5 research outputs found
Automatic threshold determination for a local approach of change detection in long-term signal recordings
CUSUM (cumulative sum) is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS) algorithm, previously developed for application to long-term electromyography (EMG) recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold) acted to stop the estimation process, the other one (upper threshold) was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events). Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm
Data fusion and type-2 fuzzy inference in contextual data stream monitoring
Data stream monitoring provides the basis for
building intelligent context-aware applications over contextual
data streams. A number of wireless sensors could be spread in a
specific area and monitor contextual parameters for identifying
phenomena e.g., fire or flood. A back-end system receives
measurements and derives decisions for possible abnormalities
related to negative effects. We propose a mechanism, which
based on multivariate sensors data streams, provides real-time
identification of phenomena. The proposed framework performs
contextual information fusion over consensus theory for the
efficient measurements aggregation while time-series prediction
is adopted to result future insights on the aggregated values. The
unanimous fused and predicted pieces of context are fed into a
Type-2 fuzzy inference system to derive highly accurate
identification of events. The Type-2 inference process offers
reasoning capabilities under the uncertainty of the phenomena
identification. We provide comprehensive experimental
evaluation over real contextual data and report on the
advantages and disadvantages of the proposed mechanism. Our
mechanism is further compared with Type-1 fuzzy inference and
other mechanisms to demonstrate its false alarms minimization
capability
Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings
CUSUM (cumulative sum) is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS) algorithm, previously developed for application to long-term electromyography (EMG) recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold) acted to stop the estimation process, the other one (upper threshold) was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events). Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm.</p