Statistical Control Techniques Research Articles

Follow-up on the U.S. Central Intelligence Agency's (CIA) remote viewing experiments☆.

Since 1972, the U.S. Central Intelligence Agency (CIA) commissioned several research programs on remote viewing (RV) that were progressively declassified from 1995 to 2003. The main objectives of this research were to statistically replicate the original findings and address the question: What are the underlying cognitive mechanisms involved in RV? The research focused on emotional intelligence (EI) theory and intuitive information processing as possible hypothetical mechanisms. We used a quasi-experimental design with new statistical control techniques based on structural equation modeling, analysis of invariance, and forced-choice experiments to accurately objectify results. We measured emotional intelligence with the Mayer-Salovey-Caruso Emotional Intelligence Test. A total of 347 participants who were nonbelievers in psychic experiences completed an RV experiment using targets based on location coordinates. A total of 287 participants reported beliefs in psychic experiences and completed another RV experiment using targets based on images of places. Moreover, we divided the total sample into further subsamples for the purpose of replicating the findings and also used different thresholds on standard deviations to test for variation in effect sizes. The hit rates on the psi-RV task were contrasted with the estimated chance. The results of our first group analysis were nonsignificant, but the analysis applied to the second group produced significant RV-related effects corresponding to the positive influence of EI (i.e., hits in the RV experiments were 19.5% predicted from EI) with small to moderate effect sizes (between 0. 457 and 0.853). These findings have profound implications for a new hypothesis of anomalous cognitions relative to RV protocols. Emotions perceived during RV sessions may play an important role in the production of anomalous cognitions. We propose the Production-Identification-Comprehension (PIC) emotional model as a function of behavior that could enhance VR test success.

Multivariate statistical monitoring strategy for an automotive manufacturing part facility

In this article an attempt has been made to devise a statistical monitoring strategy based on multivariate projection based method. The projection based method that was employed for development for the strategy was Principle Component Analysis (PCA) which is primarily used as a dimension reduction technique. The strategy thus devised was applied to a connecting rod manufacturing facility. Connecting rods are primarily produced via hot forging process accompanied by a litany of other finishing processes. Each process in turn is characterized by their own set of process characteristics which requires simultaneous monitoring so as to produce good quality end product. Simultaneous monitoring of characteristics and moreover correlated characteristics can be achieved via the employment of multivariate statistical control technique. Projection based techniques viz., PCA and partial least squares regression fall under special category MSPC techniques owning to their ability to deal with highly correlated and ill-conditioned data. The PCA based monitoring strategy thus devised in this article was found to be efficient in detection of faults indicated by out-of-control observations and thus aiding in producing good quality connecting rods.

Multivariate analyses to determine fungicide efficacy on Ecuadorian bananas for consumption

Half maximal effective concentration EC 50 is considered the main reference for evaluating the efficacy of the products in any plantation using doses and inhibition percentages from laboratory data. However, EC 50 is not the best representation when other relevant variables and their relationships could be involved. As an agricultural case study, fungicide sensitivity of Pseudocercospora fijiensis , the causal agent of black sigatoka, was evaluated on bananas’ plantations in three provinces of Ecuador. In this study, multivariate statistical process control was adjusted to a fungicide efficacy evaluation case considering multiple data tables from different locations and years at the same time. The threshold conveyed by inhibition percentages, related to the EC , along with locations and years allowed the multivariate analyses carried out in the proposal. The multivariate statistical control techniques applied were Multilinear Principal Component Analysis (MPCA) and Dual STATIS-Parallel Coordinates approach (DS-PC). A comparison was developed and showed that both methods discriminate correctly between the normal and anomalous conditions within plantations along years, validating the ability of the novel method DS-PC for exhibiting better signaling of anomalous plantations and performing variable-wise analysis to find out possible causes of this behavior in an easier time-saving graphical framework.

Performance Assessment of Shell and Tube Heat Exchangers in an Ammonia Plant

The main objective of this work is to assess the performance of two heat exchanger units (Stripper/Gas Overhead Condenser and Methanator Effluent Cooler) operating under steady state conditions in two stages. Two different methods are employed in monitoring the heat exchanger fouling, namely dirt factor trend method and a statistical control technique where a Cumulative Sum (CuSum) chart is used to check the stability of the process. Data were obtained through steady state monitoring and direct measurements from the plant. The data were analyzed using various energy equations and a computer program to determine the overall heat transfer coefficient, heat duty, capacity ratio, corrected log-mean-temperature difference, fouling factor, temperature range of both fluids and effectiveness. The result shows that for the Stripper/Gas Overhead Condenser, the overall heat transfer coefficient was 63.13% less than the design value in stage1 and 12.59% less in stage2. For the Methanator Effluent Cooler the value of heat duty and the overall heat transfer coefficient were 51.76% and 59.62% less respectively than the design value in stage1 and 30.72% and 30.16% less respectively in stage2. This was traceable to increase in heat transfer rate as a result of injecting the tubes of the exchangers with NALCO fluid. In order to detect small changes in the heat exchanger operation and to know the actual time fouling starts to build up, a Cusum chart is used. This work made use of QI Macros software to check the stability of the heat exchanger units and to know if processes are on-target.

