Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of “volunteer mappers”. Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protection

Safety Hazard and Risk Identification and Management In Infrastructure Management

Infrastructure such as transportation networks improves the condition of everyday lives by facilitating public services and systems necessary for economic activity and growth. However, constructing and maintaining transportation infrastructure poses safety hazards and risks to those working at the sharp end, leading to serious injuries and fatalities. Therefore, the identification of hazards and managing the risks they create is integral towards continually improving safety levels in Infrastructure Management. This work seeks to fully understand this problem and highlight past, present and future issues concerning safety in a comprehensive literature review. A decision support tool is proposed to improve the safety of transportation workers by facilitating hazard identification and management of associated control measures. This Tool facilitates the extraction of safety knowledge from real paper-based safety documents, capturing existing worker’s knowledge and experiences from industrial ‘corporate memory’. The Tool suggests the most appropriate control measures for new scenarios based on existing knowledge from previous work tasks. This is achieved by classifying work tasks using a new method based on unilateral UK legislation (Reporting of Injuries, Diseases and Dangerous Occurrences (1995) Regulations) and the innovative use of Artificial Intelligence method Case Based Reasoning. Case Based Reasoning (CBR) allows transparency in the Tool processes and has many benefits over other safety tools which may suffer from ‘black box’ stigmatism. The Tool is populated with knowledge extracted from a real transportation project and is hosted via the internet (www.Total-Safety.com). The end product of the Tool is the generation of bespoke method statements detailing appropriate control measures. These generated paper documents are shown to have financial and quality control benefits over traditional method statements. The Tool has undergone testing and analysis and is shown to be robust. Finally, the overall conclusions and opportunities for further research are presented and progress of the work against each of the five research objectives is assessed

A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

"ALTERNATIF PENERAPAN TEKNOLOGI INFORMASI DALAM PENENTUAN SUPPLIER INDUSTRI MANUFAKTUR BERBASIS BILL of MATERIAL DAN GROUP TECHNOLOGY"

"Pemilihan supplier merupakan permasalahan yang komplek pada era Industri 4.0 sekarang ini. Banyaknya jumlah supplier dengan kualitas performansi yang berbeda-beda menyebabkan sulitnya pihak internal perusahaan untuk memilih supplier yang sesuai. Di sisi lain macam-macam bahan baku yang dibutuhkan untuk membuat produk jadi, sangat beragam. Kesesuaian supplier berkualitas yang diperlukan untuk memasok bahan baku yang dibutuhkan oleh industri menjadi hal yang penting untuk diselesaikan. Begitupun halnya dengan industri perakitan traktor tangan, industri kecil menengah ini juga sangat tergantung pada ketersediaan bahan pasokan, dan sudah pasti tergantung pula dengan pemilihan supplier itu sendiri. Penelitian disertasi ini bertujuan untuk memperoleh metode terbaru untuk memilih supplier pada industri manufaktur dengan studi kasus pada perakitan industri kecil traktor tangan. Penelitian disertasi ini diawali dengan kegiatan studi literatur melalui FGD, dan studi pustaka, kemudian diikuti dengan pembuatan desain prototipe aplikasi. Dimana untuk menyusun database bahan baku disusun menggunakan struktur produk pada Bill of Material, penentuan bobot kriteria optimal menggunakan Genetic Algorythms dan pemilihan supplier menggunakan metode multi criteria decision making. Studi kasus penelitian ini di sentra Industri Logam Ceper Klaten Solo, yaitu di Politeknik Manufaktur Ceper. Sedangkan pelaksanaan penelitiannya di Lab Komputasional dan Sistem Informasi serta Laboratorium Rekayasa Sistem Informasi Politeknik Negeri Jember. Uji coba aplikasi diimplementasikan pada studi kasus sesungguhnya, dengan data supplier 153, data bahan baku 70 bahan baku dengan variabel kriteria pemilihan supplier sebanyak 10 variabel. Pada tahap akhir diverifikasi menggunakan kuesioner online Google Form, dengan data responden sebanyak 101, banyaknya responden yg memilih “Sangat mudah” dan “Mudah” atau “Sangat lengkap” dan “Lengkap” atau “Sangat tepat” dan “Tepat” > 80 %, ini menunjukkan bahwa aplikasi / web yang dihasilkan dalam penelitian ini sesuai dengan harapan IKM pengguna (Verified). Kata kunci : Pemilihan pemasok, Computational intelegence, Bill of Material, Group Technology, Multi Criteria Decision Making dan Genetic Algorythms.

Knowledge search for new product development: a multi-agent based methodology

Manufacturers are the leaders in developing new products to drive productivity. Higher productivity means more products based on the same materials, energy, labour, and capitals. New product development plays a critical role in the success of manufacturing firms. Activities in the product development process are dependent on the knowledge of new product development team members. Increasingly, many enterprises consider effective knowledge search to be a source of competitive advantage. This research presents an exploratory case study conducted at an aircraft manufacturer. This investigation uncovered six, empirically derived and theoretically informed, problems to enterprise knowledge search. They have been articulated as (i) the effectual web bandwidth limits search speed; (ii) less relevant search results based on word-frequency recognition models of search engine; (iii) un-useable techniques for enterprise search; (iv) rigour security, reliability, and company policy; (v) poor search performance about unstructured enterprise knowledge; (vi) the lack of tacit knowledge sharing. Existing search methodologies have focused on the internet search, rather than providing effective search for enterprise. This research aim is developed to assist the manufacturing enterprise in meeting the industrial requirements in the following way: a methodology and system that can improve the information and knowledge search performance in new product development process. Based on the exploratory case findings, a knowledge search methodology and system has been developed. Agent technology is used to fulfil the requirements of enterprise search. Some initial tests were conducted to better understand implementation issues and future deployment of the methodology and system in practice