This document presents a method for combining domain separation and adaptive active learning. It aims to efficiently label samples from a new target domain using available labeled data from a related source domain. The method first uses domain separation to identify informative regions of the target domain distinct from the source. It then applies adaptive active learning via breaking ties to iteratively select the most uncertain target samples for labeling. A TrAdaBoost algorithm is used to differentially reweight source and target samples as more target data is incorporated, in order to adapt the classifier to the target domain. The method is tested on QuickBird imagery of Zurich from different seasons, demonstrating a dataset shift. Results show the combined approach can effectively label samples for the target domain using minimal supervision
The Factors Influencing the Hospital Utilization9145
The document discusses the main factors that influence hospital utilization, including internal facilities, site, and location. Internal facilities should have modern equipment, specializations, professional staff, good infrastructure, and medical services. The site should have available facilities, appropriate land costs, no nearby similar hospitals, and supportive utilities. The location should be convenient for people through affordable transportation, have nearby emergency services, and supportive amenities while being eco-friendly and avoiding cross-infection. Locality support, facilities, site, and location are essential for hospital utilization.
The document discusses equipment maintenance, providing details on why it is important, its objectives and scope. It describes the key components of an effective maintenance program, including planning, management, implementation and performance monitoring. Planning involves inventorying equipment, determining necessary resources and selecting appropriate maintenance methods. Management covers financial, personnel and operational aspects. Implementation focuses on inspections, preventive maintenance, corrective maintenance and addressing environmental and safety factors. Performance is monitored through key metrics to identify opportunities for improvement. The overall goal is to keep medical equipment reliable, safe and available through all stages from procurement to disposal.
This document summarizes a study that analyzed the measurement system of an optical micrometer machine using gauge repeatability and reproducibility (Gage R&R) techniques. Five operators performed measurements on test parts using the machine. The measurements were statistically analyzed using methods like analysis of variance to determine sources of variability and ensure accuracy of the measurement system. Factors like temperature effects, precision of measurements for different part features, and comparisons of measurement analysis methods were also examined. The goal was to enhance understanding of the machine's measurement capabilities and identify ways to improve measurement quality.
This document describes using a particle swarm optimization (PSO) algorithm to tune the parameters of a proportional-integral-derivative (PID) controller. PID controllers are commonly used in industrial processes but can be difficult to manually tune. The PSO algorithm is applied to optimize the PID parameters to minimize errors and improve closed-loop performance compared to traditional tuning methods like Ziegler-Nichols. Simulation results show the PSO-tuned PID controller provides better step response characteristics like reducing steady-state error, rise time, settling time and overshoot in controlling a DC motor speed compared to the Ziegler-Nichols method.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
Accelerated life tests (ALTs) are employed to generate failure time data at higher-than-normal-use stress levels. ALT planning is critical for achieving statistical efficiency and reducing experimental cost through design of experiments (DOE). In this talk, I will describe a real world example of ALT planning and its impact on decision making. I will present models for regression with failure time data, including exponential and Weibull regression. Censoring, which is present in many life testing experiments, and its effect on regression models is discussed. Graphical methods for data analysis of life testing experiments are discussed, as well as the software for ALT planning and data analysis.
This document presents a method for combining domain separation and adaptive active learning. It aims to efficiently label samples from a new target domain using available labeled data from a related source domain. The method first uses domain separation to identify informative regions of the target domain distinct from the source. It then applies adaptive active learning via breaking ties to iteratively select the most uncertain target samples for labeling. A TrAdaBoost algorithm is used to differentially reweight source and target samples as more target data is incorporated, in order to adapt the classifier to the target domain. The method is tested on QuickBird imagery of Zurich from different seasons, demonstrating a dataset shift. Results show the combined approach can effectively label samples for the target domain using minimal supervision
The Factors Influencing the Hospital Utilization9145
The document discusses the main factors that influence hospital utilization, including internal facilities, site, and location. Internal facilities should have modern equipment, specializations, professional staff, good infrastructure, and medical services. The site should have available facilities, appropriate land costs, no nearby similar hospitals, and supportive utilities. The location should be convenient for people through affordable transportation, have nearby emergency services, and supportive amenities while being eco-friendly and avoiding cross-infection. Locality support, facilities, site, and location are essential for hospital utilization.
