Pub Date : 2024-04-10DOI: 10.1109/OJIES.2024.3386948
Lukas Antonio Budiwicaksana;Dong-Choon Lee
This article proposes a novel submodule capacitor voltage balancing control for multiport modular multilevel converter (MMC)-based solid-state transformers. To balance each submodule capacitor voltage, the balancing control is usually applied to the MMC. However, the low switching frequency operation and bulky arm inductance of the MMC limit the controller bandwidth and stability margin. These issues are addressed by moving the balancing control to the back-end dc/dc converter, which is operated at high switching frequency. The controller gains are designed based on the small-signal model. The superiority of the proposed controller over the conventional one has been verified through Bode plot, pole-zero map, and Nyquist path analyses. Experimental results for a 2.4-kW prototype system have also verified the accuracy of the model and effectiveness of the proposed balancing control for step load changes.
{"title":"Submodule Capacitor Voltage Balancing Through High-Frequency-Side Control for Multiport MMC-Based Solid-State Transformers","authors":"Lukas Antonio Budiwicaksana;Dong-Choon Lee","doi":"10.1109/OJIES.2024.3386948","DOIUrl":"10.1109/OJIES.2024.3386948","url":null,"abstract":"This article proposes a novel submodule capacitor voltage balancing control for multiport modular multilevel converter (MMC)-based solid-state transformers. To balance each submodule capacitor voltage, the balancing control is usually applied to the MMC. However, the low switching frequency operation and bulky arm inductance of the MMC limit the controller bandwidth and stability margin. These issues are addressed by moving the balancing control to the back-end dc/dc converter, which is operated at high switching frequency. The controller gains are designed based on the small-signal model. The superiority of the proposed controller over the conventional one has been verified through Bode plot, pole-zero map, and Nyquist path analyses. Experimental results for a 2.4-kW prototype system have also verified the accuracy of the model and effectiveness of the proposed balancing control for step load changes.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"302-316"},"PeriodicalIF":8.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1109/OJIES.2024.3385865
Rosario V. Giuffrida;Andreas Horat;Dominik Bortis;Tim Bierewirtz;Krishnaraj Narayanaswamy;Marcus Granegger;Johann W. Kolar
A novel implantable total artificial heart, hereinafter referred to as the ShuttlePump , is currently under development in a research collaboration between the Medical University of Vienna, the Power Electronic Systems Laboratory of ETH Zurich and Charite Berlin. Its novel, low-complexity, pulsatile pumping principle requires a specially shaped piston performing a controlled, synchronized linear-rotary motion while providing the necessary hydraulic force and torque. The machine design of the Permanent Magnet Synchronous Machine (PMSM)-based linear-rotary actuator was conducted in previous work of the authors, leading to the construction of a hardware prototype satisfying the application requirements in terms of electromechanical force, torque, power losses, and volume. This article provides the details of the closed-loop linear-rotary position control system required to operate the ShuttlePump . The design of the position control system targets tight reference tracking ( $pm$ 8 mm linear stroke and continuous rotation) up to an operational frequency of 5 Hz, under the heavy disturbance introduced by the axial hydraulic load force, as high as 45 N. The experimental measurements show successful linear-rotary position tracking under the specified axial load, with a maximum error of 1 mm and 5 $^{circ }$ .
