An effective neuro-fuzzy paradigm for machinery condition healthmonitoring

An innovative neuro-fuzzy network appropriate for fault detection and classification in a machinery condition health monitoring environment is proposed. The network, called an incremental learning fuzzy neural (ILFN) network, uses localized neurons to represent the distributions of the input space and is trained using a one-pass, on-line, and incremental learning algorithm that is fast and can operate in real time. The ILFN network employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized structure with the ability to adaptively learn new classes of failure modes and update its parameters continuously while monitoring a system. To demonstrate the feasibility and effectiveness of the proposed network, numerical simulations have been performed using some well-known benchmark data sets, such as the Fisher's Iris data and the Deterding vowel data set. Comparison studies with other well-known classifiers were performed and the ILFN network was found competitive with or even superior to many existing classifiers. The ILFN network was applied on the vibration data known as Westland data set collected from a U.S. Navy CH-46E helicopter test stand, in order to assess its efficiency in machinery condition health monitoring. Using a simple fast Fourier transform (FFT) technique for feature extraction, the ILFN network has shown promising results. With various torque levels for training the network, 100% correct classification was achieved for the same torque Levels of the test data.     

Academic effectiveness of podcasting: A comparative study of integrated versus supplemental use of podcasting in second language classes

With podcasting gaining more mainstream adoption in higher education, it’s critical to examine its effectiveness in improving the student learning experience. To this end, this paper examines the effectiveness of podcasts integrated into the curriculum (PIC) versus podcasts as supplemental material (PSM). Considering recent empirical work on the effectiveness of podcasting, this study collected data from students enrolled in lower level and upper level language courses. Results revealed an inconclusive relationship among PIC students’ learning outcomes (as measured by their final grades). In contrast, however, our findings indicate a strong relationship between the use of PSM and students’ final grades, particularly in upper level courses.     

Holography: a behavior‐based profiler for malware analysis

Behavior‐based detection and signature‐based detection are two popular approaches to malware (malicious software) analysis. The security industry, such as the sector selling antivirus tools, has been using signature and heuristic‐based technologies for years. However, this approach has been proven to be inefficient in identifying unknown malware strains. On the other hand, the behavior‐based malware detection approach has a greater potential in identifying previously unknown instances of malicious software. The accuracy of this approach relies on techniques to profile and recognize accurate behavior models. Unfortunately, with the increasing complexity of malicious software and limitations of existing automatic tools, the current behavior‐based approach cannot discover many newer forms of malware either. In this paper, we implement ‘holography platform’, a behavior‐based profiler on top of a virtual machine emulator that intercepts the system processes and analyzes the CPU instructions, CPU registers, and memory. The captured information is stored in a relational database, and data mining techniques are used to extract information. We demonstrate the breadth of the ‘holography platform’ by conducting two experiments: a packed binary behavior analysis and a malvertising (malicious advertising) incident tracing. Both tasks are known to be very difficult to do efficiently using existing methods and tools. We demonstrate how the precise behavior information can be easily obtained using the ‘holography platform’ tool. With these two experiments, we show that the ‘holography platform’ can provide security researchers and automatic malware detection systems with an efficient malicious software behavior analysis solution. Copyright © 2011 John Wiley & Sons, Ltd.     

Comprehensive Study of Emitter-Ledge Thickness of InGaP/GaAs HBTs

A comprehensive study of emitter-ledge thickness of InGaP/GaAs heterojunction bipolar transistors (HBTs) has been undertaken. It is shown that the recombination rate and electron densities are drastically increased near the exposed base surface between the base contact and the emitter ledge. In contrast, the corresponding hole densities are decreased. If the emitter ledge is too thick, current will flow through the undepleted ledge, which increases the emitter-size effect. In contrast, if the emitter ledge is too thin, it may not effectively passivate the surface. Therefore, the thickness of the emitter ledge is a crucial issue and should be carefully considered. It is shown that, from simulated and experimental results, the optimum emitter-ledge thickness of InGaP/GaAs HBT is 100-200 Aring     

Recent Life Events Preceding Suicide Attempts in a Personality Disorder Sample: Findings From the Collaborative Longitudinal Personality Disorders Study.

Few studies have examined the relationship between life events, suicide attempts, and personality disorders (PDs), in spite of the strong associations between PDs and suicidal behavior, and the poor coping strategies often exhibited by these individuals. The authors examined whether participants with PDs who attempted suicide during the first 3 years of a prospective, longitudinal study were more likely to experience specific life events in the month during and preceding the suicide attempt. Of 489 participants with PDs, 61 attempted suicide during the 3-year, follow-up interval. Results indicated that negative life events, particularly those pertaining to love-marriage or crime-legal matters, were significant predictors of suicide attempts, even after controlling for baseline diagnoses of borderline PD, major depressive disorders, substance use disorders, and a history of childhood sexual abuse. Therefore, certain types of negative life events are unique risk factors for imminent suicide attempts among individuals with PDs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Combined numerical and linguistic knowledge representation and its application to medical diagnosis

In this paper, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system (FES), optimized via the genetic algorithm (GA). The ILFN is a self-organizing network. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. The resulted HIS is capable of dealing with low-level numerical computation and higher level linguistic computation. After the system is completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the system's performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the proposed HIS performs better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods. Our interest is not only on improving the accuracy of the system, but also enhancing the comprehensibility of the resulted knowledge representation.     

Priority conflict-free Petri nets

A number of problems concerning priority conflict-free Petri nets are investigated in this paper. We show the reachability problem for such Petri nets to be NP-complete. (Using a similar technique, the NP-completeness result applies to the class of priority BPP-nets as well.) As for the boundedness problem, an NP-completeness result is demonstrated for priority conflict-free Petri nets with two types of prioritized transitions. (In contrast, the problem is known to be P-complete for conflict-free Petri nets without priorities.) We also investigate the home state problem, i.e., the problem of determining whether home states exist in a given a Petri net, for conflict-free Petri nets with and without priorities. As it turns out, home states always exist for bounded conflict-free Petri nets without priorities. If an additional liveness constraint is imposed, such Petri nets are guaranteed to be ‘reversible’ (i.e., their initial states are home states). For priority conflict-free Petri nets, being bounded and live is sufficient for the existence of home states. However, if the liveness assumption is dropped, the existence of home states is no longer guaranteed. Received: 1 April 1997 / 8 December 1997