Technology and Indonesia's Industrial Competitiveness

This paper will discuss the major factors, which affect Indonesia’s industrial competitiveness, specifically the determinants of its industrial technology development, which is crucial to raising Indonesia’s competitiveness. After a brief overview of industrial development before and after the Asian economic crisis, the paper discusses some recent assessments of the country’s competitiveness. It considers the determinants of Indonesia’s industrial technological development, including policy options open to the government. The author recommends that as Indonesia’s technology support services, specifically the public MSTQ services, have in general not performed adequately in meeting the needs of firms, privatizing these services would be advisable. This will not only lessen the fiscal burden, but more important, it will enable these important services to aim their services specifically at the needs of private industry. These efforts, however, will only be successful if the government also manages to eliminate the various factors, which currently account for the poor investment climate which, in turn, imposes high costs on firms which reduces their competitiveness relative to firms in the other East Asian countries

Pelajar spastik daftar USM

Pelajar Cemerlang STPM 2011 peringkat kebangsaan kategori calon istimewa turut mendaftar di Universiti Sains Malaysia (USM) di Pulau Pinang, semalam

A Foreword by the Guest Editors: Professor Anne Booth, Eminent and Prolific Scholar, Generous Friend and Colleague

This Festschrift in honour of Professor Anne Booth on the occasion of herimpending retirement from the Department of Economics at the School ofOriental and African Studies (SOAS), University of London, is to celebrateAnne's formidable scholarly achievements and her generosity in sharing herresearch findings with her many colleagues, friends, and students

Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid