A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text

The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and approaches in Natural Language Processing (NLP) based on English text are shown to be ineffective when attending to these language specific problems in late-started Chinese NLP. Relation Extraction is an area under NLP, looking to identify semantic relationships between entities in the text. The term “Effect Relation” is introduced in this research to refer to a specific content type of relationship between two entities, where one entity has a certain “effect” on the other entity. In this research project, a case study on Chinese text from Traditional Chinese Medicine (TCM) journal publications is built, to closely examine the forms of Effect Relation in this text domain. This case study targets the effect of a prescription or herb, in treatment of a disease, symptom or body part. A rule-based methodology is introduced in this thesis. It utilises predetermined rules and templates, derived from the characteristics and pattern observed in the dataset. This methodology achieves the F-score of 0.85 in its Named Entity Recognition (NER) module; 0.79 in its Semantic Relationship Extraction (SRE) module; and the overall performance of 0.46. A second methodology taking a feature-based approach is also introduced in this thesis. It views the RE task as a classification problem and utilises mathematical classification model and features consisting of contextual information and rules. It achieves the F-scores of: 0.73 (NER), 0.88 (SRE) and overall performance of 0.41. The role of functional words in the contemporary Chinese language and in relation to the ERs in this research is explored. Functional words have been found to be effective in detecting the complex structure ER entities as rules in the rule-based methodology

Experimental study on the generated pyroshock level under different amount of explosive

The purpose of this study is to evaluate the effect of the amount of explosive on generated pyroshock of a typical igniter. In this study, pyrotechnic experiments of the igniter are performed. The output pressure is measured with a pressure transducer while acceleration data is obtained using piezoelectric accelerometers. Finally, the effects of the amount of explosive on the generated pyroshock are discussed based on results in time and frequency domain. Results show that the relation between the amount of explosive and peak pressure of typical igniter shows good agreement with Nobel-Abel equation of state (EOS). Moreover, peak acceleration and SRS both show an approximate linear growth with increased amount of explosive

Interconnect and Memory Design for Intelligent Mobile System

Technology scaling has driven the transistor to a smaller area, higher performance and lower power consuming which leads us into the mobile and edge computing era. However, the benefits of technology scaling are diminishing today, as the wire delay and energy scales far behind that of the logics, which makes communication more expensive than computation. Moreover, emerging data centric algorithms like deep learning have a growing demand on SRAM capacity and bandwidth. High access energy and huge leakage of the large on-chip SRAM have become the main limiter of realizing an energy efficient low power smart sensor platform. This thesis presents several architecture and circuit solutions to enable intelligent mobile systems, including voltage scalable interconnect scheme, Compute-In-Memory (CIM), low power memory system from edge deep learning processor and an ultra-low leakage stacked voltage domain SRAM for low power smart image signal processor (ISP). Four prototypes are implemented for demonstration and verification. The first two seek the solutions to the slow and high energy global on-chip interconnect: the first prototype proposes a reconfigurable self-timed regenerator based global interconnect scheme to achieve higher performance and energy-efficiency in wide voltage range, while the second one presents a non Von Neumann architecture, a hybrid in-/near-memory Compute SRAM (CRAM), to address the locality issue. The next two works focus on low-power low-leakage SRAM design for Intelligent sensors. The third prototype is a low power memory design for a deep learning processor with 270KB custom SRAM and Non-Uniform Memory Access architecture. The fourth prototype is an ultra-low leakage SRAM for motion-triggered low power smart imager sensor system with voltage domain stacking and a novel array swapping mechanism. The work presented in this dissertation exploits various optimizations in both architecture level (exploiting temporal and spatial locality) and circuit customization to overcome the main challenges in making extremely energy-efficient battery-powered intelligent mobile devices. The impact of the work is significant in the era of Internet-of-Things (IoT) and the age of AI when the mobile computing systems get ubiquitous, intelligent and longer battery life, powered by these proposed solutions.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155232/1/jiwang_1.pd

