Optical sensing for nondestructive structural evaluation and additive manufacturing process monitoring

Sensing is a significant engineering science which quantify parameters from the physical world and discover the physics running behind the measurement process. Optical sensing makes use of electromagnetic waves from infrared to ultraviolet on the light spectrum as a medium to measure variables, such as position, temperature and strain. Image sensing and fiber sensing are two of the most widely applied optical sensing methods in industries and daily life. They have been studied by the academia for decades, due to their immunity to electromagnetic interference and ease of installation. This dissertation introduced the research on the intelligent and flexible metrology methodologies for real-time structure and process monitoring based on optical sensing. The works focused on two major topics: 1) structural health monitoring for compact heat exchanger (CHE), and 2) bimetallic additive manufacturing process monitoring. For the structural health test, a novel online sensing method capable of detecting internal cracks for Compact Heat Exchanger (CHE) was designed and developed through optical fiber sensor based strain measurement. A crack diagnosis model was built to evaluate crack positions based on limited sampling data in mechanical structure. The model established a physical basis to correlate crack position and distributed strain variation that can be detected by the optical fiber sensors. A physical model quantifying the strain transfer from the sensor embedded mechanical structure to the fiber sensor was built to describe the performance of the sensors at different working conditions. A good match has been observed in the comparison of the data from experimental tests and analytical models, with an average relative error 2.4%. Finally, an experimental platform was designed and setup to validate introduced nondestructive test method. The experimental results showed that strain variations can be detected by optical fiber sensors when crack presented in CHE during elastic deformation, plastic deformation and crack growth process. For bimetallic additive manufacturing process monitoring, an in-situ sensing method for measuring material composition in the printed alloy was modeled and developed based on infrared imaging. The method takes the size of temperature contours surrounding the heated spots during additive manufacturing process as an indicator of the material composition variation. The relationship between material composition and dimensions of the temperature contour was analytically modeled based on Fourier’s law of thermal conduction. The thermal images acquisition by IR camera were processed through a series of designed algorithms to extract geometrical features such as the length and width of the contours, which showed consistent trend through the theoretical analysis. The extracted features and actual weight percentage of copper in the alloy were further used to train an Artificial Neuron Network (ANN) model. The results showed that the accuracy of 94% was achieved when using the trained ANN model to estimate the composition of alloy from the thermal image data. The analytical/numerical models, simulations, experiments, and data analysis included in this thesis were expected to provide solid support for testing the research hypotheses and developing new hardware/software in advanced manufacturing systems

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt pre-training plus fine-tuning paradigm that is effective and tunes only extremely light parameters. The soft prompts include continuous input embeddings across an encoder and a decoder to fit the structure of the generation models. Importantly, a novel inner-prompt placed in the text is introduced to capture document-level information. The aim is to devote attention to understanding the document that better prompts the model to generate document-related content. The first step in the summarization procedure is to conduct prompt pre-training with self-supervised pseudo-data. This teaches the model basic summarizing capabilities. The model is then fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail and XSum datasets show that our method, with only 0.1% of the parameters, outperforms full-model tuning where all model parameters are tuned. It also surpasses Prompt Tuning by a large margin and delivers competitive results against Prefix-Tuning with 3% of the parameters.Comment: 12 page

Two-Period Inventory Control with Manufacturing and Remanufacturing under Return Compensation Policy

As an effective way of decreasing production cost, remanufacturing has attracted more and more attention from firms. However, it also brings many difficulties to firms, especial when firms remanufacture products which they produce. A primary problem for the case is how to acquire the used product sold by the firm itself. In this paper, we consider a return compensation policy for acquiring used product from customers. Under this policy, the return quantity of used product is a proportion of demand. We study an inventory replenishment and production planning problem for a two-period inventory system with dependent return and demand. We formulate the problem into a three-stage stochastic programming problem, where the firm needs to make decisions on the replenishment quantity of new raw material inventory in each period and the production quantities of manufacturing and remanufacturing ways. We give the optimal production policy of manufacturing and remanufacturing ways for the realized demand and prove the objective function for each stage to be concave in the inventory replenishment quantity. Moreover, we prove that the basic inventory policy is still optimal for each period and give the analytical conditions of the optimal inventory levels which are unrelated to acquisition price. Finally, we investigate numerical studies to analyze managerial insights

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization

Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source

Overexpression of RRM2 decreases thrombspondin-1 and increases VEGF production in human cancer cells in vitro and in vivo: implication of RRM2 in angiogenesis

