An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network

Estimating shaft power of a crew boat is very important to be analysed because it has high-speed operational characteristics along with limited routes. To understand the phenomena, 3 sister crew-boats with operational distance about 40-60 nautical miles every day are investigated. The daily operational time is 8 hours and the configurations are: 4.04% full speed, 13.63% economical speed, 1.81% slow speed, 7.65% snatching, 1.25% manoeuvring, 5.29% idle, and the remaining time is in standby condition. The crew boats are fitted with a monitoring system namely SHIMOS®, in which data is sent to a server in the centre office every 2 minutes. The data consists of time capture, boat position (latitude and longitude), speed, left and right RPM engine, left and right flow-meter data engine, and average of fuel consumption data in everyday operation. Three years of data has been collected for the vessel. The present study proposed characteristics of crew-boat shaft power, which affected by external factors using Artificial Neural Network (ANN) back propagation method and optimisation in 4 hidden layers and 40 neurons with relative error 6.2%. The results demonstrates good agreement with previous popular method that using statistical models

Segmentasi Citra Berwarna dengan Menggunakan Metode Clustering Berbasis Patch untuk Identifikasi Mycobacterium Tuberculosis

Citra berwarna memiliki banyak variasi nilai intensitas pada masing-masing piksel dalam satu citra. Dengan kasat mata, citra seperti terlihat memiliki warna yang sama dengan citra yang lain. Namun bila diolah oleh komputer suatu piksel dengan warna yang sama dengan piksel yang lain, ternyata memiliki kombinasi nilai intensitas yang berbeda. Variasi nilai intensitas ini akan sangat mempengaruhi hasil proses pengolahan citra oleh komputer.Dalam penelitian ini, dilakukan uji coba proses segmentasi citra berwarna pada citra mikroskopis bakteri TBC (mycobacterium tuberculosis) yang berasal dari dahak atau sputum pasien, sebagai sampel citra warna yang memiliki variasi nilai intensitas yang begitu kompleks. Sputum yang diperoleh dari pasien dilakukan pewarnaan dengan metode pewarnaan Ziehl – Neelsen. Metode pewarnaan ini umum digunakan di puskesmas, karena di puskesmas pada umumnya menggunakan mikroskop optik untuk memeriksa slide sputum. Hasil pewarnaan memberikan efek warna merah untuk bakteri TB dan background berwarna biru. Hasil pewarnaan ini memberikan citra slide yang kompleks, akibat hasil pewarnaan yang berbeda bergantung pada skill tenaga laboran, sehingga petugas klinis mengalami kesulitan ketika melakukan pemeriksaan slide secara manual. Untuk membantu petugas klinis dalam melakukan pembacaan slide, maka pada penelitian ini dilakukan segmentasi warna citra slide untuk mengekstrasi citra bakteri TB dan menghilangkan citra background.Beberapa metode telah dilakukan dalam penelitian ini, yaitu adaptive color thresholding pada ruang warna RGB, HSV, CIE L*a*b, yang memberikan hasil segmentasi yang baik pada ruang warna CIE L*a*b. Kemudian dicoba metode segmentasi k-means clustering dan k-nearest neighbors untuk memperbaiki performansi segmentasi warna adaptive color thresholding, dan metode k-nearest neighbors memberikan akurasi yang paling baik 97,90% , namun belum mampu memberikan hasil yang bagus pada citra utuh dan waktu komputasi proses pembelajaran yang lama. Untuk memperbaiki performansi hasil segmentasi citra berwarna pada citra sputum penyakit TBC ini, maka pada penelitian ini dilakukan metode segmentasi Fast k-means clustering, yang membutuhkan waktu komputasi yang lebih cepat dari metode k-nearest neihgbors dan hasil segmentasi yang lebih baik. Metode Fast k-means clustering yang digunakan ditunjang dengan penerapan pengolahan citra berbasis patch, untuk menghindari variasi global yang dapat mempengaruhi hasil segmentasi. Dengan metode segmentasi citra berbasis patch ini ternyata memberikan hasil yang lebih baik dibanding metode segmentasi yang diterapkan pada citra utuh yang secara serentak dilakukan pengolahan citranya

