Price-based demand response (PBDR) offers a significant opportunity for electricity consumers to dynamically balance their energy demand in response to time-varying electricity prices, and therefore ease the burden on the grid during peak times. However, despite being the primary energy consumers, there is little research carried out on implementing PBDR in industrial facilities, especially on real-time price (RTP) based DR. In this study, we propose a DR scheme based on hour-ahead RTP for industrial facilities. The scheme implements an artificial neural network based price forecasting model to forecast unknown future prices to support global time horizon optimization. Based on the forecasting price, the energy cost minimization problem is formulated by mixed integer linear programming. This paper includes a practical case study of the whole process of steel powder manufacturing for performance analysis. The results show that the proposed scheme is capable of balancing the energy demand and reducing energy costs while satisfying production targets.

In modern manufacturing systems, various industrial communication systems (e.g., fieldbus systems and industrial Ethernet networks) have been used to realize reliable information exchange. However, these industrial communication solutions are largely incompatible with each other, which do not satisfy the new requirements of Industry 4.0. Recently Time-Sensitive Networking (TSN) has been developed to improve the real-time capabilities to the standard Ethernet, and is considered to be a promising real-time communication solution for Industry 4.0. In this work, we propose a communication architecture for a manufacturing system using the Open Platform Communications Unified Architecture (OPC UA) and TSN technologies. TSN is adopted as the communication backbone to connect heterogeneous industrial automation subsystems. The OPC UA is adopted to realize horizontal and vertical communication between subsystems in the field layer and the entities of the upper layers. We implement a laboratory-level manufacturing system to validate the proposed architecture. The experimental results demonstrate the feasibility and capability of the proposed architecture. Moreover, we evaluate the performance of a key TSN substandard, i.e., IEEE 802.1Qbv, in the laboratory-level manufacturing system. The evaluation results demonstrate that IEEE 802.1Qbv can indeed provide excellent real-time capabilities for industrial applications.

Industrial and automation control systems require that data be delivered in a highly predictable manner in terms of time. Time-sensitive Networking (TSN), an extension of the Ethernet, is a set of protocols developed and maintained by the IEEE 802.1 Task Group; the protocols deal with time synchronization, traffic scheduling, and network configuration, etc. TSN yields promising solutions for real-time and deterministic networks. Here, we develop a TSN simulation model based on OMNET++; we model a TSN-enabled switch that schedules traffic using gate control lists (GCLs). Simulation verified that the model guaranteed deterministic end-to-end latency.

Recently, Time-Sensitive Networking (TSN) has been developed to incorporate real-time capabilities to the standard Ethernet for the incompatible issue in existing industrial communication solutions and is considered a promising solution to satisfy the requirements of the Industrial Internet of Things (IIoT). However, the routing and scheduling methods are left out of the TSN standard. This study developed a novel joint traffic routing and scheduling algorithm to compute routes for time-critical flows and construct the schedules. The validity of the proposed algorithm was verified using a random synthetic test scenario. The simulation results demonstrated that the real-time requirements of time-critical traffic could be satisfied with the proposed algorithm. Subsequently, we evaluated the scalability of the proposed algorithm from the perspective of the number of flows that can be scheduled and the network size. The results showed that the computational times for 4000 flows in the realistic industrial network topology and random network topologies (with up to 21 switches and 105 end stations) are at the sub-second level, which indicated the perfect scalability of the proposed algorithm. In addition, compared with the well-known Integer Linear Programming (ILP)-based approach, Degree of Conflict (DoC)-Aware Iterative Routing and Scheduling (DA/IRS) approach and Hybrid Genetic Algorithm (HGA)-based approach, the proposed algorithm is faster than the three approaches by at least an order of magnitude.

The aftermath of a natural disaster leaves victims trapped in rubble which is challenging to detect by smart drones due to the victims in low visibility under the adverse disaster environments and victims in various sizes. To overcome the above challenges, a transformer fusion-based scale-aware attention network (TFSANet) is proposed to overcome adverse environmental impacts in disaster areas by robustly integrating the latent interactions between RGB and thermal images and to address the problem of various-sized victim detection. Firstly, a transformer fusion model is developed to incorporate a two-stream backbone network to effectively fuse the complementary characteristics between RGB and thermal images. This aims to solve the problem that the victims cannot be seen clearly due to the adverse disaster area, such as smog and heavy rain. In addition, a scale-aware attention mechanism is designed to be embedded into the head network to adaptively adjust the size of receptive fields aiming to capture victims with different scales. Extensive experiments on two challenging datasets indicate that our TFSANet achieves superior results. The proposed method achieves 86.56% average precision (AP) on the National Institute of Informatics—Chiba University (NII-CU) multispectral aerial person detection dataset, outperforming the state-of-the-art approach by 4.38%. On the drone-captured RGBT person detection (RGBTDronePerson) dataset, the proposed method significantly improves the AP of the state-of-the-art approach by 4.33%.