Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)

Çevrimiçi ( XIV, 67 pages

A Tutorial on Learning Human Welder\u27s Behavior: Sensing, Modeling, and Control

Human welder\u27s experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder\u27s behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder\u27s intelligence are surveyed, including sensing of the weld pool, modeling of the welder\u27s adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder\u27s intelligence into robotic welding system. Finally future research directions in this field are presented

Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models

制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward

A GODFIP Control Algorithm for an IRC Grain Dryer

Drying is an energy intensive and complex nonlinear process and it is difficult to control and make the traditional control meet the challenges. In order to effectively control the output grain moisture content of a combined infrared radiation and convection (IRC) grain dryer, taking into account the superiority of the fuzzy control method in dealing with complex systems, in this article, a genetic optimization dual fuzzy immune PID (Proportional-Integral-Derivative) (GODFIP) controller was proposed from the aspects of energy savings, stability, accuracy, and rapidity. The structure of the GODFIP controller consists of two fuzzy controllers, a PID controller, an immune algorithm, and a genetic optimization algorithm. In addition, a NARX model which can give relatively good predictive output information of the IRC dryer was established and used to represent the actual drying process to verify the control performance in the control simulation and anti-interference tests. The effectiveness of this controller was demonstrated by computer simulations, and the anti-interference performance comparative study with the other controllers further confirmed the superiority of the proposed grain drying controller which has the least value of performance objective function, the shortest rising time, and the best anti-interference ability compared to the other three compared controllers

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems

Modelling and control of dynamic systems are faced by multiple technical challenges, mainly due to the nature of uncertain complex, nonlinear, and time-varying systems. Traditional modelling techniques require a complete understanding of system dynamics and obtaining comprehensive mathematical models is not always achievable due to limited knowledge of the systems as well as the presence of multiple uncertainties in the environment. As universal approximators, fuzzy logic systems (FLSs), neural networks (NNs) and neuro-fuzzy systems have proved to be successful computational tools for representing the behaviour of complex dynamical systems. Moreover, FLSs, NNs and learning-based techniques have been gaining popularity for controlling complex, ill-defined, nonlinear, and time-varying systems in the face of uncertainties. However, fuzzy rules derived by experts can be too ad-hoc, and the performance is less than optimum. In other words, generating fuzzy rules and membership functions in fuzzy systems is a potential challenge especially for systems with many variables. Moreover, under the umbrella of FLSs, although type-1 fuzzy logic control systems (T1-FLCs) have been applied to control various complex nonlinear systems, they have limited capability to handle uncertainties. Aiming to accommodate uncertainties, type-2 fuzzy logic control systems (T2-FLCs) were established. This thesis aims to address the shortcomings of existing fuzzy techniques by utilisation of type-2 FLCs with novel adaptive capabilities. The first contribution of this thesis is a novel online system identification technique by means of a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) to accommodate the footprint-of-uncertainties (FoUs). This development is meant to specifically address the shortcomings of type-1 fuzzy systems in capturing the footprint-of-uncertainties such as mechanical wear, rotor damage, battery drain and sensor and actuator faults. Unlike previous type-2 TS fuzzy models, the proposed method constructs two fuzzifiers (upper and lower) and two regression coefficients in the consequent part to handle uncertainties. The weighted least square method is employed to compute the regression coefficients. The proposed method is validated using two benchmarks, namely, real flight test data of a quadcopter drone and Mackey-Glass time series data. The algorithm has the capability to model uncertainties (e.g., noisy dataset). The second contribution of this thesis is the development of a novel self-adaptive interval type-2 fuzzy controller named the SAF2C for controlling multi-input multi-output (MIMO) nonlinear systems. The adaptation law is derived using sliding mode control (SMC) theory to reduce the computation time so that the learning process can be expedited by 80% compared to separate single-input single-output (SISO) controllers. The system employs the `Enhanced Iterative Algorithm with Stop Condition' (EIASC) type-reduction method, which is more computationally efficient than the `Karnik-Mendel' type-reduction algorithm. The stability of the SAF2C is proven using the Lyapunov technique. To ensure the applicability of the proposed control scheme, SAF2C is implemented to control several dynamical systems, including a simulated MIMO hexacopter unmanned aerial vehicle (UAV) in the face of external disturbance and parameter variations. The ability of SAF2C to filter the measurement noise is demonstrated, where significant improvement is obtained using the proposed controller in the face of measurement noise. Also, the proposed closed-loop control system is applied to control other benchmark dynamic systems (e.g., a simulated autonomous underwater vehicle and inverted pendulum on a cart system) demonstrating high accuracy and robustness to variations in system parameters and external disturbance. Another contribution of this thesis is a novel stand-alone enhanced self-adaptive interval type-2 fuzzy controller named the ESAF2C algorithm, whose type-2 fuzzy parameters are tuned online using the SMC theory. This way, we expect to design a computationally efficient adaptive Type-2 fuzzy system, suitable for real-time applications by introducing the EIASC type-reducer. The proposed technique is applied on a quadcopter UAV (QUAV), where extensive simulations and real-time flight tests for a hovering QUAV under wind disturbances are also conducted to validate the efficacy of the ESAF2C. Specifically, the control performance is investigated in the face of external wind gust disturbances, generated using an industrial fan. Stability analysis of the ESAF2C control system is investigated using the Lyapunov theory. Yet another contribution of this thesis is the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). T2-EFCS has two phases, namely, the structure learning and the parameters learning. The structure of T2-EFCS does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one rule. The rules are then added and pruned in an online fashion to achieve the desired set-point. The proposed technique is applied to control an unmanned ground vehicle (UGV) in the presence of multiple external disturbances demonstrating the robustness of the proposed control systems. The proposed approach turns out to be computationally efficient as the system employs fewer fuzzy parameters while maintaining superior control performance

