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

Linhang Ju, Hanze Jia, Yanggang Feng et al.

Inter‐day motion intent recognition using wearable sensors, due to the change in position during multiple donning and doffing, i.e., surface electromyography, remains a challenge. Herein, an optimal optical sensing sleeve using a multilayer perceptron is introduced to achieve an accurate inter‐day motion intent recognition. This sleeve, demonstrating a high correlation (R2 = 0.93) with grasping force, incorporates six novel optical waveguides. Each waveguide is specifically designed to respond to pressing with high linearity, achieved by minimizing bending with a 3D‐printed base and limiting elongation through carbon fiber reinforcement. This novel configuration enhances the generalization of the optical waveguides across multiple donning and doffing sessions. Furthermore, the multilayer perceptron model, which maps sensing signals to grasping forces, shows optimal performance compared to linear, quadratic, cubic, and quartic polynomial models. Remarkably, the correlation in mapping does not decrease in inter‐day experiments; instead, it increases by 4.54%, indicating improved model generalization. Additionally, 12 commonly used items are grasped and held by a prosthetic hand, controlled by the optical sensing sleeve, which suggests the robustness in daily life, for an amputee. The optimal optical sensing sleeve holds promise for contributing to advancements in other wearable robots and achieving an inter‐day model generalization.

Yacun Guan, Bin Jiang, Youmin Zhang et al.

This paper investigates the issue of fault-tolerant control for swarm systems subject to switched graphs, actuator faults and obstacles. A geometric-based partial differential equation (PDE) framework is proposed to unify collision-free trajectory generation and fault-tolerant control. To deal with the fault-induced force imbalances, the Riemannian metric is proposed to coordinate nominal controllers and the global one. Then, Riemannian-based trajectory length optimization is solved by gradient’s dynamic model–heat flow PDE, under which a feasible trajectory satisfying motion constraints is achieved to guide the faulty system. Such virtual control force emerges autonomously through this metric adjustments. Further, the tracking error is rigorously proven to be exponential boundedness. Simulation results confirm the validity of these theoretical findings.

Walid Alqaisi, Mostafa Soliman, Ahmed Badawi et al.

Abstract This paper presents a quadrotor detection and tracking system that uses a single camera and the Speeded Up Robust Features (SURF) algorithm to extract the image structure and estimate the target's position. The goal is to develop an independent system capable of detecting a followed object’s motion without prior knowledge of its trajectory, without requiring communication with the target, and without relying on external sensors. Feedback Linearization (FL) combined with sliding mode control is used to ensure target tracking. The control system avoids the complex nonlinear control solutions and the highly coupled dynamic behavior of the quadrotor. Nonlinear uncertain disturbances are overcome by using Time Delay Estimation (TDE) of the disturbance.

Wei Liu, Ailun Yu, Daoguang Zan et al.

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs’ general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.CCS Concepts• Software and its engineering → Search-based software engineering; • Information systems → Language models; Query representation; • Mathematics of computing → Graph algorithms.

Tuan Dung Lai, Anj Simmons, Scott Barnett et al.

Mudit Verma, Sandeep Hans, Diptikalyan Saha et al.

With the proliferation of cloud-native systems supported by container technology and the widespread deployment of 5G and Edge use-cases, modern applications have become increasingly distributed and complex, often consisting of hundreds of components. Ensuring the reliability of these workloads has grown increasingly intricate as a consequence, only further complicated by the continuous evolution of systems supported by CI/CD practices. In this context, Chaos Engineering can play a crucial role in assessing the reliability of these large-scale systems by intentionally introducing adverse conditions and gauging their resilience in inter-connected environments. This controlled approach enables organizations to identify and learn from potential failure points before they escalate into full-blown service degradation and production outages. Yet, the effectiveness of chaos testing hinges on the relevance of the targeted fault scenarios and often relies on arbitrary or intuitive fault injection practices, leading to inefficiencies and suboptimal outcomes. Addressing these challenges, we have developed a chaos-recommendation tool. This tool assesses the real-time behavior and characteristics of workloads and suggests fault injections that can cause disruptions. In this demo, we will illustrate how the Chaos recommendation tool can be used to automatically identify potential failure points for a system and suggest corresponding chaos test cases. This tool, part of Redhat's Chaos Engineering project Kraken, is open-source and available at: https://github.com/redhat-chaos/krkn/blob/main/utils/chaos_recommender/README.md

