Piglet mortality is a current issue in the pig production and the large variability between herds suggests a management component to the mortality. Studies show that the mortality may be reduced by the supervision of farrowing or through climate regulation in the farrowing pens. However, this is possible only if the farrowing time is known and thus provides sufficient time for the management to make and execute the decisions. The gestation period of a sow is approximately 115 (SD=2) days. However, an initial cost-benefit analysis recommended increased precision in the prediction of farrowing to make the increased management efforts cost-effective for the pig producer. Recently, a wide range of sensor technology have become available to monitor the behavioural and physiological changes of sow. Evidence show that appropriate utilization of sensor technology may increase the precision of prediction of onset of farrowing. Prediction is feasible only if the prediction is online and automated.
The thesis is focused on constructing a system that can give predictions about the expected time to farrowing of individual sows based on automatic sensor recordings such as water consumption, video based activity measurements, and photo-cells based activity measurements. The warnings could serve to activate the floor heating system to ensure a sufficiently high temperature for the new born piglets, as well as to help the farmer to organize extra surveillance around farrowing. The thesis is based on three submitted manuscripts, describing the system at different stages.
The kernel in the thesis is a Markov process with four subsequent states Before Nest-Building, Nest-Building, Resting and the absorbing state Farrowing; the states were selected based on ethological knowledge about sow behaviour. However, the sojourn time distribution in each state is not exponential. Therefore a continuous time discrete state semi-Markov process based on a Phase-Type distribution (in this case Erlang distributed) was formulated. Finally, the Markov process was transformed to a discrete time process. A Hidden Markov Model (HMM) was used for this process. The model is called Hidden Phase-type Markov Model (HPMM), and the time steps corresponded to each updating with sensor information at which the time to farrowing was predicted.
The POMDP model for optimizing the floor-heat regulation system was used to demonstrate the decision support tool as an extension to the prediction algorithm.
The tools for the prediction of onset of farrowing, estimation of HPMM and optimal decision making provides a framework for handling a large amount of sensor data available and gives an overview of how to integrate information from several sensors on the pen level. The complexity of the models imply that the prediction algorithm and decision tool may be run on the herd level computer; whereas the parameters were estimated on the central level computers.
Anne Clark - Overview of the Recent Amino Acids Work in Growing PigsJohn Blue
Overview of the Recent Amino Acids Work in Growing Pigs - Anne Clark, Kansas State Research and Extension, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Paulo Arruda - Continuing Diagnostic Investigation–Novel SapelovirusJohn Blue
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More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Sorghum in poultry feed brings extra value to the crop-livestock system (French)ICRISAT
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Dr. Jenny Patterson - How Synchronization Works and How to Make It BetterJohn Blue
How Synchronization Works and How to Make It Better - Dr. Jenny Patterson, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Anne Clark - Overview of the Recent Amino Acids Work in Growing PigsJohn Blue
Overview of the Recent Amino Acids Work in Growing Pigs - Anne Clark, Kansas State Research and Extension, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Paulo Arruda - Continuing Diagnostic Investigation–Novel SapelovirusJohn Blue
Continuing Diagnostic Investigation–Novel Sapelovirus - Dr. Paulo Arruda, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Sorghum in poultry feed brings extra value to the crop-livestock system (French)ICRISAT
Poultry farmers’ concerns about using sorghum instead of maize in poultry feed have been overcome thanks to dietary trials by ICRISAT in Niger, Nigeria and India. A key problem facing poultry production in Niger and Nigeria is the inadequate supply and high cost of feed ingredients, for which maize is the main energy source. Alternative energy sources such as sorghum may help reduce the high cost of poultry feed.
Dr. Jenny Patterson - How Synchronization Works and How to Make It BetterJohn Blue
How Synchronization Works and How to Make It Better - Dr. Jenny Patterson, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
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0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
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https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
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How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
This article is all about what AI trends will emerge in the field of creative operations in 2024. All the marketers and brand builders should be aware of these trends for their further use and save themselves some time!
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
This article is all about what AI trends will emerge in the field of creative operations in 2024. All the marketers and brand builders should be aware of these trends for their further use and save themselves some time!
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
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The six step guide to practical project management
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Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The Intelligent Farrowing Pen
1. Prediction Estimation Decision Tool Final Remarks
Methods for Sensor Based Farrowing Prediction and
Floor-heat Regulation
The Intelligent Farrowing Pen
Ph.D. Dissertation
Aparna U.
