Virtualized Welding Based Learning of Human Welder Behaviors for Intelligent Robotic Welding

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Synergistic Mechanism of Designing Information Granules with the Use of the Principle of Justifiable Granularity

The construction of information granules is a significant and interesting topic of Granular Computing (GrC) in which information granules play a vital role in representing and describing data, and it has become one of the most effective frameworks for solving complex problems. In this study, we are interested in the collaborative impacts of several different characteristics on constructing information granules, and a novel synergistic mechanism of the principle of justifiable granularity is utilized in developing information granules. The synergistic mechanism is finalized with a two-phase process—to start with, the principle of justifiable granularity and Fuzzy C-Means Clustering method are combined to develop a collection of information granules. First, the available experimental data is transformed (normalized) into fuzzy sets following the standard Fuzzy C-Means Clustering method. Then, information granules are developed based on the elements located in different clusters with the use of the principle of justifiable granularity. In the sequel, the positions of information granules are updated by considering the collaborative impacts of the other information granules with the parameters of specifying the level of influence. Experimental studies are conducted to illustrate the nature and feasibility of the proposed framework based on the synthetic data as well as a series of publicly available datasets coming from KEEL machine learning repositories

Travel route planning method to avoid epidemic hot-spots in the post-epidemic era

The Chinese government adhered to the “dynamic clearance” epidemic prevention strategy from August 2021 to December 7, 2022, during the post-epidemic era (this study started in March 2022 and ended in September 2022). People are gradually resuming their daily routines, and demand for travel is rising again. Nonetheless, the epidemic occasionally breaks out on a smaller scale, causing social concern. As a social reaction, the essential issue is how to avoid COVID-19 hot-spots effectively by offering secure travel options for local residents who tend to travel privately. Two travel route planning models are proposed to avoid COVID-19 hot-spots based on the invalid road sections which are affected by epidemic. Specifically, the static model aims at generating the shortest travel distance after detours, with the constraint of avoiding COVID-19 hot-spots; the dynamic model takes real-time data into account, which includes epidemic risk levels, road grades, and real-time traffic information on road selection. Shenzhen, China, is illustrated as an example of the research area in this paper. To assess the effectiveness and efficiency of the suggested approaches, data regarding the road network, the prevalence of epidemics, and traffic congestion are collected. The experimental results demonstrate that 1) the proposed two route planning models can effectively bypass areas with high levels of epidemic risk. 2) The static route planning model increases the travel distance by 12.24% and 13.03%, while the dynamic route planning model increases the travel distance by 24.33% and 27.69% compared with the conventional shortest route, given the same origin and destination and the same impact radii of the COVID-19 hot-spots (300 and 500 m respectively). When taking detour routes to avoid COVID-19 hot-spots, the average increase in trip distance does not surpass 50%, which is acceptable psychologically for travelers. 3) The static travel route planning model is suitable for the severe epidemic situation for it can strictly avoid the epidemic hot-spots; The dynamic travel route planning model is applicable to the situation where the epidemic situation is relatively mild. Ultimately, the route planning models can be utilized to develop a framework to provide travelers with detour options, which would make a practical difference to ensure travelers’ safety during traveling and contribute to preventing the spread of the epidemic