• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.4
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• pp.445-453
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• 2015

A fully integrated cost-effective and low-power single chip Lithium-Ion (Li-Ion) battery protection IC (BPIC) for portable devices is presented. The control unit of the battery protection system and the MOSFET switches are integrated in a single package to protect the battery from over-charge, over-discharge, and over-current. The proposed BPIC enters into low-power standby mode when the battery becomes over-discharged. A new auto release function (ARF) is adopted to release the BPIC from standby mode and safely return it to normal operation mode. A new delay shorten mode (DSM) is also proposed to reduce the test time without increasing pin counts. The BPIC implemented in a $0.18-{\mu}m$ CMOS process occupies an area of $750{\mu}m{\times}610{\mu}m$. With DSM enabled, the measured test time is dramatically reduced from 56.82 s to 0.15 s. The BPIC chip consumes $3{\mu}A$ under normal operating conditions and $0.45{\mu}A$ under standby mode.

• Journal of Environmental Health Sciences
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• v.35 no.3
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• pp.191-200
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• 2009

We investigated a trait of odor and the relationship between odor unit and odorous compounds using multiple regression analysis based on data compiled from Sihwa (SIC), Banwol (BIC), Banwol plating (BPIC) and Poseung industrial complex (PIC). These areas are odor control areas in Gyeonggi province. It was revealed that $NH_3$ and styrene concentrations in SIC and BPIC were relatively higher and $H_2S$ concentration especially in mc was more than five times higher than other areas. As a result of regression analysis using SAS, intensity of odor unit was highly related to concentrations of $H_2S$, TMA, styrene and n-valeraldehyde in SIC, $H_2S$, acetaldehyde, and butyraldehyde in BPIC and $NH_3$ in BIC.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1217-1220
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• 2003

A low-power design technique for lithium-ion Battery-Protection Integrated Circuit (BPIC) for multi cell configuration is proposed. The hardware sharing scheme with more precisely divided operating states in the detection range could reduce the power consumption significantly, especially during the normal state. The usefulness of the proposed scheme was confirmed through HSPICE simulations.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.