Dept. of Animal Science
Aarhus University
Denmark
18 March, 2014
Aparna U. The Intelligent Farrowing Pen 1 / 42
2. Prediction Estimation Decision Tool Final Remarks
Objective
To develop and validate an automated system that
monitors the pre-parturition behaviour of the sow in the
farrowing pen,
predicts the onset of farrowing
would further help the farm manager to optimize the decisions
related to parturition and post-parturition - e.g. optimal
oor-heat regulation system
by integrating several sensor information
purpose
to reduce the piglet mortality
*Study was supported by The Danish National Advanced Technology
Foundation
Aparna U. The Intelligent Farrowing Pen 2 / 42
3. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
4. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
5. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
6. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
7. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
8. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm
9. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm Estimation Algorithm
10. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm Estimation Algorithm
Optimal Floor-Heat Regulation System
11. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm Estimation Algorithm
Optimal Floor-Heat Regulation SystemAparna U. The Intelligent Farrowing Pen 3 / 42
12. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm
Aparna U. The Intelligent Farrowing Pen 4 / 42
13. Prediction Estimation Decision Tool Final Remarks
Gestation Period
Behavioural States
Mating Farrowing
115±2 days
Aparna U. The Intelligent Farrowing Pen 5 / 42
14. Prediction Estimation Decision Tool Final Remarks
Gestation Period
Behavioural States
Mating Farrowing
115±2 days
Nest-Building
Aparna U. The Intelligent Farrowing Pen 5 / 42
15. Prediction Estimation Decision Tool Final Remarks
Gestation Period
Behavioural States
Mating Farrowing
115±2 days
Nest-Building
Resting
Aparna U. The Intelligent Farrowing Pen 5 / 42
16. Prediction Estimation Decision Tool Final Remarks
Gestation Period
Behavioural States
Mating Farrowing
115±2 days
Nest-Building
RestingBefore Nest-Building
Aparna U. The Intelligent Farrowing Pen 5 / 42
17. Prediction Estimation Decision Tool Final Remarks
Gestation Period
Behavioural States
Mating Farrowing
115±2 days
Nest-Building
RestingBefore Nest-Building
110 days Into Farrowing
Pen
Aparna U. The Intelligent Farrowing Pen 5 / 42
18. Prediction Estimation Decision Tool Final Remarks
Farrowing Pen
Number of sows observed: 64
Aparna U. The Intelligent Farrowing Pen 6 / 42
19. Prediction Estimation Decision Tool Final Remarks
Sensor set-up at the pen level
Number of sows observed: 64
Aparna U. The Intelligent Farrowing Pen 7 / 42
20. Prediction Estimation Decision Tool Final Remarks
Online Recording of Sensor Observation for a Sow - An idea
Water consumption
Aparna U. The Intelligent Farrowing Pen 8 / 42
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days since mating
waterconsumption(log)
17:30:00
22. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
time line:-
t0
t
Tt: remaining time to onset of farrowing
Stochastic process
23. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
time line:-
t0
t
Tt: remaining time to onset of farrowing
Stochastic process
?Markov process
24. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
time line:-
t0
t
Tt: remaining time to onset of farrowing
Stochastic process
?Markov process
(ω1, ς1) (ω2, ς2) (ω3, ς3)
25. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
time line:-
t0
t
Tt: remaining time to onset of farrowing
Stochastic process
(ω1, ς1) (ω2, ς2) (ω3, ς3)
Markov process semi-Markov process
26. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
time line:-
t0
t
Tt: remaining time to onset of farrowing
Stochastic process
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
Markov process semi-Markov process
27. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
28. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
29. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
30. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
31. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
32. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
33. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
34. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
35. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
36. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
37. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
38. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
39. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
40. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
41. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
42. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
States into smaller divisions - Behavioural Phases
'phases' reect the time since mating
43. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
(m1, λ1) (m2, λ2) (m3, λ3)
'semi' Markov Process over phases
Aparna U. The Intelligent Farrowing Pen 10 / 42
44. Prediction Estimation Decision Tool Final Remarks
Farrowing process as a Markov Model
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
(ω1, ς1) (ω2, ς2) (ω3, ς3)Erlang
(m1, λ1) (m2, λ2) (m3, λ3)
'semi' Markov Process over phases
Phase-type
Convolution of three Phase-type distributions
Aparna U. The Intelligent Farrowing Pen 10 / 42
45. Prediction Estimation Decision Tool Final Remarks
Farrowing and Prediction Process
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
'Markov Model'
Aparna U. The Intelligent Farrowing Pen 11 / 42
46. Prediction Estimation Decision Tool Final Remarks
Farrowing and Prediction Process
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
'Markov Model'
Prediction process
t0 tI t2 t3
- discrete time points
Aparna U. The Intelligent Farrowing Pen 11 / 42
47. Prediction Estimation Decision Tool Final Remarks
Farrowing and Prediction Process
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
'Hidden' + 'Markov Model'αt : distribution of phases
Tt :time to onset of farrowing ∼ PH(αt, S)
Prediction process
t0 tI t2 t3
- discrete time points
Aparna U. The Intelligent Farrowing Pen 11 / 42
48. Prediction Estimation Decision Tool Final Remarks
Farrowing and Prediction Process
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
'Hidden' + 'Markov Model'αt : distribution of phases
Tt :time to onset of farrowing ∼ PH(αt, S)
Prediction process
t0 tI t2 t3
- discrete time points
Sensor
Pr(Yt | Phaset)
YI Y2 Y3
Aparna U. The Intelligent Farrowing Pen 11 / 42
49. Prediction Estimation Decision Tool Final Remarks
Farrowing and Prediction Process
Farrowing process - continuous time
Before Nest-Building Nest-Building Resting F
'Hidden' + 'Markov Model'αt : distribution of phases
Tt :time to onset of farrowing ∼ PH(αt, S)
Prediction process
t0 tI t2 t3 tN
- discrete time points
Sensor
Pr(Yt | Phaset)
YI Y2 Y3
Aparna U. The Intelligent Farrowing Pen 11 / 42
50. Prediction Estimation Decision Tool Final Remarks
Prediction of Onset of Farrowing
t0 tI t t + δ tI + Nδ
?
Yt
αt+δ: distribution of phases in the next prediction point
Prediction of αt+δ
At each prediction point,
1 calculates αt+δ using time transition (Markov chain)
2 updates αt+δ using the sensor information
Aparna U. The Intelligent Farrowing Pen 12 / 42
51. Prediction Estimation Decision Tool Final Remarks
Prediction of Distribution of Phases αt+δ
Aparna U. The Intelligent Farrowing Pen 13 / 42
400 600 800 1000
0.000.020.040.060.080.10
Behavioural Phases
probabilityofphases
Before
Nest−Building
Nest−Building
day−109.4
52. Prediction Estimation Decision Tool Final Remarks
Prediction of Onset of Farrowing
Prediction of αt+δ
At each prediction point,
1 calculates αt+δ using time transition (Markov chain)
2 updates αt+δ using the sensor information
Tt+δ: time to onset of farrowing ∼ PH(αt+δ, S)
Statistical measures...
Expected time to onset of farrowing
Aparna U. The Intelligent Farrowing Pen 14 / 42
53. Prediction Estimation Decision Tool Final Remarks
Prediction of Onset of Farrowing
Prediction of αt+δ
At each prediction point,
1 calculates αt+δ using time transition (Markov chain)
2 updates αt+δ using the sensor information
Tt+δ: time to onset of farrowing ∼ PH(αt+δ, S)
Statistical measures...
Expected time to onset of farrowing
Probability of onset of farrowing in 12 hours
Aparna U. The Intelligent Farrowing Pen 14 / 42
54. Prediction Estimation Decision Tool Final Remarks
Validating the Prediction - how???
Aparna U. The Intelligent Farrowing Pen 15 / 42
55. Prediction Estimation Decision Tool Final Remarks
Validating the Prediction - how???
Aparna U. The Intelligent Farrowing Pen 15 / 42
56. Prediction Estimation Decision Tool Final Remarks
Online Prediction Curve - an example
Aparna U. The Intelligent Farrowing Pen 16 / 42
q
107 108 109 110 111
050100150
days since mating
ETF(hours)
57. Prediction Estimation Decision Tool Final Remarks
Warning periods - success Vs failure
Aparna U. The Intelligent Farrowing Pen 17 / 42
58. Prediction Estimation Decision Tool Final Remarks
Example of False-warning Period
Aparna U. The Intelligent Farrowing Pen 18 / 42
qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
q
q
q
q
q
q
qq
q
qq
q
q
q
q
q
q
qqq
qqqqqqqqqq
112 113 114 115
050100150200250
days since mating
ETF(hours)
59. Prediction Estimation Decision Tool Final Remarks
Validating the Prediction Algorithm
Sensor
Sample True Warning Dur.