The document discusses equipment maintenance, providing details on why it is important, its objectives and scope. It describes the key components of an effective maintenance program, including planning, management, implementation and performance monitoring. Planning involves inventorying equipment, determining necessary resources and selecting appropriate maintenance methods. Management covers financial, personnel and operational aspects. Implementation focuses on inspections, preventive maintenance, corrective maintenance and addressing environmental and safety factors. Performance is monitored through key metrics to identify opportunities for improvement. The overall goal is to keep medical equipment reliable, safe and available through all stages from procurement to disposal.
This document summarizes a study that analyzed the measurement system of an optical micrometer machine using gauge repeatability and reproducibility (Gage R&R) techniques. Five operators performed measurements on test parts using the machine. The measurements were statistically analyzed using methods like analysis of variance to determine sources of variability and ensure accuracy of the measurement system. Factors like temperature effects, precision of measurements for different part features, and comparisons of measurement analysis methods were also examined. The goal was to enhance understanding of the machine's measurement capabilities and identify ways to improve measurement quality.
This document describes using a particle swarm optimization (PSO) algorithm to tune the parameters of a proportional-integral-derivative (PID) controller. PID controllers are commonly used in industrial processes but can be difficult to manually tune. The PSO algorithm is applied to optimize the PID parameters to minimize errors and improve closed-loop performance compared to traditional tuning methods like Ziegler-Nichols. Simulation results show the PSO-tuned PID controller provides better step response characteristics like reducing steady-state error, rise time, settling time and overshoot in controlling a DC motor speed compared to the Ziegler-Nichols method.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
Accelerated life tests (ALTs) are employed to generate failure time data at higher-than-normal-use stress levels. ALT planning is critical for achieving statistical efficiency and reducing experimental cost through design of experiments (DOE). In this talk, I will describe a real world example of ALT planning and its impact on decision making. I will present models for regression with failure time data, including exponential and Weibull regression. Censoring, which is present in many life testing experiments, and its effect on regression models is discussed. Graphical methods for data analysis of life testing experiments are discussed, as well as the software for ALT planning and data analysis.
Project #4 Due 121015 by 500pm Upload one PowerPoint and .docxwkyra78
Project #4 Due 12/10/15 by 5:00pm
Upload one PowerPoint and one Excel file into Blackboard by the deadline above (e-mailed copies will not be
accepted).
For this project you select a Fortune 500 company of your choice and assess performance data via the SEC
website EDGAR; here are the minimal requirements:
1. (PowerPoint ) Describe the company including their ticker symbol, fiscal year, major line(s) of business,
history, financial projections for the future, and any affiliations currently existing. Good sources include
Yahoo finance and EDGAR but you are free to use others as well.
2. (Excel and PowerPoint) Using the 10-K reports create an Excel spreadsheet (label 2 to indicate the sheet
corresponds to question 2) with a column for Fiscal Years (i.e. 2014, 2013, etc.), Fiscal Year Sales.
3. (Excel and PowerPoint) Using the 10-Q reports create an Excel spreadsheet (label 3 to indicate the sheet
corresponds to question 3) with a column for Quarters, (i.e. Q1-2015, Q2-2015, etc.) and Quarterly Sales.
Complete a total of 40 quarters (10 years of data) with the last quarter of 2014 representing quarter 40.
Since the 10-Q reports are not released for the 4
th
quarter of each year you will need to use the
information from question 2 to derive 4
th
quarter data (e.g. if you find that for fiscal year 2012 ABC Inc.
made 1.5 billion in sales and you calculate that the sum of quarters 1, 2, & 3 net income equals 1.2
billion, then it stands to reason that the 4
th
quarter net income must be 300 million).
4. (Excel and PowerPoint) Using Excel’s scatterplot function, estimate a linear trend equations [Y = α + β t]
for Sales. Plot the time series data on the X-axis (Quarters from lowest to highest) and the sales on the Y-
Axis (in millions of dollars). In the graph make sure to show the trend line and regression metrics
(equation & R-squared). Using the equation forecast Sales for Q= 41 (Q1-2015).
5. (Excel and PowerPoint) Using Excel’s Data Analysis Regression function create a spreadsheet that
provides the computer-generated statistical details (label 5 to indicate the sheet corresponds to question
5). The resulting coefficients should mirror those calculated in question 4 with the scatterplot. If they
don’t then you may have reversed your dependent and independent variables so you will need to correct
this before proceeding.