Performance Assessment of Shell and Tube Heat Exchangers in an Ammonia Plant

The main objective of this work is to assess the performance of two heat exchanger units (Stripper/Gas Overhead Condenser and Methanator Effluent Cooler) operating under steady state conditions in two stages. Two different methods are employed in monitoring the heat exchanger fouling, namely dirt factor trend method and a statistical control technique where a Cumulative Sum (CuSum) chart is used to check the stability of the process. Data were obtained through steady state monitoring and direct measurements from the plant. The data were analyzed using various energy equations and a computer program to determine the overall heat transfer coefficient, heat duty, capacity ratio, corrected log-mean-temperature difference, fouling factor, temperature range of both fluids and effectiveness. The result shows that for the Stripper/Gas Overhead Condenser, the overall heat transfer coefficient was 63.13% less than the design value in stage1 and 12.59% less in stage2. For the Methanator Effluent Cooler the value of heat duty and the overall heat transfer coefficient were 51.76% and 59.62% less respectively than the design value in stage1 and 30.72% and 30.16% less respectively in stage2. This was traceable to increase in heat transfer rate as a result of injecting the tubes of the exchangers with NALCO fluid. In order to detect small changes in the heat exchanger operation and to know the actual time fouling starts to build up, a Cusum chart is used. This work made use of QI Macros software to check the stability of the heat exchanger units and to know if processes are on-target.

Is treated HIV infection still toxic to the brain?

Clinically apparent HIV infection, accompanied by CNS opportunistic infections and HIV encephalopathy, was often associated with profound structural and functional brain effects prior to the introduction of anti-retroviral therapy (ART). With treatment, HIV structural and functional brain effects are smaller and have not been as easily detected. With near complete elimination of CNS opportunistic infections, the HIV neuroimaging research community now grapples with the problem of detecting subtler structural and functional changes against a background of persisting confounds, such as comorbidities and clinical features common in the HIV infected population. This situation also raises the question of whether imaging measure changes that are reported as HIV brain effects are purely related to viral infection, rather than originating from confounding effects that might include age, substance use, hepatitis C coinfection, cerebrovascular risk factors, ART, premorbid cognitive skills and illness duration. In addition to cohort characteristics, variation in image acquisition and analysis techniques may also contribute to study outcome heterogeneity. We review the potential effects of these confounds on detection of HIV infection effects and discuss strategies to avoid or mitigate the effects of these confounds. We then present a systematic approach to measurement, design and analysis in HIV neuroimaging studies, combining both experimental and statistical control techniques to determine if HIV infection effects persist, fluctuate or worsen in groups achieving viral suppression from ART.

Sensor Location for Enhancing Fault Diagnosis

Multivariate statistical control techniques have been successfully applied to the detection and isolation of process faults. Because those strategies evaluate the current process state using the measurement values and the normal operation model, their performance is strongly influenced by the sensor network installed in the plant. Nevertheless, very few sensor location approaches have been presented to enhance faults isolation, and they do not guarantee that a fault can be distinguished from the other ones before its magnitude reaches a certain critical value. In this work a strategy for updating process instrumentation is presented that aims at detecting and isolating a given set of process failures using statistical monitoring procedures before the fault magnitudes exceed predefined threshold values. In this sense, fault isolation constraints are formulated and incorporated to the instrumentation update optimization problem. The proposed restrictions are expressed in terms of the variable contributions ...

APPLICATION OF MASS MEASUREMENT TECHNIQUE IN CALIBRATION AND CONTROL FOR GAS CYLINDERS

This paper deals with measurement and control of the standard gas cylinder. In CMS (Center of Measurement Standard), the mass measurement technology is used to calibrate the concentration of standard gas cylinders. This measurement method is based on the mass change of gas cylinder before and after filling. By using balance and calculating air buoyancy the mass difference of gas cylinder before and after filling could be obtained. In order to understand stability of the gas cylinder, we use statistical control techniques to monitor the change of concentration of the standard gas cylinders, to take immediate measures when the concentration of the standard gas cylinders is changed.

Study on VoIP Service Quality Management

VoIP transmits voices over IP-based networks and it is the abbreviation of Voice over Internet Protocol. Recently, VoIP provides various services in addition to voices. Since VoIP services' provision is extending, VoIP service quality management is becoming an important issue. Therefore, in this paper, we study VoIP service quality management. We examine VoIP technology, service types, and network architecture. Then, we investigate key quality indicators(KQIs)/key performance indicators(KPIs) in terms of customers, not network service providers. Toward this, we also study the concept of general service quality management as well as the concept of telecommunication related service quality management. Moreover, we apply and R charts to show how to use statistical quality control techniques in real telecommunication companies with one KQI.

