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

Ya-Fei Shi, Hui Yang, Dong Liu et al.

This paper presents a novel data-based adaptive control strategy based on feedback linearization to address the asymptotic tracking control problem of position and speed for underactuated high-speed train (HST). The proposed strategy accounts for basic resistances, in-train forces, multiple unknown disturbances, and unknown system parameters. The key contributions of this paper are threefold. First, the proposed strategy eliminates the reliance on exact model parameters by incorporating adaptive mechanism, which is a significant advancement over traditional feedback linearization method. Second, by combining Lyapunov stability theory with a novel output redefinition approach, the stability of the zero dynamics system for underdriven HST is rigorously demonstrated. Third, an improved equivalent control law is introduced, which not only suppresses unknown disturbances automatically but also mitigates severe chattering phenomenon. Simulation on a HST with 2 motor cars and 6 trailer cars is provided for verifying the theoretical results. Simulation results show that the proposed strategy achieves asymptotic tracking of the locomotive to the desired position and speed trajectories as well as ensures the uniformly ultimately bounded stability of the internal dynamics of all trailers. Note to Practitioners—The underactuated high-speed train (HST) system, as a practical engineering system, is characterized by multi-input multi-output, multivariable coupling, and nonlinearity. During the operation of an underactuated HST, the system is inevitably affected by basic resistances, in-train forces, multiple unknown disturbances, and time-varying system parameters. This paper aims to achieve high-precision tracking of the position and speed of underactuated HST under accounting for these factors. The proposed data-based control scheme enhances practicality by applying the feedback linearization combined with adaptive mechanism to deal with the nonlinearity of the underactuated HST. To address the challenges posed by the complex operating environments of HST, adaptive control is introduced. Additionally, an adaptive sliding mode control method is employed to enhance the robustness and fast convergence of the proposed control scheme, while effectively mitigating severe chattering. The simulation results show that the HST can achieve high-precision tracking of the desired position and speed curves in the presence of unknown system parameters and disturbances.

Xiaolong Ma, Xiyuan Chen, Linzhouting Chen

Xue Hou

Abstract With the rapid development of science and technology, various types of industrial robots occupy an important role in industrial production. Therefore, the performance testing of industrial robots is very important. In response to the low accuracy in performance testing of industrial robots operating under extreme working conditions using traditional testing devices, a performance testing device for industrial robots is designed using a dedicated servo motor model and programmable logic controller. To test the proposed testing device, comparative experiments are conducted. The results showed that when the temperature varied between − 40 ℃ and 80 ℃, the motor operating speed of the proposed device was around − 2,993 r/min, which was better than the comparative device. It was not significantly affected by temperature. Under over-speed conditions of 1.0 times, 1.2 times, 1.4 times, and 1.6 times, the motor temperatures of the proposed device after operating 20 min were 36 ℃, 40 ℃, 45 ℃, and 53 ℃, respectively, which were significantly lower than those of the comparative device. In summary, the proposed industrial robot performance testing device based on ECMA servo motor and PLC can maintain a stable state under extreme conditions, providing an appropriate idea for the performance testing of industrial robots and ensuring a guarantee for the design of industrial robots.

SUN Qiming, ZHANG Chao, QIU Yaqin

To enable nonlinear strict feedback systems to achieve stable control with efficient computation and guaranteed performance under complex real-world conditions such as unknown models and resource constraints, this paper proposes an adaptive control method based on a multi-dimensional Taylor network (MTN), enabling system outputs to track given signals. First, the strict feedback system was transformed and new state variables were defined to establish a standard form, introducing two parameters to be identified. Next, a state observer was designed and the two parameters were identified using the approximation characteristics of the multi-dimensional Taylor network. Based on these results, the controller design and system stability analysis were completed. Finally, the nonlinear system was numerically simulated. Simulation results showed tracking errors approaching zero, suggesting strong tracking performance and thereby verifying the effectiveness of the proposed method.

Yi Zheng, Xiao‐Ke Lu, Jia‐Ni Li et al.

Artificial compound eye technology has been a research hotspot due to its advantages, including a large field of view (FOV). However, the lack of adjustability limits its applications. Here, an adaptive compound eye (ACE) imaging device based on electrowetting liquid aperture with adjustable shape is proposed to achieve both large FOV and adaptive imaging. A method to adjust the aperture based on the electrowetting effect is proposed, which dispenses with any mechanical moving components, enabling fast adjustment, compact structure, and low power consumption. The liquid aperture can be flexibly adjusted to a roughly circular shape with a variable diameter between 0 and 5.07 mm or a horizontal or vertical elongated shape with a maximum aspect ratio of 9.5. Experimental results demonstrate the feasibility of achieving both large FOV and adaptive imaging, including light intensity adaptability and transmittable information frequency adaptability. Therefore, the proposed ACE imaging device can operate under different lighting conditions and can be used to distinguish between target and background images. Its distributed control capability also ensures that it can adapt to locally changing imaging scenes. The proposed ACE imaging device is expected to be applied in many fields such as machine vision, detection, and measurement.

