A parallel algorithm for robust fault detection in semiconductor manufacturing processes

The semiconductor manufacturing consists of a number of processes, and even a small fault occurring at any point can damage the product quality. The fast and accurate detection of such faults is essential to maintain high manufacturing yields. In this paper, we propose a parallel algorithm for fault detection in semiconductor manufacturing processes. The algorithm is a modification of the discord detection algorithm called HOT SAX, which adopted the SAX representation of time-series for efficient storage and computation. We first propose a sequential algorithm and then extend it to a parallel version. We evaluate our algorithm through experiments using the data obtained from a real-world semiconductor plasma etching process. As a result, our fault detection algorithm achieved 100 % accuracy without any false positive or false negative.     

Crowd intelligence for the classification of fractures and beyond

Background

Medical diagnosis, like all products of human cognition, is subject to error. We tested the hypothesis that errors of diagnosis in the realm of fracture classification can be reduced by a consensus (group) diagnosis; and that digital imaging and Internet access makes feasible the compilation of a diagnostic consensus in real time.

Methods

Twelve orthopaedic surgeons were asked to evaluate 20 hip radiographs demonstrating a femoral neck fracture. The surgeons were asked to determine if the fractures were displaced or not. Because no reference standard is available, the maximal accuracy of the diagnosis of displacement can be inferred from inter-observer reliability: if two readers disagree about displacement, one of them must be wrong. That method was employed here. Additionally, virtual reader groups of 3 and 5 individual members were amalgamated, with the response of those groups defined by majority vote. The purpose of this step was to see if increasing the number of readers would improve accuracy. In a second experiment, to study the feasibility of amassing a reader group on the Internet in real time, 40 volunteers were sent 10 periodic email requests to answer questions and their response times were assessed.

Results

The mean kappa coefficient for individual inter-observer reliability for the diagnosis of displacement was 0.69, comparable to prior published values. For 3-member virtual reader groups, inter-observer reliability was 0.77; and for 5-member groups, it was 0.80. In the experiment studying the feasibility of amassing a reader group in real time, the mean response time was 594 minutes. For all cases, a 9-member group (theoretically 99% accurate) was amassed in 135.8 minutes or less.

Conclusions

Consensus may improve diagnosis. Amassing a group for this purpose on the Internet is feasible.     

A specification language for planning and fault recovery in manufacturing systems

This paper presents the syntax and semantics of a component-oriented rule-based language for specifying the formal models of manufacturing systems. A model captures the state of a component of the system in a set of first-order logic predicates, and it captures the semantics of the operations performed by this component in a set of rules that determine the preconditions and postconditions of an operation. The models are then used to plan the sequence of operations of each class of jobs to be manufactured by these systems. A plan-oriented fault detection and correction strategy is proposed. This strategy can automatically handle any combination of faults that may occur when monitoring the operations of manufacturing systems. A fault-tree is consulted prior to executing the scheduled operations of a plan, and the faults that affect the execution of these operations are handled subsequently. Resuming the original cyclic schedule is attempted, whenever feasible. As a proof of concept, a prototype implementation of both the main constructs of the component-oriented rule-based language and the planning and fault-recovery algorithms presented in this paper have been completed. This prototype is implemented on a Unix-based system in the Ada programming language. The specification of a manufacturing system is first expressed in the proposed language. These statements are then translated into Ada code. This code is next compiled by a Verdix Ada compiler and is executed in order to create and populate the model data structure of the system. A detailed plan of execution and a set of fault-recovery plans may then be derived for a job to be manufactured on this system.     

Pyrosequencing enhancement for better detection limit and sequencing homopolymers

Pyrosequencing is a DNA sequencing technique based on sequencing-by-synthesis enabling rapid and real-time sequence determination. Although ample genomic research has been undertaken using pyrosequencing, the requirement of relatively high amount of DNA template and the difficulty in sequencing the homopolymeric regions limit its key advantages in the applications directing towards clinical research. In this study, we demonstrate that pyrosequencing on homopolymeric regions with 10 identical nucleotides can be successfully performed with optimal amount of DNA (0.3125-5 pmol) immobilized on conventional non-porous Sepharose beads. We also validate that by using porous silica beads, the sequencing signal increased 3.5-folds as compared to that produced from same amount of DNA immobilized on solid Sepharose beads. Our results strongly indicate that with optimized quantity of DNA and suitable solid support, the performance of pyrosequencing on homopolymeric regions and its detection limit has been significantly improved.     