6. (PowerPoint) Summarize and explain your forecasted and statistical results (i.e. R Square, Coefficients,
T-stat, P-value, and the 95% confidence interval).
WHAT ARE STANDARDS?1
Standards are published documents that establish specifications and procedures designed to maximize the reliability of the materials, products, methods, and/or services people use every day. Standards address a range of issues, including but not limited to various protocols to help maximize product functionality and compatibility, facilitate interoperability and support consumer safety and pub ...
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
LabVIEW Interface for Controlling a Test Bench for Photovoltaic Modules and E...IAES-IJPEDS
The document describes a LabVIEW graphical user interface (GUI) developed to control an outdoor photovoltaic module test bench and extract performance parameters. The GUI allows [1] measuring current-voltage (I-V) characteristics of modules under natural conditions, [2] extracting module parameters using a numerical method, and [3] simulating I-V curves and translating results to standard test conditions. The test bench utilizes an electronic load controlled via the GUI to rapidly vary module loading. Measured data, extracted parameters, and simulated results are displayed on the GUI for analysis.
Design of Experiment (DOE) has been widely applied on improving product performance. It is an important part of Design for Six Sigma (DFSS). However, due to its limitation on data requirement and model assumptions, it is not popularly used in life test. In this presentation, a method combining regular DOE technique with proper life data analysis method is presented. This method can be used to identify factors that affect product life and also can be used to optimize design variables to improve product reliability.
This document provides guidance for developing a medical device from concept to market. It outlines key steps including establishing intended use and regulatory classification, developing specifications and risk analysis, verification and validation testing, and determining regulatory approval pathways. Main target markets are identified as the EU and USA. The document emphasizes establishing a regulatory strategy upfront to guide development and clinical plans based on risk classification. Standards, guidelines, and a traceability matrix are recommended to ensure safety and effectiveness.
This FDA warning letter outlines issues with a clinical investigation conducted by Dr. Thomas Beilke between 2008-2009. The FDA inspection found that Dr. Beilke failed to properly conduct or supervise the clinical investigation according to regulations. Specifically, the letter cites that Dr. Beilke did not personally conduct or supervise the investigation as required. The FDA concluded that Dr. Beilke did not adhere to statutory requirements and regulations governing clinical investigations.
This document outlines Donghyun Lee's research capability and plan. It summarizes his research experience, statistical analysis skills, and programming abilities. His ongoing research focuses on the pharmaceutical industry, with a literature review examining R&D investment and corporate performance. His research plan is to study open innovation models in pharmaceutical R&D with a global focus, as well as biosimilars, pricing, and simulation. To strengthen his methodology, he outlines a course plan focusing on econometrics, modeling, and innovation management.
This document outlines the steps to calculate the unit costs for various health interventions including counseling and testing, PMTCT, TB clinics, ART, and lab services. It describes calculating the cost per case for each intervention by determining the target population, required resources, and typical patient flow. Infrastructure, program fixed, and variable costs will be calculated. Infrastructure costs include building space, equipment, and manpower. Program fixed costs cover managerial and technical staff. Variable costs involve operational expenses. Data will be collected from comprehensive sites to test the costing methodology.
A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
Detection of Attentiveness from Periocular InformationIRJET Journal
This document presents an approach to detect attentiveness from the periocular region surrounding the eyes. It first detects faces in an image using a classifier trained on facial features. It then isolates the periocular region by reducing the height of the bounding box around the detected face. Features are extracted from the periocular region using HOG descriptors and fed into an SVM classifier trained to identify attentiveness. The approach aims to predict attentiveness with minimal computational cost by focusing analysis on the periocular region rather than full face recognition or extensive image processing.
Industrial Engineering is concerned with designing integrated systems involving people, materials, equipment and energy. Some significant events in its development include the division of labor, standardized parts, scientific management, the assembly line, and quality control methods. Productivity is a measure of output over input, with higher productivity indicating more output is generated from the same level of inputs. Factors like technology, capacity utilization, and training can affect productivity levels in an organization.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
The Quality Control Program (QCP) provides laboratories with statistical reports and tools to improve quality and compare performance to peer groups. It collects data from 800 labs worldwide. The QCP includes 8 statistical comparison reports that provide indicators of precision, accuracy, and uncertainty to help laboratories evaluate their results over time, identify errors, and improve performance relative to international standards. Primary users can enroll laboratories and instruments and enter quality control data either manually or via automatic daily uploads from certain instruments. Secondary users can be added to manage specific instruments.