目前，维也纳医科大学、苏黎世联邦理工学院动力电子系统实验室和柏林夏里特大学正在合作研发一种新型植入式全人工心脏（以下简称 ShuttlePump）。其新颖、低复杂度、脉冲式泵送原理要求一个特殊形状的活塞在提供必要的液压力和扭矩的同时，执行受控、同步的线性旋转运动。作者在之前的工作中对基于永磁同步电机（PMSM）的线性旋转执行器进行了机器设计，最终构建了一个硬件原型，满足了机电力、扭矩、功率损耗和体积方面的应用要求。本文详细介绍了操作 ShuttlePump 所需的闭环线性旋转位置控制系统。位置控制系统的设计目标是在高达 45 N 的轴向液压负载力带来的严重干扰下，在高达 5 Hz 的工作频率下实现紧密的参考跟踪（8 mm 线性行程和连续旋转）。实验测量显示，在指定的轴向负载下，线性旋转位置跟踪成功，最大误差为 1 mm 和 5$^{circ }$。
{"title":"Linear-Rotary Position Control System With Enhanced Disturbance Rejection for a Novel Total Artificial Heart","authors":"Rosario V. Giuffrida;Andreas Horat;Dominik Bortis;Tim Bierewirtz;Krishnaraj Narayanaswamy;Marcus Granegger;Johann W. Kolar","doi":"10.1109/OJIES.2024.3385865","DOIUrl":"10.1109/OJIES.2024.3385865","url":null,"abstract":"A novel implantable total artificial heart, hereinafter referred to as the \u0000<italic>ShuttlePump</i>\u0000, is currently under development in a research collaboration between the Medical University of Vienna, the Power Electronic Systems Laboratory of ETH Zurich and Charite Berlin. Its novel, low-complexity, pulsatile pumping principle requires a specially shaped piston performing a controlled, synchronized linear-rotary motion while providing the necessary hydraulic force and torque. The machine design of the Permanent Magnet Synchronous Machine (PMSM)-based linear-rotary actuator was conducted in previous work of the authors, leading to the construction of a hardware prototype satisfying the application requirements in terms of electromechanical force, torque, power losses, and volume. This article provides the details of the closed-loop linear-rotary position control system required to operate the \u0000<italic>ShuttlePump</i>\u0000. The design of the position control system targets tight reference tracking (\u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u00008 mm linear stroke and continuous rotation) up to an operational frequency of 5 Hz, under the heavy disturbance introduced by the axial hydraulic load force, as high as 45 N. The experimental measurements show successful linear-rotary position tracking under the specified axial load, with a maximum error of 1 mm and 5\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"359-375"},"PeriodicalIF":8.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494377","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.1109/OJIES.2024.3406732
Héctor Sarnago;Óscar Lucía
Modern electric vehicles require power electronic systems capable of operating under a wide variety of operating conditions, including on-board chargers (OBCs) and dc–dc converters. These systems must function across a wide range of parameters, such as phase number, input voltage, and output battery voltage. Considering modern design standards, achieving a high-performance implementation featuring high efficiency and low cost is also mandatory, adding additional technical challenges. To address these challenges, this article proposes a novel OBC architecture designed to operate in both three-phase and single-phase configurations across the full output power range. This is achieved without requiring additional power components or degrading performance. As a consequence, the proposed solution is a universal-charging single-power-processing block that features a cost-effective implementation while achieving high power density and efficiency. In this article, a bidirectional 11-kW 800-V-battery-voltage prototype of the system is designed and constructed for a 400-V (line-to-line) mains supply.
{"title":"Design and Experimental Verification of a Bidirectional EV On-Board Charger Featuring Multiphase Operation in Full Power/Voltage Ranges","authors":"Héctor Sarnago;Óscar Lucía","doi":"10.1109/OJIES.2024.3406732","DOIUrl":"10.1109/OJIES.2024.3406732","url":null,"abstract":"Modern electric vehicles require power electronic systems capable of operating under a wide variety of operating conditions, including on-board chargers (OBCs) and dc–dc converters. These systems must function across a wide range of parameters, such as phase number, input voltage, and output battery voltage. Considering modern design standards, achieving a high-performance implementation featuring high efficiency and low cost is also mandatory, adding additional technical challenges. To address these challenges, this article proposes a novel OBC architecture designed to operate in both three-phase and single-phase configurations across the full output power range. This is achieved without requiring additional power components or degrading performance. As a consequence, the proposed solution is a universal-charging single-power-processing block that features a cost-effective implementation while achieving high power density and efficiency. In this article, a bidirectional 11-kW 800-V-battery-voltage prototype of the system is designed and constructed for a 400-V (line-to-line) mains supply.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"458-467"},"PeriodicalIF":8.5,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/OJIES.2024.3406226
Md Abu Taher;Milad Behnamfar;Arif I. Sarwat;Mohd Tariq
This study investigates the vulnerability of dc microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a nonlinear autoregressive network with exogenous input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates dc currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output dc voltages and currents of distributed energy resources. An attack mitigation strategy using a proportional–integral controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.
