Many techniques for data analysis also based on gaussian noise assumption which is often valid in various situations. However, the sensitivity of gravitational wave searches are limited by their non-gaussian and non-stationary noise. We introduce various on-going efforts to overcome this limitation in Korean Gravitational Wave Group. First, artificial neural networks are applied to discriminate non-gaussian noise artefacts and gravitational-wave signals using auxiliary channels of a gravitational wave detector. Second, viability of applying Hilbert-Huang transform is investigated to deal with non-stationary data of gravitational wave detectors. We also report progress in acceleration of low-latency gravitational search using GPGPU.