RICE INSECTS CLASSIFICATION USIING TRANSFER LEARNING AND CNNIRJET Journal
This document summarizes a study that used transfer learning and convolutional neural networks (CNNs) to classify different rice insect pests from images. The researchers used pre-trained CNN models like AlexNet and VGG16 and fine-tuned them on a dataset of rice insect images. AlexNet achieved the highest classification accuracy of 98%. Transfer learning helped address the classification problem with minimal training requirements compared to training CNNs from scratch. The study aims to help with early detection of insect pests to prevent crop damage.
The ITAB is an interactive aptitude battery that measures fluid intelligence through game-like tests. It aims to assess intelligence in a way that is engaging for test-takers who dislike traditional tests. The ITAB measures how well users incorporate feedback to solve problems efficiently. It provides scores on prediction, work style, and intelligence that can be customized. Research shows the ITAB predicts technical training outcomes incrementally over an ASVAB composite. The ITAB is a valid, reliable tool for measuring fluid intelligence relevant for technical jobs.
The document summarizes 5 papers from Zhejiang University of Finance and Economics that were included in the Ei Compendex database in 2005. It provides the title, authors, source, and brief summaries for each of the 5 papers.
Determination of Optimum Parameters Affecting the Properties of O RingsIRJET Journal
This document summarizes a study that used design of experiments (DOE) to determine the optimal parameters for curing and heat treating EPDM G2 flange O-rings. A 2^4 factorial design was used to test the effects of four factors (curing temperature, curing time, heat treatment temperature, and heat treatment time) on the mechanical properties of O-rings, including tensile strength, elongation at break, and load at break. Interaction plots and ANOVA were used to analyze the results. The analysis found a significant interaction between curing temperature and curing time for elongation at break and percentage elongation, but other interactions were insignificant. The study aims to optimize the curing and heat treatment parameters to achieve
Determination of Optimum Parameters Affecting the Properties of O RingsIRJET Journal
This document discusses using design of experiments (DOE) to determine the optimal parameters for producing O-rings made of EPDM G2 flange material. DOE was used to study how curing temperature, curing time, heat treatment temperature, and heat treatment time affect the tensile strength, load at break, and elongation of the O-rings. MINITAB software was used to generate plots and analyze the experimental results to identify the parameter settings that produce optimal mechanical properties for the O-rings. The goal of the study was to determine the optimum temperature parameters for curing and heat treating the O-rings.
Performance Measurement of Individual Manufacturing Firm Under Fuzzy Performa...IRJET Journal
This document presents a study that develops a hierarchical model using fuzzy performance index to measure the overall performance of an individual manufacturing firm under lean-resilient supply chain strategies. It constructs a 2-level hierarchical structure of lean-resilient supply chain measures and corresponding metrics. An empirical case study is conducted to apply the model to measure the performance of a crank and shaft manufacturing firm. Expert ratings and weights are gathered on the metrics and aggregated using fuzzy operators to calculate an overall fuzzy performance index for the firm. The index is then defuzzified to provide a single numerical performance score.
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Healt...HTAi Bilbao 2012
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Health Technology Assessment
Jackie Tran, MD
University of Medicine and Dentistry of New Jersey, USA
HTAi 9th Annual Meeting, Bilbao
Integrated Care for a Patient Centered System
25 June, 2012
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Project #4 Due 121015 by 500pm Upload one PowerPoint and .docxwkyra78
Project #4 Due 12/10/15 by 5:00pm
Upload one PowerPoint and one Excel file into Blackboard by the deadline above (e-mailed copies will not be
accepted).
For this project you select a Fortune 500 company of your choice and assess performance data via the SEC
website EDGAR; here are the minimal requirements:
1. (PowerPoint ) Describe the company including their ticker symbol, fiscal year, major line(s) of business,
history, financial projections for the future, and any affiliations currently existing. Good sources include
Yahoo finance and EDGAR but you are free to use others as well.
2. (Excel and PowerPoint) Using the 10-K reports create an Excel spreadsheet (label 2 to indicate the sheet
corresponds to question 2) with a column for Fiscal Years (i.e. 2014, 2013, etc.), Fiscal Year Sales.
3. (Excel and PowerPoint) Using the 10-Q reports create an Excel spreadsheet (label 3 to indicate the sheet
corresponds to question 3) with a column for Quarters, (i.e. Q1-2015, Q2-2015, etc.) and Quarterly Sales.
