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The Dynamic Models, Control Strategies and Applications for Magnetorheological Damping Systems: A Systematic Review

JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2021年第1期9卷 131-147页

作者： Lv, Hongzhan Zhang, Songsong Sun, Qi Chen, Rui Zhang, W. J. Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 5A9 Canada

Purpose The magnetorheological (MR) damping devices have attracted an increasing amount of attention in the field of vibration control for their excellent performance of the vibration absorption. Systematically, the constitutive mechanical models of the MR fluids affect the control accuracy for the control strategies and the applications of the MR dampers. Methods This work elaborately gives a systematic review on the control issues with the MR devices, thus, covering the dynamic models of the MR dampers, the state-of-the-art research, and the damping control strategies and its applications, which provide the necessary fundamental theories and the references for the damping control design of an MR damping device. Results According to the advanced degree of the control algorithms that are discussed in detail, they can be classified into three categories, namely, the classical control algorithms, the advanced control algorithms, and the intelligent control algorithms. The damping control strategies determine the control quality, which is the soul of an MR damping control system. There is still room to improve the algorithm for the controller for MR dampers. Conclusions Employing the smart algorithms of machine learning, the work directs much more attention to the foundational research on the application of the artificial intelligence algorithms in damping control, outlining a perspective to combine the advantages of the individual different intelligent algorithms that could be an alternative solution to control of an MR damping device for the complex problems.

关键词： Magnetorheological (MR) damper Dynamic models Control strategies intelligent algorithms Vibration attenuation

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ECG artefact identification and removal in mHealth systems for continuous patient monitoring

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HEALTHCARE TECHNOLOGY LETTERS 2016年第3期3卷 171-176页

作者： Imtiaz, Syed Anas Mardell, James Saremi-Yarahmadi, Siavash Rodriguez-Villegas, Esther Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

关键词： ECG artefact identification ECG artefact removal automatic artefact identification algorithms automatic processing biomechanics biomedical equipment computational complexity continuous patient monitoring systems data acquired sections data acquisition electrocardiography electrocardiography data filtering algorithms filtering theory intelligent algorithms interpretation quality mHealth systems medical diagnosis medical signal processing patient monitoring patient movement sensor interference sensor placement signal quality telemedicine wireless sensor network wireless sensor networks

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A comparison study of two implemented fuzzy-based models for decision of logical trust

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JOURNAL OF HIGH SPEED NETWORKS 2024年第2期30卷 279-295页

作者： Higashi, Shunya Ampririt, Phudit Qafzezi, Ermioni Ikeda, Makoto Matsuo, Keita Barolli, Leonard Fukuoka Inst Technol Grad Sch Engn 3-30-1 Wajiro HigashiHigashi Ku Fukuoka 8110295 Japan Fukuoka Inst Technol Dept Informat & Commun Engn 3-30-1 Wajiro HigashiHigashi Ku Fukuoka 8110295 Japan

In recent years, the human-to-human and human-to-things relationships are becoming complicated and unreliable, which makes harder decisions in a variety of situations. As a result, trust computing is gaining interest in a number of research fields. The Logical Trust (LT) is one of trust computing concepts. In this paper, we design a Fuzzy-based System for Decision of Logical Trust (FSDLT). We implement two models: FSDLTM1 and FSDLTM2. The FSDLTM1 considers three input parameters: Belief (Be), Experience (Ep), Rationality (Ra) and the output parameter is LT. In FSDLTM2, we consider Reliability (Re) as a new parameter. We evaluated the implemented models by computer simulations. The simulation results show that when Be, Ep, Ra and Re are increasing, the LT is increased. For FSDLTM1, when Ep value is 0.9, all LT values are greater than 0.5. While for FSDLTM2, in case when Be is 0.9, for all values of Ra and Re, when Ep is 0.5 and 0.9, all LT values are higher than 0.5. This shows that the person or device is trustworthy. The FSDLTM2 is more complex than FSALTM1 but it makes a better decision for LT by considering four input parameters.