<p>Abstract</p> <p>Background</p> <p>In addition to its essential role in ribonucleotide reduction, ribonucleotide reductase (RNR) small subunit, RRM2, has been known to play a critical role in determining tumor malignancy. Overexpression of RRM2 significantly enhances the invasive and metastatic potential of tumor. Angiogenesis is critical to tumor malignancy; it plays an essential role in tumor growth and metastasis. It is important to investigate whether the angiogenic potential of tumor is affected by RRM2.</p> <p>Results</p> <p>We examined the expression of antiangiogenic thrombospondin-1 (TSP-1) and proangiogenic vascular endothelial growth factor (VEGF) in two RRM2-overexpressing KB cells: KB-M2-D and KB-HURs. We found that TSP-1 was significantly decreased in both KB-M2-D and KB-HURs cells compared to the parental KB and mock transfected KB-V. Simultaneously, RRM2-overexpressing KB cells showed increased production of VEGF mRNA and protein. In contrast, attenuating RRM2 expression via siRNA resulted in a significant increased TSP-1 expression in both KB and LNCaP cells; while the expression of VEGF by the two cells was significantly decreased under both normoxia and hypoxia. In comparison with KB-V, overexpression of RRM2 had no significant effect on proliferation in vitro, but it dramatically accelerated in vivo subcutaneous growth of KB-M2-D. KB-M2-D possessed more angiogenic potential than KB-V, as shown in vitro by its increased chemotaxis for endothelial cells and in vivo by the generation of more vascularized tumor xenografts.</p> <p>Conclusion</p> <p>These findings suggest a positive role of RRM2 in tumor angiogenesis and growth through regulation of the expression of TSP-1 and VEGF.</p

Studies on structural, electrical, and optical properties of Cu doped As-Se-Te chalcogenide glasses

Cu doped chalcogenide (ChG) glassy films in the As-Se-Te glass system have been prepared using thermal evaporation techniques. Single-source evaporation from bulk (1-x) As(0.40)Se(0.35)Te(0.25)+x Cu glasses with x=0.05, 0.075, 0.10, 0.125, and 0.15, as well as dual-source coevaporation from As-chalcogenide and Cu-chalcogenide binary glasses as source materials, has been explored. We have shown that it is not possible to deposit high concentration Cu doped ChG glassy films, from the Cu doped bulk samples using single-source evaporation. However, using the dual-source coevaporation technique, we have demonstrated that the films can be doped with high concentrations of Cu. Micro-Raman spectroscopy has been utilized to verify that Cu is introduced into the glass network without disrupting the basic As-chalcogen units. Optical measurements have shown that introduction of Cu decreases the band gap of As-Se-Te glasses. The electrical properties of the investigated films have been measured at different temperatures and it has been shown that Cu incorporation in the As-Se-Te glass system vastly improves electrical conductivity. Moreover, we have shown that the temperature dependence of electrical conductivity can be fitted assuming variable range hopping between states near the Fermi level

Ferro-rotational domain walls revealed by electric quadrupole second harmonic generation microscopy

Domain walls are ubiquitous in materials that undergo phase transitions driven by spontaneous symmetry breaking. Domain walls in ferroics and multiferroics have received tremendous attention recently due to their emergent properties distinct from their domain counterparts, for example, their high mobility and controllability, as well as their potential applications in nanoelectronics. However, it is extremely challenging to detect, visualize and study the ferro-rotational (FR) domain walls because the FR order, in contrast to ferromagnetism (FM) and ferroelectricity (FE), is invariant under both the spatial-inversion and the time-reversal operations and thus hardly couple with conventional experimental probes. Here, an FR candidate $\mathrm{NiTiO_{3}}$ is investigated by ultrasensitive electric quadrupole (EQ) second harmonic generation rotational anisotropy (SHG RA) to probe the point symmetries of the two degenerate FR domain states, showing their relation by the vertical mirror operations that are broken below the FR critical temperature. We then visualize the real-space FR domains by scanning EQ SHG microscopy, and further resolve the FR domain walls by revealing a suppressed SHG intensity at domain walls. By taking local EQ SHG RA measurements, we show the restoration of the mirror symmetry at FR domain walls and prove their unconventional nonpolar nature. Our findings not only provide a comprehensive insight into FR domain walls, but also demonstrate a unique and powerful tool for future studies on domain walls of unconventional ferroics, both of which pave the way towards future manipulations and applications of FR domain walls