• The Journal of Information Systems
• /
• v.15 no.3
• /
• pp.187-211
• /
• 2006

As port competitiveness is becoming more important in the global market environment RFID (Radio Frequency Identification) Is also becoming a crucial enabler In implement efficient visible, secure and productive ports. However there Is a lack of practical validated RFID technology acceptance cases in the port logistics industry until now, even though various related projects have been undertaken. In this study, we applied 13.56MHz passive RFID readers, tags, and their applications into existing bar-code based gate systems to improve the port logistics process, and we analyzed results of a pilot test in economic and non-economic perspectives. The main purpose of this study is to develop the RFID based automatic gate passing system in container terminals, and is to validate its economic and non-economic feasibility. In order to accomplish the purpose of this study, first, we examined previous researches on RFID technology acceptance in the port logistics industry, second, we Identified and analyzed the business process of existing gate systems in container terminals, third, we build RFID gate systems with 13.56Mhz tags, readers, and its middle-ware, finally we tested the system and its performance. The results were successful and showed the feasibility of the system in real container terminal gates. Economic and non-economic contribution was confirmed. Although the system has technological limitations with short range passive type, we clearly identified its potential capability and its economic validity in the field, which are the implications of this study.

• Asian review of World Histories
• /
• v.4 no.1
• /
• pp.3-34
• /
• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• Journal of Korea Multimedia Society
• /
• v.15 no.5
• /
• pp.615-623
• /
• 2012

This paper presents T-DMB AEAS (automatic emergency alerting service) receiver model considering emergency region. The proposed receiver model determines the geographical location of the terminal by analysing the received T-DMB signal and displays the AEAS messages only if the location of terminal is similar to the emergency site. First, to determine the geographical location of the terminal, we extract the TII value from the null symbol of the SC and, based on it, calculate the location of transmitter by analysing FIG 0/22 delivering the TII-related data. The proposed algorithm sets the location of transmitter as that of receiver and displays the emergency message only in the case of the similar region. The experiment was conducted in the test environment of low power T-DMB generator based on the T-DMB AEAS messages.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.7
• /
• pp.1026-1031
• /
• 2002

In this Paper, We analysed the technical characteristics of a automatic identification system that will introduce in aviation and marine radio stations. IMO's Marine Safety Committee approved revision of chapter V of the Safety of Life at Sea(SOLAS) Convention in 73rd meeting. According to this, AIS will become a mandatory carriage requirement by 01 July 2002. AIS as a surveillance system continuously receives its own position from the GNSS and then repeatedly broadcasts it on a W:.u data link for avoiding traffic conflicts and possible disasters. VHF data link is organized so that a specified number of time slots make up a repeatable frame. Each radio station can autonomously allocate and deallocate slots within the frame using selection algorithm which is called SOTDMA(Self-Organized Time Division Multiple Access). The results can be an aid in the continued of understanding technical characteristics for AIS as a broad surveillance system.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.9-21
• /
• 2003

This paper proposes an automatic construction system for transliteration dictionary from English-Korean parallel corpus. The system works in 3 steps: it extracts all nouns from Korean documents as the first step, filters transliterated foreign word nouns out of them with the language identification method as the second step, and extracts the corresponding English words by using a probabilistic alignment method as the final step. Specially, the fact that there is a corresponding English word in most cases, is utilized to extract the purely transliterated part from a Koreans word phrase, which is usually used in combined forms with Korean endings(Eomi) or particles(Josa). Moreover, the direct phonetic comparison is done to the words in two different alphabet systems without converting them to the same alphabet system. The experiment showed that the performance was influenced by the first and the second preprocessing steps; the most efficient model among manually preprocessed ones showed 85.4% recall, 91.0% precision and the most efficient model among fully automated ones got 68.3% recall, 89.2% precision.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.310-315
• /
• 2014

The domestic maritime environment shows higher frequency of maritime accidents amidst greater traffic volume arising from increasing international seaborne trade and maritime leisure activities. To reduce such maritime accidents, there exist various kinds of safety navigation devices in the ship bridge aimed to mitigate burdens of navigators and support their accurate decision making. Amongst is the AIS considered very important, which is an automatic tracking system to assist understanding of the circumstances in the vicinity by receiving information of other ships and also sending its own; where the information contains errors initially, however, such wrong information is periodically transmitted, accordingly giving rise to hindrance sometimes in decision making by shore operators or ship navigators at sea. This study is to propose the error data and field management algorithm using fuzzy theory toward improving reliability and accuracy in ship related information received from AIS.

• Journal of KIISE
• /
• v.42 no.11
• /
• pp.1372-1379
• /
• 2015

This paper presents an analysis of feature extraction methods used for distinguishing the speech of patients with cleft palates and people with normal palates. This research is a basic study on the development of a software system for automatic recognition and restoration of speech disorders, in pursuit of improving the welfare of speech disabled persons. Monosyllable voice data for experiments were collected for three groups: normal speech, cleft palate speech, and simulated clef palate speech. The data consists of 14 basic Korean consonants, 5 complex consonants, and 7 vowels. Feature extractions are performed using three well-known methods: LPC, MFCC, and PLP. The pattern recognition process is executed using the acoustic model GMM. From our experiments, we concluded that the MFCC method is generally the most effective way to identify speech distortions. These results may contribute to the automatic detection and correction of the distorted speech of cleft palate patients, along with the development of an identification tool for levels of speech distortion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.11a
• /
• pp.187-191
• /
• 2002

In this paper, We analysed the technical characteristics of a automatic identification system that will introduce in aviation and marine radio stations. IMO's Marine Safety Committee approved revision of chapter V of the Safety of Life at Sea(SOLAS) Convention in 73rd meeting. According to this, AIS will become a mandatory carriage requirement by 01 July 2002. AIS as a surveillance system continuously receives its own position from the GNSS and then repeatedly broadcasts it on a VHF data link for avoiding traffic conflicts and possible disasters. VHF data link is organized so that a specified number of time slots make up a repeatable frame. Each radio station can autonomously allocate and deallocate slots within the frame using selection algorithm which is called SOTDMA(Self-Organized Time Division Multiple Access). The results can be an aid in the continued of understanding technical characteristics for AIS as a broad surveillance system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.1765-1794
• /
• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.420-426
• /
• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.