Model predictive control of water quality in drinking water distribution systems considering disinfection by-products

The shortage in water resources have been observed all over the world. However, the safety of drinking water has been given much attention by scientists because the disinfection will react with organic matters in drinking water to generate disinfection by-products (DBPs) which are considered as the cancerigenic matters. Although much research has been carried out on the water quality control problem in DWDS, the water quality model considered is linear with only chlorine dynamics. Compared to the linear water quality model, the nonlinear water quality model considers the interaction between chlorine and DBPs dynamics. The thesis proposes a nonlinear model predictive controller which utilises the newly derived nonlinear water quality model as a control alternative for controlling water quality. EPANET and EPANET-MSN are simulators utilised for modelling in the developed nonlinear MPC controller. Uncertainty is not considered in these simulators. This thesis proposes the bounded PPM in a form of multi-input multi-output to robustly bound parameters of chlorine and DBPs jointly and to robustly predict water quality control outputs for quality control purpose. The methodologies and algorithms developed in this thesis are verified by applying extended case studies to the example DWDS. The simulation results are presented and critically analysed

INTELLIGENT REGRESSION TESTING FOR INTERNET OF THINGS WIRELESS DEVICE USING MIXED MACHINE LEARNING METHODS

Techniques are described herein for a mixed Machine Learning (ML) method which contains both the Ant Colony Optimization (ACO) algorithm and Bayesian method. The regression test is very common for a Quality Assurance (QA) team in its daily work, but usually has too many cases for a very little-changed patch. The techniques described herein may pick up as few desired test cases as possible but still guarantee the same testing performance as before. This may improve the efficiency of the QA team, shorten its work time, and reduce the requirement for testing devices as well

ADAPTIVE TRANSMISSION POWER IN LOW-POWER AND LOSSY NETWORK

Techniques are provided herein for intelligent transmission power control under different transmission patterns in a connected grid mesh. The transmission patterns include asynchronized transmission, broadcast transmission, and unicast transmission. They also provide a mechanism to help data packets compete against interference on specific channels and help high priority Quality of Service (QoS) packet have a greater chance to be received when congestion occurs. This enables the connected grid mesh to achieve higher reliability of communication with efficient power consumption

Towards Free Data Selection with General-Purpose Models

A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. In this paper, we challenge this status quo by designing a distinct data selection pipeline that utilizes existing general-purpose models to select data from various datasets with a single-pass inference without the need for additional training or supervision. A novel free data selection (FreeSel) method is proposed following this new pipeline. Specifically, we define semantic patterns extracted from inter-mediate features of the general-purpose model to capture subtle local information in each image. We then enable the selection of all data samples in a single pass through distance-based sampling at the fine-grained semantic pattern level. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods. Extensive experiments verify the effectiveness of FreeSel on various computer vision tasks. Our code is available at https://github.com/yichen928/FreeSel.Comment: accepted by NeurIPS 202

DYNAMICALLY MITIGATING BOTTLENECK EFFECT TO GUARANTEE QUALITY OF SERVICE IN LOW-POWER AND LOSSY NETWORKS

Techniques are described herein for providing an intelligent and dynamic routing policy for Quality of Service (QoS) based on Routing Protocol for Low-Power and Lossy Networks (RPL) Directed Acyclic Graph (DAG). This helps mitigate the bottleneck effect in a connected grid mesh by forecasting the capacity of the routing path. Each sender device may be able to forward packets based on QoS requirements to the proper next hop before RPL DAG updates by Expected Transmission Count (ETX) change. With this approach, the QoS of latency sensitive or low packet loss tolerance services can be better satisfied in the connected grid mesh network

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE