Ez Slam V7
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The recent trend manufacturer’s shows keen interest in safety and pleasure riding. The seat belts and the airbags which are now a important attachment of the cars must be utilized fully for safer riding. The weakest and most harzdous part the glasses in cars are also now made to add safety with the advanced technology. If this condition persist the safety factor in cars will reach a pioneer position. With these kind of improved technology will pay way for usage of large number of passenger cars
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Bumper is one of the most important parts in passenger cars for which the material and structure should
be considered in order to reduce the impact of collision. Since suitable impact strength is the main expectation
for such a structure, the authors survey the variables that directly give impact characteristics and
wished for easily achievable modifications resulting from impact modeling on commercial bumpers. Many
researchers have studied that accident always occurs in front side. The impressed the authors to study
and analyses the component related to frontal crash and therefore, the authors selected bumper.
Aim of the project work is reduce the effort of car bumper by introducing honey comb structure instead of
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Air suspension system
Air Suspension System is commonly use in BMW,Mercedes,Audi types luxurious types Cars for protect from damaging, increasing life of the vehicle ,increases the handling , increases comfort of passengers and many more..
So according to me if you remove the suspension system, then you feel like in bull-cart in Audi, Mercedes, BMW type luxurious cars . The only diffrence is speed.
So the scope of Suspension System is Too Bright !!!
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The document discusses the steering system of vehicles. It describes the steering system as the interface between the driver and vehicle that allows the driver to guide the vehicle by controlling the path. It converts rotational motion of the steering wheel into angular turns of the wheels. Factors like steering ratio, geometry, power steering determine the steering effort required and turning radius. Proper alignment of components like caster, camber, toe is important for steering stability and reducing tire wear.
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The document discusses the design challenges for unmanned vehicular video streaming. It proposes using multiple low-power microprocessors in parallel to achieve high processing speeds while minimizing power consumption. Test results show that combining horizontal and vertical image scanning provides the best picture quality. The document also describes the architecture of a low-power camera system designed for unmanned vehicles.
Mixed Integer Programming: Analyzing 12 Years of Progress
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
Willump: Optimizing Feature Computation in ML Inference
Systems for performing ML inference are increasingly important, but are far slower than they could be because they use techniques designed for conventional data serving workloads, neglecting the statistical nature of ML inference. As an alternative, this talk presents Willump, an optimizer for ML inference.
Using Bayesian Optimization to Tune Machine Learning Models
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.
Using Bayesian Optimization to Tune Machine Learning Models
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.
digit_twin.pptx
The document discusses using a digital twin approach to optimize availability for complex systems like electronics. It describes two main approaches - a knowledge-based one using limited failure data and models, and a data-driven one requiring large failure datasets. The case study presented learns from failure data of existing power converters under different usage conditions to build a reliability model. This allows simulating optimal maintenance strategies for new converters operating under different conditions to minimize life cycle costs. Criticism notes the approach may be difficult with limited early data, and emphasizes reliability testing and knowledge reuse.
Chapter11a
Simulation may be defined as a technique that imitates the
operation of a real-world system as it evolves over time. This is normally
done by developing a simulation model.
A simulation model take the form of a set of assumptions about
the operation of the system, expressed as mathematical or logical
relations between the objects of interest in the system.
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry...
SfMLearner + KF selectionを提案した"Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM [ICCV19]"を論文読み会で紹介した時の資料です.
digit_twin.pptx
- The document discusses using a digital twin approach to optimize availability for complex systems like vehicles or electronics.
- A digital twin is a simulation that mirrors a physical system using models, sensor data, and other information to monitor and predict performance.
- For electronics, failures are often rare and complex, so a digital twin must integrate all available data and knowledge from past and current systems to learn across different operating conditions and account for uncertainty.
- The case study describes using a digital twin to predict failure modes in power converters based on past failure data under various conditions to optimize maintenance strategies and minimize life cycle costs for new systems.
Using SigOpt to Tune Deep Learning Models with Nervana Cloud
This document discusses using SigOpt to tune deep learning models. It notes that tuning deep learning systems is non-intuitive and expert-intensive using traditional random search or grid search methods. SigOpt provides a more efficient approach using Bayesian optimization to suggest optimal hyperparameters after each trial, reducing wasted expert time and computation. The document provides examples applying SigOpt to tune convolutional neural networks on CIFAR10, demonstrating a 1.6% reduction in error rate over expert tuning with no wasted trials.
A Systematic Approach to Creating Behavioral Models (CDNLive Slides)
This document outlines a systematic approach to creating behavioral models for mixed-signal system-on-chip verification. It discusses keeping schematic views in sync with models, taking a top-down approach using SMG, learning the circuit details, planning what to include in models, writing the model code, and validating models through collaborative test benches. It also covers maintaining models over the project lifetime using a model validator, applying models for analog and digital verification using a common verification platform with real number models, and summarizing that behavioral models require collaboration between designers and verification to create accurate, targeted models for efficient design verification.
Cool Supercomputing: Keepin' it Real
Panel talk given at the SC'12 Birds of a Feather session entitled, "Cool Supercomputing: Achieving Energy Efficiency at the Extreme Scales". Salt Lake City, Utah, 14 November 2012.
High Fidelity Wind Model Software for Real-Time Simulation Platforms
A High Fidelity Wind Model Software for Real-Time Simulation Platforms was published and presented for the first time at the 2015 I/ITSEC. I/ITSEC is the The World’s Largest Modeling & Training Conference.
