Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body

Predict the compressive strength of ultra high-performance concrete by a hybrid method of machine learning

Abstract Ultra-high performance concrete (UHPC) benefits the construction industry due to its improved flexibility, high workability, durability, and performance compared to normal concrete. Some investigators have conducted observed papers on the UHPC’s mechanical properties for establishing a reliable analytical approach for calculating the compressive strength, tensile strength, slump, etc. However, most of these studies were performed with limited samples because of the UHPC’s high cost. This study aims to predict the compressive strength (CS) of UHPC through hybrid machine-learning approaches. The model is included Adaptive-Network Fuzzy Inference System (ANFIS). Moreover, three meta-heuristic algorithms were employed to improve the developed model's accuracy, including the Generalized Normal Distribution Optimization, the COOT optimization algorithm, and the Honey Badger Algorithm. Several metrics were used to compare and assess the performance of the hybrid models in the framework of ANGN, ANCO, and ANHB. A comparison of the predicted and measured results generally shows that the proposed developed models can reasonably estimate the mechanical properties of UHPC. The results indicated that the ANHB model could estimate the CS of UHPC with the most suitable accuracy

Pulse Waveform Classification Using Support Vector Machine with Gaussian Time Warp Edit Distance Kernel

Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods

Classification of Pulse Waveforms Using Edit Distance with Real Penalty

<p>Abstract</p> <p>Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD). Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP) and the recent progress in <inline-formula> <graphic file="1687-6180-2010-303140-i1.gif"/></inline-formula>-nearest neighbors (KNN) classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.</p

Risk factors associated with atrial fibrillation in early period after operation of lung cancer

Background and objective Atrial fibrillation (AF) is a common complication after operation of lung cancer. Atrial fibrillation is often associated with longer hospital stay time and higher hospital cost, as well as increased postoperative mortality. The aim of this study is to explore the risk factors of atrial fibrillation (AF) in early period after operation of lung cancer and analyze its impact on short-term mortality, hospital stay time and hospitalization cost.Methods From January 2006 to December 2007, 416 consecutive lung cancer patients underwent operation in our hospital were chosen. Postoperative ECG (electrocardiography) was used to diagnose AF. The cases were divided into two groups: AF group and control group (Non-AF group). Statistic χ2 test was used to compare numeration data and Logistic regression was performed to find risk factors of postoperative AF. Results In the 416 lung cancer patients, 52 cases (12.5%) were with AF and most occurred in the 1 to 3 days after operation. Multivariate analysis showed that the patients with age older than 65, preoperative pulmonary infection, low preoperative FEV1%pre, Intrapericardia operation and postoperative hypoxemia are prone to occur AF after operation. In AF group, the hospital stay time was longer, the hospitalization cost was higher. No obvious difference was observed with short-term mortality between the two groups. Conclusion The incidence of AF after operation with lung cancer is 12.5% (52/416). Patients with age older than 65, preoperative pulmonary infection, low FEV1%pre, intrapericardia operation and postoperative hypoxemia have a higher risk of AF following operation. Although postoperative AF has no obvious impact on short-term mortality, it is associated with longer impatient time and higher hospitalization cost