{"title":"False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid","authors":"Md Abu Taher;Milad Behnamfar;Arif I. Sarwat;Mohd Tariq","doi":"10.1109/OJIES.2024.3406226","DOIUrl":"10.1109/OJIES.2024.3406226","url":null,"abstract":"This study investigates the vulnerability of dc microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a nonlinear autoregressive network with exogenous input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates dc currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output dc voltages and currents of distributed energy resources. An attack mitigation strategy using a proportional–integral controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"441-457"},"PeriodicalIF":8.5,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-15DOI: 10.1109/OJIES.2024.3401541
Giovanni Marini;Alessandro Lidozzi;Marco di Benedetto;M. Moranchel Pérez;Luca Solero
This article presents a real-time active damping methodology for front-end inverters connected to the railway catenary in energy recovery applications. The system arrangement comprises a three-phase 2.5 MW inverter connected to the ac grid with a suitable filter. On the opposite side it shares the dc-side with the railway plant where traction inverters and auxiliary systems are connected. The proposed method tries to solve a problem when the energy recovery converter, operating with an almost constant power load, stimulates the catenary power line. This method estimates the dc-side resonant frequency, isolates the dc voltage oscillations around the resonant frequency, and finally attenuates the related effects by acting on the inverter current control strategy. Experimental tests are shown to validate the method using the hardware-in-the-loop real-time emulator. Thanks to the HIL, the complete catenary system has been modeled according to the real data provided by the train operator. The control algorithm and the related control board have the same structure as the architecture used in the field. The results show the effectiveness of the proposed method in detecting the resonance and reducing its effects, increasing the catenary robustness, and making the proper integration of energy recovery systems possible.
{"title":"Real-Time Resonance Detection and Active Damping in Energy Recovery Railways Applications","authors":"Giovanni Marini;Alessandro Lidozzi;Marco di Benedetto;M. Moranchel Pérez;Luca Solero","doi":"10.1109/OJIES.2024.3401541","DOIUrl":"10.1109/OJIES.2024.3401541","url":null,"abstract":"This article presents a real-time active damping methodology for front-end inverters connected to the railway catenary in energy recovery applications. The system arrangement comprises a three-phase 2.5 MW inverter connected to the ac grid with a suitable filter. On the opposite side it shares the dc-side with the railway plant where traction inverters and auxiliary systems are connected. The proposed method tries to solve a problem when the energy recovery converter, operating with an almost constant power load, stimulates the catenary power line. This method estimates the dc-side resonant frequency, isolates the dc voltage oscillations around the resonant frequency, and finally attenuates the related effects by acting on the inverter current control strategy. Experimental tests are shown to validate the method using the hardware-in-the-loop real-time emulator. Thanks to the HIL, the complete catenary system has been modeled according to the real data provided by the train operator. The control algorithm and the related control board have the same structure as the architecture used in the field. The results show the effectiveness of the proposed method in detecting the resonance and reducing its effects, increasing the catenary robustness, and making the proper integration of energy recovery systems possible.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"916-927"},"PeriodicalIF":5.2,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for Fault Detection and Diagnosis (FDD) in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a new defense strategy based on combining adversarial training and data quantization. This research contributes several insights into securing machine learning within ACS, ensuring robust FDD in industrial processes.
