Aircraft is an important means of travel and the most convenient and fast vehicle in long-distance transportation. The aircraft engine is one of the most critical parts of an aircraft, and its reliability and safety are extremely important. In this paper, we consider that the operating conditions of aero-engines are complex and changeable, and a multi-resolution long short-term memory (MR-LSTM) model is proposed. The model can effectively predict the remaining useful life (RUL) of an aero-engine, which is a priority issue within the Prognostics and Health Management (PHM) framework - and thus it can support maintenance decisions. Sequences with multiple temporal resolutions are generated by a reconstruction of the decomposed wavelets. A two-layer LSTM model is then designed: 1) the first layer LSTM is used to learn attention at different time resolutions as well as to generate an integrated historical representation; 2) the second layer LSTM is used to learn the long and short-term time dependencies in the integrated historical representation. Experimental evaluations using the C-MAPSS datasets (FD002 and FD004) and the N-CMAPSS dataset showed that compared to other state-of-the-art RUL prediction methods, the FD002 sub-dataset showed a 12.1% reduction in RMSE and a 3.8% reduction in Score; the FD004 sub-dataset showed a 21.8% reduction in RMSE and a decreased by 62.1%; the RMSE of the N-CMAPSS dataset decreased by at most 25.8%.

Specific emitter identification (SEI) is a technology that extracts subtle features from signals sent by emitters to identify different individuals. It can effectively improve the security of the Industrial Internet of Things (IIoT) by acting on the physical layer of the Internet. Recent research on SEI has focused on deep learning (DL) models that can automatically learn effective inherent emitter features from raw signals. Nevertheless, training popular DL models is computationally expensive because of the numerous hyperparameters and nonscalable structures. This limits the application of DL-based SEI models in certain practical IIoT scenarios. To address this concern, we propose an adaptive broad learning (ABL) method to build a lightweight SEI model. In the proposed model, the raw signal samples are mapped to feature nodes, and the emitters are denoted as the output nodes. The hidden nodes are directly connected to the output nodes by a broad network. Through this flat structure, the size and calculation amount of the model can be effectively reduced. To further economize the computational cost, we designed an adaptive node expansion strategy for rapidly obtaining the optimal hyperparameters of the models. The results of experiments on real-world data prove the superiority of ABL over popular state-of-the-art DL-based SEI models.

Underwater image enhancement (UIE) is an essential task for intelligent environment perception in underwater remote visual sensing scenarios. However, the computing power of mobile platforms limits the usage of larger-scale models. In this paper, we propose a lightweight encoder-decoder architecture (UIENet) to enhance underwater images from visual sensors. We also involve the architecture into a generative adversarial model (UIEGAN) against a supervised discriminator to further perfect its corrective capabilities for the photo-realistic images with more global appearance and local details. The multi-resolution counterparts are embedded into the generator to diversify the feature representation of the original inputs. Further, UIEGAN guides the spatial attention module and the channel attention module to jointly enhance the global-local connection of the image. We evaluate the proposed method on benchmark datasets of UIEB and UFO-120 and report better performance than the state-of-the-art schemes, exceeding 11.15% and 12.85% on peak signal-to-noise ratio (PSNR) than the baselines of these datasets. Besides, by testing on the UIEB challenge, URPC and SQUID datasets without any reference images, our scheme outperforms the other methods on evaluation metrics to validate its generalization performance, and meanwhile uses a series of ablation study demonstrates the effectiveness of the functional modules.

Remaining useful life (RUL) prediction of aero-engines is one of the important issues in research related to engine health management. Although deep learning has made great progress in fault diagnosis research, successful training of deep learning models is very time-consuming and difficult to meet the real-time requirements of online RUL prediction applications. Broad learning systems (BLS) provide an alternative to deep learning networks with low computational resource requirements, fast training time, and incremental scalability. Based on the typical BLS, we propose a new lightweight multiscale BLS (MSBLS). Considering that RUL is influenced by the working condition factor, the discrete wavelet transform is used to generate multiresolution components, and then feature nodes are extracted on top of the components. An elastic net regularization technique is used to constrain the output weights of the nodes, preserving the significant nodes, and finally obtaining a more sparse MSBLS. Experiments are conducted using the NASA publicly available commercial modular aero-propulsion system simulation (C-MAPSS) dataset and the N-CMAPSS dataset, and our proposed MSBLS not only improves the accuracy of RUL prediction but also has a very short training time compared with the latest research methods nowadays.

In industrial Internet of Things, the combination of specific emitter identification (SEI) and key authenticated technologies can effectively resist spoofing attacks and improve system security. However, most existing SEI approaches extract features based on real valued operations and only work in static scenario. This motivated us to develop a novel SEI method tasked with: exploiting the high potential model for SEI based on the inphase/quadrature (I/Q) signal that is represented by complex number, and realizing rapid reconstruction of the model in the face of dynamic scenarios. To this end, in this article, we introduce a two-stage cross-domain identification model. First, a complex-valued separate residual network (CVSRN) with novel separate residual modules is proposed as the pretrained model. The CVSRN can automatically extract effective inherent features directly from raw signals in an end-to-end manner, which favors complex-valued signals that are found in two distinct signal paths. Second, three transfer strategies are proposed to achieve rapid construction of the target SEI model. They leverage the knowledge learned from the pretrained CVSRN to facilitate the recognition of a new but similar emitters. We benchmark our proposed approach against four state-of-the-art SEI methods on real-world data and exhibit that it is not only competitive but also able to cope with complex dynamic scenarios.

In this study, we propose a highly efficient energy control framework supported by edge computing to reduce energy waste and increase the benefit for industrial users. To this end, battery energy storage systems are currently being employed to store energy for stability of supply and quality of power. The optimal load patterns and corresponding energy storage capacities of the battery energy storage systems can be obtained through the framework, according to the energy market and the historical load data of industrial users. However, computing these requires considering the trade-off between equipment cost, time-of-use electricity price, running expenses, and other related factors, which would be an NP-hard problem. To address this challenge, we also propose an adaptive mixed differential evolution algorithm with a novel mutation strategy. Experiments on real-world data demonstrate the effectiveness of the proposed algorithm and framework.