二等獎

This paper describes a power-profile-based approach to CanSat picosatellite energy usage estimation. We measured the power profile of individual CanSat components on a test bench. The collected data was used to create a power consumption model for a simple flight control algorithm. The power consumption model was implemented in Python programming language and was used to simulate the CanSat flight. The simulation results were compared with the measured power profile of a fully integrated CanSat running the same flight control algorithm. The model showed an error of 2.2% and proved to be suitable for use in battery capacity optimization.

楊氏矩陣是由有限多個相鄰的方格排列而成的表格，各橫列的左邊對齊，格子數由下而上遞增，而標準楊氏矩陣中每列與每行的元素皆嚴格遞增。我們將楊氏矩陣的「方格」變形為「三角形」，制定與原先楊氏矩陣相似的規則，並命名之為三角楊氏陣列。 本篇研究中，我們首先求得了將某些特定形狀的兩列三角楊氏陣列的遞迴關係式、生成函數、一般項，其與組合學上著名的卡特蘭數亦有相關。後來更是一般化至任意的兩列三角楊氏陣列，得到能夠求其方法數的通式。研究中使用的推算邏輯與方法，也許對未來再研究更一般(或云更多列)的三角楊氏陣列會有所幫助，另外，此研究與偏序集合(Partially Ordered Set)有關，可能可以應用於資訊領域的排序問題。

Cardiac Arrhythmia is one of the conditions in the group of heart and blood vessel diseases that can lead to sudden cardiac arrest (sudden death) and other conditions if not diagnosed quickly and accurately. According to research, heart and blood vessel diseases are the most common diseases and have a mortality rate of one-half of all non-communicable diseases. According to WHO statistics in 2012, it was found that there were 7.4 million deaths from heart and blood vessel diseases, and in 2017, the number of deaths increased to 177 million people, or about 94,444 people per day. Diagnosis of heart and blood vessel diseases can be done by measuring the electrical activity of the heart, and after the examination, a specialized physician will read and analyze the graph to find abnormal patterns. Currently, the shortage of qualified heart specialists to read the graph and screen for heart disease is a medical position shortage, which requires transferring data to hospitals with specialists, resulting in delays in diagnosis and treatment and even death. The project "EIPCA: Electrocardiogram Interpretation Pattern for Cardiovascular Abnormalities prediction" is an application program that assists in screening for fatal diseases that arise from abnormal heart rhythm. It employs artificial intelligence to aid in the screening and analysis of the electrical waveforms generated by an ECG machine, thus reducing diagnosis time and addressing the shortage of cardiology experts. EIPCA is comprised of two systems: (1) a system for screening and analyzing ECG waveforms using artificial intelligence to solve the problem of a shortage of specialized cardiology physicians, and (2) a system for risk assessment of fatal diseases by analyzing the ECG waveform data. The target group of the project is Rural hospitals, as well as health-related agencies. The project team hopes that the development of this project will significantly improve the efficiency and speed of screening for heart-related diseases, ultimately reducing the mortality rate from these diseases in the future.

In recent years, there has been an extreme rise in population and economic development, which requires a great demand for energy worldwide. Global energy consumption has been increasing nearly every year for over half a century [1]; it is rapidly rising in the form of nonrenewable energy, such as coal, oil, natural gas, and fossil fuel. Fossil fuel overreliance has resulted in a dramatic rise in atmospheric carbon dioxide (CO2) concentrations.