AbstrakSistem sensor robot selalu didukung oleh sebuah sistem komputer yang dikenal sebagai 'visi komputer'. Konsep penting dalam visi komputer adalah klasifi kasi objek. Dalam kajian ini, dua buah algoritma untuk klasifi kasi objek akan dibandingkan. Pertama adalah metode sederhana yang tidak memerlukan komputasi komplek yang dianggap sebagai metode informal, disebut sebagai metode pohon keputusan biner. Metode ini bertumpu pada ciri deskriptor yang sederhana dari suatu objek seperti garis vertikal, garis horisontal atau elip. Sayangnya metode ini memiliki kelemahan dalam mengenali objek yang terkontaminasi oleh noise. Metode yang kedua adalah metode yang lebih formal dengan deskriptor yang bervariasi tinggi. Dalam konteks ini pendekatan statistik multivariat dengan metode yang disebut analisis diskriminan diajukan sebagai alternatif untuk klasifi kasi objek. Metode ini dijalankan dengan menghitung suatu fungsi yang disebut fungsi diskriminan Fisher yang dapat digunakan untuk memisahkan objek. Dari simulasi data dan analisis untuk klaifi kasi dua objek, yaitu skrup dan baut dan tiga objek ,yaitu huruf T,O dan S dapat ditunjukkan bahwa analisis diskriminan dapat mengklasifi kasi objek dengan lebih baik dari pada metode pohon keputusan biner. Kelebihan ditunjukkan terutama pada objek yang mengalami noise. AbstractA robotic sensor system is always supported by a computer system called 'computer vision'. The important concept of computer vision is object classfi fi cation. In this study two algorithms for object classifi cation in this system will be compared. Firstly, A simple method that do not need complex computation and that considered as an informal method is called binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as vertical line, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by a noise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approach named discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computation of a function called Fisher discriminant function that can be used for separating an object. From the data simulation and analysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown that discriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminant method is especially seen when this method is applied for classifi cation of a noisy image of object. PendahuluanAplikasi otomasi industri saat ini memerlukan penggunaan robot industri yang lebih intensif dari masa-masa sebelumnya. Beberapa contoh aplikasi yang dapat disebutkan antara lain pemeriksaan otomatis benda kerja secara visual, indikasi komponen yang jenisnya dibatasi pada ban berjalan, pembacaan label pada otomasi sistem pergudangan dan pengendalian kualitas produk. Sistem sensor robot denga...
Resource Scheduling is one of the most challenging parts of grid computing. A number of algorithms have been designed and developed to create effective resource scheduling. In this research, the algorithms that have been used are the improvised prioritized deadline scheduling algorithm (IPDSA), and the parallel virtual machine version 3 (PVM3) has been used for efficient task execution, with a deadline limit for each task. PVM3 is a software library that optimizes resources flexibly and heterogeneously on a computer. These resources have been connected to various architectures in parallel, so that they can complete tasks well, even though they are very large and complex. This research has implemented the IPDSA resource scheduling algorithm to optimize scheduling and Grid resources in a computer laboratory as a grid environment, where the computers (hosts) are the Grid resource. This research has also developed an IPDSA resource scheduling algorithm by giving priority to each task and implemented using PVM3. The IPDSA resource scheduling algorithm has been successfully implemented using PVM3, with average Tardiness showing a stable value and getting a Non-Delayed Task value above 97.3%, because the resources and tasks that are carried out can be distributed evenly according to the number of hosts used.
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