Yield improvement of an eeprom for automotive applications
In order to manufacture high quality and cost effective EEPROMs suitable for automotive under-hood applications several topics must be taken into account. As well as a high reliability EEPROM technology the choice of an advanced memory architecture including ECC and a highly sophisticated screening methodology in production test is necessary to achieve high quality in the field. The EEPROM production test flow must not only be able to screen out weaknesses of the process but must also be cost efficient. A majority of the tests executed in the EEPROM test flow are needed to check the quality of the processed oxides which are the basic elements to realize the EEPROM function of the memory. Most of these tests are complex and time consuming. This work will present an optimization of an existing EEPROM production test flow by means of thorough analysis of the faulty dice and the test flow leading to an increase of the yield without reducing quality.
autonomus Bike Progress
The document describes the design of an autonomous bicycle capable of traveling at a constant speed on flat surfaces without human intervention. Key aspects include:
- The system requirements for the bicycle to operate at a constant speed, wirelessly, with a graphical user interface and safety devices.
- The system level design includes components like a standard mountain bike, 24V battery, steering and drive motors, sensors like a potentiometer and inclinometer, and an Arduino microcontroller.
- The control system design involves developing a state space model and using LQR control. Simulink is used to simulate the system response with disturbances.
- A timeline outlines tasks from research to the final report over 10 months.
Using capability assessment during product design
The document discusses using capability assessment during product design. It covers conducting Monte Carlo analysis early in the design process to identify potential issues. An example is provided of using Monte Carlo analysis on a simple voltage regulator design to evaluate the output variation given input tolerances. Different design revisions are analyzed, showing how tighter component tolerances and controlling ratios between components can improve capability. The benefits of conducting capability assessments throughout the design and prototype stages are discussed.
D2_MTV2012-EnergyEffPrf-Mattwandel
The document describes a new process for measuring and modeling energy efficient performance of microprocessors. It involves synchronously measuring power and performance, calculating energy from power measurements, and graphing energy versus performance on a single plot. This allows optimization of performance and energy efficiency by identifying configurations with high energy/low performance or low energy/high performance. The process has been applied at Intel to quantify generation-to-generation improvements in energy efficiency, tune processors for best energy efficient performance, and optimize future designs. Challenges include automation, timing of measurements, and cross-generational comparisons.
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Mixed Integer Programming: Analyzing 12 Years of Progress
Mixed Integer Programming: Analyzing 12 Years of Progress
Willump: Optimizing Feature Computation in ML Inference
Willump: Optimizing Feature Computation in ML Inference
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry...
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry...
Using SigOpt to Tune Deep Learning Models with Nervana Cloud
Using SigOpt to Tune Deep Learning Models with Nervana Cloud
A Systematic Approach to Creating Behavioral Models (CDNLive Slides)
A Systematic Approach to Creating Behavioral Models (CDNLive Slides)
High Fidelity Wind Model Software for Real-Time Simulation Platforms
High Fidelity Wind Model Software for Real-Time Simulation Platforms
Yield improvement of an eeprom for automotive applications
Yield improvement of an eeprom for automotive applications
Ez Slam V7
- 1. The World’s First Dedicated Closure Measurement System . EZSLAM
- 4. 1. Introduction: A. Closure Challenge: WHAT is a GOOD door? HOW does that TRANSLATE to your quality? HOW do you BENCHMARK your door? HOW do you CHECK your door?
- 5. 1. Introduction: B. Closure Solution: Until now, it took hours and days of testing to obtain a handful of metrics on 1 single door.
- 7. 2. EZSLAM Concept: A. The Basics of EZSLAM:
- 9. 2. EZSLAM Concept: C. Standard Test Procedure:
- 11. 3. Technology: A. Bundle of Technology collects all sensor data at minimal 3600Hz:
- 12. 3. Technology: B. Example 1: YZ Trajectory: Door drop Over slam Door motion Latch – Striker interaction Latched position
- 13. Initial Speed End of Pulse 3. Technology: C. Example 2: Speed in function of Angle: Impact Speed Door Motion Acceleration Open Closed 90mm from Latch Impact
- 14. 3. Technology: D. Example 3: Force in function of Y: Static Force to Latch (N) Compression Energy (J) Door motion (Slow) Latch Point or Minimum required over slam to latch (mm) Closed: Flush (0mm)
- 16. 4. C.I.M .: C ombined I ntegrated M odel: A. Definition: C.I.M. C ombined I ntegrated M odel: In the analysis to determine key values or characteristics, EZSLAM tests are undergoing a mathematical iteration where results from one test will be used in other test and the other way around .
- 17. 4. C.I.M. : C ombined I ntegrated M odel: B. Principle:
- 18. 4. C.I.M. : C ombined I ntegrated M odel: C. Example: Minimum Closing Speed - Step 1 : Actual Samples Speed vs.. Over slam Minimal Speed to latch. Latch Point or Minimum required over slam to latch (Push test) Curve Fit Regression
- 19. 4. C.I.M. : C ombined I ntegrated M odel: C. Example: Minimum Closing Speed - Step 2 : Closing Speed Curves Closing Speed Door Check Slope Minimal Closing Speed Curve Minimal Closing Speed Door Check Linearity
- 20. 4. C.I.M. : C ombined I ntegrated M odel: D. Other Examples:
- 22. 5. Measured Key Metrics:
- 23. 5. Measured Key Metrics: Define Your New Standard: “ Quantify the Quality with A Standard User independent Procedure.”
- 29. 7. Troubleshooting Using EZSLAM: A. Important Time Saving: EZSLAM requires ONLY 10 MINUTES : CMM 2 Seals? Striker aligned? CMM 1 Force 3 Seal gap? General classic method requires Hours or even Days :
- 30. 7. Troubleshooting Using EZSLAM: B. Examples:
- 36. 9. Your EZSLAM: A. Hardware Levels:
- 37. 9. Your EZSLAM : B. Maintenance Levels:
- 38. Thank you