Robust Controller Design for Modified Projective Synchronization of Chen-Lee Chaotic Systems with Nonlinear Inputs

This study demonstrates the modified projective synchronization in Chen-Lee chaotic system. The variable structure control technology is used to design the synchronization controller with input nonlinearity. Based on Lyapunov stability theory, a nonlinear controller and some generic sufficient conditions can be obtained to guarantee the modified projective synchronization, including synchronization, antisynchronization, and projective synchronization in spite of the input nonlinearity. The numerical simulation results show that the synchronization and antisynchronization can coexist in Chen-Lee chaotic systems. It demonstrates the validity and feasibility of the proposed controller

Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC) neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF) neural network and back propagation (BP) neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields

Robust Exponential Converge Controller Design for a Unified Chaotic System with Structured Uncertainties via LMI

This paper focuses on the chaos control problem of the unified chaotic systems with structured uncertainties. Applying Schur-complement and some matrix manipulation techniques, the controlled uncertain unified chaotic system is then transformed into the linear matrix inequality (LMI) form. Based on Lyapunov stability theory and linear matrix inequality (LMI) formulation, a simple linear feedback control law is obtained to enforce the prespecified exponential decay dynamics of the uncertain unified chaotic system. Numerical results validate the effectiveness of the proposed robust control scheme

Implementation of a Tour Guide Robot System Using RFID Technology and Viterbi Algorithm-Based HMM for Speech Recognition

This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC). Then, the Hidden Markov Model (HMM) was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot

Number of Convolution Layers and Convolution Kernel Determination and Validation for Multilayer Convolutional Neural Network: Case Study in Breast Lesion Screening of Mammographic Images

Mammography is a low-dose X-ray imaging technique that can detect breast tumors, cysts, and calcifications, which can aid in detecting potential breast cancer in the early stage and reduce the mortality rate. This study employed a multilayer convolutional neural network (MCNN) to screen breast lesions with mammographic images. Within the region of interest, a specific bounding box is used to extract feature maps before automatic image segmentation and feature classification are conducted. These include three classes, namely, normal, benign tumor, and malignant tumor. Multiconvolution processes with kernel convolution operations have noise removal and sharpening effects that are better than other image processing methods, which can strengthen the features of the desired object and contour and increase the classifier’s classification accuracy. However, excessive convolution layers and kernel convolution operations will increase the computational complexity, computational time, and training time for training the classifier. Thus, this study aimed to determine a suitable number of convolution layers and kernels to achieve a classifier with high learning performance and classification accuracy, with a case study in the breast lesion screening of mammographic images. The Mammographic Image Analysis Society Digital Mammogram Database (United Kingdom National Breast Screening Program) was used for experimental tests to determine the number of convolution layers and kernels. The optimal classifier’s performance is evaluated using accuracy (%), precision (%), recall (%), and F1 score to test and validate the most suitable MCNN model architecture

Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening

Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosclerosis are high-risk PADs for patients with Type 2 diabetes or with both diabetes and end-stage renal disease. For early UEVD detection, a fingertip-based, toe-based, or wrist-based photoplethysmography (PPG) tool is a simple and noninvasive measurement system for vital sign monitoring and healthcare applications. Based on time-domain PPG analysis, a Duffing–Holmes system with a master system and a slave system is used to extract self-synchronization dynamic errors, which can track the differences in PPG morphology (in amplitudes (systolic peak) and time delay (systolic peak to diastolic peak)) between healthy subjects and PAD patients. In the preliminary analysis, the self-synchronization dynamic errors can be used to evaluate risk levels based on the reflection index (RI), which includes normal condition, lower PAD, and higher PAD. Then, a one-dimensional convolutional neural network is established as a multilayer classifier for automatic UEVD screening. The experimental results indicated that the self-synchronization dynamic errors have a positive correlation with the RI (R2 = 0.6694). The K-fold cross-validation is used to verify the performance of the proposed classifier with recall (%), precision (%), accuracy (%), and F1 score

Cost-effectiveness of Drug-eluting Stents in Patients With Stable Coronary Artery Disease

Drug-eluting stents (DESs) have been shown to reduce in-stent restenosis and target vessel revascularization (TVR) in large clinical trials. We conducted this study to elucidate the differences in the cost and clinical outcome of DESs and bare metal stents (BMSs). Methods: We retrospectively analyzed the clinical data and costs of patients with stable angina treated with coronary stents from September 2003 to January 2005 at the National Taiwan University Hospital, Taipei, Taiwan. Results: We enrolled 186 patients treated with DESs and 194 patients treated with BMSs. The use of DESs is associated with a lower rate of TVR compared with that with BMSs (12% vs. 22%, p = 0.011). Compared with the BMS group, the overall costs were significantly higher in the DES group (NT$352,495 ± 140,408 vs. NT$298,947 ± 131,289, p<0.001). The incremental cost to avoid one TVR at 2 years was NT$546,444 (95$151,071-2,565,793). Conclusion: The use of DESs reduces the rate of TVR at 2 years after intervention, but is probably not cost-effective compared with BMSs in patients with stable coronary artery disease

Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems

In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination-based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes as input variables are parameterized to quantify the degradation between estimated maximum PV output power and metered PV output power, which can be categorized into normal condition, grounded faults, open-circuit faults, bridged faults, and mismatch faults. Bidirectional hetero-associative memory (BHAM) networks are then used to associate the inputs and locate multiple faults as output variables within the PV array. For a rooftop PV system with two strings, experimental results demonstrate that the proposed model has computational efficiency in learning and detection accuracies for real-time applications, and that its algorithm is easily implemented in a mobile intelligent vehicle