Complete a total of 40 quarters (10 years of data) with the last quarter of 2014 representing quarter 40.
Since the 10-Q reports are not released for the 4
th
quarter of each year you will need to use the
information from question 2 to derive 4
th
quarter data (e.g. if you find that for fiscal year 2012 ABC Inc.
made 1.5 billion in sales and you calculate that the sum of quarters 1, 2, & 3 net income equals 1.2
billion, then it stands to reason that the 4
th
quarter net income must be 300 million).
4. (Excel and PowerPoint) Using Excel’s scatterplot function, estimate a linear trend equations [Y = α + β t]
for Sales. Plot the time series data on the X-axis (Quarters from lowest to highest) and the sales on the Y-
Axis (in millions of dollars). In the graph make sure to show the trend line and regression metrics
(equation & R-squared). Using the equation forecast Sales for Q= 41 (Q1-2015).
5. (Excel and PowerPoint) Using Excel’s Data Analysis Regression function create a spreadsheet that
provides the computer-generated statistical details (label 5 to indicate the sheet corresponds to question
5). The resulting coefficients should mirror those calculated in question 4 with the scatterplot. If they
don’t then you may have reversed your dependent and independent variables so you will need to correct
this before proceeding.
6. (PowerPoint) Summarize and explain your forecasted and statistical results (i.e. R Square, Coefficients,
T-stat, P-value, and the 95% confidence interval).
WHAT ARE STANDARDS?1
Standards are published documents that establish specifications and procedures designed to maximize the reliability of the materials, products, methods, and/or services people use every day. Standards address a range of issues, including but not limited to various protocols to help maximize product functionality and compatibility, facilitate interoperability and support consumer safety and pub ...
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
LabVIEW Interface for Controlling a Test Bench for Photovoltaic Modules and E...IAES-IJPEDS
The document describes a LabVIEW graphical user interface (GUI) developed to control an outdoor photovoltaic module test bench and extract performance parameters. The GUI allows [1] measuring current-voltage (I-V) characteristics of modules under natural conditions, [2] extracting module parameters using a numerical method, and [3] simulating I-V curves and translating results to standard test conditions. The test bench utilizes an electronic load controlled via the GUI to rapidly vary module loading. Measured data, extracted parameters, and simulated results are displayed on the GUI for analysis.
Design of Experiment (DOE) has been widely applied on improving product performance. It is an important part of Design for Six Sigma (DFSS). However, due to its limitation on data requirement and model assumptions, it is not popularly used in life test. In this presentation, a method combining regular DOE technique with proper life data analysis method is presented. This method can be used to identify factors that affect product life and also can be used to optimize design variables to improve product reliability.
This document provides guidance for developing a medical device from concept to market. It outlines key steps including establishing intended use and regulatory classification, developing specifications and risk analysis, verification and validation testing, and determining regulatory approval pathways. Main target markets are identified as the EU and USA. The document emphasizes establishing a regulatory strategy upfront to guide development and clinical plans based on risk classification. Standards, guidelines, and a traceability matrix are recommended to ensure safety and effectiveness.
This FDA warning letter outlines issues with a clinical investigation conducted by Dr. Thomas Beilke between 2008-2009. The FDA inspection found that Dr. Beilke failed to properly conduct or supervise the clinical investigation according to regulations. Specifically, the letter cites that Dr. Beilke did not personally conduct or supervise the investigation as required. The FDA concluded that Dr. Beilke did not adhere to statutory requirements and regulations governing clinical investigations.
This document outlines Donghyun Lee's research capability and plan. It summarizes his research experience, statistical analysis skills, and programming abilities. His ongoing research focuses on the pharmaceutical industry, with a literature review examining R&D investment and corporate performance. His research plan is to study open innovation models in pharmaceutical R&D with a global focus, as well as biosimilars, pricing, and simulation. To strengthen his methodology, he outlines a course plan focusing on econometrics, modeling, and innovation management.
This document outlines the steps to calculate the unit costs for various health interventions including counseling and testing, PMTCT, TB clinics, ART, and lab services. It describes calculating the cost per case for each intervention by determining the target population, required resources, and typical patient flow. Infrastructure, program fixed, and variable costs will be calculated. Infrastructure costs include building space, equipment, and manpower. Program fixed costs cover managerial and technical staff. Variable costs involve operational expenses. Data will be collected from comprehensive sites to test the costing methodology.
