• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.151-164
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• 2009

Strategic alliances developed as formalized inter-organizational relationships are core vehicles to share information, resources and knowledge. The structural characteristics of strategic network constructed by strategic alliances have been important agenda in strategic management discipline. This paper has two folds in analysing the strategic network formulated by project level alliances in Korean construction industry. First, we investigate the strategic network using the tools and methods of social network analysis, such as centrality, cohesion, structural equivalence, and power law. Second, the performance of firms within networks are analysed longitudinally with panel data analysis. We have found that the strategic networks in this industry has scale-free characteristics, where the degree distribution fits the power law, and the vertically equivalent structure is clear. We also present that the performance of firms are continuously affected by the degree centrality of firms in this network for the last 10 years.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Journal of KIISE
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• v.44 no.7
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• pp.742-747
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• 2017

In this paper, we propose a novel approach to improve the performance of the Convolutional Neural Network(CNN) word embedding model on top of word2vec with the result of performing like doc2vec in conducting a document classification task. The Word Piece Model(WPM) is empirically proven to outperform other tokenization methods such as the phrase unit, a part-of-speech tagger with substantial experimental evidence (classification rate: 79.5%). Further, we conducted an experiment to classify ten categories of news articles written in Korean by feeding words and document vectors generated by an application of WPM to the baseline and the proposed model. From the results of the experiment, we report the model we proposed showed a higher classification rate (89.88%) than its counterpart model (86.89%), achieving a 22.80% improvement. Throughout this research, it is demonstrated that applying doc2vec in the document classification task yields more effective results because doc2vec generates similar document vector representation for documents belonging to the same category.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.179-185
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• 2001

Mobile agents have the unique ability to transport themselves from one system in a network to another. The ability to travel allows mobile agents to move to a system that contains services with which they want to interact and then to take advantage of being in the same host or network as the service. But most of conventional mobile agent systems require that the users or the programmer should give the mobile agent its detail behavioral script for accomplishing the given task. And during its runtime, such mobile agents just behave according to the fixed script given by its user. Therefore it is impossible that conventional mobile agents autonomously build their own plants and execute them in considering their ultimate goals and the dynamic world states. One way to overcome such limitations of conventional mobile agent systems is to develop an intelligent mobile agent system embedding a reactive planner. In this paper, we design both a model of agent mobility and a model of inter-agent communication based upon the representative reactive planning agent architecture called JAM. An then we develop an intelligent mobile agent system with reactive planning capability, IMAS, by implementing additional basic actions for agent moves and inter-agent communication within JAM according to the predefined models. Unlike conventional mobile agents. IMAS agents can be able to adapt their behaviors to the dynamic changes of their environments as well as build their own plans autonomously. Thus IMAS agents can show higher flexibility and robustness than the conventional ones.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.168-173
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• 2010

Error concealment techniques are significant due to the growing interest in imagery transmission over error-prone channels. This paper presents a spatial error concealment technique for losslessly compressed images using least significant bit (LSB)-based data hiding to reconstruct a close approximation after the loss of image blocks during image transmission. Before transmission, block description information (BDI) is generated by applying quantization following discrete wavelet transform. This is then embedded into the LSB plane of the original image itself at the encoder. At the decoder, this BDI is used to conceal blocks that may have been dropped during the transmission. Although the original image is modified slightly by the message embedding process, no perceptible artifacts are introduced and the visual quality is sufficient for analysis and diagnosis. In comparisons with previous methods at various loss rates, the proposed technique is shown to be promising due to its good performance in the case of a loss of isolated and continuous blocks.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.92-98
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• 1994

The hypercube-like interconncetion network,X-hypercubes,has the same number of nodes and edges as conventional hypercubes.By slightly changing the interconneton way between nodes,however,X-hypercubes reduces the diameter by almost half.Thus the communication delay in X-hypercubes can be expected to be much lower than that in hypercubes. This paper gives a new definition of X-hypercubes establishing clear-cut condition of connection between two nodes.As appliction examples of the new definition,this paper presents simple embeddings of hypercubes in X-hypercubes and vice versa.This means that any programs written for hypercubes can be transported onto X-hypercubes and vice versa with minimal overhead.This paper also present bitonic merge sort for X-hypercubes by simulation that for hypercubes.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.427-442
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• 2022

MP3 is one of the most widely used file formats for encoding and representing audio data. One of the reasons for this popularity is their significant ability to reduce audio file sizes in comparison to other encoding techniques. Additionally, other reasons also include ease of implementation, its availability and good technical support. Steganography is the art of shielding the communication between two parties from the eyes of attackers. In steganography, a secret message in the form of a copyright mark, concealed communication, or serial number can be embedded in an innocuous file (e.g., computer code, video film, or audio recording), making it impossible for the wrong party to access the hidden message during the exchange of data. This paper describes a new steganography algorithm for encoding secret messages in MP3 audio files using an improved least significant bit (LSB) technique with high embedding capacity. Test results obtained shows that the efficiency of this technique is higher compared to other LSB techniques.