Resolving VisiCad Questions With GIS
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The document discusses tools and methodologies for answering two common questions faced by emergency dispatchers: "Why is the address wrong?" and "Why weren't we chosen for the run?". It provides examples of each question and the analysis that can be done using tools like the Incident Editor, Unit Activity Log, geovalidation, and travel time modeling to determine the most likely causes. Other resources mentioned include support from the software vendor and collaboration with operations staff.
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Numerical example on page 203
Period
1
2
3
4
5
6
7
8
9
10
11
12
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
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Demand
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A = $54r = $0.02/monthv = $20/box
Decide on replenishment time and quantities for the above problem using the solution approaches listed below and calculate carrying and setup costs, as well as total cost for each approach.
a) 3-months’ demand replenishment
b) Fixed EOQ
c) Wagner-Whitin algorithm
d) Silver-Meal algorithm
e) Periodic order quantity
f) Lot-for-lot
g) Least unit cost
h) Part-period balancing
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Period
1
2
3
4
Demand
100
75
175
200
A = $50v = $5r = $0.01/month
Decide on replenishment time and quantities for the above problem using the solution approaches listed below and calculate carrying and setup costs, as well as total cost for each approach.
a) 3-months’ demand replenishment
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c) Wagner-Whitin algorithm
d) Silver-Meal algorithm
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Jan
Feb
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Apr
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Oct
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Dec
18
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Jan
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Apr
May
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Aug
Sep
Oct
Nov
Dec
20
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110
120
60
30
20
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80
120
130
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Exercise Problems for Chapter 5Numerical example on page 203Pe.docx
Exercise Problems for Chapter 5
Numerical example on page 203
Period
1
2
3
4
5
6
7
8
9
10
11
12
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Demand
10
62
12
130
154
129
88
52
124
160
238
41
A = $54r = $0.02/monthv = $20/box
Decide on replenishment time and quantities for the above problem using the solution approaches listed below and calculate carrying and setup costs, as well as total cost for each approach.
a) 3-months’ demand replenishment
b) Fixed EOQ
c) Wagner-Whitin algorithm
d) Silver-Meal algorithm
e) Periodic order quantity
f) Lot-for-lot
g) Least unit cost
h) Part-period balancing
Toy Problem
Period
1
2
3
4
Demand
100
75
175
200
A = $50v = $5r = $0.01/month
Decide on replenishment time and quantities for the above problem using the solution approaches listed below and calculate carrying and setup costs, as well as total cost for each approach.
a) 3-months’ demand replenishment
b) Fixed EOQ
c) Wagner-Whitin algorithm
d) Silver-Meal algorithm
e) Periodic order quantity
f) Lot-for-lot
g) Least unit cost
h) Part-period balancing
Problem 5.2
The demand pattern for a type of filter is given below. These filters cost the company $4.75 each; ordering and carrying costs are $35 and $0.24/yr, respectively. Use the Silver-Meal heuristic to determine the sizes and timing of replenishment of stock.
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Nov
Dec
18
31
23
95
29
37
50
39
30
88
22
36
Problem 5.10
Consider a company facing a demand pattern provided below. Each item costs $4.00. Ordering cost is $25 per order and carrying inventory costs the company $0.05/month. Using a 3-month decision rule total replenishment cost of the company is $256.
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Nov
Dec
20
40
110
120
60
30
20
30
80
120
130
40
a) Construct a replenishment schedule and calculate the associated costs using the fixed EOQ method.
b) Repeat using Wagner-Whitin algorithm
c) Repeat using Silver-Meal heuristic
d) Repeat using LUC.
e) Repeat using PPB.
f) Repeat using POQ.
CSC-317-03 – Final Assignment
You are to develop a website that can receive input from a vehicle via query URLs that will
record the input into a database and use that data to map its relative position. This is an
INDIVIDUAL assignment.
Data Acquisition:
The following relative URL’s (or routes) are used by the vehicle to provide data to the website:
/register?name=XXXX&width=###.###
Adds a new vehicle run to the system, should return a cookie called USER=[name] that would
be included for the other commands. Width is the width of the vehicle in cm.
