Neural network analysis of telemetry data of on-board equipment of space crafts

Goal. Research and development of methodology and software tools of machine automated analysis of telemetry data of onboard equipment (OE) of space crafts (SC). Research methods. The developed software tools and methodology are based on the machine learning, neural networks and image processing methods and algorithms. Results. The paper presents solutions for the actual tasks of machine analysis of telemetry data of OE SC with the purpose of detecting the states of its functioning and analyzing the reliability and operability. Software tools and methodology of neural network clustering-classification analysis of OE SC telemetry data based on the application of the neural networks such as the Kohonen SOM and image processing methods have been developed. The software tools were implemented in desktop and web versions and has a flexible modular service-oriented architecture. Conclusion. The presented software tools and the methodology of neural network analysis of OE SC telemetry data were tested on real telemetry data of the Belarusian spacecraft and the SC group AIST and showed the results with a confidence probability value of at least 0.9. The proposed tools of neural network analysis of the OE SC telemetry data make it possible to develop recommendations for improving the indicators of OE SC reliability during design and operation, detecting the OE SC states, making the correct control and operational decisions of the ground control complex. Key words: neural network, Kohonen SOM, image processing, modular service-oriented architecture, machine neural network telemetry data analysis, onboard equipment of space crafts.

Las ocupaciones laborales en función de los requisitos de competencias lingüísticas: una categorización mediante redes neuronales artificiales

Este trabajo tiene como objetivo agrupar las distintas ocupaciones laborales en diversas categorías en función de la semejanza en los requisitos que presenta cada ocupación en cuanto a competencias lingüísticas. Para ello, se utilizan los datos para las ocupaciones definidas en la O’Net-SOC-2010 para EE.UU. en 2015. Se consideran tres clasificaciones, en función del nivel de desagregación que presentan los datos de O’Net. El análisis se efectúa mediante redes neuronales artificiales, en concreto, los mapas autoorganizativos de Kohonen (SOM). Se pretende así analizar si el nivel de desagregación de las ocupaciones influye en la clasificación de dichas ocupaciones en función del nivel requerido de competencias lingüísticas. La aportación que se realiza es novedosa, ya que, hasta donde conocemos, no existe ningún trabajo previo que utilice los SOM para la clasificación de las ocupaciones laborales considerando las competencias lingüísticas requeridas. Dicho análisis puede ayudar a los investigadores sociales en el estudio del impacto e influencia que presentan los componentes lingüís- ticos del trabajo en la productividad laboral, la empleabilidad de los trabajadores, los resultados empresariales y la generación de ventajas competitivas basadas en el lenguaje, entre otras.

Digital Signs Security System using AES-Blowfish-RSA Hybrid Cryptography Approach

Increasing application of digital signatures in legitimate authentication of administrative documents in both public and private environments is one of the points of concern, especially the issue of security and integrity of ownership of signatures. Digital signature is a mathematical scheme, which a unit to identify and prove the authenticity of the owner of the message or document. The study aims to analyze security patterns and identification of digital signatures on documents using the RSA-AES-Blowfish hybrid cryptographic method approach for securing digital signatures, while the Kohonen SOM method is applied to identify ownership recognition of signature images. The analysis framework used in this study is each signature will be stored in the form of a digital image file that has been encrypted using hybrid method of AES-Blowfish with the SHA 256 hash function. Process of forming private keys and public keys in the signature image using the RSA algorithm. Authentic verification of the use of digital signatures on the document has 2 (two) stages, the first stage is signature will be valid used on the document if the result of hashing the selected signature image is the same based on the private key and public key entered by the user, while the second stage identification is done using the Kohonen SOM method to validate the similarity of the chosen signature with the ownership of the signature.