Soliton mode-locked pulse generation with a bulk structured MXene Ti 3 AlC 2 deposited onto a D-shaped fiber

We propose a bulk structured MXene, Ti3AlC2 deposited onto D-shaped fiber for soliton generation in an erbium-doped fiber laser (EDFL) cavity. Our saturable absorber (SA) device, based on MAX phase, was prepared by using stirring and ultrasonic vibration, which offer easier sample preparation compared with its 2D counterparts. By means of the polishing wheel technique, we fabricated a D-shaped fiber with a controlled polishing depth and incorporated the MAX phase Ti3AlC2 solution onto its polishing region. We obtained a mode-locked soliton pulse with the proposed MAX phase D-shaped (MAX-DS) SA in EDFL cavity. The pulse width, repetition rate, and central wavelength of the pulse train are 2.21 ps, 1.89 MHz, and 1557.63 nm, respectively. The polarization-insensitive EDFL cavity initiated a soliton operation with superior stability as the pump power tuned from 21 to 131 mW; further, the ML laser exhibits an average power of 15.3 mW, peak power of 3.8 kW, and pump efficiency of 12.5%. The MAX-DS SA incorporated inside the EDFL reveals efficient output performance, with a pulse energy of 8.14 nJ, the highest ever reported, to our best knowledge, among D-shaped fiber-based SA

Classification of adeno carcinoma, high squamous intraephithelial lesion, and squamous cell carcinoma in Pap smear images based on extreme learning machine

Cervical cancer is a malignant tumour that attacks the female genital area originating from epithelial metaplasia in the squamous protocol junction area. One method of diagnosis of cervical cancer is to do a Pap smear examination by taking a cervical cell smear from the woman�s cervix and observing its cell development. However, examination of cervical cancer from Pap smear results usually takes a long time. This is because medical practitioners still rely on visual observations in the analysis of the results of Pap smear so that the results are subjective. Therefore, we need a programme that can help the classification process in establishing a diagnosis of cervical cancer with high accuracy results. In this study, a cervical cancer classification program was developed using a combination of the Grey Level Co-occurrence Matrix (GLCM) and Extreme Learning Machine (ELM) methods. There are three classes of cervical cell images classified, namely adenocarcinoma, High Squamous Intraepithelial Lesion (HSIL) and Squamous Cell Carcinoma (SCC). From the results of the training program obtained an accuracy 100 and from the testing program obtained an accuracy of 80. © 2020 Informa UK Limited, trading as Taylor & Francis Group

Implementation of Ict Based Pediatric Telehealth Care Posyandu as Automatic Monitor and Identification of Infant's Growth and Development

Background: Posyandu is one of the Indonesian government's attempt in order to monitor and improve the health and life quality of the community, especially infant. However, the implementation of Posyandu is facing some issues such as low effectiveness and low accuracy during the data collecting process of the infant's growth and development. Purpose: This study aims to develop an automatic telehealth care product in order to help to increase the effectivity and accuracy in the implementation of Posyandu. Methods: (1) Development of the Telehealth Care Posyandu Application, (2) Implementation of the application in the form of social service program. Result: (1) “Toddler” Telehealth Care Application based in Android and ICT was buith with artificial intelligence of Decision Tree and Random Forest method. Program testing was done with 97.89% accuration score from total 85 infant's growth data. While from 47 questionnaire data of infant's development, accuracy score of 83.33% was obtained. (2) Target's respond on the Telehealth Care Posyandu Application shown the status of “Very Satistified” based on the score of 81% from the satisfaction survey. The satisfaction survey covered three aspects which are: System, User, and Interaction. Conclusion: “Toddler” Telehealth Care Posyandu Application was proven to has high accuracy, sensitivity, and sensitivity score and also resulted in “Very Satisfied” user respond