关键词： Trust computing fuzzy logic intelligent algorithms decision making systems

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FACS-MP: A fuzzy admission control system with many priorities for wireless cellular networks and its performance evaluation

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JOURNAL OF HIGH SPEED NETWORKS 2015年第1期21卷 1-14页

作者： Matsuo, Keita Elmazi, Donald Liu, Yi Sakamoto, Shinji Mino, Gjergji Barolli, Leonard Fukuoka Prefectural Fukuoka Tech High Sch Fukuoka Japan Polytech Univ Tirana Dept Elect & Telecommun Tirana Albania Fukuoka Inst Technol Grad Sch Engn Fukuoka Japan Comp Technol Solut Salem NH USA Fukuoka Inst Technol Dept Informat & Commun Engn Fukuoka Japan

Call Admission Control (CAC) is one of the resource management functions which regulates network access to ensure QoS provisioning. However, the decision for CAC is very challenging issue due to user mobility, limited radio spectrum and multimedia traffic characteristics. Several schemes have been proposed for CAC in wireless cellular networks. However, during to the complexity of CAC in wireless environment, many simplified models and assumptions are made. Some schemes consider that each mobile node will make hand-over to neighbouring cells with equal probability, which may be not accurate in general. For this reason, the intelligent and heuristic methods are needed. We proposed a fuzzy-based CAC system by considering the priority of the on-going connections. We called this system FACS-P. In FACS-P, as priority parameter, we considered only one parameter (service request). We extended our work by adding different priorities. We call this system FACS-MP. In this paper, we evaluate by simulations the performance of the proposed system. From the simulation results, we conclude that the FACS-MP makes a good differentiation of different services and has better behaviour than FACS-P.

关键词： Cellular networks CAC fuzzy logic priorities intelligent algorithms

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An overview of self-adaptive technologies within virtual reality training

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COMPUTER SCIENCE REVIEW 2016年 22卷 65-87页

作者： Vaughan, Neil Gabrys, Bodgan Dubey, Venketesh N. Bournemouth Univ Talbot Campus Poole BH12 5BB Dorset England

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Virtual reality Adaptive systems intelligent algorithms Training

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UAV Assisted Traffic Offloading in Air Ground Integrated Networks With Mixed User Traffic

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2022年第8期23卷 12601-12611页

作者： Fan, Bo Jiang, Li Chen, Yanyan Zhang, Ye Wu, Yuan Univ Macau State Key Lab Internet Things Smart City Taipa Macao Peoples R China Beijing Univ Technol Coll Metropolitan Transportat Beijing Key Lab Traff Engn Beijing 100124 Peoples R China Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Univ Macau Beijing Key Lab Traffic Engn Coll Metropolitan Transportat Beijing 100124 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macao Peoples R China Zhuhai UM Sci & Technol Res Inst Zhuhai 519000 Peoples R China

The air ground integrated networks can leverage unmanned aerial vehicle (UAV) communications to tackle the ever-increasing and unbalanced traffic load in future communication systems. This paper investigates the UAV enabled traffic offloading problem in air ground integrated networks with mixed user traffic. The problem jointly maximizes the system load balance and the total UAV reward, which can be formulated under a two-layer network graph model. In the cellular network graph, the association between the delay-sensitive users and the access points (APs) as well as the association between the UAVs and the APs are formulated. In the UAV network graph, the association between the delay-insensitive users and the UAVs is formulated. By observing the coupling relationship of the decision variables, we decouple the problem into three sub-problems and solve the first two sub-problems with reduced complexity. Then, we devise a Deep Neural Network (DNN) empowered genetic algorithm to solve the last sub-problem. The DNN can be leveraged to filter out the non-optimal solutions in the initialization operator of the genetic algorithm for improving the efficiency. Performance comparisons are provided between the proposed traffic offloading scheme and the existing ones, which validate the advantages of the DNN empowered genetic algorithm regarding its convergence, accuracy, and robustness.

关键词： Unmanned aerial vehicles Trajectory Genetic algorithms Delays Load modeling Cellular networks Transportation Air ground integrated networks traffic offloading deep learning intelligent algorithms

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Tiny Machine Learning for Efficient Channel Selection in LoRaWAN

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 30714-30724页

作者： Ali Lodhi, Muhammad Obaidat, Mohammad S. Wang, Lei Mahmood, Khalid Ibrahim Qureshi, Khalid Chen, Jenhui Hsiao, Kuei-Fang Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 114319 Peoples R China Univ Jordan King Abdullah Sch Informat Technol 2 Amman 11942 Jordan Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China SRM Univ Sch Comp Dept Computat Intelligence Kattankulathur 603203 India Amity Univ Sch Engn Noida 201301 India Natl Yunlin Univ Sci & Technol Grad Sch Intelligent Data Sci Touliu 64002 Taiwan Chang Gung Univ Dept Comp Sci & Informat Engn Taoyuan 33375 Taiwan Chang Gung Mem Hosp Dept Surg Div Breast Surg & Gen Surg Taoyuan 33375 Taiwan Univ Jordan KASIT Amman 11942 Jordan