Celal Onur Gökçe

In this study a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, design of optimal neural-network based controller is done automatically. Several common reference types are studied including step, square, sine, sawtooth and trapezoid functions. Expected reference-disturbance pairs are used to train the system for finding optimal neural-network controller parameters. A separate test set is used to test the system for unexpected reference-disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. Real DC motor’s parameters are estimated using particle swarm optimization (PSO) algorithm. Initially, a proportional-integral (PI) controller is designed using PSO algorithm for finding simple controller’s parameters optimally and automatically. Starting with neural-network equivalent of optimal PI controller, optimal neural-network controller is designed using PSO algorithm for training again. Simulations are conducted with estimated parameters for diverse set of training and test patterns. Results are compared with optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained suggesting further research in the proposed direction.

LYU Maoyin, NIU Xuexin, CHEN Qishen et al.

In view of bottlenecks encountered in the process of turnout laying and replacement on high-speed railways such as substantial construction difficulties, low efficiency, intensive labor workload, and high risk, this paper proposes a control method of intelligent turnout laying and replacement equipment with combined gantry cranes for high-speed railways. It is proposed to utilize a control strategy of asynchronous transverse movement and synchronous tilting of the combined gantry cranes to handle the rotation and longitudinal motion of large components respectively. A theoretical mathematical modeling analysis is performed for the turnout laying and replacement equipment with combined gantry cranes focusing on the rotation and longitudinal movement operations. Moreover, considering the flexible nature of chains in the real construction process of combined gantry cranes, a self-learning algorithm is incorporated to facilitate flexible and highly precise multi-crane cooperative micro-motion control. The experimental results from field operations showcases adopting the control method proposed in this article, stable and reliable rotation and longitudinal movement by the combined gantry cranes, with an operational error of less than 10 mm, achieving full compliance with the requirements of engineering applications demonstrates.

Alonso Ingar Romero, Qianru Jin, Kevin Kit Parker et al.

Studying the behavior of electroactive cells, such as firing dynamics and chemical secretion, is crucial for developing human disease models and therapeutics. Following the recent advances in cell culture technology, traditional monolayers are optimized to resemble more 3D, organ‐like structures. The biological and electrochemical complexity of these structures requires devices with adaptive shapes and novel features, such as precise electrophysiological mapping and stimulation in the case of brain‐ and heart‐derived tissues. However, conventional organ‐on‐chip platforms often fall short, as they do not recreate the native environment of the cells and lack the functional interfaces necessary for long‐term monitoring. Origami‐on‐a‐chip platforms offer a solution for this problem, as they can flexibly adapt to the structure of the desired biological sample and can be integrated with functional components enabled by chosen materials. In this review, the evolution of origami‐on‐a‐chip biointerfaces is discussed, emphasizing folding stimuli, materials, and critical findings. In the prospects, microfluidic integration, functional tissue engineering scaffolds, and multi‐organoid networks are included, allowing patient‐specific diagnoses and therapies through computational and in vitro disease modeling.

Abderaouf M. Moustari, Youcef Brik, Bilal Attallah et al.

The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it's crucial to enhance the visibility of important regions and focus the recognition system on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to enhance image quality. We propose a novel two-branch attention-guided convolutional neural network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially establishes overall attention to the entire image with the global branch and then incorporates a local branch to compensate for any lost discriminative cues. We conduct extensive experiments using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in state-of-the-art performance within the field.

Luca Castri, Sariah Mghames, Marc Hanheide et al.

The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.

Shumaila, Rabia Jahangir, Faiza Saba et al.

