Adaptive Control for Estimating Insulation Resistance of High- Voltage Battery System in Electric Vehicles

To ensure electrical safety and reliability in electric vehicles equipped with a high-voltage battery pack, an insulation monitoring circuit is indispensable to continuously monitor the insulation resistance during charging or driving. Existing methods such as injecting specific signals into the monitoring circuit and earth help to extract the resistance value from the voltage waveform. However, parasitic or stray capacitances in the monitoring circuit, which might introduce higher order dynamics into the waveform, are ignored. To avoid estimation error, the insulation resistance must be known in advance to carry out parameter calibration. In this chapter, one parasitic capacitance is applied in the circuit model and a new adaptive algorithm based on Lyapunov stability is employed to estimate the insulation resistance. This new online monitoring method and circuit are verified through simulation and experimentation, respectively. The results demonstrate that the proposed method can quickly react and track variations of insulation resistance on both positive and negative direct current (DC) lines

Reused Lithium-Ion Battery Applied in Water Treatment Plants

For stabilizing renewable energies and shaving peak power at noon, both the energy consumption and potential renewable energies in Dihua waste water treatment plant (WWTP) in Taiwan are analyzed. Under the consideration of environment, cost, and performance, automotive reused lithium-ion battery (RLIB) is employed. Two typical automotive lithium-ion batteries are used in this study after the selection of suitable battery cells. In particular, one simple, converterless energy management system (EMS) is developed and integrated in new RLIB packs. The control strategy between RLIB and an additional physical battery is adjusted by simulation. An online estimation of RLIB’s internal resistance and open-circuit voltage monitoring scheme is applied in EMS to ensure the safety of RLIB. The bench test and rough economical estimation reveal that EMS shows great potential in elongating life cycle and possibly benefits from feed-in tariff and peak shift of electricity charges

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Increased risk of endometriosis in patients with endometritis — a nationwide cohort study involving 84,150 individuals

Objectives: To evaluate the incidence of endometriosis among endometritis patients and its association with confoundingcomorbidities.Material and methods: A population-based, retrospective cohort study of women aged between 20 to 55 years, who werenewly diagnosed with endometritis between 2000 to 2013. A total of 16,830 endometritis patients and 67,230 non-endometritisindividuals were enrolled by accessing data from the National Health Insurance Research Database of Taiwan.The comorbidities accessed were uterine leiomyoma, rheumatoid arthritis, ovarian cancer, infertility and allergic diseases.Results: The mean follow-up period was 9.15 years for the non-endometritis cohort and 9.13 years for the endometritiscohort. There were significantly higher percentages of uterine leiomyoma, rheumatoid arthritis, infertility, ovarian cancerand allergic diseases in the endometritis cohort than in the non-endometritis cohort. Patients with endometritis hada 1.5-fold increased risk of their condition advancing to endometriosis (HR 1.58, 95% CI 1.48–1.68).Conclusions: Our results suggest that patients with endometritis exhibited a positive correlation in developing endometriosis

Oromotor variability in children with mild spastic cerebral palsy: a kinematic study of speech motor control

<p>Abstract</p> <p>Background</p> <p>Treating motor speech dysfunction in children with CP requires an understanding of the mechanism underlying speech motor control. However, there is a lack of literature in quantitative measures of motor control, which may potentially characterize the nature of the speech impairments in these children. This study investigated speech motor control in children with cerebral palsy (CP) using kinematic analysis.</p> <p>Methods</p> <p>We collected 10 children with mild spastic CP, aged 4.8 to 7.5 years, and 10 age-matched children with typical development (TD) from rehabilitation department at a tertiary hospital. All children underwent analysis of percentage of consonants correct (PCC) and kinematic analysis of speech tasks: poly-syllable (PS) and mono-syllable (MS) tasks using the Vicon Motion 370 system integrated with a digital camcorder. Kinematic parameters included spatiotemporal indexes (STIs), and average values and coefficients of variation (CVs) of utterance duration, peak oral opening displacement and velocity. An ANOVA was conducted to determine whether PCC and kinematic data significantly differed between groups.</p> <p>Results</p> <p>CP group had relatively lower PCCs (80.0-99.0%) than TD group (<it>p </it>= 0.039). CP group had higher STIs in PS speech tasks, but not in MS tasks, than TD group did (<it>p </it>= 0.001). The CVs of utterance duration for MS and PS tasks of children with CP were at least three times as large as those of TD children (<it>p </it>< 0.01). However, average values of utterance duration, peak oral opening displacement and velocity and CVs of other kinematic data for both tasks did not significantly differ between two groups.</p> <p>Conclusion</p> <p>High STI values and high variability on utterance durations in children with CP reflect deficits in relative spatial and/or especially temporal control for speech in the CP participants compared to the TD participants. Children with mild spastic CP may have more difficulty in processing increased articulatory demands and resulted in greater oromotor variability than normal children. The kinematic data such as STIs can be used as indices for detection of speech motor control impairments in children with mild CP and assessment of the effectiveness in the treatment.</p

Effects of natto extract on endothelial injury in a rat model

Vascular endothelial damage has been found to be associated with thrombus formation, which is considered to be a risk factor for cardiovascular disease. A diet of natto leads to a low prevalence of cardiovascular disease. The aim of the present study was to investigate the effects of natto extract on vascular endothelia damage with exposure to laser irradiation. Endothelial damage both in vitro and in vivo was induced by irradiation of rose bengal using a DPSS green laser. Cell viability was determined by MTS assay, and the intimal thickening was verified by a histological approach. The antioxidant content of natto extract was determined for the free radical scavenging activity. Endothelial cells were injured in the presence of rose bengal irradiated in a dose-dependent manner. Natto extract exhibits high levels of antioxidant activity compared with purified natto kinase. Apoptosis of laser-injured endothelial cells was significantly reduced in the presence of natto extract. Both the natto extract and natto kinase suppressed intimal thickening in rats with endothelial injury. The present findings suggest that natto extract suppresses vessel thickening as a synergic effect attributed to its antioxidant and anti-apoptosis properties