Implementation of Provably Stable MaxNet

MaxNet TCP is a congestion control protocol that uses explicit multi-bit signalling from routers to achieve desirable properties such as high throughput and low latency. In this paper we present an implementation of an extended version of MaxNet. Our contributions are threefold. First, we extend the original algorithm to give both provable stability and rate fairness. Second, we introduce the MaxStart algorithm which allows new MaxNet connections to reach their fair rates quickly. Third, we provide a Linux kernel implementation of the protocol. With no overhead but 24-bit price signals, our implementation scales from 32 bit/s to 1 peta-bit/s with a 0.001% rate accuracy. We confirm the theoretically predicted properties by performing a range of experiments at speeds up to 1 Gbit/sec and delays up to 180 ms on the WAN-in-Lab facility

Rancang Bangun Program Aplikasi Sistem Pembelajaran Mata Kuliah Jaringan Syaraf Tiruan model Jaringan Kompetitif

Jaringan Syaraf Tiruan merupakan salah satu mata kuliah jurusan teknik informatika. Jaringan Syaraf Tiruan memiliki beberapa model diantaranya neuron, hebb, perceptron, adaline, back propagation, jaringan kompetisi dan jaringan kohonen. Model jaringan kompetisi merupakan salah satu model Jaringan Syaraf Tiruan yang cukup sulit dalam pembelajaran. Pembelajaran dengan metode konvensional pada model jaringan ini dirasakan kurang efektif sehingga dibutuhkan waktu yang lebih untuk memahami dan mengerti model jaringan ini. Salah satu solusi yang dapat dilakukan untuk mengatasi masalah ini yaitu dengan membuat suatu program aplikasi berbasis desktop. Perancangan dan pengembangan sistem pembelajaran jaringan syaraf tiruan model jaringan kompetitif menggunakan bahasa pemrograman java development tool Neetbeans 6.9.1. Tahapan perancangan dan pengembangan sistem pembelajaran jaringan syaraf tiruan model jaringan kompetitif meliputi pengumpulan data, perancangan aplikasi, pembuatan aplikasi, pengujian aplkasi, perbaikan aplikasi, implementasi aplikasi, pembuatan laporan. Hasil perancangan dan pengembangan sistem pembelajaran Jaringan Syaraf Tiruan model jaringan kompetitif adalah terwujudnya aplikasi perhitungan Jaringan Syaraf Tiruan model Jaringan Kompetitif yang dapat menghitung dan menampilkan grafik pergerakan input

Pemanfaatan Jaringan Saraf Tiruan Hamming dan MAXNET Dalam Mendeteksi Nomor Plat Kendaraan

Abstrak:.        Jaringan Hamming awalnya diteliti oleh Lippmann dan lembaga DARPA, jaringan Hamming bekerja berdasarkan prinsip kecocokan atau kesesuaian data vektor input dengan pola vektor prototip yang tersimpan dalam matriks bobot, sehingga jaringan ini tidak memerlukan “pelatih” dalam proses pembelajaran. Kesederhanaan cara kerja jaringan Hamming membawa konsekuensi akan persyaratan tertentu yang memungkinkan kinerja dalam mendeteksi data vektor input secara akurat. Persyaratan yang diperlukan jaringan Hamming menjadi fokus penelitian pada laporan ini. Variabel data vektor prototip dan variabel data vektor input diteliti untuk melihat pengaruhnya terhadap keakuratan jaringan Hamming dalam mendeteksi data vektor input yang terdapat efek pengaburan dari lingkungan. Hasil penelitian berupa persyaratan yang diperlukan dan selanjutnya dapat diaplikasikan pada sistem Pemanfaatan Jarinngan Saraf Tiruan Hamming dan MAXNET Dalam Mendeteksi Nomor Plat Kendaraan.   Kata kunci: Hamming Network, MAXNET, dimensi, keakuratan, bit   Abstract:    The Hamming network was originally researched by Lippmann and DARPA department, the Hamming network works based on the principle of matching or matching the input vector data with the prototype vector pattern stored in the weight matrix, so this network does not need a "trainer" in the learning process. The simplicity of how the Hamming network works has consequences for certain requirements that enable performance in detecting input vector data accurately. The requirements needed by the Hamming network are the focus of research in this report. Prototype vector data variables and input vector data variables were examined to see their effect on the accuracy of the Hamming network in detecting input vector data that has a defocusing effect from the environment. The results of the research are in the form of requirements that are needed and can then be applied to the system of Utilizing Hamming and MAXNET Artificial Neural Networks in Detecting Vehicle Plate Numbers.   Keywords: Hamming Network, MAXNET, Dimension, Acurate, bi

The development of a generic systems-level model for combustion-based domestic cogeneration

The provision of heat and power to dwellings from micro-cogeneration systems is gaining credence around the developed world as a possible means to reduce the significant carbon emissions associated with the domestic sector. However, achieving the optimum performance for these systems requires that building design practitioners are equipped with robust, integrated models, which will provide a realistic picture of the cogeneration performance in-situ. A long established and appropriate means to evaluate the energy performance of buildings and their energy systems is through the use of dynamic building simulation tools. However, until now, only a very limited number of micro-cogeneration device models have been available to the modelling community and generally these have not been appropriate for use within building simulation codes. This paper describes work undertaken within the International Energy Agency's Energy Conservation in Building and Community Systems Annex 42 to address this problem through the development of a generic, combustion based cogeneration device model that is suitable for integration within building simulation tools and can be used to simulate the variety of Internal Combustion Engine (ICE) and Stirling Engine (SE) cogeneration devices that are and will be available for integration into dwellings. The model is described in detail along with details of how it has been integrated into the ESP-r, Energy Plus and TRNSYS simulation platforms

TOFA: Transfer-Once-for-All

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract a number of models from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios. Given a task, TOFA obtains custom neural networks, both the topology and the weights, optimized for any number of edge deployment scenarios. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all subnets within the supernet, coupled with on-the-fly architecture selection at deployment time

Training Winner-Take-All Simultaneous Recurrent Neural Networks

The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter algorithms for the task of finding the maximum among N numbers. The simulation demonstrates the effectiveness of our training approach under conditions of a shared-weight SRN architecture. A more general SRN also succeeds in solving a real classification application on car engine data