The characteristics of (non)linear dust-ion acoustic waves (DIAWs) in a collisionless, magnetized dusty plasma are investigated. The current model is composed of ( r , q )-distributed electrons along with warm ions and stationary dust grains with negative charge. Both linear and nonlinear waves are considered to progress in x - z plane. The properties of linear waves are studied by deriving the dispersion relation for the plasma parameters of Saturn’s magnetosphere. The fluid equations of the current model are reduced to the universal Korteweg-de Vries (KdV) equation in order to study the characteristics of oblique propagation of DIA solitary waves (DIASWs). The critical point at which the nature/polarity of solitons changes is determined precisely. The influence of various plasma parameters, namely, obliqueness, magnetic field, densities, temperatures, and double spectral indices of the ( r , q )-distributed electrons on DIASWs is investigated for Saturn’s magnetosphere. The DIASWs of ( r , q )-distributed electrons are also compared with Maxwellian electrons. This work would be helpful to study other astrophysical and laboratory plasma systems where dusty plasmas and ( r , q ) distribution are predicted.

YUAN Chao, PAN Wenbo, CHEN Zhiwei et al.

At present, the use of mobile LiDAR systems (MLS) to collect environmental information and generate roadside pole-shaped object inventories is limited by equipment costs and has poor real-time performance. Although LiDAR-based simultaneous localization and mapping (SLAM) techniques have been widely applied in the navigation field, research on simultaneous real-time localization and creation of roadside pole-shaped object inventories remains absent. In this regard, this paper proposes an approach that utilizes LiDAR technology to achieve vehicle localization and create roadside pole-shaped object inventories complete with absolute locations. The aim is to devise an accurate and robust system for vehicle localization and the creation of roadside pole-shaped object inventories. Firstly, LiDAR was integrated with inertial measurement units (IMU) and global navigation satellite system (GNSS), to achieve accurate pose estimations and simultaneous generation of global maps. Secondly, an optimized fusion positioning algorithm based on sliding windows is constructed, to enhance system robustness through effectively integrating data from multiple sensors. Then, a method for creating pole-shaped object inventories is proposed using an SLAM feature extraction algorithm, thereby reducing the computational expense for simultaneous vehicle positioning and roadside pole-shaped object inventory creation. Finally, extensive evaluations are conducted using real datasets covering various road scenarios, including urban and suburban areas. The experimental results demonstrated the centimeter-level vehicle positioning accuracy of the proposed system in real-time automatic creation of roadside pole-shaped object inventories, boasting an average positioning error within 3 cm.

Wei Zhang, Zhong Zheng, Hongpeng Liu

In general, the output power of PV inverter should be coordinated with the load. In the initial stage of the design, when the PV inverter operating in islanded mode, the load power is supposed to be no more than the maximum PV output power with the purpose to stabilize the system. However, it may waste PV cells' energy. Consequently, more and more solar panels are linked to other energy sources in order to create a parallel inverter system, which makes it feasible to fit the power output of each source. The power distribution of parallel inverter is achieved by the use of droop control in microgrid system. For such a parallel inverter system consisted of PV inverters and non-regeneration energy sources inverters without energy storage devices in islanded mode, if the shared load power is no more than the available maximum PV inverter output power, there is a power waste for PV inverter; Moreover, due to the intermittency of PV sources, the system may become unstable if the shared load power is more than the available maximum output power of PV inverter. However, the maximum utilization of the PV output energy cannot be achieved automatically with the traditional methods to add virtual impedance or adjust the droop coefficients. Thus, in order to avoid power waste and potential instability caused by insufficient PV power by traditional droop control, this paper recommends an improved droop control scheme to maximize the power output of PV units. Required by the load, the remaining power is composed of the other inverters, which could effectively improve the utilization rating of renewable energy sources and system stability. At the same time, according to the system stability analysis based on the small signal modeling, it has been designed about the droop coefficients of the improved droop control loop. In the end, the experimental results have showed that the suggested scheme has a varied validity and robustness.