Yongxiang Zhao, Wei Luo, Zhiguo Wang et al.

This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.

Dongge Lei, Lulu Cai, Fei Wu

Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBL-ICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.

Nan Liu

The internal simulation market system can stimulate employee initiative, reduce costs and improve information processing efficiency. However, the complexity of the internal simulation market poses a challenge to computing resources. Efficient data processing techniques are crucial for internal simulation market systems. In this paper, the internal simulation market model and the value chain theory are first put forward. Second, the internal simulation market construction within power grid enterprises is proposed. Then we propose a data mining-based collaborative fusion and processing method for multi-value chain quantitative data in power grid internal simulation markets to conduct data processing, including data fusion, abnormal data elimination and data dimensionality reduction, thereby improving the accuracy of information processing and data sharing. Finally, we validate the superior performance of the proposed method through simulations.

In-Su Kim, Bumjoo Kim, Seok-June Chae et al.

[001]‐oriented NaNbO3 films are deposited on Sr2Nb3O10/TiN/SiO2/Si substrates at 300 °C. The Sr2Nb3O10 nanosheets are used as a template to form crystalline NaNbO3 films at low temperature. The NaNbO3 films deposited on one Sr2Nb3O10 monolayer exhibit a bipolar switching curve due to the construction and destruction of oxygen vacancy filaments. Because the Sr2Nb3O10 monolayer does not act as an insulating layer, the film does not exhibit self‐rectifying properties. Self‐rectifying properties are observed in the NaNbO3 memristor, which forms on two Sr2Nb3O10 monolayers that act as tunnel barriers in the memristor. The memristor exhibits extensive rectification and on/off ratios of 48 and 15.7, respectively. Tunneling is the current conduction mechanism of the device in the low‐resistance state, and Schottky emission and tunneling are responsible for the conduction mechanism in the high‐resistance state at low and high voltages, respectively. The piezoelectric nanogenerator produced using the [001]‐oriented NaNbO3 film generates high voltage (1.8 V) and power (3.2 μW). Furthermore, endurance of the resistive random‐access memory and nonlinear transmission characteristics of the biological synapse are accomplished in the NaNbO3 memristor powered by the NaNbO3 nanogenerator. Therefore, the [001]‐oriented crystalline NaNbO3 film formed at 300 °C may be utilized for self‐rectifying and self‐powered artificial synapses.

Masahiko Hoshi, Yoshitaka Hara, Sousuke Nakamura

Abstract This paper describes a graph-based SLAM approach using wall detection and floor plan constraints without relying on loop closure. In SLAM, loop closure is widely used to address cumulative errors. Although loop closure helps maintain the map’s relative consistency, it does not ensure the accuracy of absolute positions. Therefore, we focus on floor plans that accurately depict the environmental geometry and propose a SLAM method that leverages this information. However, floor plans do not depict semi-static objects such as bookshelves and other fixtures. Thus, our study aims to build accurate maps based on floor plans and represent actual environments. The proposed method achieves this goal by integrating wall detection and floor plan constraints within the framework of graph-based SLAM. We evaluated the proposed method based on qualitative assessments of mapping results and quantitative evaluations of robot trajectories and processing time. Experiments were conducted using datasets obtained from both simulation and real-world environments. The results demonstrate that the proposed method can build a map with accurate absolute positions in a low processing time by leveraging wall detection and floor plan constraints.

Phu Pham, Loan T. T. Nguyen, Bay Vo et al.

Abstract Recently, due to the rapid growth of o nline s ocial n etworks (OSNs) such as Facebook, Twitter, Weibo, etc. the number of machine accounts/social bots that mimic human users has increased. Along with the development of a rtificial i ntelligence (AI), social bots are designed to become smarter and more sophisticated in their efforts at replicating the normal behaviors of human accounts. Constructing reliable and effective bot detection mechanisms is this considered crucial to keep OSNs clean and safe for users. Despite the rapid development of social bot detection platforms, recent state-of-the-art systems still encounter challenges which are related to the model’s generalization (and whether it can be adaptable for multiple types of OSNs) as well as the great efforts needed for feature engineering. In this paper, we propose a novel approach of applying network representation learning (NRL) to bot/spammer detection, called Bot2Vec. Our proposed Bot2Vec model is designed to automatically preserve both local neighborhood relations and the intra-community structure of user nodes while learning the representation of given OSNs, without using any extra features based on the user’s profile. By applying the intra-community random walk strategy, Bot2Vec promises to achieve better user node embedding outputs than recent state-of-the-art network embedding baselines for bot detection tasks. Extensive experiments on two different types of real-word social networks (Twitter and Tagged) demonstrate the effectiveness of our proposed model. The source code for implementing the Bot2Vec model is available at: https://github.com/phamtheanhphu/bot2vec