Error
size Warnings (hours)
% Mean SD (hours)
Water 38 21 11.7 2.2 3.4
Video 55 98 14.4 12.5 1.6
Water-Video 35 97 11.5 4.6 0.7
*threshold set at 12 hours
Aparna U. The Intelligent Farrowing Pen 19 / 42
60. Prediction Estimation Decision Tool Final Remarks
Validating the Prediction Algorithm
Sensor
Sample True Warning Dur.
Error
size Warnings (hours)
% Mean SD (hours)
Water 38 21 11.7 2.2 3.4
Video 55 98 14.4 12.5 1.6
Water-Video 35 97 11.5 4.6 0.7
*threshold set at 12 hours
Aparna U. The Intelligent Farrowing Pen 19 / 42
61. Prediction Estimation Decision Tool Final Remarks
Validating the Prediction Algorithm
Sensor
Sample True Warning Dur.
Error
size Warnings (hours)
% Mean SD (hours)
Water 38 21 11.7 2.2 3.4
Video 55 98 14.4 12.5 1.6
Water-Video 35 97 11.5 4.6 0.7
*threshold set at 12 hours
Aparna U. The Intelligent Farrowing Pen 19 / 42
62. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
63. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Estimation Algorithm
Aparna U. The Intelligent Farrowing Pen 20 / 42
64. Prediction Estimation Decision Tool Final Remarks
Estimation of HPMM Parameters
by maximizing the likelihood function
Aparna U. The Intelligent Farrowing Pen 21 / 42
65. Prediction Estimation Decision Tool Final Remarks
Estimation of HPMM Parameters
by maximizing the likelihood function
Stochastic Expectation-Maximization algorithm (SEM algorithm)
-iterative method
Uses time of mating and farrowing information in addition to
sensor information
Phases are allocated by weighted sampling - Stochastic
Aparna U. The Intelligent Farrowing Pen 21 / 42
66. Prediction Estimation Decision Tool Final Remarks
Estimated Sojourn time distribution
Behavioural State
Duration Number Rate
(hours) of (per hour)
Mean SD Phases
Before Nest-Building 751.20∗ 29.58 645 0.86
Nest-Building 17.02 0.80 458 26.91
Resting 0.53 0.22 6 11.40
Gestation period, days 117 1.2 1109 -
*in addition to 85 days
Aparna U. The Intelligent Farrowing Pen 22 / 42
67. Prediction Estimation Decision Tool Final Remarks
Estimated Sojourn time distribution
Behavioural State
Duration Number Rate
(hours) of (per hour)
Mean SD Phases
Before Nest-Building 751.20∗ 29.58 645 0.86
Nest-Building 17.02 0.80 458 26.91
Resting 0.53 0.22 6 11.40
Gestation period, days 117 1.2 1109 -
*in addition to 85 days
Aparna U. The Intelligent Farrowing Pen 22 / 42
68. Prediction Estimation Decision Tool Final Remarks
Estimated Sojourn time distribution
Behavioural State
Duration Number Rate
(hours) of (per hour)
Mean SD Phases
Before Nest-Building 751.20∗ 29.58 645 0.86
Nest-Building 17.02 0.80 458 26.91
Resting 0.53 0.22 6 11.40
Gestation period, days 117 1.2 1109 -
*in addition to 85 days
Aparna U. The Intelligent Farrowing Pen 22 / 42
69. Prediction Estimation Decision Tool Final Remarks
Estimated Sojourn time distribution
Behavioural State
Duration Number Rate
(hours) of (per hour)
Mean SD Phases
Before Nest-Building 751.20∗ 29.58 645 0.86
Nest-Building 17.02 0.80 458 26.91
Resting 0.53 0.22 6 11.40
Gestation period, days 117 1.2 1109 -
*in addition to 85 days
Aparna U. The Intelligent Farrowing Pen 22 / 42
70. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
Aparna U. The Intelligent Farrowing Pen 23 / 42
71. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
diurnal rhythm conditioned on the Phase/State
Aparna U. The Intelligent Farrowing Pen 23 / 42
72. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
diurnal rhythm conditioned on the Phase/State
model selection
Aparna U. The Intelligent Farrowing Pen 23 / 42
73. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
diurnal rhythm conditioned on the Phase/State
model selection
dependency of the variables
Aparna U. The Intelligent Farrowing Pen 23 / 42
74. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
diurnal rhythm conditioned on the Phase/State
model selection
dependency of the variables
dependency on the phases
Aparna U. The Intelligent Farrowing Pen 23 / 42
75. Prediction Estimation Decision Tool Final Remarks