A fault identification and classification scheme for an automobile door assembly process

A process fault identification and classification scheme for an automobile door assembly process is presented based on multivariate in-line dimensional measurements and principal component factor analysis. First, the door assembly process and the dimensional measurement system are briefly introduced. Second, the technique of principal component factor analysis is presented for process fault identification. Process faults are summarized based on off-line identified case studies. Finally a machine classification scheme based on principal components and principal factors is presented and evaluated, using the pattern knowledge obtained off-line. This scheme is shown to be effective in classifying process faults using production data.     

Molecular methods for the detection and classification of Cryptosporidium

Cryptosporidiosis is a zoonotic disease caused by a parasitic protozoan belonging to the coccidial genus Cryptosporidium. Current laboratory methods are adequate for the detection of the infection when oocysts are present in great numbers, but more-sensitive means of identification are urgently required. In a recent issue of Parasitology Today, Carolyn Petersen has presented a review of the cell biology of this parasite'. Here, Kath Webster draws attention to the various methods involved in its detection and classification.     

A fault detection service for wide area distributed computations

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to trade off timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver. This revised version was published online in July 2006 with corrections to the Cover Date.     

Use of DNA methylation for cancer detection and molecular classification

Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.     

Resistance to androstanes as an approach for androstandienedione yield enhancement in industrial mycobacteria

The resistance to androstandienedione (ADD) of industrial mycobacteria was demonstrated as a valuable approach to increasing ADD yield in sterol fermentations. Colonies growing at 1 mg/ml ADD in culture medium after nitrosoguanidine mutagenesis showed a differential behavior in respect to parentals in cholesterol biotransformation. In the presence of exogenous ADD, a substantial depletion of ADD production was observed in parental strains B3683 and Ex4, whereas it was unaffected, and even increased, in resistant colonies. An apparent reduction from ADD to androstandione and testosterone was also noticed. Furthermore, the ADD resistance phenotype may be related to the increase in steroid 1,2 dehydrogenase activity.     

Synthesizing cellular intelligence and artificial intelligence for bioprocesses

Microbial processes operated under realistic conditions are difficult to describe by mechanistic models, thereby limiting their optimization and control. Responses of living cells to their environment suggest that they possess some "innate intelligence". Such responses have been modeled by a cybernetic approach. Furthermore, the overall behavior of a bioreactor containing a population of cells may be described and controlled through artificial intelligence methods. Therefore, it seems logical to combine cybernetic models with artificial intelligence to evolve an integrated intelligence-based strategy that is physiologically more faithful than the current approaches. This possibility is discussed, together with practical considerations favoring a hybrid approach that includes some mathematical modeling.     

Performance evaluation of fault detection methods for wastewater treatment processes

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.     

SuperPartitions: detection and classification of orthologs

The proper detection of orthologs is crucial for evolutionary studies of genes and species. Despite large efforts to solve this problem the methodological situation appears unsettled to a large extent and the “quest for orthologs” is still an ongoing task in large-scale genome comparisons.Here, we introduce a simple operational framework for the detection of orthologs and their classification. The operational framework relies on well-established principles, optimizing their implementation for the considered purposes, and chaining components in coherent procedures: 1) We take advantage of the efficiency and simplicity of the Reciprocal Best Hit (RBH) detections, remedying (by design) the drawback concerning the limitations in terms of 1:1 detections. The procedure is based on the partitioning of Reciprocal Best Hits, with the further merging of partitions including members of the same paralogous classes (“SuperPartition of Orthologs” (SPOs)). 2) We then resort to the conservation profiles of the obtained clusters, allowing simple detection of SPOs containing duplicated members. Based on accepted evolutionary principles, such members can be further tagged as in-paralogs (co-orthologs) or out-paralogs.The method is illustrated and validated by extensive genomic analyses. The performances of the overall approach are characterized in global terms for three sets of species (Chlamydiae, Mycobacteria, Aspergilli), showing that at least 75% of the sets of orthologs contain at most one protein from a given species. The sets including more than one protein from a given species are shown to contain in-paralogs in proportions varying from 28% to 58%. The characterizations also show that the large majority of SPOs are associated with ancestral motifs, and accordingly not prone to chaining effects that might be triggered by multi-domain proteins. Further the SPO formulation is compared to other similarity based ortholog detection methods. Beyond core common results, significant differences are observed between various methods, which can be accounted for to a large extent on conceptual grounds, relative to the different merging schemes involved. Such comparisons highlight a major advantage of the SPO approach concerning the proper clustering of associated paralogs, which appear to be often dispatched spuriously into distinct orthologous classes.Finally the perspectives for future applications and elaborations of SPO-based compositional analyses are discussed.     

