Research

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20 pages, 2552 KiB

Open AccessArticle

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

by Robert Nebeluk and Maciej Ławryńczuk

Sensors 2023, 23(23), 9539; https://doi.org/10.3390/s23239539 - 30 Nov 2023

Viewed by 617

Abstract

The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to [...] Read more.

The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to offer better modeling accuracy and increase the MPC control quality. This work discusses the benefits of using the parallel Wiener model in MPC. It has three objectives. Firstly, it describes a fast MPC algorithm in which parallel Wiener models are used for online prediction. In the presented approach, sophisticated trajectory linearization is performed online, which leads to computationally fast quadratic optimization. The second objective of this work is to study the influence of the model structure on modeling accuracy. The well-known neutralization benchmark process is considered. It is shown that the parallel Wiener models in the open-loop mode generate significantly fewer errors than the classical structure. This work’s third objective is to validate the efficiency of parallel Wiener models in closed-loop MPC. For the neutralization process, it is demonstrated that parallel models demonstrate better control quality using various indicators, but the difference between the classical and parallel models is not significant. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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27 pages, 903 KiB

Open AccessArticle

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

by Krzysztof Zarzycki and Maciej Ławryńczuk

Sensors 2023, 23(21), 8898; https://doi.org/10.3390/s23218898 - 01 Nov 2023

Cited by 1 | Viewed by 1204

Abstract

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion [...] Read more.

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion block that relies on fuzzy logic. The second objective of this work is to detail a computationally efficient model predictive control (MPC) algorithm that employs the PIHNN model. The validity of the presented modeling and MPC approaches is demonstrated for a simulated polymerization reactor. It is shown that the PIHNN structure gives very good modeling results, while the MPC controller results in excellent control quality. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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19 pages, 3271 KiB

Open AccessArticle

Deep Learning-Empowered Digital Twin Using Acoustic Signal for Welding Quality Inspection

by Tao Ji and Norzalilah Mohamad Nor

Sensors 2023, 23(5), 2643; https://doi.org/10.3390/s23052643 - 28 Feb 2023

Cited by 7 | Viewed by 1886

Abstract

Weld site inspection is a research area of interest in the manufacturing industry. In this study, a digital twin system for welding robots to examine various weld flaws that might happen during welding using the acoustics of the weld site is presented. Additionally, [...] Read more.

Weld site inspection is a research area of interest in the manufacturing industry. In this study, a digital twin system for welding robots to examine various weld flaws that might happen during welding using the acoustics of the weld site is presented. Additionally, a wavelet filtering technique is implemented to remove the acoustic signal originating from machine noise. Then, an SeCNN-LSTM model is applied to recognize and categorize weld acoustic signals according to the traits of strong acoustic signal time sequences. The model verification accuracy was found to be 91%. In addition, using numerous indicators, the model was compared with seven other models, namely, CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. A deep learning model, and acoustic signal filtering and preprocessing techniques are integrated into the proposed digital twin system. The goal of this work was to propose a systematic on-site weld flaw detection approach encompassing data processing, system modeling, and identification methods. In addition, our proposed method could serve as a resource for pertinent research. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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55 pages, 29708 KiB

Open AccessArticle

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

by Muhammad Asim Shahid, Muhammad Mansoor Alam and Mazliham Mohd Su’ud

Sensors 2023, 23(4), 1965; https://doi.org/10.3390/s23041965 - 09 Feb 2023

Cited by 3 | Viewed by 1797

Abstract

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability [...] Read more.

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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28 pages, 9377 KiB

Open AccessArticle

Contributions to Power Grid System Analysis Based on Clustering Techniques

by Gheorghe Grigoraș, Maria Simona Raboaca, Catalin Dumitrescu, Daniela Lucia Manea, Traian Candin Mihaltan, Violeta-Carolina Niculescu and Bogdan Constantin Neagu

Sensors 2023, 23(4), 1895; https://doi.org/10.3390/s23041895 - 08 Feb 2023

Cited by 4 | Viewed by 1449

Abstract

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea [...] Read more.