A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
Detection of Attentiveness from Periocular InformationIRJET Journal
This document presents an approach to detect attentiveness from the periocular region surrounding the eyes. It first detects faces in an image using a classifier trained on facial features. It then isolates the periocular region by reducing the height of the bounding box around the detected face. Features are extracted from the periocular region using HOG descriptors and fed into an SVM classifier trained to identify attentiveness. The approach aims to predict attentiveness with minimal computational cost by focusing analysis on the periocular region rather than full face recognition or extensive image processing.
Industrial Engineering is concerned with designing integrated systems involving people, materials, equipment and energy. Some significant events in its development include the division of labor, standardized parts, scientific management, the assembly line, and quality control methods. Productivity is a measure of output over input, with higher productivity indicating more output is generated from the same level of inputs. Factors like technology, capacity utilization, and training can affect productivity levels in an organization.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
The Quality Control Program (QCP) provides laboratories with statistical reports and tools to improve quality and compare performance to peer groups. It collects data from 800 labs worldwide. The QCP includes 8 statistical comparison reports that provide indicators of precision, accuracy, and uncertainty to help laboratories evaluate their results over time, identify errors, and improve performance relative to international standards. Primary users can enroll laboratories and instruments and enter quality control data either manually or via automatic daily uploads from certain instruments. Secondary users can be added to manage specific instruments.
RICE INSECTS CLASSIFICATION USIING TRANSFER LEARNING AND CNNIRJET Journal
This document summarizes a study that used transfer learning and convolutional neural networks (CNNs) to classify different rice insect pests from images. The researchers used pre-trained CNN models like AlexNet and VGG16 and fine-tuned them on a dataset of rice insect images. AlexNet achieved the highest classification accuracy of 98%. Transfer learning helped address the classification problem with minimal training requirements compared to training CNNs from scratch. The study aims to help with early detection of insect pests to prevent crop damage.
The ITAB is an interactive aptitude battery that measures fluid intelligence through game-like tests. It aims to assess intelligence in a way that is engaging for test-takers who dislike traditional tests. The ITAB measures how well users incorporate feedback to solve problems efficiently. It provides scores on prediction, work style, and intelligence that can be customized. Research shows the ITAB predicts technical training outcomes incrementally over an ASVAB composite. The ITAB is a valid, reliable tool for measuring fluid intelligence relevant for technical jobs.
The document summarizes 5 papers from Zhejiang University of Finance and Economics that were included in the Ei Compendex database in 2005. It provides the title, authors, source, and brief summaries for each of the 5 papers.
Determination of Optimum Parameters Affecting the Properties of O RingsIRJET Journal
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Disinvestment. Utilization indexes for medical equipment a pilot model.
1. UTILIZATION INDEXES FOR MEDICAL EQUIPMENT
A PILOT MODEL
U. Nocco1, G. Lolli Ceroni2, E. Lettieri2, S. del Torchio1
1
Clinical Engineering Department, Varese Town and University Hospital, Varese – Italy
2
Politecnico di Milano School of Engineering, Milan - Italy
Corresponding author:
eng. Umberto Nocco
Clinical Engineering Department
AOU Ospedale di Circolo e Fondazione Macchi
Viale Borri, 57 – 21100 Varese – Italy
Tel +39 0332 278301 - mobile: +39 335 7882671
e-mail: umberto.nocco@ospedale.varese.it
2. Medical Equipment
BACKGROUND
To a Healthcare Institution medical
equipment represents:
-investment;
-source for clinical information;
-stakeholder to process definition;
What we don’t know…
- How much is each device used?
- Can we define theoretical productivity?
- Which devices are eligible for utilization
assessment?
- Can utilization assessment be used in a
disinvestment process?
But, first of all
- Do we have data?
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
2
3. Which devices are eligible for evaluation?
BACKGROUND
Mandatory:
There are devices that are needed in each ward:
-defibrillators
-ekg machines
-….
due to regulation
Strategic:
Other devices are needed “just in case”: a pediatric bed in a ICU is needed
although it might be used once a year
Eligible devices are those related to a specific procedure, those that require
a high investment (US machines), or those who have little cost but big
numbers (infusion pumps).
The more complete an IT scheduling and reporting system the better.