M. Lenzi, L. F. Fadel Miguel, R. Lopez et al.

Abdelhamid Bounemeur, M. Chemachema, Salah Bouzina

This paper deals with the problem of fault-tolerant control for a class of perturbed nonlinear systems with nonlinear non-affine actuator faults. Fuzzy systems are integrated into the design of the control law to get rid of the system nonlinearities and the considered actuator faults. Two adaptive controllers are proposed in order to reach the control objective and ensure stability. The first term is an adaptive controller involved to mollify the system uncertainties and the considered actuator faults. Therefore, the second term is known as a robust controller introduced for the purpose of dealing with approximation errors and exogenous disturbances. In general, the designed controller allows to deal automatically with the exogenous disturbances and actuator faults with the help of an online adaption protocol. A Butterworth low-pass filter is utilized to avoid the algebraic loop issue and allows a reliable approximation of the ideal control law. A stability study is performed based on Lyapunov's theory. Two inverted pendulum example is carried out to prove the accuracy of the controller.

Xuegui Li, Shuo Feng, Nan Hou et al.

Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.

Jinxin Liu, Maojun Xu, Xingwu Zhang et al.

Multiline spectral vibration (or noise) is very common in rotating machinery like motor, gearbox, compressor, propeller, etc. Active reshaping of these vibrations is one of the most important branches for active vibration control, which may find application in fields of sound quality control, psychoacoustics, military camouflage, etc. The traditional filtered-reference LMS is the most popular structure for multiline spectral reshaping control. However, with the increase of spectral lines, vibration sources and observation points (feedback channels), the traditional structure will have extremely heavy computational complexity, especially for the system whose dynamic of secondary path is complex, since it requires two long filters for every single spectral line and secondary path to adjust the reference signals. In this study, we propose a fast-multiline spectrum reshaping algorithm for active vibration control. We first construct an equivalent system of the traditional multiline spectrum reshaping algorithm by introducing two extra control branches, which is able to improve the convergence property of the system. Then, we generate a group of pre-phase-scheduled reference signals using the phase information at certain frequencies of the secondary paths instead of using filters. Afterwards, we conduct a theoretical analysis of computational complexity of multiline spectrum reshaping and fast multiline spectrum reshaping algorithms. Finally, we carried out two case studies based on the FEMilS test-bed to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm performs an accurate residual vibration control with uniformed convergence speed and reduced computational complexity.

Chihun Lee, Juwon Na, Kyongho Park et al.

This study combines an artificial neural network (ANN) and a random search to develop a system to recommend process conditions for injection molding. Both simulation and experimental results are collected using a mixed sampling method that combines Taguchi and random sampling. The dataset consists of 3600 simulations and 476 experiments from 36 different molds. Each datum has five process and 15 geometry features as input and one weight feature as output. Hyper‐parameter tuning is conducted to find the optimal ANN model. Then, transfer learning is introduced, which allows the use of simultaneous experimental and simulation data to reduce the error. The final prediction model has a root mean‐square error of 0.846. To develop a recommender system, random search is conducted using the trained ANN forward model. As a result, the weight‐prediction model based on simulated data has a relative error (RE) of 0.73%, and the weight prediction using the transfer model has an RE of 0.662%. A user interface system is also developed, which can be used directly with the injection‐molding machine. This method enables the setting of process conditions that yield parts having weights close to the target, by considering only the geometry and target weight.

Xin Lou

Recent attacks against cyber-physical systems (CPSes) show that traditional reliance on isolation for security is insufficient. This paper develops efficient assessment and mitigation of an attack's impact as a system's built-in mechanisms. We focus on a general class of attacks, which we call time delay attack, that delays the transmissions of control data packets in a linear CPS control system. Our attack impact assessment, which is based on a joint stability-safety criterion, consists of (i) a machine learning (ML) based safety classification, and (ii) a tandem stability-safety classification that exploits a basic relationship between stability and safety, namely that an unstable system must be unsafe whereas a stable system may not be safe. The ML addresses a state explosion problem in the safety classification, whereas the tandem structure reduces false negatives in detecting unsafety arising from imperfect ML. We apply our approach to assess the impact of the attack on power grid automatic generation control, and accordingly develop a two-tiered mitigation that tunes the control gain automatically to restore safety where necessary and shed load only if the tuning is insufficient. Extensive simulations based on a 37-bus system model are conducted to evaluate the effectiveness of our assessment and mitigation approaches.