Addressing the programming challenges of practical interferometric mesh based optical processors

We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for programmable optical processors. The proposed mesh, referred to as Bokun mesh, is an architecture that merges the attributes of the prior topologies Diamond and Clements. Similar to Diamond, Bokun provides diagonal paths passing through every individual MZI enabling direct phase monitoring. However, unlike Diamond and similar to Clements, Bokun maintains a minimum optical depth leading to better scalability. Providing the monitoring option, Bokun's programming is faster improving the total energy efficiency of the processor. The performance of Bokun mesh enabled by an optimal optical depth is also more resilient to the loss and fabrication imperfections compared to architectures with longer depth such as Reck and Diamond. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz

Engine gas path component fault diagnosis based on a sparse deep stacking network

Accurate engine gas path component fault diagnosis methods are key to ensuring the reliability and safety of engine operations. At present, the effectiveness of the data-driven gas path component fault diagnosis methods has been widely verified in engineering applications. The deep stack neural network (DSN), as a common deep learning neural network, has been gaining more attention in gas path fault diagnosis studies. However, various gas path component faults with strong coupling effects could occur simultaneously, resulting the DSN method less effective for engine gas path fault diagnosis. In order to improve the prediction performance of the DSN handling multiple gas path component fault diagnosis, a sparse regularization and representation method was proposed. The sparse regularization term is used to expand the traditional deep stacking neural network in the sparse representation, and the predicted output tag is close to the target output tag through this term. The diagnosis performance of six different neural network methods were compared by various engine gas path component fault diagnosis types. The results show that the proposed sparse regularization method significantly improves the prediction performance of the DSN, with an accuracy rate 99.9% under various gas path component fault conditions, which is higher than other methods. The proposed engine gas path component fault diagnosis method can handle multiple coupling gas path faults, and help engine operators to develop maintenance plans for the purpose of engine health management

Performance and Hydration Mechanism of Modified Tabia with Composite-Activated Coal Gangue

The feasibility of modified tabia (MT) with composite-activated coal gangue (CACG) as the subgrade material of low-grade highways was experimentally investigated. A composite activation method was employed to improve the pozzolanic activity of coal gangue. The effect of CACG content on the mechanical properties of MT was investigated through a series of experiments. It was found that the pozzolanic reactivity of coal gangue was remarkably enhanced by the composite activation method. Compared with traditional tabia (TT), the unconfined compressive strength, splitting strength, and flexural tensile strength of the MT with 50% of CACG content increased by 5.03 times, 9.71 times, and 1.50 times, respectively. The impermeability of specimens with CACG significantly improved. Furthermore, the mass loss rate of MT was less than 2.83%, while it reached up to 34.20% in TT after being conditioned to 40 freeze–thaw cycles. Finally, the microstructure change and hydration mechanism of MT are discussed and revealed

Performance and Hydration Mechanism of Modified Tabia with Composite-Activated Coal Gangue

The feasibility of modified tabia (MT) with composite-activated coal gangue (CACG) as the subgrade material of low-grade highways was experimentally investigated. A composite activation method was employed to improve the pozzolanic activity of coal gangue. The effect of CACG content on the mechanical properties of MT was investigated through a series of experiments. It was found that the pozzolanic reactivity of coal gangue was remarkably enhanced by the composite activation method. Compared with traditional tabia (TT), the unconfined compressive strength, splitting strength, and flexural tensile strength of the MT with 50% of CACG content increased by 5.03 times, 9.71 times, and 1.50 times, respectively. The impermeability of specimens with CACG significantly improved. Furthermore, the mass loss rate of MT was less than 2.83%, while it reached up to 34.20% in TT after being conditioned to 40 freeze–thaw cycles. Finally, the microstructure change and hydration mechanism of MT are discussed and revealed

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%