3 research outputs found
Study of Shipbuilding Competitiveness: Benchmarking analysis as a tool to measure shipyards' competitiveness with a focus on Asian yards
No full textThe shipbuilding industry is not a standalone industry. It integrates with the shipping market, and therefore one characteristic of the shipbuilding industry is complexity. Competitiveness is a multi-dimensional concept that can be measured in numerous ways. The author found that these multi-dimensional concepts can better be translated into three objectives. Three methods are evaluated to find which one is the most appropriate when measuring the shipyard's competitiveness. To find which method is the most appropriate, the author conducted the Analytical Hierarchy Process and found that benchmarking analysis is the best method. Data Envelopment Analysis (DEA) is chosen due to its versatility, cautious estimation, and non-parametric characteristics. Data Envelopment Analysis is an operation research method that uses mathematical formulation to find benchmarks among units under study. The first model uses deliveries as output, dock area and number of employees as inputs. The second model uses price/CGT and duration/CGT as inputs, and new contracts as outputs. The models investigated 20 shipyards from Japan, China, South Korea, and Vietnam. The results show that Chinese yards are very efficient when it comes to attracting new orders. However, in terms of allocating its resources, Chinese yards are very inefficient. Japanese yards are very efficient in both models. Most of the Japanese yards are frontiers. Korean yards, on the other hand, are the winner for mega-sized yards, but not in the medium-sized shipyards. Data Envelopment Analysis (DEA)'s results show the efficiency between the output and input of a system with a quantifiable value and provide a point of improvement by increasing output (for output-oriented) or decreasing input (for input-oriented). The insight can be derived from the results by analyzing the efficiency score and lambda values.Marine Technology | Ship Design, Production and Operation
Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching
No full textSign Language is a way to communicate to people with disabilities. American Sign Language (ASL) is one among other sign languages. Sign language image would be extracted using Edge Oriented Histogram (EOH). In Content-Based Image Retrieval, a feature from query image will be compared to database image to find out the best matching method so three matching methods will be used. The matching methods are Earth Mover Distance, Hausdorff Distance, and Sum of Absolute Difference. The smallest distance shows the strong similarity between query image and database image. The Sum of Absolute Difference is outperformed of other in case the most of relevant image can be retrieved. The order of methods to recognize alphabet (from the best one) is Sum of Absolute Difference following by Earth Mover Distance and Hausdorff Distance. Hausdorff Distance has smallest running time using 4 bin features. Earth Mover Distance has smallest running time using 6 bin features. Sum of Absolute Difference has smallest running time using 9 bin features, so the method can be recommended to recognize ASL
Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching
No full textSign Language is a way to communicate to people with disabilities. American Sign Language (ASL) is one among other sign languages. Sign language image would be extracted using Edge Oriented Histogram (EOH). In Content-Based Image Retrieval, a feature from query image will be compared to database image to find out the best matching method so three matching methods will be used. The matching methods are Earth Mover Distance, Hausdorff Distance, and Sum of Absolute Difference. The smallest distance shows the strong similarity between query image and database image. The Sum of Absolute Difference is outperformed of other in case the most of relevant image can be retrieved. The order of methods to recognize alphabet (from the best one) is Sum of Absolute Difference following by Earth Mover Distance and Hausdorff Distance. Hausdorff Distance has smallest running time using 4 bin features. Earth Mover Distance has smallest running time using 6 bin features. Sum of Absolute Difference has smallest running time using 9 bin features, so the method can be recommended to recognize ASL