It should overwrite any other “active” session for that named vehicle
/wheels?left=###.###&right=###.###
Rescords the speed of the left and right wheel in cm/sec for that vehicle in the current session
/echo?dist=###.###
Records the result of the echo sensor in cm for the vehicle in the current session
/line?l1=##&l2=##&l3=##
Records the result of ONE or MORE l1, l2, l3, etc. Line sensors on/o ...
Oral Report Rev2
The document summarizes the Advanced Robotic Mapper (ARM) project. The ARM is a robot that can build a map of its environment. It includes components for position and orientation tracking, mapping sensors, a main controller and LCD, and software for mapping, navigation and user interfacing. Initial tests showed the robot could accurately map a surface 4+ feet away and travel a square path, but position tracking degraded over time. The compass was unstable when motors operated due to magnetic interference.
AAS National Conference 2008: Kris Zacny
Honeybee Robotics is a 25-year old engineering company specializing in robotics for space exploration. It has expertise in sampling and excavation tools, spacecraft mechanisms, and field robotics. Honeybee leverages its experience across government agencies and commercial sectors to reduce costs. It develops technologies for NASA, DoD, and industry by adapting tools to different applications and environments like the moon, Mars, and hazardous sites on Earth.
Hailey_Database_Performance_Made_Easy_through_Graphics.pdf
1. The document discusses using graphics and data visualization to improve understanding of database performance issues and SQL tuning. It provides examples of how visualizations can clearly show relationships in complex SQL queries and data that are difficult to understand from text or code alone.
2. Key steps in visual SQL tuning are laid out, including drawing tables as nodes, joins as connection lines, and filters as markings on tables. This helps identify optimization opportunities like missing indexes or stale statistics.
3. The document emphasizes that a lack of clarity in visualizing complex data and queries can have devastating consequences, while graphics enable easy understanding and effective problem-solving.
Robertson.brent
The document discusses lessons learned from developing the Solar Dynamics Observatory (SDO) satellite. It notes that 484 problem reports were filed during development and testing, with 19 resulting in serious project issues or risks at launch. Many issues were due to design errors or interactive complexity between subsystems that were only discovered after system integration testing. The two instruments with the most complexity, HMI and AIA, accounted for many problem reports. On-orbit, SDO has exceeded science requirements with only two minor issues occurring after launch.
20151009 Amplify PVInsider
- As solar capacity increases, better understanding performance in the field is needed as PV systems often underperform models due to various factors like defects, installation issues, and weather events.
- Novel methods are required to characterize field performance as current approaches have limitations due to signal-to-noise ratios in production and weather data.
- Remote performance analysis and on-site inspection/testing provide different insights and both play a role in properly diagnosing underperformance through high-resolution data analysis and visualization of system issues.
Mounir_Sardouk_Final_Report_touch
The document provides an overview of the author's summer training activities at a mobile telecommunications company. Some of the key tasks discussed include conducting site surveys and drive tests to validate capacity and coverage complaints, optimizing antenna tilts on temporary mobile sites to improve stadium coverage, programming an Excel macro to manage network data, planning microwave links for a remote village, and evaluating options like adding a new sector to address weak coverage in a resort complex. The training covered various aspects of radio network planning and optimization work.
GPS Group 9 Project
The document describes a GPS group project to determine the location of receiver sites using satellite ephemeris and observation data. It involves:
1) Calculating satellite positions over time from orbital elements in NAV data using an algorithm. This provides satellite positions for CORS and IGS observation data.
2) Using a least squares algorithm to calculate site vectors from CORS observation data, validating the method by comparing to the known CORS location.
3) Applying the same method to calculate an unknown IGS site location from its observation data. Tables of satellite data and site vector results are provided for both CORS validation and IGS location determination.
Clustering
Clustering has been one of the most widely studied topics
in data mining and k-means clustering has been one of
the popular clustering algorithms. K-means requires several
passes on the entire dataset, which can make it very expensive
for large disk-resident datasets. In view of this, a lot of work
has been done on various approximate versions of k-means,
which require only one or a small number of passes on the
entire dataset.
In this paper, we present a new algorithm, called Fast and
Exact K-means Clustering (FEKM), which typically requires
only one or a small number of passes on the entire dataset,
and provably produces the same cluster centers as reported
by the original k-means algorithm. The algorithm uses sampling
to create initial cluster centers, and then takes one or
more passes over the entire dataset to adjust these cluster
centers. We provide theoretical analysis to show that the cluster
centers thus reported are the same as the ones computed
by the original k-means algorithm. Experimental results from
a number of real and synthetic datasets show speedup between
a factor of 2 and 4.5, as compared to k-means.