Conditional Distribution of Sensor Observation
meanActivity - sdActivity - grid activity
Pr(Yt | Si ) ∼ N(µ
(Y )
i , σ2
i
(Y )
)ζ
Simple linear model
sine-cosine functions - harmonic variables
Aparna U. The Intelligent Farrowing Pen 24 / 42
79. Prediction Estimation Decision Tool Final Remarks
Probability of Water Consumption
at given time of day and state
0 5 10 15 20
0.00.20.40.60.8
time of day (hours)
probabilityofdrinking
q q
q
q
q
q
q
q q q q q q
q
q
q
q
q
q q
q
q
q
q q
q
q
q
q
q
q
q
q
q
q
q
q
q
q q q q q q q q q q
state−1
state−2
Aparna U. The Intelligent Farrowing Pen 27 / 42
80. Prediction Estimation Decision Tool Final Remarks
Estimation of HPMM Parameters
Challenges with conditional model
changing pattern with calender time - Behavioural
Phases/States
diurnal rhythm conditioned on the Phase/State
model selection
dependency of the variables ?
dependency on the phases ?
Conclusion
Duration of the Nest-Building state is similar to other studies
Computational time - 26mins per iteration
Aparna U. The Intelligent Farrowing Pen 28 / 42
81. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
82. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Optimal Floor-Heat Regulation System
83. Prediction Estimation Decision Tool Final Remarks
Overview of the Study
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Optimal Floor-Heat Regulation System
Aparna U. The Intelligent Farrowing Pen 29 / 42
84. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Malmkvist et. al. (2006)→ survival of one extra piglet per litter
85. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Malmkvist et. al. (2006)→ survival of one extra piglet per litter
86. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Malmkvist et. al. (2006)→ survival of one extra piglet per litter
87. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Malmkvist et. al. (2006)→ survival of one extra piglet per litter
88. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Malmkvist et. al. (2006)→ survival of one extra piglet per litter
89. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Phase-(A)
Phase-(B)
Phase-(C)Malmkvist et. al. (2006)→ survival of one extra piglet per litter
Aparna U. The Intelligent Farrowing Pen 30 / 42
90. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
HeatOn
HeatO
Farrowing
Phase-(A)
Phase-(B)
Phase-(C)Malmkvist et. al. (2006)→ survival of one extra piglet per litter
Aparna U. The Intelligent Farrowing Pen 30 / 42
91. Prediction Estimation Decision Tool Final Remarks
Floor-heat Regulation on Pen Level
Time since onset of farrowing
Floor-temperature
−20 0 20 40 60 80
10
15
20
25
30
35
40
Farrowing
HeatOn
HeatO
Phase-(A)
Phase-(B)
Phase-(C)Aparna U. The Intelligent Farrowing Pen 30 / 42
92. Prediction Estimation Decision Tool Final Remarks
How to choose threshold???
Aparna U. The Intelligent Farrowing Pen 31 / 42
93. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
Aparna U. The Intelligent Farrowing Pen 32 / 42
94. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
Aparna U. The Intelligent Farrowing Pen 32 / 42
95. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2
U1 U2
Y1
C1
d1
H1
Aparna U. The Intelligent Farrowing Pen 32 / 42
96. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2
U1 U2
Y1
C1 C2
d1
H1
Aparna U. The Intelligent Farrowing Pen 32 / 42
97. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2
U1 U2
Y1
C1 C2
d1 d2
H1
Aparna U. The Intelligent Farrowing Pen 32 / 42
98. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2
U1 U2
Y1
C1 C2
d1 d2
H1 H2
Aparna U. The Intelligent Farrowing Pen 32 / 42
99. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2 t3
U1 U2 U3
Y1 Y2
C1 C2
d1 d2
H1 H2
Aparna U. The Intelligent Farrowing Pen 32 / 42
100. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2 t3
U1 U2 U3
Y1 Y2
C1 C2 C3
d1 d2
H1 H2
Aparna U. The Intelligent Farrowing Pen 32 / 42
101. Prediction Estimation Decision Tool Final Remarks
Floor-heat regulation System
Partially Observable Markov Decision Process (POMDP)
belief
time
phase
sensor obs.
