Sekiur My Thoughts
Dan Ariely, a professor of behavioral economics at Duke University, presents examples of cognitive illusions that help illustrate why humans make predictably irrational decisions.
EG is the celebration of the American entertainment industry. Since 1984, Richard Saul Wurman has created extraordinary gatherings about learning and understanding. EG is a rich extension of these ideas – a conference that explores the attitude of understanding in music, film, television, radio, technology, advertising, gaming, interactivity and the web – The Entertainment Gathering
Dan Ariely is the Alfred P. Sloan Professor of Behavioral Economics at MIT Sloan School of Management. He also holds an appointment at the MIT Media Lab where he is the head of the eRationality research group. He is considered to be one of the leading behavioral economists. Currently, Ariely is serving as a Visiting Professor at the Duke University, Fuqua School of Business where he is teaching a course based upon his findings in Predictably Irrational.
Ariely was an undergraduate at Tel Aviv University and received a Ph.D. and M.A. in cognitive psychology from the University of North Carolina at Chapel Hill, and a Ph.D. in business from Duke University. His research focuses on discovering and measuring how people make decisions. He models the human decision making process and in particular the irrational decisions that we all make every day.
Ariely is the author of the book, Predictably Irrational: The Hidden Forces That Shape Our Decisions, which was published on February 21, 2008 by HarperCollins.
Psychologist Barry Schwartz takes aim at a central tenet of western societies: freedom of choice. In Schwartz’s estimation, choice has made us not freer but more paralyzed, not happier but more dissatisfied.
Our lives, our cultures, are composed of many overlapping stories. Novelist Chimamanda Adichie tells the story of how she found her authentic cultural voice — and warns that if we hear only a single story about another person or country, we risk a critical misunderstanding.
In my last post I put forth the idea of using the unique capabilities and UX of the iPhone to help track defects in railways, which came about after my initial conversations with a friend from the railroad industry.
Hours into our conversation I was perplexed at the lack of proactive monitoring of the today’s bridges used by trains for transport.
Should a uniquely located bridge collapse, an energy crisis could ensue as a result of the coal fields in the northeast/midwest being severed from the southwest.
I looked at several existing methods and solutions used today to address this issue and drew from each to conclude in a refined approach to monitoring the health of railway bridges.
There were basically 3 design considerations which needed to be met:
- Easy to deploy
- Low Maintenance
- Long Term
The system had to be easily deploy-able were an electrician in the field could install the components of the solution. Obviously low maintenance is also key, reducing the total cost of ownership and Long Term reducing the need for personnel to visit these bridges.
Application Requirements:
In order to monitor the health of a structure, vibrations of the structure need to be gathered and analyzed to develop a baseline under normal conditions. Subsequent measurements of vibrations can then be compared to the baseline to determine if an anomaly exists.
To accomplish this requirement sensors (3-axis accelerometers) are placed throughout the span of the bridge collecting data. The frequency components of interest range between 0.25-20Hz, the measurements would need to take place 40 secs before and after the passage of the train and time synchronization between the sensors would also be a factor to take into account.
Existing approaches use technology such as Solar panels to supply power in remote areas, GSM for data transmission, GPS for time synchronization and a star topology for the sensors to communicate to a head node which would collect and transmit the data for analysis.
There are multiple problems here since solar panels are expensive, prone to theft, vandalism and damage; GSM data transmission isn’t always viable when there isn’t network coverage in remoter areas and relying on a head node to collect and transmit the data would be like putting all your eggs in one basket. If the head node failed, the system would stop working.
The techniques I came across with basically fell into 2 categories: Existing bridges and new bridges.
I focused on existing bridges since there are very sophisticated things being done with new bridges. Today engineers are embedding sensors and fiber in the concrete while the bridges are being built in order to take measurements, but this approach is obviously not viable for existing bridges.
The methods in use for existing bridges included visual inspection, wired solutions which were bulky, expensive and time consuming to setup and a few wireless solutions some of which were proprietary, not scalable and interesting work from India.
In summary there are several challenges in deploying such a solution at sometimes remote and hostile locations. A lack of power which calls for alternate sources of energy, a way to effectively and reliably collect and transmit the data for analysis and keeping installation and maintenance costs low.
Since the train comes and goes, so can the data collected by the sensors. The train would activate the standby sensors as it approaches the bridge and then collect the data buffered by the sensors after passing the bridge. This approach would deal with the transmission of data limitations while at the same time eliminating the need of power for this component of the system. The train would carry the data and uploaded it to a collection station.
To deal with reliability and power requirements the linear path Star Topology would be dropped in favor of a Mesh Network which provides TRUE self-organizing and self-healing properties. On top of the Mesh Network, TSMP (Time Synchronized Mesh Protocol) would be used providing more than 99.9% reliability and the lowest power consumption per delivered packet.
The key for achieving maximum reliability is to use channel hopping, in which each packet is sent on a different channel. In this case, transient failures in links on a given channel are handled gracefully, and persistent link failures that develop after the site survey do not destabilize the network.
Sensors of this type using this approach can last 7-10 years on a small battery meeting the application requirements.
Now to raise some money, build a working prototype and demo it to the Railway companies.