This paper also describes and evaluates a distributed version
of FEKM, which we refer to as DFEKM. This algorithm
is suitable for analyzing data that is distributed across loosely
coupled machines. Unlike the previous work in this area,
DFEKM provably produces the same results as the original
k-means algorithm. Our experimental results show that
DFEKM is clearly better than two other possible options for
exact clustering on distributed data, which are down-loading
all data and running sequential k-means, or running parallel
k-means on a loosely coupled configuration. Moreover, even
in a tightly coupled environment, DFEKM can outperform
parallel k-means if there is a significant load imbalance
Airline delay prediction
The aim of the project is to track the on-time performance of major domestic carriers in the US. The complete information on air travel report including raw data and summary statistics is available which enables to make predictions about possible delays in flights
Prashant_CV
Prashant Naik has over 5 years of experience in systems engineering. He has a Bachelor's degree in IT and worked on several projects involving control panel design, hardware design, and commissioning for companies like SUNLUX Technologies, Space Application Centre, and Alstom T&D India. Some of his responsibilities include system requirements analysis, hardware design, testing, documentation, and customer communication. He is proficient in programming languages like PLC Ladder Logic and C, and software like LabVIEW, AutoCAD Electrical, and Microsoft Office.
Light Detection and Ranging Technology for Energy Sector
The advancement of technologies in the last decade is no surprise to anyone. LiDAR although new to some has been around for some time, haven’t heard of it? What are you missing?
This presentation will briefly explain what LiDAR is and how it is gathered, but the primary focus will be on how the Energy Sector can benefit from its use. We will show several example project datasets, tools developed and available to industry, and how this growing technology can change how you look at your projects.
Additionally, we will show how involving all members of your exploration team in a LiDAR planning process will save time and money while increasing safety and reducing environmental impact. Your well location may begin with Geology calculating back to a surface location, but the finial location can affect many other decisions your company may make…good planning makes for good decisions.
Chris Martin is a Survey Technologist with 17 years of experience in the surveying profession. His field survey career has involved major projects with TransCanada Pipelines and Alliance Pipelines along with many conventional oil and gas programs. For the last 4 years he has primarily focussed on business development and is currently a Marketing Representative for Can-Am Geomatics.
2local.pdf
The document discusses local search algorithms used for optimization problems. It describes how local search starts with an initial solution and iteratively moves to better neighboring solutions until a local optimum is reached. It then explains how simulated annealing is a metaheuristic that guides local search to avoid getting stuck in poor local optima by allowing occasional moves to worse neighbors based on a probability that decreases over time like the cooling process of annealing. The document provides details on implementing simulated annealing including setting the starting temperature, cooling schedule, and number of iterations at each temperature level.
IRJET- Simultaneous Localization and Mapping for Automatic Chair Re-Arran...
This document describes a simultaneous localization and mapping (SLAM) technique implemented in an automatic chair re-arrangement system. The system uses an overhead camera and image processing to track a chair's position and orientation. It determines the angle between the chair and its original location, represented by a red dot. The chair has a magnetometer to determine its heading relative to a defined north. The system calculates needed rotations and movements to maneuver the chair back to its original position when displaced. This demonstrates a SLAM solution to automate returning objects like chairs to their proper locations.
Spatial station
Spatial station, also known as ground-based sensing, is an instrument that captures and combines scanning, imaging, and surveying capabilities into a single solution. It has several advantages over a total station, including automatic acquisition of control points, less time consumption, integration of high-resolution digital images with 3D data, and lower probability of error. Spatial stations are preferred for surveying as they have all the functions of a total station while allowing for faster and more accurate data acquisition and enabling new applications through complementary technologies.
Presentation by Lolade.pptx
The internship took place at NASRDA (National Space Research and Development Agency) from September 2021. Activities included connecting a dark detector circuit, drawing circuit diagrams, research on ground control stations, and assembling an A350 3D printer. The intern learned about satellite systems, ground control stations, and how to interpret circuit layouts. Challenges included issues with screw sizes during assembly and miscommunication leading to wrong parts. Overall, the internship improved the intern's skills in circuit interpretation and provided substantial knowledge about space science.