Machine learning (ML) has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying ML algorithms on resource-constrained edge devices poses significant challenges due to their limited computational capabilities and storage capacities. In this study, we propose leveraging tiny ML (TinyML) techniques to address these challenges and optimize channel allocation within long range wide area network (LoRaWAN) deployments. Our key innovation lies in replacing traditional random channel allocation methods with TinyML-based approaches, wherein each edge device autonomously utilizes TinyML to select the most efficient channel prior to each uplink transmission. Furthermore, we conduct comprehensive comparisons between TinyML and conventional channel allocation techniques implemented on edge devices. Through extensive simulations, our results demonstrate that TinyML outperforms existing channel allocation mechanisms in terms of packet success ratio (PSR). Notably, when evaluating TinyML against conventional ML approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

关键词： Channel allocation Internet of Things Software Resource management Multiaccess communication Standards Signal to noise ratio intelligent algorithms Internet of Things (IoT) long range wide area network (LoRaWAN) mobility tiny machine learning (TinyML)

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Online Tuning of PID controller using Black Box Multi-Objective Optimization and Reinforcement Learning

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IFAC-PapersOnLine 2018年第32期51卷 844-849页

作者： Pandit, Ashwad Hingu, Bipin Cummins Technical Centre India Pune India

A PID Controller is the most widely used controller due to its ease and convenience of use. Manual tuning of a PID Controller is a time-consuming task. Hence, employing intelligent algorithms is necessary. The Cummins engine controller has a complex structure. To fine-tune it, a substantial amount of time is required. To reduce this time requirement, a black box approach was selected for online tuning. This would not only reduce the required time, but also reduce the efforts. Black box optimization would mean the engineers have to spend less time trying to understand the controller structure. With this aim in mind, a PID system simulation was set up in MATLAB. A function would randomize a system, resulting in a true black box to tune. This removed any bias the authors might have. The algorithm has shown promising results, with tuned controller gains in just over 20 iterations on average. This could then be extended to not only Cummins controllers, but other industrial controllers as well. © 2018

关键词： Multiobjective optimization Controllers E learning Electric control equipment Genetic algorithms Learning algorithms MATLAB Proportional control systems Reinforcement learning Three term control systems Black box approach Black box optimization Controller structures Industrial controllers intelligent algorithms Parameter optimization PID controllers Time consuming tasks

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Machine learning-based scheduling: a bibliometric perspective

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE intelligent MANUFACTURING 2021年第2期3卷 131-146页

作者： Li, Shiyun Yu, Tianzong Cao, Xu Pei, Zhi Yi, Wenchao Chen, Yong Lv, Ruifeng Zhejiang Univ Technol Coll Mech Engn Hangzhou Peoples R China

In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non-linear in nature, time varying, multi-objective, and subject to interruptions. Ergo, the machine learning-based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning-based algorithms. To reveal the value of machine learning-based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning-based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

关键词： data science intelligent algorithms deep learning complex shop floor resources reinforcement learning machine learning-based scheduling method deep learning (artificial intelligence) scheduling problems data driven algorithms combinatorial mathematics information analysis production data computing power bibliometric analysis mathematical programming inherent complexity combinatorial optimization artificial intelligence reinforcement learning-based algorithms flow shop scheduling heuristic rules

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Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

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OPEN GEOSCIENCES 2019年第1期11卷 391-413页

作者： Yi, Jiao-Hong Wang, Jian Wang, Gai-Ge China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Shandong Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Jilin Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China

China has one of the highest rates of natural disasters in the world. In recent years, the Chinese government has placed a high value on improving emergency natural disaster relief. The goal of this research was to resolve a key issue for emergency natural disaster relief: the emergency vehicle routing problem (EmVRP) with relief materials in sudden disasters. First, we provided a description of the EmVRP, and defined the boundary conditions. On this basis, we constructed an optimization model of EmVRP with relief materials in sudden disasters. To reach the best solution in the least amount of time, we proposed an enhanced monarch butterfly optimization (EMBO) algorithm, incorporating two modifications to the basic MBO: a self-adaptive strategy and a crossover operator. Finally, the EMBO algorithm was used to solve the EmVRP. Our experiments using two examples EmVRP with relief materials in a sudden-onset disaster proved the suitability of EMBO. In addition, an array of comparative studies showed that the proposed EMBO algorithm can achieve satisfactory solutions in less time than the basic MBO algorithm and seven other intelligent algorithms.

关键词： Emergency vehicle routing problem relief materials sudden disasters intelligent algorithms monarch butterfly optimization self-adaptive crossover operator

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