temperature
decision
heating cost
t1 t2 tF−1 tF tF+1
U1 U2 UF−1 UF UF+1
Y1 Y2 YF−1
C1 C2 CF−1 CF
d1 d2 dF−1
H1 H2 HF−1
I
Aparna U. The Intelligent Farrowing Pen 32 / 42
102. Prediction Estimation Decision Tool Final Remarks
Approximate Solution
Approximate POMDP solution
Markov Decision Process - optimization for known phases
Value Iteration Method
Optimize the total expected utility
w.r.t. phase number and oor-temperature
Greedy Strategies
Aparna U. The Intelligent Farrowing Pen 33 / 42
104. Prediction Estimation Decision Tool Final Remarks
Approximating to POMDP
Decision Vs Belief state for the given oor-temperature
400 600 800 1000
0.000.010.020.030.040.050.060.07
phases
αt
t=112
t=114
t=117.6
t=118.1
t=118.3
Aparna U. The Intelligent Farrowing Pen 35 / 42
105. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Aparna U. The Intelligent Farrowing Pen 36 / 42
106. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Most likely phase
Aparna U. The Intelligent Farrowing Pen 36 / 42
107. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Most likely phase
Random phase
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108. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Most likely phase
Random phase
Voting
Aparna U. The Intelligent Farrowing Pen 36 / 42
109. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Most likely phase
Random phase
Voting
Random action
Aparna U. The Intelligent Farrowing Pen 36 / 42
110. Prediction Estimation Decision Tool Final Remarks
POMDP Greedy Strategies
QMDP - expectation over all the phases
Most likely phase
Random phase
Voting
Random action
*Above greedy strategies return almost identical rewards.
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111. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
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112. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
energy supply,
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113. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
energy supply, mortality model
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114. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
energy supply, mortality model and price of a piglet
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115. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
energy supply, mortality model and price of a piglet
POMDP strategy performed better than simple heuristic
strategy especially when the room-temperature and energy
supply was varied
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116. Prediction Estimation Decision Tool Final Remarks
Robustness
POMDP for Floor-heat Regulation Strategy
Decision tool is robust to the changes in room temperature,
energy supply, mortality model and price of a piglet
POMDP strategy performed better than simple heuristic
strategy especially when the room-temperature and energy
supply was varied
POMDP does not suggest to turn on the heater if it is not
benecial
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118. Prediction Estimation Decision Tool Final Remarks
Desired System - data management to decision
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm Estimation Algorithm
Optimal Floor-Heat Regulation Strategy
119. Prediction Estimation Decision Tool Final Remarks
Desired System - data management to decision
HerdLevelComputer
at the pen level
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
Prediction Algorithm Estimation Algorithm
Optimal Floor-Heat Regulation StrategyAparna U. The Intelligent Farrowing Pen 39 / 42
120. Prediction Estimation Decision Tool Final Remarks
Future works...
Studying the corners of the desired system
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121. Prediction Estimation Decision Tool Final Remarks
Future works...
Studying the corners of the desired system
Including farmer's observation on farrowing into model
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122. Prediction Estimation Decision Tool Final Remarks
Future works...
Studying the corners of the desired system
Including farmer's observation on farrowing into model
Improving the methods and the calculation speed of
Estimation algorithm
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123. Prediction Estimation Decision Tool Final Remarks
Future works...
Studying the corners of the desired system
Including farmer's observation on farrowing into model
Improving the methods and the calculation speed of
Estimation algorithm
Similar decision support system for other applications
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124. Prediction Estimation Decision Tool Final Remarks
Take home message...
Contributions
Framework of an automated system starting from data
management to decision The Intelligent Farrowing Pen
Model for monitoring the pre-parturition behaviour of an
individual sow
Prediction of onset of farrowing - can directly calculate the
expected time to farrowing for an individual sow
Prediction model as the kernel of a decision support system
Modelling sows diurnal rhythm in sensor observations
Integrating information from several sensors
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125. Prediction Estimation Decision Tool Final Remarks
The Intelligent Farrowing Pen
HerdLevelComputer
Sensor
Observations
Herd
Database
Prediction
Algorithm
Warning
Strategy
CentralLevelComputer
Historical Data
Estimation
Algorithm
Herd Specic
Parameters
Heat Parameters
Mortality
Model
Costs
Optimization
Algorithm
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