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Background Stress urinary incontinence (SUI) is a major health problem affecting approximately 50% of the female population over 45 years of age. We evaluated the therapeutic effects of a home-based non-invasive wireless sensor pelvic floor muscle training (PFMT) device with assisted Kegel exercise for SUI. Methods We included sixty women aged 40 to 60 years who were diagnosed with urodynamic SUI (mean pad test, 10.52 g). The PFMT device applicator was clamped on the upper inner thigh, and the patients could self-train at home. The signal was recorded and delivered to a 3G/4G smartphone via Bluetooth, which also allows guided feedback via the smartphone's voice. To evaluate the therapeutic effect, all patients completed the following questionnaires: a 3-day bladder diary, the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), the Urogenital Distress Inventory-Short Form (UDI-6-SF), and the Incontinence Impact Questionnaire-7 (IIQ-7). One-hour pad test measurements were performed before the test (M0) and at 1 (M1), 2 months (M2), and 3 months (M3) after the PFMT device-assisted Kegel exercise. Results The one-hour pad test and the scores of the ICIQ-SF, UDI-6, and IIQ-7 questionnaires were improved at M1, M2, and M3, compared with the M0 values. The mean value of the post-voiding residual urine (PVR) significantly decreased at M2 and M3. The subjective and objective improvement rates at M3 were 80% and 72%, respectively. Conclusion The data demonstrated that 3 months of Kegel exercise assisted with a home-based PFMT device improved the number and severity of episodes, post-voiding residual urine, and quality of life (QoL) in patients with SUI, suggesting that this device might serve as an alternative non-invasive therapy for mild and moderate SUI.

Pregnancy weight gain may be associated with adverse pregnancy outcomes. The article aims to explore the relationship between weight change and pregnancy outcome in the Taiwanese pregnant women. The retrospective cohort study enrolled women with vertex singleton pregnancy at University-associated Hospital between 2011 and 2014. Pregnancy weight change was separated into 3 groups, based on the Institute of Medicine (IOM) guidelines: below (n = 221); within (n = 544); and above (n = 382). Analysis of variance, χ2 tests, generalized linear models, and logistic regression models were used for statistical comparisons. Pregnant women with weight change above IOM guidelines had a significant increase in both maternal and perinatal complications compared with normal controls (odds ratio [OR] 1.65, 95% confidence interval [CI] 1.03–1.98; P = .043; OR 1.45, 95% CI 1.01–1.87; P = .049, respectively). This finding was not found in pregnant women with weight gain below IOM guidelines. Moreover, age (OR 1.08, 95% CI 1.02–1.15; P = .0011), pre-pregnancy weight (OR 1.04, 95% CI 1.01–1.09; P = .0008), pre-pregnancy body mass index (BMI; OR 1.15, 95% CI 1.06–1.30; P

We propose a novel adaptive fast learning (AFL) algorithm for two-dimensional principal component analysis (2DPCA) in this paper. As opposite to conventional PCA which is based on 1D data vectors, 2DPCA is based on 2D image matrices and thus has higher accuracy than conventional PCA when applied to applications such as face recognition, facial expression recognition, palmprint recognition, etc. Our proposed AFL algorithm simultaneously estimates both eigenvectors and corresponding eigenvalues, and then adaptively sets the learning rate parameters of neurons to ensure all neurons learning with almost the same fast speed. Requiring no image covariance matrix evaluation, the desired multiple eigenvectors of 2DPCA can thus be learned effectively in the form of weight vectors of neurons. The proposed AFL algorithm can also be applied to learning for T-2DPCA. Simulation experiments performed on face database such as the YaleB database clearly demonstrate that the proposed AFL algorithm performs very well and thus is a very effective computational tool for both 2DPCA and T-2DPCA.

Inexhaustible solar power is booming for carbon reduction in recent years. The current efficiency of solar power generation is still low. Therefore, improvement of the power generation efficiency will be the research objective to scientists all over the world. The maximum power point tracking (MPPT) is an effective way to improve efficiency. Existing maximum power point tracking methods are incremental conductance method, perturbation and observation method, voltage feedback method and so on. Perturbation and observation method has the simple, easy to understand and the most common structure. In this paper, the nonlinear phenomena of buck-boost DC-DC converting solar cell system with perturbation and observation method for MPPT, considering converter operation in continuous conduction mode (CCM) with inductor current by varying load resistance is studied. The system is from periodic steady-state into period-doubling bifurcation to the chaos of nonlinear phenomena. Firstly the solar cell maximum power tracking system and converter working principle are explained. Secondly, the construction of solar maximum power tracking with perturbation and observation method and buck-boost DC - DC converter equivalent circuit model, then mathematical model is derived, and simulated by MATLAB / SIMULINK software package. Through the time-domain waveforms, phase plane and bifurcation diagrams, to explore the changing of load resistance system from a periodic steady-state into the chaos of nonlinear phenomena, chaos evolution mechanism of the system is analyzed.

Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D)2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.

The subtractive clustering algorithm (SC) is a popular method for data clustering. But the radius of each cluster is an important factor which affects the performances of clustering results. This paper proposes an objective function for the genetic algorithm to estimate the optimal value of this parameter. Two experiments show that the proposed method can automatically obtain this parameter for the subtractive clustering algorithm.

Objective To evaluate the risk and protective factors for functional decline of veteran home residents in Taiwan to develop disability prevention programs in long term care settings. Design A prospective cohort study. Setting Two veteran homes in Taiwan. Participants A total of 1045 residents of veteran homes. Intervention None. Measurements Minimum data set (MDS), resident assessment protocols (RAP), MDS-COGS, Resource Utilization Group-III Activities of Daily Living scale, MDS Social engagement scale, MDS Pain scale. Results A total of 1045 (83.0 ± 5.1 years, all men) residents completed the study, and 11.3% of them developed significant functional decline during the study period. Participants with long-term institutionalization history (odds ratio [OR] 2.966, confidence interval [CI] 1.270–6.927), underlying cerebrovascular disease (OR 4.432, CI 1.994–9.852) and dementia (OR 2.150, CI 1.029–4.504), and higher sum of RAP triggers (OR 1.366, CI 1.230–1.517) were more likely to develop functional decline, whereas those who had better social engagement were significantly protective from functional decline (OR 0.528, CI 0.399–0.697). Conclusions Cerebrovascular disease, dementia, higher sum of RAP triggers, and long-term institutionalization were independent risk factors for functional decline, whereas better social engagement had a protective effect on functional dependence. Further study is needed to develop an integrated disability prevention program based on the discoveries of this study.

This paper proposes related research of the nonlinear dynamic behaviors in voltage-mode controlled single-phase half-bridge inverters. The system exhibits nonlinear dynamic behaviors from period-l operation through period-doubling bifurcations to chaos state using the proportional gain changeable, variable DC input voltage and load resistance. First, a mathematical model of single-phase half-bridge inverter is derived. Then, SIMULINK software tools are used to construct models, simulate results and verify the nonlinear dynamic behaviors in this inverter.

With wireless communication network and GPS positioning technology, most of current existing systems using IP protocol to provide various information services such as traffic and navigation. In this paper, we propose a location-based system with WAVE/DSRC present a low delay and high mobility system architecture constructed on the road. It provides vehicles with more intelligent real time information such as news and location-based contents. We also implement the system, which shows location-based content in the service area.

Based on the characteristics of electric vehicles (EV), this paper proposes a conceptual framework utilizing the Controller Area Network (CAN) bus technology, OBD-II connector and the existing telematics service systems to construct a novel telematics gateway for EV. Vehicle gateway interface has been applied to telematics service systems for many years but not to EV. The proposed framework of telematics gateway establishes a three layers' architecture, namely, the physical layer, data link layer, and application layer. The telematics gateway needs to resolve two issues: One is to simplify the framework of EV gateway interface and the other is to ensure the application scenarios for power saving and location based service (LBS). Using the framework of telematics gateway, the entire service network can meet different application scenarios for EV.

This paper proposes study of the nonlinear dynamic behaviors in voltage-mode controlled single-phase half-bridge inverters. The system exhibits nonlinear dynamic behaviors from period-1 operation through period-doubling bifurcations to chaos state using the proportional gain changeable. First, a mathematical model of single-phase half-bridge inverter is derived. Then, SIMULINK and IsSpice software tools are used to construct models, simulate results and verify the nonlinear dynamic behaviors in this inverter

This paper proposes a robust validity index for Fuzzy c-Means (FCM) algorithm. The Fuzzy c-Means algorithm has become of most widely used method in fuzzy clustering. After clustering, it is often necessary to evaluate its results. Such assessment techniques are called cluster validity. The disadvantage of FCM is that the number of clusters must be predetermined. Even if the number of clusters is given, the clustering results of these algorithms are influenced by the choice of initial cluster centers. In this paper, a new cluster validity index is proposed to evaluate the fitness of clusters obtained by FCM. The example shows the result of proposed index have good performances than other cluster validities.

