow models and then proceeds to study their automated interconnection into network models, which subsume the algebraic constraints of bond graph or standard fluid modeling. The models are validated and their errors are quantified by referring them to operational data from a commercial gas compressor test facility. For linear time-invariant models, the interconnection method to generate network models is shown to coincide with automation of Mason’s gain formula. These pipe network models based on engineering data are the first part of the development of general facility process control tools.

Loli Burgueño, Jordi Cabot, Shuai Li et al.

Models capture relevant properties of systems. During the models’ life-cycle, they are subjected to manipulations with different goals such as managing software evolution, performing analysis, increasing developers’ productivity, and reducing human errors. Typically, these manipulation operations are implemented as model transformations. Examples of these transformations are (i) model-to-model transformations for model evolution, model refactoring, model merging, model migration, model refinement, etc., (ii) model-to-text transformations for code generation and (iii) text-to-model ones for reverse engineering. These operations are usually manually implemented, using general-purpose languages such as Java, or domain-specific languages (DSLs) such as ATL or Acceleo. Even when using such DSLs, transformations are still time-consuming and error-prone. We propose using the advances in artificial intelligence techniques to learn these manipulation operations on models and automate the process, freeing the developer from building specific pieces of code. In particular, our proposal is a generic neural network architecture suitable for heterogeneous model transformations. Our architecture comprises an encoder–decoder long short-term memory with an attention mechanism. It is fed with pairs of input–output examples and, once trained, given an input, automatically produces the expected output. We present the architecture and illustrate the feasibility and potential of our approach through its application in two main operations on models: model-to-model transformations and code generation. The results confirm that neural networks are able to faithfully learn how to perform these tasks as long as enough data are provided and no contradictory examples are given.

Zihao Wang, Aojie Zhang, Xinyue Xia et al.

Among diverse intelligent assistive systems developed for amyotrophic lateral sclerosis (ALS) patients, headwear eye tracking based ones trigger broad interests due to their merits such as noninvasive, cost effective, and high operation freedom. However, with headwear eye trackers, patients are easy to feel tired during human–machine interactivities (HMIs), and the operation accuracy is not satisfied compared with its counterparts. To address these two issues, herein, a visual feedback technique is developed which allows users to recognize machine's vision by positioning a laser spot to the user watched object, according to the location information interpreted from user's eye movement. Through the visual feedback technique, users not only obtain real‐time feedback, but also can fine‐tune the laser spot to the desired location before performing further operations. Experimental results demonstrate that the presented work can successfully reduce user's fatigue and boost operation accuracy by 25.1% and 27.6%, respectively, therefore, advancing the field of intelligent assistive technologies.

Javier Carrón, Y. Campos-Roca, Mario Madruga et al.

Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.