Accurate prediction of reaction temperature in rotary kiln is essential to realize its advanced process control and operational optimizations. However, the complexity of the physical and chemical reactions in the rotary kiln makes it difficult for the traditional mechanism model to characterize the dynamic kiln process. In this study, a deep learning-based temperature prediction model is proposed to accurately track the temperature changes during the production process of rotary kiln. The proposed model integrates a temporal convolutional network (TCN) with a long short-term memory (LSTM) network. The former enables the proposed model to be aware of local context and can effectively compute local characteristics, whereas the latter with its time-memory capability can better capture the long-term dependencies of data to extract deep-level features. To enhance the prediction accuracy, the proposed model is further improved by introducing a novel feature fusion and hybrid pooling layer to merge the original input with TCN output features, which efficiently preserve the detailed information and significantly reduce model complexity. Attention mechanism is also incorporated after the LSTM network to concentrate on the key moment information and improve the model performance. Monitoring data from a zinc rotary kiln at a field site is used for model training and testing. Results demonstrate that the proposed model can achieve the best mean square error, 0.165, exhibiting a promising prediction accuracy of the rotary kiln’s temperature. It outperforms the state-of-the-art machine learning-based prediction models such as LSTM, Gated Recurrent Unit (GRU) and TCN. Note to Practitioners —This work considers high-accuracy prediction of the temperature changes during the production process of rotary kiln. It is critical to well predict kiln tail temperature by capturing long-term dependencies and extracting global and local features from time series data. Yet, this is an extremely challenging task because rotary kiln operation is a complex thermal process with multivariate, pure hysteresis, strong interference, nonlinearity and strong coupling characteristics. As a result, these issues inhibit the establishment of a model that can accurately characterize the production process of the rotary kiln and impedes the realization of automation in volatile kiln production. Current prediction methods usually adopt mechanism-based and data-driven, there are suffer from long computation time, unknown kinetic parameters and poor accuracy. Thus, they fail to effectively learn features and accurately establish model. This work proposes a temperature prediction dynamic model named enhanced TCN-LSTM, which combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, incorporating a unique feature fusion technique along with a hybrid pooling layer and attention layer. This integration enables the model to extract both global and local features, thereby preserving detailed information efficiently while significantly reducing model complexity and enhance the stability and robustness. Experimental results based on the zinc rotary kiln dataset demonstrate that the proposed model exhibits a promising prediction accuracy of the rotary kiln’s temperature. In the future, it can be readily implemented and applied in many industrial areas such as systems modeling, time series forecasting, and predictive maintenance.

Submarine optical transmission systems are commonly operated with constant output power amplifiers. The Gaussian Noise (GN) model of propagation was generally developed in the context of constant gain amplification and accounts for amplified spontaneous emission (ASE) noise and nonlinear interference (NLI) noise. Recently, new models have been proposed and developed to predict signal-to-noise ratio (SNR) models for systems with constant output power amplification that account for the phenomenon known as signal droop, or more broadly, generalized droop (GD). Here we describe a GD model within the context of noises as generated in the GN model, and then further including other distributed noise generation such as might occur for crosstalk in multicore optical fibers. The model results in simple expressions for SNR, signal power, and noise powers as a function of the number of identical spans. We provide numerical modeling confirmation of the analytical model for different formats, and experimental transmission data that is fully consistent with the model.

We assess the potential applicability and performance of multicore fibers (MCFs) against conventional single-core fibers (SCFs) in submarine transmission systems. In particular, we assume 4-core MCFs with nominally uncoupled cores and separate amplification of each MCF core with conventional erbium doped fiber amplifiers (EDFAs) using fan-in/fan-out (FI/FO) devices in the repeaters. We examine the effects of the number of physical fibers accommodated in the cable design, MCF and FI/FO crosstalk, FI/FO loss, span length, transceiver implementation penalty, and minimum required signal-to-noise ratio (SNR). The relative cable capacities offered by the MCF and SCF systems are evaluated under the different conditions. Then the relative cost/capacity for systems built with the two types of fiber are calculated using a system cost model for two conditions: 1) with maximum cable capacity and 2) for minimum overall cost/capacity. The potential role of multicore fibers (MCFs) in submarine cable systems is investigated in the context of electrical power limitations and physical fiber count limitations. Cable capacities are estimated based on ideal Shannon-limited capacity unless a transceiver implementation penalty, or gap-to-Shannon capacity, is applied. The systems are modeled assuming constant output power amplifiers as are commonly deployed in submarine systems. We find that MCFs offer the greatest cable capacity increase for low fiber count cables, but that the cost/capacity can be significantly higher. When minimization of cost/capacity is the design goal, MCF systems remain higher in this metric than SCF systems, while enabling only a small capacity increase at best.

Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation.

We analyze the potential suitability or strength of arguments for the application of multicore optical fibers in high capacity submarine cable systems via transmission and techno-economic modeling. We consider hypothetical multicore fibers (MCFs) with 2–4 weakly coupled cores and compare capacity and cost/bit against conventional single-core fibers (SCFs). The analysis is performed in the context of a trans-Atlantic link length system and we evaluate the relative fiber performance with three different, but related, system design approaches. Two SCF coating diameters are assessed in terms of how this parameter affects the cost/bit through fiber density in submarine cables and resulting cable cost. We find that MCFs may enable higher cable capacity when fiber pair limits are imposed, but likely not at lower cost/bit unless optimistic and best case assumptions are made with respect to MCF relative fiber cost. We also find that reduced diameter SCFs can deliver much of the density and cable cost savings that motivates interest in MCF without the challenges of a new eco-system as required by MCF. However, MCF may enable the design of the largest cable capacities such as 1 Pb/s or more that might not be attainable with SCFs without significant cable changes.

Fringe projection profilometry (FPP) is an extensively used active three-dimensional (3D) measurement technique. However, it faces challenges in achieving synchronous improvement of measurement range, speed, accuracy and shadow issues. To meet the demand for rapid 3D reconstruction of two projection views only using a single-shot phase-shifting grating, we initially propose a fast and large-scale 3D reconstruction system based on deep learning with a dual-projector and single-camera configuration, named DL_DPSL. The key feature is that the entire measurement process only requires two projectors to project one image simultaneously. To validate the effect, we constructed a simulated and a real system and corresponding simulation ( ˜3300 sets) and real dataset ( ˜1000 sets) respectively. The experimental results show that based ResUNet the DL_DPSL system can recover the phases of two projection views from one-shot superimposed phase-shifting grating within 0.018 s inclusive projection time when using two NVIDIA GeForce RTX 3090 graphics cards. Additionally, despite reducing the projection time by half, in the simulation dataset, exists the mean average error was reduced by a maximum of 20%. And similar performance improvements in the continuous area of the measured objects in the real dataset. The DL_DPSL system has better accuracy and stability compared to the current fastest deep learning system. DL_DPSL breaks the existing dual-projector structured light 3D measurement system paradigm, providing a new solution to promote the development and industrial application of FPP technology, with broad application prospects. (Simulation and real datasets are publicly available on the following GitHub repository: https://github.com/LiYiMingM/DPSL3D-measurement).