Traditional Pattern Matching Research Articles

Evaluation of GLOCK 9 mm Firing Pin Aperture Shear Mark Individuality Based on 3,156 Different Pistols (Manufactured Over a 30 Year Period in Two Countries) Using Additional Pattern Matching and IBIS Pattern Recognition

Over a period of 30 years, a number of fired GLOCK cartridge cases have been evaluated. A total of 3156 GLOCK firearms were used to generate a sample of the same size. Our research hypothesis was that no cartridge cases fired from different 9 mm semiautomatic GLOCK pistols would be mistaken as coming from the same gun (a false match). Using optical comparison microscopy, two separate experiments were carried out to test this hypothesis. A subsample of 617 test-fired cartridge cases were subjected to algorithmic comparison by the Integrated Ballistics Identification System (IBIS). The second experiment subjected the full set of 3,156 cases to manual comparisons using traditional pattern matching. None of the cartridge cases were “matched” by either of these two experiments. Using these empirical findings, an established conservative Bayesian probability model was used to estimate the chance that a 9 mm cartridge case, fired from a GLOCK, could be mistaken as coming from the same firearm when in fact it did not (i.e., the false match probability).

Evaluation of GLOCK 9 mm Firing Pin Aperture Shear Mark Individuality Based On 1,632 Different Pistols by Traditional Pattern Matching and IBIS Pattern Recognition.

Over a period of 21 years, a number of fired GLOCK cartridge cases have been evaluated. A total of 1632 GLOCK firearms were used to generate a sample of the same size. Our research hypothesis was that no cartridge cases fired from different 9-mm semiautomatic GLOCK pistols would be mistaken as coming from the same gun. Using optical comparison microscopy, two separate experiments were carried out to test this hypothesis. A subsample of 617 test-fired cases were subjected to algorithmic comparison by the Integrated Ballistics Identification System (IBIS). The second experiment subjected the full set of 1632 cases to manual comparisons using traditional pattern matching. None of the cartridge cases were "matched" by either of these two experiments. Using these empirical findings, an established Bayesian probability model was used to estimate the chance that a 9-mm cartridge case, fired from a GLOCK, could be mistaken as coming from the same firearm when in fact it did not (i.e., the random match probability).

A Digest and Pattern Matching-Based Intrusion Detection Engine

Intrusion detection/prevention systems (IDSs/IPSs) heavily rely on signature databases and pattern matching (PM) techniques to identify network attacks. The engines of such systems often employ traditional PM algorithms to search for telltale patterns in network flows. The observations that real-world network traffic is largely legitimate and that telltales manifested by exploits rarely appear in network streams lead us to the proposal of Fingerprinter. This framework integrates fingerprinting and PM methods to rapidly distinguish well-behaved from malicious traffic. Fingerprinter produces concise digests or fingerprints for attack signatures during its programming phase. In its querying phase, the framework quickly identifies attack-free connections by transforming input traffic into its fingerprint space and matching its digest against those of attack signatures. If the legitimacy of a stream cannot be determined by fingerprints alone, our framework uses the Boyer–Moore algorithm to ascertain whether attack signatures appear in the stream. To reduce false matches, we resort to multiple fingerprinting techniques including Bloom–Filter and Rabin–Fingerprint. Experimentation with a prototype and a variety of traces has helped us establish that Fingerprinter significantly accelerates the attack detection process.

Application of new Apriori algorithm MDNC to TFT-LCD array manufacturing yield improvement

With the trend and demand of larger panels and higher resolution in Thin Film Transistor-Liquid Crystal Display (TFT-LCD) panels, yield improvement has become the key factor in TFT-LCD manufacturing. This paper presents a successful and effective data mining methodology for TFT-LCD manufacturing yield improvement. We have modified an Apriori algorithm called Multi-Dimension Non-Continuous (MDNC) by eliminating the limitations imposed by traditional pattern matching of continuous data, to mine the association rules in the cross-day discrete manufacturing data and find out some valuable information. The results show how to effectively locate any machine with low yield and vastly improve it in TFT-LCD large panel manufacturing yield-rate, thereby reducing the manufacturing cycle time, the frequency of holding lot by adaptation of MDNC.

A generalized pattern matching approach for multi-step prediction of crude oil price

This paper applies pattern matching technique to multi-step prediction of crude oil prices and proposes a new approach: generalized pattern matching based on genetic algorithm (GPMGA), which can be used to forecast future crude oil price based on historical observations. This approach can detect the most similar pattern in contemporary crude oil prices from the historical data. Based on the similar historical pattern, a multi-step prediction of future crude oil prices can be figured out. In GPMGA modeling process, the traditional pattern matching is not directly employed. Historical data is transformed to larger or smaller scales in the x-axis and the y-axis directions, so that a generalized price pattern reflecting current price movement can be obtained. This treatment overcomes the local deficiency of the traditional pattern modeling in recognition system approach (PMRS), and in addition to this, a matched historical pattern in a larger pattern size can be found. Since the approach takes not only historical similarities but also differences into account, the concept of “generalized pattern matching” is proposed here. It proves a new basis for multi-step prediction by finding out more essential similarities through various transformations. The related empirical study is constructed for a one-month forecasting of the Brent and WTI crude oil prices, and satisfying forecasting results are attained. At the end, comparisons with some other time series prediction approaches, such as PMRS and Elman network, demonstrate the effectiveness and superiority of GPMGA over others.