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Resolving VisiCad Questions With GIS
- 1. Resolve Two of the Biggest VisiCAD Questions with GIS Peter Di Turi GIS Analyst Seattle Fire Department
- 4. Seattle Fire has a database to collect MIS Work Requests.
- 5. There are query options by status, assigner, date range, work type, priority. Also tracked are events/notes related to each request.
- 6. Details of each request are kept, such as contact information. Assignments can be delegated for each task and time tracked.
- 7. Web page front-end is used to collect requests from SFD staff.
- 9. ArcView project having Seattle Fire network data
- 10. SQL Server query of sfdmain_system database
- 12. Initial assign on a test system can simulate send response and additional recommendations
- 13. Use Incident Editor to research and query incidents effectively.
- 14. Three great sources of information in the Incident Editor: a) The Incident Times tab…
- 15. … b) the Edit Log tab, and…
- 16. … c) the Call Activities tab.
- 17. The Unit Activity log can be queried by unit for a given day. The “Show AVL Data” box should ALWAYS be checked for analyses.
- 18. Use VisiCAD Explorer’s Tools/Show Directions functionality to determine Shortest Path unweighted times and distances
- 19. VisiNET Browser can be used to acquire analysis data if you don’t have direct access to CAD dispatch stations.
- 20. With knowledge, a user application could be developed to query data from CAD’s database system more tailored to the organization, such as Seattle’s CADview system.
- 22. The 2 nd Biggest Question…
- 25. ALI/ANI has trouble with some wireless calls, sometimes providing a cell tower address as incident location
- 26. Alarm Company notified dispatch – provided address that’s not a street, but needs to be in CAD.
- 27. Ungeoverified address shows with missed bulls-eye =AND with Edit Log NOT FOUND entry
- 28. Address geovalidates OK today as shown, but once geovalidated in CAD as 2401 4th Av (no suffix). The difference between the two is 48 blocks .
- 31. Premise Interface To CAD – From Authoritative Source… Authoritative source from Ops: Seattle’s Inspection Database…
- 32. …to QA Processing in RMS… … passing through a QA process handled by MIS within the scope of RMS…
- 33. …to its final destination in CAD … approved records in QA get updated through interface to CAD, to be available to dispatchers and Ops.
- 35. And now, the top question…
- 39. a) Check the unit that got dispatched – the response isn’t from the fire station – note the dispatch time
- 40. b) Check the unit that thought it should have been dispatched – note its location at the last timestamp before the dispatch 08/10/05 11:08:46 AVL S LUCILE ST L KING JR WAY S 08/10/05 11:16:51 AVL S BRANDON ST5TH AV S 08/10/05 11:17:17 AVL RENTON AV S BRANDON ST 08/10/05 11:17:49 AVL S LUCILE ST9TH AV S
- 41. c) Determine E5 non-weighted time from Stewart/Minor to incident location and multiply by unit weights
- 42. d) d) Determine E28 non-weighted time from MLK S/S Lucile to incident location and multiply by unit weights
- 45. a) Check the unit that got dispatched – the response is from the fire station – note the dispatch time
- 46. b) Check the unit that thought it should have been dispatched – it was AIQ at the last timestamp before the dispatch
- 47. c) Determine E5 non-weighted time from Station 5 to incident location and multiply by unit weights
- 48. d) Determine E5 shortest distance from Station 5 to incident location
- 49. e) Determine E28 non-weighted time from Station 28 to incident location and multiply by unit weights
- 50. f) Determine E28 shortest distance from Station 28 to incident location
- 51. g) Verify that higher road speeds are significant to the response from Station 5 40
- 52. h) Verify that lower road speeds are significant to the response from Station 28
- 55. a) Check the unit that got dispatched – the response is from the fire station – note the dispatch time
- 56. b) No AVL statuses are recorded from E11. Thus, going AOR doesn’t change the location from the previous on-scene location. The AIQ is too late for the run.
- 57. c) Review dispatched engine (E36) response and note its non-weighted time
- 58. d) Review expected engine (E11) response - assuming it was AIQ - and note its non-weighted time
- 59. e) Review estimated engine (E11) response from its last known location - and note its non-weighted time