In our previous work, we have developed a mechanical coupling for energy harvester from vibration source. This energy harvester uses piezoelectric with additional cantilever beam and permanent magnets. Our work propos...
In our previous work, we have developed a mechanical coupling for energy harvester from vibration source. This energy harvester uses piezoelectric with additional cantilever beam and permanent magnets. Our work proposed alternative scheme of mechanical coupling for tune the vibration input into resonant frequency of piezoelectric. Based on the experiment, correlation between the length of cantilever beam and the output power also evaluated. In this paper, we try to modelling our work into mathematical model and apply it to some case study. For example application, we apply our energy harvester system to generate electrical energy to enlighten the street. The human footsteps can be used as vibration source to generate electrical energy.
Osteoporosis is a degenerative disease characterized by low bone density and micro architectural deterioration of bone tissue with a consequent increase in bone fragility and decreasing bone mechanical force on suppor...
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Osteoporosis is a degenerative disease characterized by low bone density and micro architectural deterioration of bone tissue with a consequent increase in bone fragility and decreasing bone mechanical force on suppor...
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Osteoporosis is a degenerative disease characterized by low bone density and micro architectural deterioration of bone tissue with a consequent increase in bone fragility and decreasing bone mechanical force on supporting body normal activity. One of common technique used for measurement bone mass, bone mineral density or other aspect related bone structure is Dual Energy X-ray Absorptiometry (DXA). Previous researchers shown opportunity to utilize dental panoramic images for early detection and estimate the probability of having osteoporosis. However, a robust image quantitative is still challenge. In the paper, quantitative of dental panoramic is reported based on Gray Level Co-occurrence Matrix (GLCM). Feature extraction from GLCM will be use as an input for Support Vector Machine (SVM) algorithm to classify normal and osteoporosis. The classification result will validate using BMD data of 23 samples prepared by Dental Radiographs Department, where panoramic images are imaged from patient postmenopausal, with ages 52-73 year. Classification using SVM with kernel function multilayer perceptron for normal and osteoporosis showed that the best performance (using 9 training data and 14 test data) was 85,71% accuracy, 90,91% sensitivity, and 66,67% specificity. It's best performance result is obtained by using contrast, correlation, energy, and homogeneity combination for SVM classification input.
Parametric filters are widely used to determine arrival time of P wave. The disadvantage of its method is phase shifting occurred on filtered seismic signal. Consequently, an arrival time of P wave became less accurat...
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Parametric filters are widely used to determine arrival time of P wave. The disadvantage of its method is phase shifting occurred on filtered seismic signal. Consequently, an arrival time of P wave became less accurat...
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Parametric filters are widely used to determine arrival time of P wave. The disadvantage of its method is phase shifting occurred on filtered seismic signal. Consequently, an arrival time of P wave became less accurate. In this paper, two processing steps are proposed. On the First step, spectral density(PSD) estimation of seismic signal is used for early detection of earthquakes and to detect possible duration of information carrier P-wave. On the second step, on the specific duration of seismic signal that detected carrier P-wave is filtered using discrete wavelet denoising in order to minimize phase shift. To evaluated performance of wavelet denoising, the seismogram that recording a local earthquake in Tasikmalaya 2 September 2009 occurred at sea was used. Next, the seismic signal was segmented with data length in about 25.6 s, resulting 20 data segments. Using first step processing, the segment signal that carrier P-wave is detected on 4 th data segment. Next, wavelet basis function of Daubechies was used to decomposition seismic signal into 9 th level. The P wave arrival time is detected on wavelet decomposition on detail 2 (d2) components at 2.5-5 Hz frequency range. From this data it is found that P wave arrival time is 85.5 seconds (07.55.25.5 UTC). There is a 0.9 s difference earlier by the method of discrete wavelet filter compared to the Meteorological, Climatological and Geophysical Agency (BMKG) reports. The same process was applied on 10 recordings of aftershocks earthquake with magnitude 3.6 until 5.6 SR and resulting 0.25-0.9 s time range difference earlier than BMKG phase report.
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