Te Wu, Feng Fu, Long Wang

Spatial interactions are considered an important factor influencing a variety of evolutionary processes that take place in structured populations. It still remains an open problem to fully understand evolutionary game dynamics on networks except for certain limiting scenarios such as weak selection. Here we study the evolutionary dynamics of spatial games under strong selection where strategy evolution of individuals becomes deterministic in a fashion of winners taking all. We show that the long term behavior of the evolutionary process eventually converges to a particular basin of attraction, which is either a periodic cycle or a single fixed state depending on specific initial conditions and model parameters. In particular, we find that symmetric starting configurations can induce an exceedingly long transient phase encompassing a large number of aesthetic spatial patterns including the prominent kaleidoscopic cooperation. Our finding holds for any population structure and a broad class of finite games beyond the Prisoner’s Dilemma. Our work offers insights into understanding evolutionary dynamics of spatially extended systems ubiquitous in biology and ecology.

S. Clark, Daniel S. Lobser, M. Revelle et al.

The Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia National Laboratories is a trapped-ion qubit system designed to evaluate the potential of near-term quantum hardware in scientific computing applications for the U.S. Department of Energy and its Advanced Scientific Computing Research program. Similar to commercially available platforms, it offers quantum hardware that researchers can use to perform quantum algorithms, investigate noise properties unique to quantum systems, and test novel ideas that will be useful for larger and more powerful systems in the future. However, unlike most other quantum computing testbeds, the QSCOUT allows both quantum circuit and low-level pulse control access to study new modes of programming and optimization. The purpose of this article is to provide users and the general community with details of the QSCOUT hardware and its interface, enabling them to take maximum advantage of its capabilities.

Jae Hyuk Hwang, Jeehae Shin, Jiseok Han et al.

With the growing demand for soft robots capable of various degrees of locomotion based on reversibly actuating liquid crystalline networks (LCNs), the fabrication of complex 3D architectures from 2D LCN films via shape reconfiguration and/or assembly techniques are studied recently. However, once a system is formed and fixed into a specific 3D structure, only certain movements can be implemented using the fixed structure, and disassembly into the original 2D films is challenging. Therefore, studies to overcome this irreversible fabrication process become increasingly important. Herein, an effective and simple preparation of static and dynamic covalent dual‐cross‐linked, photo‐controllable LCN (pc‐LCN) films as building blocks for lego‐like, monolithically assembled 3D soft transformable robots is presented. By tailoring the static and dynamic covalent linkages in the networks, pc‐LCN films can be readily reconfigured and assembled into complex 3D structures under ultraviolet (UV) irradiation. Such monoliths can also be disassembled into their constituent building block films and reassembled into different architectures under the same UV stimulation. Moreover, by adopting selective visible‐light‐responsive dopant dyes to actuate pc‐LCN building blocks, 3D soft transformable robots with versatile motion capabilities, including rolling, gripping, and cargo transport in multiple directions, are demonstrated.

Dongping Qiao, Yajing Wang, Jie Pei et al.

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.

Bernhard Wally, J. Vyskočil, Petr Novák et al.

Industry 4.0 production systems must support flexibility in various dimensions, such as for the products to be produced, for the production processes to be applied, and for the available machinery. In this article, we present a novel approach to design and control smart manufacturing systems. The approach is reactive, that is responds to unplanned situations and implements an iterative refinement technique, that is, optimizes itself during runtime to better accommodate production goals. For realizing these advances, we present a model-driven methodology and we provide a prototypical implementation of such a production system. In particular, we employ Planning Domain Definition Language (PDDL) as our artificial intelligence environment for automated planning of production processes and combine it with one of the most prominent Industry 4.0 standards for the fundamental production system model: IEC 62264. We show how to plan the assembly of small trucks from available components and how to assign specific production operations to available production resources, including robotic manipulators and transportation system shuttles. Results of the evaluation indicate that the presented approach is feasible and that it is able to significantly strengthen the flexibility of production systems during runtime. Note to Practitioners—Smart production is an umbrella for a number of shifts and initiatives that deal with digitization of manufacturing/production systems and related issues and potentials. In this work, we present an approach for utilizing automated planning for creating production plans. This is in contrast to the traditional approach, where recipes are programmed into the production system ahead-of-time. However, automated planning relies on specific languages and tools that are hard to master by nonexperts, which is a factor that strongly limited the utilization of plan-driven approaches for industrial automation in practice. Thus, we propose to generate planning tasks automatically with model-driven engineering techniques. We are utilizing the industrial standard IEC 62264 for the description of the production system, and the academic standard Planning Domain Definition Language (PDDL) for planning. PDDL is handled completely transparent to the user, that is the user is shielded from its complexity by employing the IEC 62264 model as the sole frontend.

