A strategic roadmap for large-scale green hydrogen demonstration and commercialisation in China: A review and survey analysis

Hydrogen is gaining increased attention from industries and policymakers in China. However, most of the current demonstration projects in the country have relied on conventional energy sources, including industrial byproduct hydrogen and grey hydrogen produced from fossil fuels. Moreover, strategies and policy frameworks leading to a shift to green or low-carbon hydrogen have neither been explored in-depth nor been identified clearly in the context of China. This study aims at bridging such gaps. Roadmapping techniques enhanced by the Delphi method and SWOT analysis are used to survey hydrogen energy experts from government bodies, industries, and academia to achieve basic agreement on strategically enabling large-scale green hydrogen demonstrations followed by commercialisation in China. The outcome of two rounds of surveys showed that experts' opinions converged on a strategic roadmap with three stages of development. The corresponding policies needed in each stage are evaluated and selected to form a systemic framework.     

Efficient FPGA implementation and verification of difference expansion based reversible watermarking with improved time and resource utilization

This paper presents Xilinx System Generator (XSG) model design for realization of reversible watermarking algorithm using Difference Expansion (DE) approach in System-On-Chip (SoC) Field Programmable Gate Array (FPGA) environment. The reversible watermarking is verified by taking a (4 × 4) sized test image and is applicable for larger sizes of cover images. The outcomes of the result demonstrate that the proposed structural design allows combining MATLAB-Simulink and XSG during graphical user interface for image processing applications. The superiority of the algorithm is justified by using comparative analysis with some well-known methods in both software and hardware environments. The method provides effectively higher PSNR at higher embedding capacity. It is also found that the method requires less time and hardware resources with throughput of 13.516 Mb/s at operational frequency of 80 MHz for real time implementation using FPGA.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics

Due to green legislations, industries track the used products through reverse logistics contractors. A reverse logistics programme offers significant cost savings in procurement, transportation, disposal and inventory carrying. Since reverse logistics operations and the supply chains they support are considerably more complex than traditional manufacturing supply chains, it can be offered to third party contractors. But availability of more number of contractors make evaluating and selecting the most efficient Reverse Logistics Contractor (RLC) a challenging task and treated as a multi-criteria decision making problem. In this paper, a hybrid method using Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is proposed. AHP is used to obtain the initial weights and Fuzzy TOPSIS is used to get the final ranking. A case study demonstrates the application of the proposed method. Finally sensitivity analysis is carried out to confirm the robustness.     

A hybrid algorithm based on particle swarm and chemical reaction optimization

In this paper, a hybrid method for optimization is proposed, which combines the two local search operators in chemical reaction optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical reaction optimization and particle swarm optimization, it creates new molecules (particles) either operations as found in chemical reaction optimization or mechanisms of particle swarm optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm optimization. The effects of model parameters like InterRate, γ, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical reaction optimization can outperform chemical reaction optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical reaction optimization in the most of experiments.     

基于灰色灾变理论的湖南洪涝灾害预测

洪涝灾害灾变系统是一个复杂的灰色系统．根据湖南省1980—2007年洪涝灾害统计数据建立了灰色灾变预测模型GM（1,1）,发现湖南省洪涝灾害的发生呈现出周期波动性和损失越来越严重的特点,在未来10年中,2010年和2017年将可能发生轻灾,2012年、2015年和2019年将可能发生重大洪灾,2013年和2016年将可能发生特大洪涝灾害．     

Performance evaluation of anti-radiation based on the gamma degradation process

In the environment of space radiation, the high-energy charged particles or high-energy photons acting on a spacecraft can cause either temporary device degradation or permanent failure. The traditional probability model is difficult to obtain reliable estimation of unit radiation resistance performance with small samples. Considering that different products will change differently after high-energy particle radiation, we construct a model based on the gamma degradation process. This model can efficiently describe the law of unit radiation resistance variation with the total radiation dose levels under the effect of the total dose and displacement damage. Finally, the proposed model is used to assess the anti-radiation performance of the N-channel power MOSFET device STRH60N20FSY3 produced by STM to obtain average unit radiation resistance, survival probability, survival function, etc.