Research on Information Resources Dynamic Allocation Based on Carbon Management Certification

Abstract Reference to domestic and international experience, China do research and invest not only on macro-level such as policy, accountability, new energy, resources, etc., but there's urgent need for the introduction of mechanisms on monitoring and certification. We do modeling study on the level of resources and carbon emissions management certification through introducing carbon certification and measurement on information resources. This paper reference to the British Standard Institute's global carbon footprint standard “PAS 2050:2008 Specification for the assessment of the life cycle greenhouse gas emissions of goods and services , using statistical control techniques to explore the measurement methods of information resources flow, referencing Capability Maturity Model Integration(CMMI), Six Sigma, ISO9000 quality certification system and method, building carbon management capacity assessment model, providing technical support to achieve carbon emission targets.

The effect of single-axis sorting on the estimation of a linear regression

Microaggregation is one of the most important statistical disclosure control techniques for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. In this paper, we consider single-axis sorting, a frequently applied microaggregation technique where the formation of groups depends on the magnitude of a sorting variable related to the variables in the data set. The paper deals with the impact of this technique on a linear model in continuous variables. We show that parameter estimates are asymptotically biased if the sorting variable depends on the response variable of the linear model. Using this result, we develop a consistent estimator that removes the aggregation bias. Moreover, we derive the asymptotic covariance matrix of the corrected least squares estimator.

Optimisation et maîtrise des processus multivariés. La méthode FNAD

The major disadvantage with actual multivariate statistical control techniques is the great difficulty in detecting the out of control variable in the process. We are proposing in this article an original approach based on the utilisation of discriminate analysis and digital filtering, and we are showing that it is possible in this way to overcome this inconvenience.

Experience of applying statistical control techniques to the function test phase of a large telecommunications system

The software test process of a large telecommunications system is presented. The feasibility of introducing statistical process control techniques to one crucial software test phase, namely the function test is explored. This phase has been identified as a strategic one for meeting the commitments to customers with respect to quality objectives. Analysis of past released products revealed that a high percentage of the failures experienced in operation corresponded to software faults that could have been discovered during the function test phase. A brief description of the statistical estimators investigated (Classical vs. Bayesian) is provided, along with a few examples of their use over real sets of data. Far from being aimed at identifying new statistical models, the focus of this work is rather about putting measurement in practice: easy and effective steps to improve the status of control over the test process in a smooth, bottom-up approach are suggested.

Race and hypertension: science and nescience.

Although race is widely used in hypertension research as a marker of increased risk, its meaning as an etiologic quantity is obscure.1 2 3 4 5 Because of its importance as a social category in American life, separating what we “know” about race through socialization from what can be known on the basis of scientific inquiry is difficult.6 This dilemma represents a specific example of how social influences impact the conduct of science. While the process by which evidence is gathered and evaluated is usually constrained by an existing theoretical model (the “hypothetico-deductive process”), hypotheses arise more informally from the interplay of “inspiration” and “intuition.”7 Although hypothesis generation is at the core of scientific activity, it is peculiarly vulnerable to the influences of ideology. Contemporary perspectives on science as a practical human activity thus acknowledge that inspiration and intuition are the products of imagination and, as such, are derived from the investigator’s experience—both inside and outside the laboratory.8 While it is necessary to take account of the assumptions about race that are generated by our participation in a racially stratified society, it is also necessary to define exactly what we can know about race through the conduct of science. In this discussion, we are interested in moving beyond concerns about social influences on hypothesis generation and will attempt to address the logic of studies that use race comparisons to examine causal relationships. Specifically, we ask the question, “Can available statistical adjustment methods lead to valid inferences when race is regarded as an etiologic quantity, rather than a broad indicator of risk factor status?” Despite the popularity of racial hypotheses, we argue that comparative studies based on observations of broad phenotypes—such as high blood pressure, obesity, or glucose intolerance—have little potential to contribute new knowledge. The attempt …

Process simplification in DRAM manufacturing

DRAM manufacturers are driving the quest for integration friendly, simplified, and statistically-controlled development. In the DRAM field, strong emphasis must be placed on process simplification as a means of staying cost effective and competitive. In order to examine architecture and integration, both at the module and system levels, it is emphasized that using correct statistical methods in conjunction with advanced technology is important. For simplified development and integration, many aspects of the new assessment are data driven. Also, it is critical to understand the source of variation in the process, obtain stability, and assess the capability relative to specifications of manufacturability and functionality. Statistical control techniques are a requirement for interpreting the results of capability studies, as will be discussed in this paper. In this paper, we have emphasized that both statistical methods and technological innovation are required for optimization. We have investigated several examples that illustrate the methodology followed for the development of statistically controlled, production-worthy processes. The improvement strategy is described both for gate stack and the DRAM cell. The focus of the results described here is both in the context of enhancing capability for existing processes and evaluating alternate options that can be accomplished through the use of advanced technology.

Software requirements for statistical quality control

The use of statistical control techniques to improve product quality is recognized in industry today as a key factor in maintaining a competitive position. Widespread, effective use of these techniques requires that they be automated. This paper describes statistical quality control methodology and examines issues central to developing software for its implementation. Cumulative Sum (CUSUM) control methodology is emphasized. Requirements for diagnostic CUSUM plots, CUSUM control programs, and integrated on-line CUSUM control are presented.