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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14 pages, 4158 KiB

Open AccessArticle

Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM

by Muhammad Usama and In-Young Lee

Sensors 2022, 22(21), 8240; https://doi.org/10.3390/s22218240 - 27 Oct 2022

Cited by 2 | Viewed by 1325

Abstract

This study designs a simple current controller employing deep symbolic regression (DSR) in a surface-mounted permanent magnet synchronous machine (SPMSM). A novel DSR-based optimal current control scheme is proposed, which after proper training and fitting, generates an analytical dynamic numerical expression that characterizes [...] Read more.

This study designs a simple current controller employing deep symbolic regression (DSR) in a surface-mounted permanent magnet synchronous machine (SPMSM). A novel DSR-based optimal current control scheme is proposed, which after proper training and fitting, generates an analytical dynamic numerical expression that characterizes the data. This creates an understandable model and has the potential to estimate data that have not been seen before. The goal of this study was to overcome the traditional linear proportional–integral (PI) current controller because the performance of the PI is highly dependent on the system model. Moreover, the outer speed control loop gains are tuned using the cuckoo search algorithm, which yields optimal gain values. To demonstrate the efficacy of the proposed design, we apply the control design to different test cases, that is varied speed and load conditions, as well as sinusoidal speed reference, and compare the results with those of a traditional vector control design. Compared with traditional control approaches, we deduce that the DSR-based control design could be extrapolated far beyond the training dataset, laying the foundation for the use of deep learning techniques in power conversion applications. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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17 pages, 406 KiB

Open AccessFeature PaperArticle

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model

by Ayesha Sultan, Wojciech Sałabun, Shahzad Faizi, Muhammad Ismail and Andrii Shekhovtsov

Sensors 2022, 22(15), 5736; https://doi.org/10.3390/s22155736 - 31 Jul 2022

Cited by 5 | Viewed by 1479

Abstract

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. [...] Read more.

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman’s rank correlation test assesses the statistical significance of two rankings sets. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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17 pages, 537 KiB

Open AccessArticle

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment

by Parul Thakur, Bartłomiej Kizielewicz, Neeraj Gandotra, Andrii Shekhovtsov, Namita Saini and Wojciech Sałabun

Sensors 2022, 22(13), 4879; https://doi.org/10.3390/s22134879 - 28 Jun 2022

Cited by 10 | Viewed by 1356

Abstract

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain [...] Read more.

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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18 pages, 2951 KiB

Open AccessArticle

Deep Learning-Based Defect Prediction for Mobile Applications

by Manzura Jorayeva, Akhan Akbulut, Cagatay Catal and Alok Mishra

Sensors 2022, 22(13), 4734; https://doi.org/10.3390/s22134734 - 23 Jun 2022

Cited by 5 | Viewed by 2313

Abstract

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study [...] Read more.

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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14 pages, 2009 KiB

Open AccessArticle

Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data

by Ko-Chieh Chao, Chuan-Bi Chou and Ching-Hung Lee

Sensors 2022, 22(12), 4540; https://doi.org/10.3390/s22124540 - 16 Jun 2022

Cited by 5 | Viewed by 1412

Abstract

Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain [...] Read more.

Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched. In this paper, the scenarios in real manufacturing site are considered, that the target domain data is much less than source domain data at the beginning, but the number of target domain data will increase as time goes by. A novel method is proposed for fault diagnosis of rolling bearing with online imbalanced cross-domain data. Finally, the proposed method which is tested on bearing dataset (CWRU) has achieved prediction accuracy of 95.89% with only 40 target samples. The results have been compared with other traditional methods. The comparisons show that the proposed online domain adaptation fault diagnosis method has achieved significant improvements. In addition, the deep transfer learning model by adaptive- network-based fuzzy inference system (ANFIS) is introduced to interpretation the results. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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32 pages, 8732 KiB

Open AccessFeature PaperArticle

n-Player Stochastic Duel Game Model with Applied Deep Learning and Its Modern Implications

by Manik Gupta, Bhisham Sharma, Akarsh Tripathi, Shashank Singh, Abhishek Bhola, Rajani Singh and Ashutosh Dhar Dwivedi

Sensors 2022, 22(6), 2422; https://doi.org/10.3390/s22062422 - 21 Mar 2022

Cited by 7 | Viewed by 3180

Abstract

This paper provides a conceptual foundation for stochastic duels and contains a further study of the game models based on the theory of stochastic duels. Some other combat assessment techniques are looked upon briefly; a modern outlook on the applications of the theory [...] Read more.