Thermal behaviour model identification for three different office buildings

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The thermal behaviour was investigated of three offices positioned in three buildings built in different periods, one academic institute built in 1920 and two modem commercial buildings in London. The buildings chosen for this study are the Rockefeller Building, which is part of University College London (UCL), Portman House in Oxford Street and the Visa Building in Paddington. Due to the lack of specific information related to the structure of the buildings such as windows, doors, building dimensions and other information that would allow the use of physical models, in this project black-box linear and non-linear mathematical models were used. Data relating to room temperature, hot and chilled water temperature, air flow and temperature from air handling units and outside temperature were collected for one year, from the actual building management systems (BMSs) installed in these buildings. The main assumption of the model development in the three buildings was that although occupancy, computers, printers etc cause an additional internal heat gain, their impact is in part indirectly included in the model. The primary objective of the analysis was to identify the inputs (independent variables) that gave good models for the prediction of room temperature for a certain period. Consequently, the process of input selection and period of validity in obtaining models that give good thermal prediction (within the same period) were the key points in season subdivision. The first part of the analysis applied the following linear parametric mathematical models to the three office buildings selected: Box Jenkins (BJ), autoregressivem oving averagew ith exogenousi nput (ARMAX) and output error (OE) structure. The project then deals with non-linear mathematical models. The same inputs selected and assumptions made with linear analysis were used to build, in turn, models with feedforward backpropagation (FFBP), non-linear autoregressive mathematical models with parallel arrangement (NARX) and series-parallel arrangement (NARXSP). The research presented in this project is related to developing models for three real offices positioned in three different buildings whereas previous researchers have applied these models mainly to experimental rooms and HVAC plants, with the purpose of fault detection and diagnostics. Furthermore, in the past, research on thermal model development has been related to one office or HVAC plant, and for a limited period of time (a few weeks or months). In contrast, this study undertakes an overall analysis of thermal model development for three offices and for a period of one year, where the process of input selection is given priority to obtain good models. Thus, previous studies have not utilized these two types of models for such a long period of data collection nor related them to three different buildings. Finally, model development and then validation were pursued utilizing the same week, different weeks and different days (where the first part of the data in each case was used for model estimation and the following part for model validation). This was one within the period that the models gave good results for the prediction of room temperature. The best mathematical models (linear and non-linear) that predict the room temperature, in terms of the inputs selected, has been determined for each season. The procedures for how to choose the best models are based on the following techniques: final prediction error (FPE for linear models), mean squared error (mse for non-linear models), and model fits and errors between measurements and simulated model output. Overall, the results related for the prediction of room temperature with non-linear models, are better than those obtained with linear models, as a result of comparison between models' errors, FPE and mse obtained with linear and non-linear models.Funding was obtained from the Brunel University Department of Mechanical Engineering