{"title":"Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process","authors":"Vitaliy Pozdnyakov;Aleksandr Kovalenko;Ilya Makarov;Mikhail Drobyshevskiy;Kirill Lukyanov","doi":"10.1109/OJIES.2024.3401396","DOIUrl":"10.1109/OJIES.2024.3401396","url":null,"abstract":"Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for Fault Detection and Diagnosis (FDD) in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a new defense strategy based on combining adversarial training and data quantization. This research contributes several insights into securing machine learning within ACS, ensuring robust FDD in industrial processes.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"428-440"},"PeriodicalIF":8.5,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.1109/OJIES.2024.3377012
Amala Sonny;Abhinav Kumar;Linga Reddy Cenkeramaddi
Rapid advancements in communication technologies in the Internet of Things (IoT) domain have had an impact on the application of positioning technology across multiple domains. Although there have been numerous fully fledged approaches for detection and localization in outdoor scenarios, due to high path loss and shadowing, these are insufficiently accurate in indoor scenarios. The primary enabler of various healthcare and safety applications is the precise sensing and localization of targets. A cost-effective approach with little maintenance is crucial for the development of such reliable systems. To address such sensing and localization challenges in indoor scenarios, we propose a novel dynamic target detection technique based on an ensembled convolutional neural network (CNN) classifier. An AWR1843 Radar sensor is used to collect data corresponding to dynamic targets in indoor scenarios. The range of each moving target in the room is estimated using point cloud data extracted from the received signal. An ensemble-based 1-D CNN classifier is used to analyze the data. To model the ensemble classifier, we used three CNN classifiers. The performances of the state-of-the-art classifiers considered in the comparison varied between 44 $%$ and 95 $%$ in terms of accuracy. In contrast, the proposed system attained an accuracy of 97.65 $%$ during training and 96.47 $%$ during testing and outperformed the state-of-the-art approaches.
{"title":"Dynamic Targets Occupancy Status Detection Utilizing mmWave Radar Sensor and Ensemble Machine Learning","authors":"Amala Sonny;Abhinav Kumar;Linga Reddy Cenkeramaddi","doi":"10.1109/OJIES.2024.3377012","DOIUrl":"10.1109/OJIES.2024.3377012","url":null,"abstract":"Rapid advancements in communication technologies in the Internet of Things (IoT) domain have had an impact on the application of positioning technology across multiple domains. Although there have been numerous fully fledged approaches for detection and localization in outdoor scenarios, due to high path loss and shadowing, these are insufficiently accurate in indoor scenarios. The primary enabler of various healthcare and safety applications is the precise sensing and localization of targets. A cost-effective approach with little maintenance is crucial for the development of such reliable systems. To address such sensing and localization challenges in indoor scenarios, we propose a novel dynamic target detection technique based on an ensembled convolutional neural network (CNN) classifier. An AWR1843 Radar sensor is used to collect data corresponding to dynamic targets in indoor scenarios. The range of each moving target in the room is estimated using point cloud data extracted from the received signal. An ensemble-based 1-D CNN classifier is used to analyze the data. To model the ensemble classifier, we used three CNN classifiers. The performances of the state-of-the-art classifiers considered in the comparison varied between 44\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 and 95\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 in terms of accuracy. In contrast, the proposed system attained an accuracy of 97.65\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 during training and 96.47\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 during testing and outperformed the state-of-the-art approaches.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"251-263"},"PeriodicalIF":8.5,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The trend of current robotic research is to develop mobile robots that can perform highly dynamic tasks, which include jumping and running. To be employed profitably, this research necessitates a significant amount of work in the development of innovative planning and control algorithms that need experimental validation on actual robots. However, the majority of robots with highly dynamic performance capabilities are currently restricted to a few expensive platforms. This is a major obstacle that ultimately restricts the amount of contributors and the advancement of the research. Thus, a cost-effective actuator solution is needed that is also able to execute very dynamic movements. With this goal in mind, we present EM-Act, a modular series elastic actuator (SEA) for legged and multimodal dynamic robots. This work focuses on the development of the actuator solution by defining jump height as the prerequisite, identifying actuator parameters through simulations, and selecting and testing mechatronic elements of the design. The work also discusses a compact integration of desired compliance to address the impact forces. Furthermore, the work also details the implementation of the actuator solution on a 2-degree-of-freedom (DOF) robotic leg and experimentally validates its jumping performance.
