Understanding the devices technology roadmap and lifecycle support is only a part of an effective management tool. our company needs to process these alerts faster than the many EC occupying sustaining engineers inbox.
1. Discuss the structured system analysis and design methodologies
2. What is DSS? Discuss the components and capabilities of DSS.
3. Narrate the stages of SDLC
4. Define OOP. What are the applications of it?
Reverse Engineering
Definition
It is described in Wikipedia as:
… the process of extracting knowledge or design information from anything man-made. The process often involves disassembling something (a mechanical device, electronic component, computer program, or biological, chemical, or organic matter) and analyzing its components and workings in detail.
Reverse Engineering
Definition
A process of discovering the technological principles of a human made device, object or system through analysis of its structure, function and operation
Systematic evaluation of a product with the purpose of replication.
Design of a new part
Copy of an existing part
Recovery of a damaged or broken part
An important step in the product development cycle.
This presentation is intended to give the viewer a working knowledge of the practical applications of SAS in terms of Banking Analytics. Specifically, Enterprise Guide and Enterprise Miner have been discussed in detail.
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Use Cases are a formal technique taught in most IS/IT disciplines. This presentation discusses a model to take that methodology and apply it to developing Security Operations and SIEM focused uses cases. The template discussed is in use at a major SIEM provider today, and is based on 10 years of implementing SIEM and building up SecOps across 15+ organizations over 10 years.
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
This talk includes the following items:
1) discussion of the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design & implementation, testing, deployment & maintenance, online evaluation & evolution.
2) getting the ML problem formulation right
3) key tenets for different stages of application cycle.
Audio for the talk:
https://youtu.be/oBR8flk2TjQ?t=19207
MineExcellence solutions help in designing, optimizing and analyzing how blasts are performing in an integrated manner. We also have a very innovative mobile app - Smart Blasting app available in Android and iPhone. We have products for some other areas in mining such as Drilling, MineSafety App and Operational Analytics.
1. Blast Designer
2. Blast Data Collection and Management (BIMS)
3. Mobile app for Blasting (Smart Blasting)
4. Blasting Predictors – Air and Ground Vibration, Fragmentation and Fly-rock. Pattern Simulation /Analysis
5. Blast Designer and BIMSu for underground blasting
6. Drilling Platform(Drill Log, Plod Reports and Daily activity)
7. Mine Safety APP
8. Operational Analytics : (Combines drilling, blasting, loading, hauling etc)
9. Web and Mobile Custom Forms for all aspects of mining Lifecycle
10. Drone Platform for Mining Operations
1. Discuss the structured system analysis and design methodologies
2. What is DSS? Discuss the components and capabilities of DSS.
3. Narrate the stages of SDLC
4. Define OOP. What are the applications of it?
Reverse Engineering
Definition
It is described in Wikipedia as:
… the process of extracting knowledge or design information from anything man-made. The process often involves disassembling something (a mechanical device, electronic component, computer program, or biological, chemical, or organic matter) and analyzing its components and workings in detail.
Reverse Engineering
Definition
A process of discovering the technological principles of a human made device, object or system through analysis of its structure, function and operation
Systematic evaluation of a product with the purpose of replication.
Design of a new part
Copy of an existing part
Recovery of a damaged or broken part
An important step in the product development cycle.
This presentation is intended to give the viewer a working knowledge of the practical applications of SAS in terms of Banking Analytics. Specifically, Enterprise Guide and Enterprise Miner have been discussed in detail.
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Use Cases are a formal technique taught in most IS/IT disciplines. This presentation discusses a model to take that methodology and apply it to developing Security Operations and SIEM focused uses cases. The template discussed is in use at a major SIEM provider today, and is based on 10 years of implementing SIEM and building up SecOps across 15+ organizations over 10 years.
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
This talk includes the following items:
1) discussion of the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design & implementation, testing, deployment & maintenance, online evaluation & evolution.
2) getting the ML problem formulation right
3) key tenets for different stages of application cycle.
Audio for the talk:
https://youtu.be/oBR8flk2TjQ?t=19207
MineExcellence solutions help in designing, optimizing and analyzing how blasts are performing in an integrated manner. We also have a very innovative mobile app - Smart Blasting app available in Android and iPhone. We have products for some other areas in mining such as Drilling, MineSafety App and Operational Analytics.
1. Blast Designer
2. Blast Data Collection and Management (BIMS)
3. Mobile app for Blasting (Smart Blasting)
4. Blasting Predictors – Air and Ground Vibration, Fragmentation and Fly-rock. Pattern Simulation /Analysis
5. Blast Designer and BIMSu for underground blasting
6. Drilling Platform(Drill Log, Plod Reports and Daily activity)
7. Mine Safety APP
8. Operational Analytics : (Combines drilling, blasting, loading, hauling etc)
9. Web and Mobile Custom Forms for all aspects of mining Lifecycle
10. Drone Platform for Mining Operations
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. &
• The value of forecasting:
– To supplement initial part selection activities
– To support pro-active DMSMS management
– To enable strategic life cycle planning solutions
• Most existing commercial forecasting tools are good at
articulating the current state of a part’s availability and
identifying alternatives, but limited in their capability to
forecast future obsolescence dates.
Why Forecast Obsolescence?
It’s hard to make predictions -
especially about the future.
- Yogi Berra
“
“
4. &
• Ordinal scale approaches – weighted accumulation of
“scores” assigned to a set of predetermined part type,
technology and supply chain attributes.