This paper provides a conceptual foundation for stochastic duels and contains a further study of the game models based on the theory of stochastic duels. Some other combat assessment techniques are looked upon briefly; a modern outlook on the applications of the theory through video games is provided; and the possibility of usage of data generated by popular shooter-type video games is discussed. Impactful works to date are carefully chosen; a timeline of the developments in the theory of stochastic duels is provided; and a brief literature review for the same is conducted, enabling readers to have a broad outlook at the theory of stochastic duels. A new evaluation model is introduced in order to match realistic scenarios. Improvements are suggested and, additionally, a trust mechanism is introduced to identify the intent of a player in order to make the model a better fit for realistic modern problems. The concept of teaming of players is also considered in the proposed mode. A deep-learning model is developed and trained on data generated by video games to support the results of the proposed model. The proposed model is compared to previously published models in a brief comparison study. Contrary to the conventional stochastic duel game combat model, this new proposed model deals with pair-wise duels throughout the game duration. This model is explained in detail, and practical applications of it in the context of the real world are also discussed. The approach toward solving modern-day problems through the use of game theory is presented in this paper, and hence, this paper acts as a foundation for researchers looking forward to an innovation with game theory. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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13 pages, 1826 KiB

Open AccessCommunication

Neural Network Modeling and Dynamic Analysis of Different Types of Engine Mounts for Internal Combustion Engines

by Jessimon Ferreira, Bianca Marin, Giane G. Lenzi, Calequela J. T. Manuel, José M. Balthazar, Wagner B. Lenz, Adriano Kossoski and Angelo M. Tusset

Sensors 2022, 22(5), 1821; https://doi.org/10.3390/s22051821 - 25 Feb 2022

Viewed by 1884

Abstract

This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimentally and numerically analyzed, namely [...] Read more.

This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimentally and numerically analyzed, namely an elastomeric engine mount, an elastomeric engine mount with a hydraulic component and standard decoupling, and an elastomeric engine mount with a hydraulic component and a modified decoupler—with this engineering design being a novelty in the literature. Experimental tests that considered different excitation frequencies were performed for the three types of engine mounts. Experimental data for stiffness and damping were used to obtain nonlinear mathematical models of the two systems with hydraulic components through the use of an Artificial Neural Network (ANN). For the results, all of the mathematical models presented coefficients of determination, R², greater than 0.985 for both stiffness and damping, showing an excellent fit for the nonlinear experimental data. Numerical results using a quarter-car suspension model showed a large reduction in vibration amplitudes for the first vibration model when using the hydraulic systems, with values ranging between 48.58% and 66.47%, depending on the tests. The modified system presented smaller amplitudes and smoother behavior when compared to the standard hydraulic model. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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19 pages, 5830 KiB

Open AccessArticle

Design of an Automated Multiposition Dynamic Wheelchair

by Luis Antonio Aguilar-Pérez, Juan Carlos Paredes-Rojas, Jose Israel Sanchez-Cruz, Jose Alfredo Leal-Naranjo, Armando Oropeza-Osornio and Christopher Rene Torres-SanMiguel

Sensors 2021, 21(22), 7533; https://doi.org/10.3390/s21227533 - 12 Nov 2021

Cited by 4 | Viewed by 4710

Abstract

This work presents a design for an automatized multiposition dynamic wheelchair used to transport quadriplegic patients by reconfiguring a manual wheelchair structure. An electric actuator is attached to a four-bar mechanism fixed to each side of a wheelchair’s backrest to reach multiposition. The [...] Read more.

This work presents a design for an automatized multiposition dynamic wheelchair used to transport quadriplegic patients by reconfiguring a manual wheelchair structure. An electric actuator is attached to a four-bar mechanism fixed to each side of a wheelchair’s backrest to reach multiposition. The entire device is actuated through a PID controller. An experimental test is carried out in a simplified wheelchair structure. Finally, the structure of the wheelchair is evaluated through the Dynamic analysis and Finite Element Method under the payload computed with the most critical position reached by the mechanism. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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29 pages, 53017 KiB

Open AccessFeature PaperArticle

Design, Manufacturing, and Control of a Pneumatic-Driven Passive Robotic Gait Training System for Muscle-Weakness in a Lower Limb

by I-Hsum Li, Yi-Shan Lin, Lian-Wang Lee and Wei-Ting Lin

Sensors 2021, 21(20), 6709; https://doi.org/10.3390/s21206709 - 09 Oct 2021

Cited by 4 | Viewed by 2566

Abstract

We designed and manufactured a pneumatic-driven robotic passive gait training system (PRPGTS), providing the functions of body-weight support, postural support, and gait orthosis for patients who suffer from weakened lower limbs. The PRPGTS was designed as a soft-joint gait training rehabilitation system. The [...] Read more.

