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Author Archives: mathscinotes
I was listening to news the other night when I heard a reporter mention that Jimmy Carter just became the oldest US president in history. I thought verifying this fact would be a good Power Query exercise. He had just surpassed George H.W. Bush, the previous record holder. Continue reading
One common Excel task is tracking work hours. As a contractor, I encounter all sorts of approaches to recording work hours. One small company wants all of my hours captured in an Excel workbook that contains one worksheet per week. Every two weeks, an administrator goes in and captures the hours into another worksheet. Continue reading
I test high-speed serial channels every day. The most common test parameter that I need to measure is the Bit Error Rate (BER). Figure 1 shows the most common test configuration used for measuring BER. Because bit errors occur randomly, there is a certain amount of error involved in measuring the parameter. So when you state a BER measurement, you also give a confidence interval to express your level of uncertainty. Continue reading
I have been working since May 2018 as a contractor for various companies on resolving specific issues – I am a troubleshooter. This role has provided me with a number of interesting challenges. One of my recent challenges is dealing with the GPS Week Number Rollover (WNRO) issue that will occur on 7-April-2019, which involves a 10-bit counter that has been counting weeks since 21-August-1999, which is when the counter was last 0. A 10-bit counter can only count to 1023 and then it will rollover to 0 on the next count. This issue shares many similarities with the Y2K problem. Continue reading
erforming an MTBF prediction is to designing HW as putting a license plate on your car is to driving the car. You need the license to legally drive the car, but it adds no value to your driving experience. Similarly, every company I have worked for demands a predicted MTBF for every HW product, but it adds no value to the design process. In fact, I would argue that generating the MTBF predictions actually adds negative value to the product deployment because it generates a number that is often misused by customers to estimate spare requirements and field support costs. Since no one has told customers otherwise, they think the MTBF value accurately reflects the real failure rate of a product. In fact, MTBF predictions provide a gross estimate of the rate of random parts failure at product maturity. Continue reading
Most of the products that I work on are powered by lithium batteries. Lithium batteries are popular today because they have excellent energy density but there are safety concerns with using them because there have been issues with battery fires. These fires have caused the shipping industry to impose special labeling and packaging information on their transport. I recently have needed to consider shipping batteries on airplanes, so I have been looking at the International Air Transport Association (IATA) shipment guidance for lithium-ion batteries. These rules require knowing the amount of total amount of lithium mass present in a lithium-ion battery. This is not a number that is easy to get from the manufacturers, though I do have a number from one vendor. Continue reading
I have been working through the book Collect, Combine and Transform Data Using Power Query in Excel and Power BI by Gil Raviv – it is an excellent Power Query (PQ) resource. I particularly like the methods discussed in Chapter 10, which focused on how to make your queries robust, that is, insensitive to minor deviations in the input data. Chapter 10 spoke to me, and I immediately began looking for some practice data that suffered from common inconsistencies: headings in different cases, minor spelling errors in the data body, and inconsistent wording (example, "Co." instead of "Company"). I found that data in the Wikipedia's information on US WW2 cruisers. In this post, I will look at the production of cruisers by the US during WW2. See Figure 1 for a typical example of a WW2 US light cruiser. Continue reading
One WW2 topic that continues to intrigue me was how US war planners kept the Imperial Japanese Navy (IJN) at bay long enough to build a large naval force. The key was the use of submarines for commerce raiding to disrupt the war material supply chain and tie down Japanese surface forces with convoy defense duty. This post will use Power Query to scrape the Wikipedia for this data. The Wikipedia is becoming a wonderful source for WW2 information. Continue reading
I spend quite a bit of time at a cabin I have built in northern Minnesota. Technically, I spend most of my time in the garage on the site and I have decided that I need to be able to watch the local television stations in Duluth. These stations are ~75 miles away and I need to determine the bearing along which to point my antenna. This seemed like a good Excel exercise that I can also use as an example for those I tutor at the Hennepin County Library. There are web calculators available that perform this calculation (example), but it is more fun doing it myself. Continue reading
In this post, I will use Power Query )PQ) to gather (aka web scrape) the US Medal of Honor (MOH) recipient names, rank, service, and conflict from a website called the Congressional Medal of Honor Society (CMOHS), which states that there have been 3505 MOH recipients. They have a beautiful website with the records stored on 140 ages with 25 names per page and one page with 5 names. Normally, I would use Python to scrape a large number of web pages but I am trying to use PQ more because my customers all have Excel. My approach as a contractor is always to work within the existing infrastructure of my customers. A copy of my workbook is available here. Continue reading