The detection and classification of membrane-spanning proteins

Discriminant analysis can be used to precisely classify membrane proteins as integral or peripheral and to estimate the odds that the classification is correct. Specifically, using 102 membrane proteins from the National Biomedical Research Foundation (NBRF) database we find that discrimination between integral and peripheral membrane proteins can be achieved with 99% reliability. Hydrophobic segments of integral membrane proteins can also be distinguished from interior segments of globular soluble proteins with better than 95% reliability. We also propose a procedure for determining boundaries of membrane-spanning segments and apply it to several integral membrane proteins. For the limited data available (such as on transplantation antigens), the residues at the boundaries of a membrane-spanning segment are predictable to within the error inherent in the concept of boundary. As a specific indication of resolution, seven membrane-spanning segments of bacteriorhodopsin are resolved with no information other than sequence, and the predicted boundary residues agree with the experimental data on proteolytic cleavage sites. Several definitive but yet to be tested predictions are also made, and the relation to other predictive methods is briefly discussed. A computer program in FORTRAN for prediction of membrane-spanning segments is available from the authors.     

Shark detection and classification with machine learning

Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation.We created a database of 53,345 shark images covering 219 species of sharks, and packaged object-detection and image classification models into a Shark Detector bundle. The Shark Detector recognizes and classifies sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: collecting occurrence records from photographs taken by the public or citizen scientists, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity.The Shark Detector can classify 47 species pertaining to 26 genera. It sorted heterogeneous datasets of images sourced from Instagram with 91% accuracy and classified species with 70% accuracy. It located sharks in baited remote footage and YouTube videos with 89% accuracy, and classified located subjects to the species level with 69% accuracy. All data-generation methods were processed without manual interaction.As media-based remote monitoring appears to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.     

Application of particle swarm optimization and proximal support vector machines for fault detection

This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with linear and nonlinear versions of PSVM. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The features extracted from original and preprocessed signals are used as inputs to the classifiers (PSVM) for detection of machine condition. Input features are selected using a PSO algorithm. The classifiers are trained with a subset of experimental data for known machine conditions and are tested using the remaining data. The procedure is illustrated using the experimental vibration data of a rotating machine. The influences of the number of features, PSO algorithms and type of classifiers (linear or nonlinear PSVM) on the detection success are investigated. Results are compared with a genetic algorithm (GA) and principal component analysis (PCA). The PSO based approach gave test classification success above 90% which were comparable with the GA and much better than PCA. The results show the effectiveness of the selected features and classifiers in detection of machine condition.     

A bacterial reporter panel for the detection and classification of antibiotic substances

The ever‐growing use of pharmaceutical compounds, including antibacterial substances, poses a substantial pollution load on the environment. Such compounds can compromise water quality, contaminate soils, livestock and crops, enhance resistance of microorganisms to antibiotic substances, and hamper human health. We report the construction of a novel panel of genetically engineered Escherichia coli reporter strains for the detection and classification of antibiotic substances. Each of these strains harbours a plasmid that carries a fusion of a selected gene promoter to bioluminescence (luxCDABE) reporter genes and an alternative tryptophan auxotrophy‐based non‐antibiotic selection system. The bioreporter panel was tested for sensitivity and responsiveness to diverse antibiotic substances by monitoring bioluminescence as a function of time and of antibiotic concentrations. All of the tested antibiotics were detected by the panel, which displayed different response patterns for each substance. These unique responses were analysed by several algorithms that enabled clustering the compounds according to their functional properties, and allowed the classification of unknown antibiotic substances with a high degree of accuracy and confidence.     

Adjusted classification for ultrasound scoring index for antenatal detection of fetal trisomy

Ultrasound (USG) is a useful investigation in obstetrics. Its mean indications include screening for fetal anomaly, especially for Down''s syndrome and other genetic trisomy. Here, the author tries to access the compatibility between classical USG scoring index and the new likelihood ratio-based system. New recommendation on severity of studied markers is given.