{"title":"EM-Act: A Modular Series Elastic Actuator for Dynamic Robots","authors":"Ramesh krishnan Muttathil Gopanunni;Lorenzo Martignetti;Francesco Iotti;Alok Ranjan;Franco Angelini;Manolo Garabini","doi":"10.1109/OJIES.2024.3400052","DOIUrl":"10.1109/OJIES.2024.3400052","url":null,"abstract":"The trend of current robotic research is to develop mobile robots that can perform highly dynamic tasks, which include jumping and running. To be employed profitably, this research necessitates a significant amount of work in the development of innovative planning and control algorithms that need experimental validation on actual robots. However, the majority of robots with highly dynamic performance capabilities are currently restricted to a few expensive platforms. This is a major obstacle that ultimately restricts the amount of contributors and the advancement of the research. Thus, a cost-effective actuator solution is needed that is also able to execute very dynamic movements. With this goal in mind, we present EM-Act, a modular series elastic actuator (SEA) for legged and multimodal dynamic robots. This work focuses on the development of the actuator solution by defining jump height as the prerequisite, identifying actuator parameters through simulations, and selecting and testing mechatronic elements of the design. The work also discusses a compact integration of desired compliance to address the impact forces. Furthermore, the work also details the implementation of the actuator solution on a 2-degree-of-freedom (DOF) robotic leg and experimentally validates its jumping performance.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"468-480"},"PeriodicalIF":8.5,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-12DOI: 10.1109/OJIES.2024.3372577
Heyang Yao;Lei Shu;Wei Lin;Kai Huang;Miguel Martínez-García;Xiuguo Zou
The solar insecticidal lamp (SIL) is an electronic device designed for physical pest control, widely utilized in orchards and farmland. Currently, the characteristic of the phototactic rhythm of pest is commonly ignored in the design of SILs, hindering pest control. This phenomenon is particularly evident in the prolonged turning on/off lamp, which leads to inefficient energy utilization due to the lack of adjustment for peak pest activity. To address this issue, four models based on the phototactic rhythm of pests are developed to adjust the insecticidal timing of SIL for precise pest control. These mathematical models are established considering the phototactic rhythm of four pests that exert the most significant impact on crops, namely Mythimna seperata , Helicoverpa armigera , Proxenus lepigone , and Cnaphalocrocis medinalis . The results indicate that mathematical modeling of the phototactic rhythm of the pest is valuable in capturing their nocturnal activity patterns. The proposed mathematical model can help to optimize the on/off time of SIL for pest control. The integration of electronic devices, such as SIL in pest management represents a noteworthy advancement in agricultural electronics, contributing to the progress of smart and sustainable agriculture.
太阳能杀虫灯（SIL）是一种用于物理害虫控制的电子装置，广泛应用于果园和农田。目前，太阳能杀虫灯的设计普遍忽视了害虫的趋光性节律特征，从而阻碍了害虫的控制。这种现象在长时间开关灯上表现得尤为明显，由于缺乏对害虫活动高峰期的调整，导致能源利用效率低下。为解决这一问题，我们根据害虫的趋光节律建立了四个模型，以调整 SIL 的杀虫时间，实现精确的害虫控制。这些数学模型是根据对农作物影响最大的四种害虫的趋光性节律建立的，这四种害虫分别是 Mythimna seperata、Helicoverpa armigera、Proxenus lepigone 和 Cnaphalocrocis medinalis。研究结果表明，害虫趋光节律的数学模型对于捕捉害虫的夜间活动模式很有价值。所提出的数学模型有助于优化 SIL 的开/关时间，以控制害虫。将 SIL 等电子设备集成到害虫管理中代表了农业电子领域值得注意的进步，有助于推动智能和可持续农业的发展。
{"title":"Pests Phototactic Rhythm Driven Solar Insecticidal Lamp Device Evolution: Mathematical Model Preliminary Result and Future Directions","authors":"Heyang Yao;Lei Shu;Wei Lin;Kai Huang;Miguel Martínez-García;Xiuguo Zou","doi":"10.1109/OJIES.2024.3372577","DOIUrl":"10.1109/OJIES.2024.3372577","url":null,"abstract":"The solar insecticidal lamp (SIL) is an electronic device designed for physical pest control, widely utilized in orchards and farmland. Currently, the characteristic of the phototactic rhythm of pest is commonly ignored in the design of SILs, hindering pest control. This phenomenon is particularly evident in the prolonged turning \u0000<sc>on/off</small>\u0000lamp, which leads to inefficient energy utilization due to the lack of adjustment for peak pest activity. To address this issue, four models based on the phototactic rhythm of pests are developed to adjust the insecticidal