Yufei Ma, Yu Cao, S. Vrudhula et al.

A broad range of applications are increasingly benefiting from the rapid and flourishing development of convolutional neural networks (CNNs). The FPGA-based CNN inference accelerator is gaining popularity due to its high-performance and low-power as well as FPGA’s conventional advantage of reconfigurability and flexibility. Without a general compiler to automate the implementation, however, significant efforts and expertise are still required to customize the design for each CNN model. In this paper, we present an register-transfer level (RTL)-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation for a given CNN algorithm. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g., GoogLeNet and ResNet, can be compiled. The proposed methodology is demonstrated with various CNN algorithms, e.g., NiN, VGG, GoogLeNet, and ResNet, on two standalone Intel FPGAs, Arria 10, and Stratix 10, achieving end-to-end inference throughputs of 969 GOPS and 1604 GOPS, respectively, with batch size of one.

Yang You, Wanzhong Chen, Mingyang Li et al.

Abstract Surgical treatment is one of the most important methods to cure or control drug-resistant epilepsy, and preoperative localization of epileptic lesions plays an important role in the success of a surgery. Given that the manual diagnosis takes time and effort, an automatic detection system is needed to aid clinical diagnosis. Therefore, in the present study, a new automatic focal electroencephalogram (EEG) detection algorithm combining flexible analytic wavelet transform (FAWT) with entropies was put forward. The differential focal (F) and non-focal (NF) EEG signals were decomposed into 15-level sub-bands using FAWT, and this was followed by computing log energy entropy (LEE) and fuzzy distribution entropy (fDistEn) of the detail coefficients of 15 sub-bands and the differential EEG signal. Kruskal–Wallis one-way analysis of variance (ANOVA) was adopted to select the statistically significant features, and five classifiers including general regression neural network (GRNN), support vector machine (SVM), least squares support vector machine (LS-SVM), K-nearest neighbor (KNN), and fuzzy K-Nearest neighbors (fKNN) were then used to verify the effectiveness of the selected features. The proposed methodology was tested on the Bern Barcelona database, and a maximum accuracy of 94.80 % was achieved in distinguishing F and NF EEG signals via LS-SVM classifier. The results suggest that the proposed method is a valuable approach to aid clinicians in locating the epileptic focus in practical application.

L. Paredes, M. Molina, B. Serrano

Presently, electric power systems based on microgrids are reaching an important position in different locations around the world. The multiple distributed generation technologies employed in modern microgrids allow a joint operation of renewable and non-renewable energy sources with many types of loads. Nonetheless, induction motor (IM) type dynamic loads represent one of the most critical factors that make microgrid systems vulnerable to scenarios that could trigger voltage instability. This paper proposes the incorporation of FACTS (flexible ac transmission system) devices to improve the dynamic voltage stability of microgrids with high dynamic load penetration. The work focuses on the impact of including a DSTATCOM (distribution static compensator) in a microgrid with high IM type dynamic load penetration when a fault occurs that causes the microgrid isolation. Various case studies are analyzed using the CIGRÉ microgrid test system. The results show the improvements in the voltage stability of the microgrid with the coordination of distributed generation technologies and the DSTATCOM.

CHEN Guangzan, LIU Lan, HUANG Deliang et al.

Electric braking of urban rail vehicle produces considerable regenerative braking energy and secondary utilization of the surplus regenerative braking energy can improve energy saving rate of the system. This paper proposed a modularized super capacitor energy storage system. By using low voltage and high frequency converter modules, and connecting the converter modules in series and parallel, the energy storage system realizes two-way flow, stable charging and discharging voltage and current through the DC voltage division control of the series system. which can make the system respond quickly and the charging response time of the system is not more than 100 ms. Experimental verification of the proposed theoretical scheme on a 300 kW super capacitor energy storage device is carried out and the results show that its average power saving is 25 kW·h per day and the energy saving effect is good.