Adaptive Multi-level Backward Tracking for Sequential Feature Selection

In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.

Backward Adaptive Brightness Temperature Threshold Technique (BAB3T): A Methodology to Determine Extreme Convective Initiation Regions Using Satellite Infrared Imagery

Thunderstorms in southeastern South America (SESA) stand out in satellite observations as being among the strongest on Earth in terms of satellite-based convective proxies, such as lightning flash rate per storm, the prevalence for extremely tall, wide convective cores and broad stratiform regions. Accurately quantifying when and where strong convection is initiated presents great interest in operational forecasting and convective system process studies due to the relationship between convective storms and severe weather phenomena. This paper generates a novel methodology to determine convective initiation (CI) signatures associated with extreme convective systems, including extreme events. Based on the well-established area-overlapping technique, an adaptive brightness temperature threshold for identification and backward tracking with infrared data is introduced in order to better identify areas of deep convection associated with and embedded within larger cloud clusters. This is particularly important over SESA because ground-based weather radar observations are currently limited to particular areas. Extreme rain precipitation features (ERPFs) from Tropical Rainfall Measurement Mission are examined to quantify the full satellite-observed life cycle of extreme convective events, although this technique allows examination of other intense convection proxies such as the identification of overshooting tops. CI annual and diurnal cycles are analyzed and distinctive behaviors are observed for different regions over SESA. It is found that near principal mountain barriers, a bimodal diurnal CI distribution is observed denoting the existence of multiple CI triggers, while convective initiation over flat terrain has a maximum frequency in the afternoon.

Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.

A Bias Correction Method for Rainfall Forecasts Using Backward Storm Tracking

This study proposes a new method to estimate the bias correction ratio for the rainfall forecast to be used as input for a flash flood warning system. This method requires a backward tracking to locate where the forecasted storm is at the present time, and the bias correction ratio is estimated at the tracked location, not at the warning site. The proposed method was applied to the rainfall forecasts provided by the Korea Meteorological Administration. A total of 300 warning sites considered in the flash flood warning system for mountain regions in Korea (FFWS-MR) were considered as study sites, along with four different storm events in 2016. As a result, it was confirmed that the proposed method provided more reasonable results, even in the case where the number of rain gauges was small. Comparison between the observed rain rate and the corrected rainfall forecasts by applying the conventional method and the proposed method also showed that the proposed method was superior to the conventional method.

SISTEM PAKAR ANALISA KERUSAKAN PADA SEPEDA MOTOR HONDA BEAT INJECTION DENGAN METODE BACKWARD CHAINING

Expert system is one part of artificial intelligence designed to mimic the expertise of an expert in answering questions and completing a problem. This application made to resolve damages acquired from the symptoms which mostly encountered by motorcyclist. This application created by backward chaining using backward tracking method that works based on conclusions from series of happenings. In this study, collecting the data gained from the experts involved in the existing system by direct interview, direct observation and also from the literatures as references to support the explanation of the elements studied. The objective of this application is to help the mechanics or technicians in analyzing damages occured in Honda Beat Injection motorcycle at the garage in Honda Motor Festival faster.

The Atmospheric Moisture Residence Time and Reference Time for Moisture Tracking over China

This paper studies the atmospheric moisture residence times over China for the period 1980–2009 using the dynamic recycling model (DRM). We define both the residence times for atmospheric moisture of precipitation (backward tracking) and evaporation (forward tracking) and show that each has significant spatial and seasonal variations. The area-averaged precipitation-moisture residence time is approximately 8.3 days, while the evaporation residence time is approximately 6.3 days. In addition, we investigate the concept of “tracking time” or time selected for moisture tracking in numerical source–sink studies. The area-averaged backward and forward tracking times at the 90% threshold (i.e., when 90% of initial moisture is attributed for tracking) are approximately 22 and 15 days, respectively. Finally, we theoretically deduced the explicit expressions for residence and tracking times for idealized cases and found the analytical proportional relationship between these times. In this way, the analytical link between residence time and e-folding time was reestablished. This proportional relationship was further verified against the DRM-derived values. In the DRM results, the proportional relation generally fluctuates along the trajectory, which leads to the differences between the theoretical and the DRM-derived values. These results can enhance our understanding of water cycling, and they are likely to help choose tracking times in relevant studies.

A Novel Algorithm for Solving the Assembly Line Balancing Type I Problem

This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Assembly Line Balancing Type I Problem. PM introduces some new concepts that make the modeling process of large-scale complex industrial problems more systematic and their solution algorithms much faster and easily maintained. In the context of SALBP-I, PM introduces a component model to deploy the problem logic and its solution algorithm into several interacting components. The problem is represented as an object-oriented graph G (V, E, W) of vertices, edges, and workstations which enables problem solutions to start anywhere. The novel representation relaxes the only forward and backward tracking approach used in the assembly line balancing literature. A set of well-reported problems in the literature are reported and solved. The paper concludes by demonstrating the efficiency of the new modeling approach and future extensions.