Two-dimensional principal component analysis (2D-PCA) which is based on 2D image matrices as opposed to the standard PCA, which is based on 1D vectors, has been first successfully applied to face recognition and has higher accuracy than the latter. It was also successfully applied to other problems such as facial expression recognition, object recognition, etc. later. However, there exists still no neural network learning algorithm for 2D-PCA like those for PCA yet. In this paper, we propose a learning algorithm for 2D-PCA. Requiring no image covariance matrix evaluation and just repeatedly presenting training image samples to the single layer neural network, the desired multiple eigenvectors for 2D-PCA can be learned in the form of weight vectors of generalized linear neurons. It also profit from the parallel architecture of neural network. Simulation experiments are performed on YaleB face database, and the experimental results show that the proposed learning algorithm performs well as expected.

This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal number of clusters.

In recent years, the increasing demand for security in home care services has led to further improvements in surveillance activities. This paper proposes an efficient approach for motion detecting and tracking in a surveillance system based on the cellular model. The system can monitor human activities and recognize the abnormal events of an elderly man in an indoor environment. The experimental room should be divided into several unit areas. Each unit area can be considered as a simple cell in the 2D cellular model. The state of each cell can be represented by the changeable number of active pixels within corresponding unit area. The approach groups those cells with nonzero status value to represent the target. Then, the centroid of a moving target is computed by its zeroth and first moments. The variation of the centroid can be tracked to recognize abnormal events, such as the fall of an old man or children. The results of the experiments conducted in this study clearly demonstrate that the proposed approach provides a more efficient and effective detecting mechanism.

People have the need to use a novel consumer clock device for home automation, personal security and convenience. This paper presents the design and implementation of the innovative system. The system architecture which is controlled by Micro-Controller Unit (MCU) includes main alarm clock module, lucky module and security module. In the main alarm clock module, we create a novel hourglass clock model and intelligent alarm clock features based on grey relational analysis. In the lucky module, we design a unique structure of live round according to the medical theory of psychology and hashing function for randomization. In the live round, it includes the fortune of love, career, family, and health. It adopts funny and encouraged voice to transmit messages. The system also provides the function of security using infrared sensors and third-generation (3G) mobile phone unit. The system has been successful and gets the award in Taiwan.

This work investigates fast-scale instability phenomena and chaotic control of voltage-controlled single-phase full-bridge inverter via varying load resistance. First, the operational principles of a voltage-controlled single-phase full-bridge inverter are examined and the mapping state-space equations are generated. Additionally, the MATLAB/SIMULINK tool is adopted for experimental simulation in this paper. The load resistance is changed to analyze the fast-scale instability waveforms of the inductor current and output voltage, and we propose the method of time-delay feedback for chaotic control to stable state of period-1. Experimental simulation clearly indicates that MATLAB/SIMULINK can provide available results and validate the mathematical models.

It is evident that effective control over chaos in the forward converter is very important for engineering applications. In this paper, a mathematical model of the forward converter is firstly derived. Secondly, the nonlinear phenomenon in the forward converter is discussed, and then we apply the method of time-delay feedback control over chaos in the forward converter. The validity of this method is confirmed by simulation results.

In this paper, we propose a novel GreyCMAC with robust FCM (RFCM) method for function approximation. The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating. In order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method (RFCM) is proposed to effectively mitigate the influence of noise and outliers and then a GreyCMAC model is used to learn the nonlinear system's features for function approximation. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm (RFCM) is proposed to greatly mitigate the influence of data noise and outliers; and (2) A Grey-based CMAC (GreyCMAC) is proposed to locate a given fine piecewise linear data domain by RFCM so that a neural network can be constructed for function approximation. The conducted experimental results clearly indicate that the proposed approach provides good performance.

The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating, simple architecture, easily processing and hardware implementation. In the training phase, the disadvantage of some CMAC models with a larger fixed learning rate is the unstable phenomenon. The smaller learning rate would cause slower convergence speed. In the aspect, we propose grey learning rate for training phase. We incorporate the grey relational analysis with the number of training iterations to get an adequate learning rate for better convergence performance. In addition, a serious problem of learning interference reduces learning speed and accuracy. The idea is that the error correcting must be proportional to the inverse of learning times, trained input area and grey relational grade for the addressed hyper cube. A credit apportionment adopts the idea to provide fast and accurate learning effects. This paper proposes a novel learning framework of CMAC for better performance and real-time applications. From the simulation results, it is evident that the proposed algorithm provides more accurate and fast convergence in the early cycles of training phase and also becomes significant in the follow-up cycles.

The back propagation (BP) algorithm for function approximation is multi-layer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter (FDS) is used to partition the nonlinear systempsilas domain into several piecewise linear subspaces to be represented by neural networks. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.