Single-Mode Projection Filters for Modal Parameter Identification for Flexible Structures

Single-mode projection filters are developed for eigensystem parameter identification from both analytical results and test data. Explicit formulations of these projection filters are derived using the orthogonal matrices of the controllability and observability matrices in the general sense. A global minimum optimization algorithm is applied to update the filter parameters by using the interval analysis method. The updated modal parameters represent the characteristics of the test data. For illustration of this new approach, a numerical simulation for the MAST beam structure is shown by using a one-dimensional global optimization algorithm to identify modal frequencies and damping. Another numerical simulation of a ten-mode structure is also presented by using a two-dimensional global optimization algorithm to illustrate the feasibility of the new method. The projection filters are practical for parallel processing implementation

Projection filters for modal parameter estimate for flexible structures

Single-mode projection filters are developed for eigensystem parameter estimates from both analytical results and test data. Explicit formulations of these projection filters are derived using the pseudoinverse matrices of the controllability and observability matrices in general use. A global minimum optimization algorithm is developed to update the filter parameters by using interval analysis method. Modal parameters can be attracted and updated in the global sense within a specific region by passing the experimental data through the projection filters. For illustration of this method, a numerical example is shown by using a one-dimensional global optimization algorithm to estimate model frequencies and dampings

Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018), Santiago, Chil

The value of D-dimer in the detection of early deep-vein thrombosis after total knee arthroplasty in Asian patients: a cohort study

<p>Abstract</p> <p>Background and purpose</p> <p>The relationship of D-dimer and deep-vein thrombosis (DVT) after total knee arthroplasty (TKA) remains controversial. The purpose of this study was to assess the value of D-dimer in the detection of early DVT after TKA.</p> <p>Methods</p> <p>The measurements of plasma D-dimer level were obtained preoperatively and at day 7 postoperatively in 78 patients undergoing TKA. Ascending venography was performed in 7 to 10 days after surgery. The plasma D-dimer levels were correlated statistically with the venographic DVT.</p> <p>Results</p> <p>Venographic DVT was identified in 40% of patients. High plasma D-dimer level >2.0 μg/ml was found in 68% of patients with DVT and 45% without DVT (P < 0.05). Therefore, high D-dimer level greater than 2.0 μg/ml showed 68% sensitivity, 55% specificity, 60% accuracy, 50% positive predictive rate and 72% negative predictive rate in the detection of early DVT after TKA.</p> <p>Conclusion</p> <p>High plasma D-dimer level is a moderately sensitive, but less specific marker in the detection of early of DVT after TKA. Measurement of serum D-dimer alone is not accurate enough to detect DVT after TKA. Venography is recommended in patients with elevated D-dimer and clinically suspected but asymptomatic DVT after TKA.</p

Digit Recognition Using Composite Features With Decision Tree Strategy

At present, check transactions are one of the most common forms of money transfer in the market. The information for check exchange is printed using magnetic ink character recognition (MICR), widely used in the banking industry, primarily for processing check transactions. However, the magnetic ink card reader is specialized and expensive, resulting in general accounting departments or bookkeepers using manual data registration instead. An organization that deals with parts or corporate services might have to process 300 to 400 checks each day, which would require a considerable amount of labor to perform the registration process. The cost of a single-sided scanner is only 1/10 of the MICR; hence, using image recognition technology is an economical solution. In this study, we aim to use multiple features for character recognition of E13B, comprising ten numbers and four symbols. For the numeric part, we used statistical features such as image density features, geometric features, and simple decision trees for classification. The symbols of E13B are composed of three distinct rectangles, classified according to their size and relative position. Using the same sample set, MLP, LetNet-5, Alexnet, and hybrid CNN-SVM were used to train the numerical part of the artificial intelligence network as the experimental control group to verify the accuracy and speed of the proposed method. The results of this study were used to verify the performance and usability of the proposed method. Our proposed method obtained all test samples correctly, with a recognition rate close to 100%. A prediction time of less than one millisecond per character, with an average value of 0.03 ms, was achieved, over 50 times faster than state-of-the-art methods. The accuracy rate is also better than all comparative state-of-the-art methods. The proposed method was also applied to an embedded device to ensure the CPU would be used for verification instead of a high-end GPU

NumHG: A Dataset for Number-Focused Headline Generation

Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-decoder models excel based on the ROUGE metric, they often falter when it comes to the precise generation of numerals in headlines. We identify the lack of datasets providing fine-grained annotations for accurate numeral generation as a major roadblock. To address this, we introduce a new dataset, the NumHG, and provide over 27,000 annotated numeral-rich news articles for detailed investigation. Further, we evaluate five well-performing models from previous headline generation tasks using human evaluation in terms of numerical accuracy, reasonableness, and readability. Our study reveals a need for improvement in numerical accuracy, demonstrating the potential of the NumHG dataset to drive progress in number-focused headline generation and stimulate further discussions in numeral-focused text generation.Comment: NumEval@SemEval-2024 Datase