We designed and manufactured a pneumatic-driven robotic passive gait training system (PRPGTS), providing the functions of body-weight support, postural support, and gait orthosis for patients who suffer from weakened lower limbs. The PRPGTS was designed as a soft-joint gait training rehabilitation system. The soft joints provide passive safety for patients. The PRPGTS features three subsystems: a pneumatic body weight support system, a pneumatic postural support system, and a pneumatic gait orthosis system. The dynamic behavior of these three subsystems are all involved in the PRPGTS, causing an extremely complicated dynamic behavior; therefore, this paper applies five individual interval type-2 fuzzy sliding controllers (IT2FSC) to compensate for the system uncertainties and disturbances in the PRGTS. The IT2FSCs can provide accurate and correct positional trajectories under passive safety protection. The feasibility of weight reduction and gait training with the PRPGTS using the IT2FSCs is demonstrated with a healthy person, and the experimental results show that the PRPGTS is stable and provides a high-trajectory tracking performance. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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24 pages, 1784 KiB

Open AccessArticle

Multi-Phase Joint-Angle Trajectory Generation Inspired by Dog Motion for Control of Quadruped Robot

by Jungsu Choi

Sensors 2021, 21(19), 6366; https://doi.org/10.3390/s21196366 - 24 Sep 2021

Cited by 4 | Viewed by 2881

Abstract

Quadruped robots are receiving great attention as a new means of transportation for various purposes, such as military, welfare, and rehabilitation systems. The use of four legs enables a robustly stable gait; compared to the humanoid robots, the quadruped robots are particularly advantageous [...] Read more.

Quadruped robots are receiving great attention as a new means of transportation for various purposes, such as military, welfare, and rehabilitation systems. The use of four legs enables a robustly stable gait; compared to the humanoid robots, the quadruped robots are particularly advantageous in improving the locomotion speed, the maximum payload, and the robustness toward disturbances. However, the more demanding conditions robots are exposed to, the more challenging the trajectory generation of robotic legs becomes. Although various trajectory generation methods (e.x., central pattern generator, finite states machine) have been developed for this purpose, these methods have limited degrees of freedom with respect to the gait transition. The conventional methods do not consider the transition of the gait phase (i.e., walk, amble, trot, canter, and gallop) or use a pre-determined fixed gait phase. Additionally, some research teams have developed locomotion algorithms that take into account the transition of the gait phase. Still, the transition of the gait phase is limited (mostly from walking to trot), and the transition according to gait speed is not considered. In this paper, a multi-phase joint-angle trajectory generation algorithm is proposed for the quadruped robot. The joint-angles of an animal are expressed as a cyclic basis function, and an input to the basis function is manipulated to realize the joint-angle trajectories in multiple gait phases as desired. To control the desired input of a cyclic basis function, a synchronization function is formulated, by which the motions of legs are designed to have proper ground contact sequences with each other. In the gait of animals, each gait phase is optimal for a certain speed, and thus transition of the gait phases is necessary for effective increase or decrease in the locomotion speed. The classification of the gait phases, however, is discrete, and thus the resultant joint-angle trajectories may be discontinuous due to the transition. For the smooth and continuous transition of gait phases, fuzzy logic is utilized in the proposed algorithm. The proposed methods are verified by simulation studies. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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Review

Jump to: Research

36 pages, 616 KiB

Open AccessReview

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

by Selim Reza, Hugo S. Oliveira, José J. M. Machado and João Manuel R. S. Tavares

Sensors 2021, 21(22), 7705; https://doi.org/10.3390/s21227705 - 19 Nov 2021

Cited by 9 | Viewed by 4324

Abstract

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and [...] Read more.

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios. Full article

(This article belongs to the Special Issue Fuzzy Systems and Neural Networks for Engineering Applications)

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