– Accuracy increases as you get closer to the
obsolescence event
– Historical basis for the forecast is subjective
– Confidence levels and uncertainties are not generally
evaluable
Existing: Ordinal scale approaches
5. &
• Data mining approaches – mapping known part
obsolescence dates to the life cycle curve of the part
type to build vendor-specific (and vendor independent)
forecasting algorithms.
– Used for parts with clearly identifiable parametric drivers,
e.g., memory
– Based on the historical record - Produces accurate part-
type and vendor-specific forecasts
– Forecasts include confidence levels
Existing: Data mining approaches
6. & Obsolescence Forecasting Strategy
Part primary
attribute driven
forecasts
• Historical data driven
• Most accurate
forecasts available for
applicable parts
• Only forecasting
approach that provides
uncertainties or
confidence levels
Procurement
lifetime forecasts
• Used if primary
attributes can’t be
identified
• Historical data driven
• Worst case, vendor
specific, part type
specific, obsolescence
forecast
Short-term
forecasts based
on distributor
inventory levels
• … source counting
and other vendor
provided information
supersede the long-
term forecasts near the
end of a parts
procurement life
THIS PAPER
7. &
• The large electronic part databases are treasure troves
of data for predicting obsolescence, the challenge to
figuring out how to mine the data to find the significant
trends.
• Previously developed data mining approaches work
very well for parts with clear parametric evolutionary
drivers (e.g., memory, microprocessors), but they do
not work for part types that lack these drivers
Objective of this Work
8. &
• Several have postulated that the “age” of electronic
parts is not a factor in determining what gets obsoleted.
– J. Carbone, “Where are the parts,” Purchasing, pp. 44-
47, Dec. 11, 2003.
– S. Clay, “Material Risk Index (MRI) and Methods for
Calculating MRI for Electronic Components,” to be
published IEEE Trans. on Components and Packaging
Technologies, 2009.
• Not so fast! Age appears to play a role in the
obsolescence of many (not all) part types …
The “Age” Effect
12. &
A group of parts introduced on various dates, all discontinued on or
about the same date – common practice
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = -1
Discontinuance date 1
(longest life parts)
Discontinuance date 2
Top boundary of the wedge
13. &
A group of parts introduced on various dates all having identical
procurement lifetimes, i.e., everything is procurable for exactly y years
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = 0
y
xx-y
No data points after this
introduction date
Analysis date = x
14. &
If the introduction dates were wrong, e.g., they were all the same
database record creation date d, where d is some point in time after
the parts were introduced.
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = ∞
(all parts have the same introduction date)
d
15. & Understanding the Graph
Known
wedge
2008
If the data set is complete up to 2008,
nothing could ever fall in this area
Parts that were
introduced in the past
but are not obsolete
yet (note, the top of
the historical record
data need not
correspond to the
boundary of the green
area (it could be
below it). The two will
correspond only if
parts are discontinued
in the analysis year,
e.g., 2008 – lower
green boundary
moves up every year
16. &
The bottom of the wedge is where the critical information is
(not the top).
Introduction Date
ProcurementLifetime
Understanding the Graph
Bottom boundary of the wedge
There is an “age” effect
No “age” effect
Approximate first
part introduction
17. &
Parts with primary parametric evolutionary drivers do not show the
“age” effect. These parts include: memory, microprocessors.
Age Effect Examples
0
4
8
12
16
20
24
28
32
36
1969 1974 1979 1984 1989 1994 1999 2004
Introduction Year
ProcurementLifetime(years)
Flash Memory
Op Amps
No age effect
Flat
Age effect
Not Flat!
Strong parametric evolutionary driver: memory size
18. & Example ‐ Linear Regulators
Worst case forecast for linear regulators
If Introduction Date < 1997.67
Procurement Life > -2.095(introduction date) + 4188.5
If Introduction Date > 1997.67
Procurement Life > -0.1014(introduction date) + 206.77
Obsolescence date = Introduction Date + Procurement Life
23. &
• Worst case, and median vendor specific, part type
specific, obsolescence forecast
– Worst case = no known parts of this type or from this
vendor have had smaller procurement lifetimes
– Vendor specific = the upper limit on the band is the
vendor’s worst case, the lower limit is the part type’s
worst case
– Part type specific = specific to the part type or group of
part types used to create the forecast
What do we really have?
24. &
• Note, the above statement says “part type specific”
NOT “part specific”
– If you give me a specific Fairchild xxxxxx linear regulator,
I can forecast the worst case obsolescence date based
on Fairchild’s history of supporting linear regulators, but I
cannot tell you anything about Fairchild’s specific plans
for the xxxxxx linear regulator
• This methodology is applicable to long-term forecasting
(pro-active and strategic management value).
– Long-term means > 1 year from obsolescence
– Short-term (< 1 year from obsolescence), other factors
kick in
What do we really have?
28. &
• Ordinal Scale Based Obsolescence Forecasting:
– A.L. Henke and S. Lai, “Automated Parts Obsolescence
Prediction,” Proceedings of the DMSMS Conference, 1997.
– C. Josias and J.P. Terpenny, “Component obsolescence risk
assessment,” Proceedings of the 2004 Industrial Engineering
Research Conference (IERC), 2004.
• Data Mining Based Obsolescence Forecasting:
– P. Sandborn, F. Mauro, and R. Knox, "A Data Mining Based
Approach to Electronic Part Obsolescence Forecasting," IEEE
Trans. on Components and Packaging Technologies, Vol. 30,
No. 3, pp. 397-401, September 2007.
http://www.enme.umd.edu/ESCML/Papers/ObsForecastingSe
pt07.pdf
References