timing of SIL for precise pest control. These mathematical models are established considering the phototactic rhythm of four pests that exert the most significant impact on crops, namely \u0000<italic>Mythimna seperata</i>\u0000, \u0000<italic>Helicoverpa armigera</i>\u0000, \u0000<italic>Proxenus lepigone</i>\u0000, and \u0000<italic>Cnaphalocrocis medinalis</i>\u0000. The results indicate that mathematical modeling of the phototactic rhythm of the pest is valuable in capturing their nocturnal activity patterns. The proposed mathematical model can help to optimize the \u0000<sc>on/off</small>\u0000time of SIL for pest control. The integration of electronic devices, such as SIL in pest management represents a noteworthy advancement in agricultural electronics, contributing to the progress of smart and sustainable agriculture.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"236-250"},"PeriodicalIF":8.5,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10468051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140116483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-09DOI: 10.1109/OJIES.2024.3397789
Chamod Samarajeewa;Daswin De Silva;Milos Manic;Nishan Mills;Prabod Rathnayaka;Andrew Jennings
Crowd monitoring is a primary function in diverse industrial domains, such as smart cities, public transport, and public safety. Recent advancements in low-energy devices and rapid connectivity have enabled the generation of real-time data streams suitable for crowd-monitoring applications. Crowd forecasting is typically achieved using deep learning models that learn the evolving nature of data streams. The computational complexity, execution time, and opaqueness are inherent challenges of deep learning models that also overlook the latent relationships between multiple real-time data streams for improved accuracy. To address these challenges, we propose the global ensemble echo state network approach for explainable crowd forecasting using multiple WiFi data streams. This approach replaces the random input mapping layer with a clustering layer, allowing the network to learn input projections on cluster centroids. It incorporates an ensemble readout comprising a stack of reservoir layers that provide model explainability. It also learns multiple related time series in parallel to construct a global model that leverage latent relationships across the data streams. This approach was empirically evaluated in a multicampus, mixed-use tertiary education setting. The results of which confirm the effectiveness and interpretability of the proposed approach for industrial applications of crowd forecasting.
{"title":"Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks","authors":"Chamod Samarajeewa;Daswin De Silva;Milos Manic;Nishan Mills;Prabod Rathnayaka;Andrew Jennings","doi":"10.1109/OJIES.2024.3397789","DOIUrl":"10.1109/OJIES.2024.3397789","url":null,"abstract":"Crowd monitoring is a primary function in diverse industrial domains, such as smart cities, public transport, and public safety. Recent advancements in low-energy devices and rapid connectivity have enabled the generation of real-time data streams suitable for crowd-monitoring applications. Crowd forecasting is typically achieved using deep learning models that learn the evolving nature of data streams. The computational complexity, execution time, and opaqueness are inherent challenges of deep learning models that also overlook the latent relationships between multiple real-time data streams for improved accuracy. To address these challenges, we propose the global ensemble echo state network approach for explainable crowd forecasting using multiple WiFi data streams. This approach replaces the random input mapping layer with a clustering layer, allowing the network to learn input projections on cluster centroids. It incorporates an ensemble readout comprising a stack of reservoir layers that provide model explainability. It also learns multiple related time series in parallel to construct a global model that leverage latent relationships across the data streams. This approach was empirically evaluated in a multicampus, mixed-use tertiary education setting. The results of which confirm the effectiveness and interpretability of the proposed approach for industrial applications of crowd forecasting.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"415-427"},"PeriodicalIF":8.5,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：