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
3
4. Purpose
OBJECTIVES
Main objectives of the project are:
1. Propose a model to define productivity indexes for medical
equipment;
equipment
2. verify whether they can be calculated easily
3. verify which stakeholders are to be considered during data
analisys
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
4
5. Concept approach
METHODS
cal
-Asset utilization index (S)
reti
Theo
-Yearly exam index (Y)
- Daily exam index (D)
-Yearly number of exams
- Daily number of exams
- Asset daily productivity
Actu
al
- Clinical
- Organizational
- Demand
- etc.
6. Index definition and calculation
METHODS
Phase Index name Index
Annual theoretical exam Number EY = Ew ⋅ 50
Theoretical
EW
Daily Theoretical Exam Number (NTd) NTd =
G
Assett daily productivity (S) N Td and S R = N Ad
S=
M M
Ea
Acutal Yearly Exam Index (Y) Y = ⋅100
EY
Ea ( 50 ⋅ G )
Real Actual Daily Exam Index (D) D= ⋅100
N Td
Ew weekly exams per schedule Assett Utilization Index (S)
Sˆ ( *) ( E ( 50 ⋅ G ) )
= a
M ( *)
G=weekdays in schedule S ( *)
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
6
7. Index evaluation
METHODS
Given that the main hypothesis to the model is that daily schedule is
determined considering epidemiology, resources and assett availability, index
evalutation is being carried out as follows:
Saturation indexes (Y and D):
Index value judgement
Y and D >90% ok (10% mortality on booked exams is acceptable)
75%< Y and D <90% actions needed, expecially referred to production
analysis
Y and D <75% Heavy activity is needed on organization, device
condivision ect.
8. Ultrasound machines
RESULTS AND DISCUSSION
US machines were analyzed in Pediatric Cardiology
Entry data:
Ew=19 G=3 M=2 Ea=661 working weeks: 50
EY = 19 ⋅ 50 = 950
N Td = 6,3
N Ad = 661 = 4,4 Is this number of exams/day or
150 (worse) this number of
S = 3,1 exams/day/device sustainable?
Which actions should be
S R = 2,2
implemented? Device condivision
661
Y = ⋅100 = 69,5% Saturation index is quite good both
950 for yearly exams and device
661 utilization
BUT
D = 50 ⋅ 3 = 69,94% Hospital administration should
6,3 verifiy why only 70% of available
ˆ
S = 71% schedule is used by patients.
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
8
9. Gastroscopes
RESULTS AND DISCUSSION
Gastroscopes
Entry data:
Ew=100 G=5 M=14 Ea=5000
working weeks: 50
4900
EY = 100 ⋅ 50 = 5000 Y = ⋅100 = 98%
5000
N Td = 20
4900
N Ad = 4900 = 10,88
250 D = 250 = 98%
S = 1,42 20
ˆ
S = 98%
S R = 1,4
Can such a small number of
exams/day/instrument can be acceptable?
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
9
10. Gastroscopes
RESULTS AND DISCUSSION
Gastroscopes
ˆ As far as endoscopes are
S = 98% concerned, not only do we need
to consider the exam time, but
S R = 1,4 also disinfection time
If we consider
disinfection time,
14 endoscopes
are needed to
cover daily
schedule
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
10
11. Did we get to the point?
CONCLUSIONS
- Proposed indexes seem to be easy to determine and provide valuable entry
data for further discussion;
- The problem is by all means hard to model, since there are many variables
that need to be considered at different points of the analysis;
- Entry data need to be present and read correctly. A multidisciplinary
approach is needed.
Further work
- Indexes need to be tested outside the proposing facility (at the moment
being on the run);
- Stakeholders should be modeled and introduced in the core model at fixed
points to standardize the process.
U. Nocco et al. - Utilization indexes for medical equipment. A pilot model
11
12. THANK YOU FOR YOUR ATTENTION
UTILIZATION INDEXES FOR MEDICAL EQUIPMENT – A PILOT MODEL
U. Nocco1, G. Lolli Ceroni2, E. Lettieri2, S. del Torchio1
1
Clinical Engineering Department, Varese Town and University Hospital, Varese – Italy
2
Politecnico di Milano School of Engineering, Milan - Italy
Corresponding author:
eng. Umberto Nocco
Clinical Engineering Department
AOU Ospedale di Circolo e Fondazione Macchi
Viale Borri, 57 – 21100 Varese – Italy
Tel +39 0332 278301 - mobile: +39 335 7882671
e-mail: umberto.nocco@ospedale.varese.it