Measurement of the Mass Flow and Velocity Distributions of Pulverized Fuel in Primary Air Pipes Using Electrostatic Sensing Techniques

On-line measurement of pulverized fuel (PF) distribution between primary air pipes on a coal-fired power plant is of great importance to achieve balanced fuel supply to the boiler for increased combustion efficiency and reduced pollutant emissions. An instrumentation system using multiple electrostatic sensing heads are developed and installed on 510 mm bore primary air pipes on the same mill of a 600 MW coal-fired boiler unit for the measurement of PF mass flow and velocity distributions. An array of electrostatic electrodes with different axial widths is housed in a sensing head. An electrode with a greater axial width and three narrower electrodes are used to derive the electrostatic signals for the determination of PF mass flow rate and velocity, respectively. The PF velocity is determined by multiple cross-correlation of the electrostatic signals from the narrow electrodes. The measured PF velocity is applied on the root-mean-square magnitude of the measured electrostatic signal from the wide electrode for the calibration of PF mass flow rate. On-plant comparison trials of the developed system were conducted under five typical operating conditions after a system calibration test. Isokinetic sampling equipment is used to obtain reference data to evaluate the performance of the developed system. Experimental data demonstrate that the developed system is effective and reliable for the on-line continuous measurement of the mass flow and velocity distributions between the primary air pipes of the same mill

Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent

An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-party OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions

Hybrid Hierarchical Collision Detection Based on Data Reuse

To improve the efficiency of collision detection between rigid bodies in complex scenes, this paper proposes a method based on hybrid bounding volume hierarchies for collision detection. In order to improve the simulation performance, the method is based on weighted oriented bounding box and makes dense sampling on the convex hulls of the geometric models. The hierarchical bounding volume tree is composed of many layers. The uppermost layer adopts a cubic bounding box, while lower layers employ weighted oriented bounding box. In the meantime, the data of weighted oriented bounding box is reused for triangle intersection check. We test the method using two scenes. The first scene contains two Buddha models with totally 361,690 triangle facets. The second scene is composed of 200 models with totally 115, 200 triangle facets. The experiments verify the effectiveness of the proposed method

EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.Comment: submitted to ISWC 202