Real-time adaptive sensing of nuclear spins by a single-spin quantum sensor

Quantum sensing is considered to be one of the most promising subfields of quantum information to deliver practical quantum advantages in real-world applications. However, its impressive capabilities, including high sensitivity, are often hindered by the limited quantum resources available. Here, we incorporate the expected information gain (EIG) and techniques such as accelerated computation into Bayesian experimental design (BED) in order to use quantum resources more efficiently. A simulated nitrogen-vacancy center in diamond is used to demonstrate real-time operation of the BED. Instead of heuristics, the EIG is used to choose optimal control parameters in real-time. Moreover, combining the BED with accelerated computation and asynchronous operations, we find that up to a tenfold speed-up in absolute time cost can be achieved in sensing multiple surrounding C13 nuclear spins. Our work explores the possibilities of applying the EIG to BED-based quantum-sensing tasks and provides techniques useful to integrate BED into more generalized quantum sensing systems

Virtual variable sampling discrete fourier transform based selective odd-order harmonic repetitive control of DC/AC converters

This paper proposes a frequency adaptive discrete Fourier transform (DFT) based repetitive control (RC) scheme for DC/AC converters. By generating infinite magnitude on the interested harmonics, the DFT-based RC offers a selective harmonic scheme to eliminate waveform distortion. The traditional DFT-based selective harmonic RC, however, is sensitive to frequency fluctuation since even very small frequency fluctuation leads to a severe magnitude decrease. To address the problem, virtual variable sampling method, which creates an adjustable virtual delay unit to closely approximate a variable sampling delay, is proposed to enable the DFT-based selective harmonic RC to be frequency adaptive. Moreover, a selective odd-order harmonic DFT filter is developed to deal with the dominant odd order harmonic. Because it halves the number of sampling delays in the DFT filter, the system transient response gets nearly 50% improvement. A comprehensive series of experiments of the proposed VVS DFT-based selective odd-order harmonic RC controlled programmable AC power source under frequency variations are presented to verify the effectiveness of the proposed method

Free Triiodothyronine Levels Are Associated with Diabetic Nephropathy in Euthyroid Patients with Type 2 Diabetes

Objective. To investigate the association of thyroid function and diabetic nephropathy (DN) in euthyroid patients with type 2 diabetes. Methods. A total of 421 patients were included in this cross-sectional study. The following parameters were assessed: anthropometric measurements, fast plasma glucose, serum creatinine, lipid profile, HbA1c, free triiodothyronine (FT3), free thyroxine, thyroid-stimulating hormone levels, and urinary albumin-to-creatinine ratio (UACR). Patients with UACR of ≥30 mg/g were defined as those suffering from DN. Results. Of the 421 patients, 203 (48.2%) suffered from DN, and no difference was found between males and females. The patients with DN yielded significantly lower FT3 levels than those without DN (P<0.01). The prevalence of DN showed a significantly decreasing trend across the three tertiles based on FT3 levels (59.6%, 46.4%, and 38.6%, P<0.01). After adjustment for gender and age, FT3 levels were found to correlate positively with estimated glomerular filtration rate (P=0.03) and negatively with UACR (P<0.01). Multiple linear regression analysis showed that FT3 level was independently associated with UACR (β=-0.18, t=-3.70, and P<0.01). Conclusion. Serum FT3 levels are inversely associated with DN in euthyroid patients with type 2 diabetes, independent of traditional risk factors

Numerical simulation of separation shock characteristics of a piston type explosive bolt

A piston type explosive bolt is modeled by using a hydrocodes AUTODYN. The influence of the charge amount on the separation shock is analyzed. The results show that the separation shock of the piston type explosive bolt mainly includes two aspects: the shock caused by explosive detonation and the impact of the piston at the end of stroke. As the charge amount increases, the collision speed of piston first increases and then decreases, and the separation shock first increases and then stabilizes