LArge Memory STorage And Retrieval Research Articles

Simultaneous bearing fault diagnosis and severity detection using a LAMSTAR network‐based approach

Bearings are one of the most important components in many industrial machines. Effective bearing fault diagnosis and severity detection are critical for keeping the machines operate normally and safe. In this study, the problem of simultaneous bearing fault diagnosis and severity detection with deep learning is addressed. Existing solutions developed using deep learning rely on fault feature extraction using complicated signal processing techniques. They perform bearing fault diagnosis and severity detection separately and normally require extensive supervised fine tuning. This study presents an effective deep learning-based solution using a large memory storage and retrieval (LAMSTAR) neural network. The developed approach can automatically extract self-learned fault features and perform bearing fault diagnosis and severity detection simultaneously. The structure of the LAMSTAR network is determined by optimally selecting the sliding box size of the input time–frequency matrix. The effectiveness of the proposed approach is validated using data collected from rolling element bearing tests.

On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders.

The results presented in this paper indicate that future on-demand Deep Brain Stimulation (DBS) systems for chronic use in patients with movement disorders should continuously and adaptively "learn" in order to maintain high symptom control efficacy. In this work, two machine learning algorithms-Decision Tree and LArge Memory STorage And Retrieval (LAMSTAR) neural network, both with surface Electromyography and accelerometry as control signals-are used to predict onset of tremor after DBS has been switched off in two patients, one suffering from Parkinson's disease and the other from essential tremor. The novelty of this work is that training and testing are done by using different data recorded during sessions at least one week apart. The question is whether the applied algorithms are robust to long-term operation (as patient's control signal may change over time due to disease progression, displacement of the wearable sensor, etc.). Various metrics are used to compare the performance of the proposed approach to those available in the literature, where training and testing are done on data from the same recording session. It is shown that a 100% sensitivity is achieved for training and testing over the same session; however, the sensitivity reduces when tested over a different session. The ratio of predicted stimulation-off time to observed stimulation-off time value is also found to be lower when training and testing on data from separate sessions. These results point to the need of adaptive learning in on-demand DBS systems.

A LAMSTAR Network-Based Human Judgment Analysis

Judgment analysis (JA) is a technique for modeling and interpreting human judgments that is usually based on multiple linear regression. However, the linear assumptions inherent to this approach can be limiting for modeling both the human judgments and the environmental criterion. This paper addresses this by introducing a formulation of JA based on large memory storage and retrieval (LAMSTAR) artificial neural networks. We describe our LAMSTAR network JA process and use it to analyze data from an air traffic control conflict prediction task. These results are compared with those of a traditional regression-based lens model analysis. We found that the LAMSTAR-based JA did a better job of capturing human judgment, while the regression-based model was more appropriate for the criterion. This suggest that the LAMSTAR-based JA approach has utility when human judgments are not well represented by a linear model. We discuss our results with respect to both the specific application we evaluated as well as meta-analyses of the JA literature. We also explore avenues for future research.

Towards fully automated closed-loop Deep Brain Stimulation in Parkinson's disease patients: A LAMSTAR-based tremor predictor.

This paper describes the application of the LAMSTAR (LArge Memory STorage and Retrieval) neural network for prediction of onset of tremor in Parkinson's disease (PD) patients to allow for on-off adaptive control of Deep Brain Stimulation (DBS). Currently, the therapeutic treatment of PD by DBS is an open-loop system where continuous stimulation is applied to a target area in the brain. This work demonstrates a fully automated closed-loop DBS system so that stimulation can be applied on-demand only when needed to treat PD symptoms. The proposed LAMSTAR network uses spectral, entropy and recurrence rate parameters for prediction of the advent of tremor after the DBS stimulation is switched off. These parameters are extracted from non-invasively collected surface electromyography and accelerometry signals. The LAMSTAR network has useful characteristics, such as fast retrieval of patterns and ability to handle large amount of data of different types, which make it attractive for medical applications. Out of 21 trials blue from one subject, the average ratio of delay in prediction of tremor to the actual delay in observed tremor from the time stimulation was switched off achieved by the proposed LAMSTAR network is 0.77. Moreover, sensitivity of 100% and overall performance better than previously proposed Back Propagation neural networks is obtained.

Knowledge Discovery in Medical Systems Using Differential Diagnosis, LAMSTAR, and <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-NN

Medical data is an ever-growing source of information generated from the hospitals in the form of patient records. When mined properly the information hidden in these records is a huge resource bank for medical research. As of now, this data is mostly used only for clinical work. This data often contains hidden patterns and relationships, that can lead to better diagnosis, better medicines, better treatment and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Unfortunately, discovery of these hidden patterns and relationships often goes unexploited. However there is on-going research in medical diagnosis which can predict the diseases of the heart, lungs and various tumours based on the past data collected from the patients.They are mostly limited to domain specific systems that predict diseases restricted to their area of operation like heart, brain and various other domains. These are not applicable to the whole medical dataset. The system proposed in this paper uses this vast storage of information so that diagnosis based on this historical data can be made. It focuses on computing the probability of occurrence of a particular ailment from the medical data by mining it using a unique algorithm which increases accuracy of such diagnosis by combining the key points of Neural Networks, Large Memory Storage and Retrieval (LAMSTAR), k-NN and Differential Diagnosis all integrated into one single algorithm. The system uses a Service-Oriented Architecture wherein the system components of diagnosis, information portal and other miscellaneous services are provided.This algorithm can be used in solving a few common problems that are encountered in automated diagnosis these days, which include: diagnosis of multiple diseases showing similar symptoms, diagnosis of a person suffering from multiple diseases, receiving faster and more accurate second opinion and faster identification of trends present in the medical records.

