Quarter-fraction factorial designs constructed via quaternary codes

The research of developing a general methodology for the construction of good nonregular designs has been very active in the last decade. Recent research by Xu and Wong [Statist. Sinica 17 (2007) 1191--1213] suggested a new class of nonregular designs constructed from quaternary codes. This paper explores the properties and uses of quaternary codes toward the construction of quarter-fraction nonregular designs. Some theoretical results are obtained regarding the aliasing structure of such designs. Optimal designs are constructed under the maximum resolution, minimum aberration and maximum projectivity criteria. These designs often have larger generalized resolution and larger projectivity than regular designs of the same size. It is further shown that some of these designs have generalized minimum aberration and maximum projectivity among all possible designs.Comment: Published in at http://dx.doi.org/10.1214/08-AOS656 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A trigonometric approach to quaternary code designs with application to one-eighth and one-sixteenth fractions

The study of good nonregular fractional factorial designs has received significant attention over the last two decades. Recent research indicates that designs constructed from quaternary codes (QC) are very promising in this regard. The present paper shows how a trigonometric approach can facilitate a systematic understanding of such QC designs and lead to new theoretical results covering hitherto unexplored situations. We focus attention on one-eighth and one-sixteenth fractions of two-level factorials and show that optimal QC designs often have larger generalized resolution and projectivity than comparable regular designs. Moreover, some of these designs are found to have maximum projectivity among all designs.Comment: Published in at http://dx.doi.org/10.1214/10-AOS815 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Desain dan Implementasi Mekanisme Smart Forklift pada AGV Berbasis Raspberry Pi 4 Model B

Automatic Guided Vehicle (AGV) pallet truck merupakan salah satu jenis AGV yang banyak digunakan di industri. AGV jenis ini merupakan kombinasi AGV dengan mekanisme forklift. Mekanisme forklift digunakan untuk mengangkat dan membawa barang dari satu titik ke titik yang lainnya. Seiring dengan berkembangnya teknologi yang disertai dengan revolusi industri 4.0 maka kegiatan tersebut dapat dilakukan secara otomatis dengan menggunakan robot seperti AGV pallet truck. Pada dasarnya, mekanisme kerja dari AGV pallet truck sama seperti forklift pada umumnya, hanya saja AGV pallet truck sudah beroperasi secara otomatis. Motor DC (Direct Current) banyak digunakan sebagai motor penggerak untuk proses naik turun dalam mekanisme forklift pada AGV pallet truck. Motor DC dipilih karena memiliki torka yang besar untuk pengangkatan beban. Namun, motor DC juga memiliki beberapa kelemahan seperti biaya perawatannya yang mahal dan tingkat kepresisiannya masih kurang akurat. Pada penelitian ini diusulkan mekanisme smart forklift untuk AGV pallet truck yang menggunakan motor stepper dengan tingkat kepresisian tinggi dan torka yang dimiliki juga tidak kalah dengan motor DC. Mekanisme smart forklift diperlengkapi sensor jarak untuk mengukur ketinggian forklift dan jarak antara AGV dengan palet serta raspberry pi 4 model B sebagai controller

GAIN ENHANCEMENT WIRELESS SENSOR TRANSMISSION FOR AGRICULTURE SECTOR USING LINEAR POLARIZATION ANTENNA IN 2.4 GHZ FREQUENCY

Sensors are used to detect changes in the physical or chemical environment whose output is converted into electrical quantities that represent a changing environment. Sensors are widely used in agriculture, especially to detect changes in the physical or chemical environment associated with plant growth. In agricultural applications that have large areas, the problem of location and distance from the sensor to the control center is a problem that must be resolved. To overcome this problem, the sensor system is designed with a wireless connection. In wireless communication system applications, the antenna portion is a very important part that can affect the rate of sensor data transmission. To improve the performance of wireless sensors in sending data, it is necessary to integrate antennas that are compatible with the transceiver sensor system. So this research was conducted to integrate linear polarization antennas with sensors to increase the gain in transmission. From the integration then measurements are taken to get the value of the radiation pattern, gain, bandwidth and delay. From the resulting measurements there is an increase in the strengthening of the wireless sensor transmission and low bit delivery delay by using linear polarization antennas for agricultural sector applications using the 2.4 GHz frequency

Predicting Depression Progression Rates in Radiotherapy Patients

Cancer is a highly prevalent disease that affects millions of people worldwide. In addition to the physiological effects of the disease, cancer patients are more likely to be diagnosed with Major Depressive Disorder (MDD). Unfortunately, prior research has shown that MDD can also decrease the efficacy of radiotherapy cancer treatments. Currently, there is no way to predict, prevent, or mitigate this comorbidity, preventing physicians from administering supplemental therapies. In this paper, we propose a low-cost and efficient computational tool that can be utilized to quantify a patient’s likelihood of developing depression. To do so, we used PET images and a ResNet34 architecture to train a convolutional neural network to identify depression biomarkers in the brain. These brain PET images were taken from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and also provided information regarding the patient’s depression at the time of the scan. We were then able to label and classify images in our dataset based off of this data. Although our model only yielded an accuracy of 54.25%, sensitivity of 56.25% and a specificity of 53.64%, a visual evaluation of our results (GradCAM) confirmed that our algorithm was able to detect the correct regions of interest in the brain, where depression biomarkers were found. This leads us to believe that our deep learning model, with improvement, can be used to effectively help classify depression progression rates in radiotherapy patients