Evaluating the Real-World NOx Emission from a China VI Heavy-Duty Diesel Vehicle

The manufacturers of China VI heavy-duty vehicles were required to conduct in-service conformity (ISC) tests by using a portable emissions measurement system (PEMS). The moving averaging window (MAW) method was used to evaluate the NOx emission required by the China VI emission standard. This paper presented the results of four PEMS tests of a China VI (step B) N3 category vehicle. Our analyses revealed that the real NOx emission of the test route was much higher than the result evaluated by the MAW method. We also found the data produced during the urban section of a PEMS test was completely excluded from the evaluation based on the current required boundary conditions. Therefore, in order to ensure the objectivity of the evaluation, this paper proposed three different evaluation methods. Method 1 merely set the power threshold as 10% for valid MAWs; Method 2 reclassified the MAWs into “Urban MAWs”, “Rural MAWs” and “Motorway MAWs” according to the vehicle speed. Method 3 reclassified the MAWs into “Hot MAWs” and “Cold MAWs” according to engine coolant temperature. The NOx emission evaluation results for Method 1 were not satisfactory, but those for Method 2 and Method 3 were close to the real NOx emission, the errors were all within ±10%.

Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

Optimization of Averaging Window Length and Alarming Hold Time in Volume Balance RTTM Leak Detection to Minimize False Alarms and Spill Volume

Reliability and sensitivity are two main performance metrics of leak detection systems as defined by API 1130 [1]. Proper thresholding scheme is one of the primary factors in having a sensitive and reliable leak detection system with timely detection. In RTTM leak detection, if not dealt with properly, severe pipeline pressure transients can degrade the performance of the leak detection system. One of the common basic methods of reducing the effect of pressure transients is using moving averaging windows; having looser thresholds on the shorter averaging windows, while maintaining tighter thresholds on the longer ones. The thresholds are typically set to meet the API 1149 [2] curve for the pipeline. While the post-processing of filtered data and alarm assessment has been explored via different methods such as sequential probability ratio test, to the authors’ knowledge, there is currently no systematic way of selecting the averaging windows to minimize false alarms prior to the post-processing of the average-filtered data. Moreover, to be able to maintain tight thresholds, especially in shorter averaging windows, one of the common methods is to apply dynamic thresholds, i.e. temporarily expanding thresholds when transients occur. While effective in some scenarios, the main disadvantage of this method is that the imbalance caused by a transient may not clear until the entire averaging window period is passed. This causes either extended periods of degraded performance, or more false positives. This paper utilizes an alarming hold time (also referred to as alarm persistence [3]) to remedy this problem where the averaging window length is reduced while maintaining detection time and sensitivity. To find the optimal set of threshold values, hold times, and averaging window lengths, a Particle Swarm Optimization (PSO) is performed. The ‘fitness function’ of the optimization algorithm is designed to minimize total spill volume for leak scenarios and have minimum false alarms for no-leak scenarios. The former is achieved via setting the objective function to the spill volume and the latter is enforced via applying constraints to the optimization algorithm. For no-leak scenarios, the historical operational data of a pipeline system is used. For leak scenarios, the historical data is modified by introducing a bias in the inlet volume of the section to simulate a leak. The result of the PSO provides a set of alarming parameters, threshold value, averaging window length, alarm hold time, and clearing threshold that provide the minimum false alarm rate and spill volume for different detectability ranges. The optimization method proposed in this paper can be applied to any mass or volume balance-based leak detection system that utilizes moving averaging windows. However, the leak detection parameters found with this method depend on the pipeline system.

Removal of ECG Baseline Wander using Savitzky-Golay Filter Based Method

ECG (Electrocardiogram) is a measure of heart’s electrical activity. As the body is a volume conductor, ECG signal can be recorded from the body surface. The signal while being recorded from the body surface gets corrupted by various types of noise or artifact. Among these, baseline wander is a type of noise that severely hampers the ECG signal. Baseline wander is particularly of very low frequency; it causes the ECG signal to deviate from its isoelectric line and causes the signal to ride on the lower frequency artifact. The proposed method is based on Savitzky-Golay filter, which is a moving average filter that takes into consideration the polynomial order along with moving averaging when approximating the signal. It enables to approximate the baseline wander quite efficiently. Though in some cases it distorts the ECG signal to some extent, when compared with usual polynomial fitting method, it demonstrates superiority in terms of accuracy, simplicity and generalization.Bangladesh Journal of Medical Physics Vol.8 No.1 2015 32-45