Price comparison: A reliable approach to identifying shill bidding in online auctions?

Shill bidding has become a serious issue for innocent bidders with the growing popularity of online auctions. In this paper, we study the relationship between final prices of online auctions and shill activities. We conduct experiments on real auction data from eBay to examine the hypotheses that state how the difference between final auction price and expected auction price implies shill bidding. In the experiments, a neural network based approach is used to learn the expected auction price. In particular, we trained the Large Memory Storage and Retrieval (LAMSTAR) Neural Network based on features extracted from item descriptions, listings and other auction properties. The likelihood of shill bidding is determined by a previously proposed shill certification technique based on Dempster–Shafer theory. By employing the chi-square test of independence and logistic regression, the experimental results indicate that a higher-than-expected final auction price might be used as direct evidence to distinguish likely shill-infected auctions from trustworthy auctions, allowing for more focused evaluation of shill-suspected auctions. As such, this work contributes to providing a feasible way to identify suspicious auctions that may contain shill biddings. It may also help to develop trustworthy auction houses with shill detection services that can protect honest bidders and benefit the auction markets in both the short-term and long term.

Automated Prediction of Apnea and Hypopnea, Using a LAMSTAR Artificial Neural Network

The prediction of individual episodes of apnea and hypopnea in people with obstructive sleep apnea syndrome has not been thoroughly investigated. Accurate prediction of these events could improve clinical management of this prevalent disease. To evaluate the performance of a system developed to predict episodes of obstructive apnea and hypopnea in individuals with obstructive sleep apnea; to determine the most important signals for making accurate and reliable predictions. We employed LArge Memory STorage And Retrieval (LAMSTAR) artificial neural networks to predict apnea and hypopnea. Wavelet transform-based preprocessing was applied to six physiological signals obtained from a set of polysomnography studies and used to train and test the networks. We tested prediction performance during non-REM and REM sleep as a function of data segment duration and prediction lead time. Measurements included average sensitivities, specificities, positive predictive values, and negative predictive values. Prediction performed best during non-REM sleep, using 30-second segments to predict events up to 30 seconds into the future. Most events were correctly predicted up to 60 seconds in the future. Apnea prediction achieved a sensitivity and specificity up to 80.6 +/- 5.6 and 72.8 +/- 6.6%, respectively. Hypopnea prediction achieved a sensitivity and specificity up to 74.4 +/- 5.9 and 68.8 +/- 7.0%., respectively. We report, to our knowledge, the first system to predict individual episodes of apnea and hypopnea. The most important signal for apnea prediction was submental electromyography. The most important signals for hypopnea prediction were submental electromyography and heart rate variability. This prediction system may facilitate improved therapies for obstructive sleep apnea.

A MODIFIED LAMSTAR NEURAL NETWORK AND ITS APPLICATIONS

This paper describes a modification of the LArge Memory STorage And Retrieval (LAMSTAR) neural network. The purpose of the modification is to allow rare events a larger role in decision-making when they are strongly biased towards a particular decision. As a by-product, the modification also permits the introduction of a confidence measure. This measure allows comparison across different network inputs so that the user may choose the "best" solution. The authors have applied the modified LAMSTAR network to a financial forecasting problem.

A novel large-memory neural network as an aid in medical diagnosis applications.

This paper describes the application of a large memory storage and retrieval (LAMSTAR) neural network to medical diagnosis and medical information retrieval problems. The network is based on Minsky's knowledge-lines (k-lines) theory of memory storage and retrieval in the central nervous system. It employs arrays of self-organized map modules, such that the k-lines are implemented via link weights (address correlation) that are being updated by learning. The network also employs features of forgetting and of interpolation and extrapolation, thus being able to handle incomplete data sets. It can deal equally well with exact and fuzzy information, thus being specifically applicable to medical diagnosis where the diagnosis is based on exact data, fuzzy patient interview information, patient history, observed images, and test records. Furthermore, the network can be operated in closed loop with Internet search engines to intelligently use data from the Internet in a higher hierarchy of learning. All of the above features are shown to make the LAMSTAR network suitable for medical diagnosis problems that concern large data sets of many categories that are often incomplete and fuzzy. Applications of the network to three specific medical diagnosis problems are described: two from nephrology and one related to an emergency-room drug identification problem. It is shown that the LAMSTAR network is hundreds and thousands times faster in its training than back-propagation-based networks when used for the same problem and with exactly the same information.

A Large Memory Storage and Retrieval Neural Network for Adaptive Retrieval and Diagnosis

The neural network discussed in this paper is a self trained network for LArge Memory STorage And Retrieval (LAMSTAR) of information. It employs features such as forgetting, interpolation, extrapolation and filtering, to enhance processing and memory efficiency and to allow zooming in and out of memories. The network is based on modified SOM (Self-Organizing-Map) modules and on arrays of link-weight vectors to channel information vertically and horizontally throughout the network. Direct feedback and up/down counting serve to set these link weights as a higher-hierarchy performance evaluator element which also provides high level interrupts. Pseudo random modulation of the link weights prevents dogmatic network behavior. The input word is a coded vector of several sub-words (sub-vectors). These features facilitate very rapid intelligent retrieval and diagnosis of very large memories, that have properties of a self-adaptive expert system with continuously adjustable weights. The authors have applied the network to a simple medical diagnosis and fault detection problems.