Analysis of Facial Image Extraction on Facial Recognition using Kohonen SOM for UNPRI SIAKAD Online User Authentication

Academic Information System (Sistem Informasi Akademik aka SIAKAD) Online of Universitas Prima Indonesia (UNPRI) is one of the applications used to facilitate the administration process of lectures which includes the filling process of study plan cards (Kartu Rencana Studi aka KRS), study result cards (Kartu Hasil Studi aka KHS), class schedules, submission of research titles, seminars, and other processes. SIAKAD UNPRI can be accessed by students, lecturers, and academics where every user has a password that has been encrypted to maintain the security of information from people who are not responsible, password security using the encryption method needs to be changed regularly, but there are still many students, lecturers and academic community who are reluctant to change passwords. To improve the security verification stage for SIAKAD users, we propose a face recognition feature approach. Face recognition is a feature that allows the identification of someone from a digital image or video. The way the facial recognition method works is by comparing face data from the camera or images with images that were previously stored in a database. In this study, the Kohonen SOM method is proposed for face identification based on the feature extraction approach of discrete cosine transform (DCT), linear discriminant analysis (LDA) and principal component analysis (PCA) to improve the security of UNPRI SIAKAD users. The analytical framework is done by requiring students to do face taking, where each student will save 5 (five) faces extracted with facial features using the DCT, LDA and PCA model approach, feature extraction results are used as input to the Kohonen SOM network for training and testing facial recognition, then analysis of the effect of DCT, LDA and PCA feature extraction on the Kohonen network on facial recognition accuracy.

Dengue Disease Detection using K- Means, Hierarchical, Kohonen- SOM Clustering

Data Mining is the process of extracting useful information. Data Mining is about finding new information from pre-existing databases. It is the procedure of mining facts from data and deals with the kind of patterns that can be mined. Therefore, this proposed work is to detect and categorize the illness of people who are affected by Dengue through Data Mining techniques mainly as the Clustering method. Clustering is the method of finding related groups of data in a dataset and used to split the related data into a group of sub-classes. So, in this research work clustering method is used to categorize the age group of people those who are affected by mosquito-borne viral infection using K-Means and Hierarchical Clustering algorithm and Kohonen-SOM algorithm has been implemented in Tanagra tool. The scientists use the data mining algorithm for preventing and defending different diseases like Dengue disease. This paper helps to apply the algorithm for clustering of Dengue fever in Tanagra tool to detect the best results from those algorithms.

Metodologia de Classificação de Descargas Parciais em Hidrogeradores Utilizando Mapas Auto-organizáveis de Kohonen

A maneira mais utilizada para avaliação da condição da isolação estatórica em hidrogeradores é o monitoramento de descargas parciais (DPs). Neste trabalho, é apresentado um sistema de classificação de padrões de DPs usando mapas auto-organizáveis de Kohonen (SOM). Foram combinadas diversas técnicas da literatura para pré-processamento e visualização dos padrões. Propõe-se uma metodologia que obtém as fronteiras de separação no mapa Kohonen que maximizam a acurácia, além de automatizar a classificação de padrões desconhecidos. O mapa de Kohonen treinado apresentou alta taxa de acerto, generalizando o problema a ponto de evidenciar subgrupos associados a variações dos padrões de um mesmo tipo de DP.

SISTEM PAKAR PENGENALAN EKSPRESI WAJAH MANUSIA MENGGUNAKAN METODE KOHONEN SELF ORGANIZING DAN PRINCIPAL COMPONEN ANALYSIS

Abstraksistem yang dapat digunakan untuk mengenali ekspresi wajah manusia menggunakan Jaringan Syaraf Tiruan Kohonen SOM sistem tersebut menggunakan metode PCA untuk ekstraksi fitur. Hasil ekstraksi fitur dengan PCA merupakan inisialisasi untuk proses klustering pada jaringan Kohonen SOM. Jaringan Kohonen SOM digunakan untuk membagi pola masukan kedalam beberapa kelompok (cluster). Kohonen SOM dapat mengelompokkan berdasarkan vektor-vektor dari citra ekspresi wajah, hasil keluaran jaringan Kohonen SOM adalah kelompok yang paling dekat atau mirip dengan masukan yang diberikan. pengenalan ekspresi wajah dilakukan dengan ukuran citra masukan dan hasilnya 80.00% didapat pada ukuran citra 90x60, dengan jumlah data pengujian 30 citra ekspresi wajah. Kata kunci: Jaringan Syaraf Tiruan, Kohonen Self Organizing Map, Ekspresi wajah. Principal Component Analysis (PCA)