Crack AutoCAD Electrical 2014
Tutoring is the only job I have had which -- at the end of the day -- makes me feel like I have made a positive difference in the world. The instant smiles and thanks I receive after illuminating a difficult concept is almost addictive. I have a unique experience with academics (and especially mathematics) which has helped me relate to students: I used to hate school. After high school I promised myself I would never sit through another lecture or crack open a text book. My most hated subject was mathematics. I just refused to sit there and mindlessly solve for "x" fifty times an evening. I did not see the point nor did I care. Naturally, I decided to join the Army. But after being in the service for a few months, I realized that I was not being challenged intellectually, and I was craving it! After being away from academics (and having several life-changing experiences overseas) I came to realize how much I valued the pursuit of knowledge. While my time in the Army challenged me emotionally and physically, it did not give me the academic challenge that I needed. I went to South Puget Sound Community College as soon as I separated from the service. Knowing that I would have to get some math out of the way, I took College Algebra...and loved it. I mean that I fell IN LOVE with math. Instead of viewing it as a chore, I began to delve deeper into the meaning of the numbers, variables, and symbols, and had the desire to find out more on my own. But it did not come easily. I had to work for hours every evening, rewrite my notes, copy proofs in the text, find other texts with different presentations of the material, and watch dozens of YouTube videos showing example problems. When I am tutoring, I often hear from students, "Oh, well you're a natural! You just 'get' all this math stuff." I love to watch their faces when I say that I used to hate math and actually failed Algebra 2 in high school. I never "got good" at math. I put in the right amount of effort to understand the material. That is the most important lesson that I teach.After getting an A.S. from SPSCC, I went on to major in biochemistry at the University of Washington, Seattle. At the end of my Junior year, my interest in math took over, and I decided to switch majors to mathematics. I earned a B.S. in Mathematics from UW in 2013. After that, I was awarded two scholarships through the Center for Sensorimotor Neural Engineering which allowed me to do independent research until September 2014. During this time I was accepted to the Applied Mathematics Ph.D. program at UW. I began my Ph.D. studies in Autumn 2014.
Data mining is one of the data processing techniques to collect information from a set of stored data. Every day the consumption of electricity load is recorded by Electrical Company, usually at intervals of 15 or 30 minutes. This paper uses a clustering technique, which is one of data mining techniques to analyse the electrical load profiles during 2014. The three methods of clustering techniques were compared, namely K-Means (KM), Fuzzy C-Means (FCM), and K-Means Harmonics (KHM). The result shows that KHM is the most appropriate method to classify the electrical load profile. The optimum number of clusters is determined using the Davies-Bouldin Index. By grouping the load profile, the demand of variation analysis and estimation of energy loss from the group of load profile with similar pattern can be done. From the group of electric load profile, it can be known cluster load factor and a range of cluster loss factor that can help to find the range of values of coefficients for the estimated loss of energy without performing load flow studies. 2b1af7f3a8