• Journal of Communications and Networks
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• 제14권1호
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• pp.75-90
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• 2012

Thin client computing trades local processing for network bandwidth consumption by offloading application logic to remote servers. User input and display updates are exchanged between client and server through a thin client protocol. On wireless devices, the thin client protocol traffic can lead to a significantly higher power consumption of the radio interface. In this article, a cross-layer framework is presented that transitions the wireless network interface card (WNIC) to the energy-conserving sleep mode when no traffic from the server is expected. The approach is validated for different wireless channel conditions, such as path loss and available bandwidth, as well as for different network roundtrip time values. Using this cross-layer algorithm for sample scenario with a remote text editor, and through experiments based on actual user traces, a reduction of the WNIC energy consumption of up to 36.82% is obtained, without degrading the application's reactivity.

• 한국정보처리학회논문지
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• 제7권2호
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• pp.536-541
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• 2000

터보코드는 인터리버의 크기가 클수록 반복 복호횟수가 많을수록 복호 성능은 우수하지만 시스템이 복잡해져 한 개의 정보비트를 복호할 때 많은 시간지연을 발생시켜 실시간 통신에는 부적합하다는 단점이 있다. 따라서 본 논문에서는 터보코드 부$\cdot$복호기에 사용되는 인터리버의 크기를 감소시켜 한 개의 정보비트를 복호할 때 소요되는 시간지연을 줄이는 새로운 세미 랜덤(Semi-Random) 인터리버 알고리즘을 제안하였다. 세미 랜덤 인터리버 알고리즘은 입력 데이터 길이의 1/2 크기만큼 인터리버를 구성하고, 인터리버 내에 쓸때는 블록 인터리버처럼 행으로 쓰고, 읽을 때는 랜덤하게 읽음과 동시에 다음 데이터가 그 주소 번지에 위치하게 된다. 따라서 기존의 블록, 대각, 랜덤 인터리버와 알고리즘의 복잡도를 비교할 시 그 복잡도를 1/2로 감소시킬 수 있게 된다.

• 대한원격탐사학회지
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• 제22권5호
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• 디지털융복합연구
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• 제19권5호
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• pp.31-39
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• 2021

본 연구는 국내외 자율주행자동차 연구개발과정에서 나타나고 있는 다양한 이슈를 파악하기 위해 자율주행자동차 연구개발 관련 영문 뉴스 기사 1,870개를 수집하고 데이터 전처리 과정을 거쳐 토픽 모델링을 수행하였다. 토픽모델링 결과 20개의 토픽을 추출하였으며, 토픽에 대한 명명작업을 수행하고 의미를 해석하였다. 도출된 토픽을 투입, 활동, 산출, 성과의 연구개발과정에 대응시켜 자율주행자동차 연구개발사업 논리모형을 제시하였다. 본 연구의 분석결과는 국내외 자율주행자동차 연구개발사업의 추진 상황을 정확하게 판단하고 빠르게 변화하고 있는 기술개발에 대비할 수 있는 기초자료로 활용할 수 있을 것이다.

• ETRI Journal
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• 제43권2호
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• pp.299-312
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• 2021

It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep-learning-based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high-quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine-tuned a pretrained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence-transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end-to-end learning by disallowing any use of hand-crafted or dependency-parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.

• 대한한의학원전학회지
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• 제32권1호
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• pp.171-189
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• 2019

Objectives : This study analyzes herbs combinations and primary virtues that constitute the prescriptions of Gyeji-tang, Mahwang-tang, and Daecheongryong-tang to effectively analyze medication preparations described in "Shanghanlun" using database, and test the results of the data structure and effectiveness of queries. Methods : This study enters "Sanghanjapbyeongronyeonggudaeseong" as the original text into the database, and even uses one provision as independent knowledge by dividing provisions in accordance with the content. Results : Five tables and 12 queries were created and used for data input and analysis. Using the types of Herbs included in the prescriptions, this study manages to search for the same or supplemented prescriptions, and the primary virtues of the prescriptions were collected and compared. Conclusions : Subject to the combination of herbs and pathology for the prescriptions described in "Shanghanlun" utilizing the database, this study found relevant texts and combinations of herbs and pathologies, and using this, the time required for theoretical research between prescriptions could be shortened.

• 정보보호학회논문지
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• 제22권3호
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• pp.485-494
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• 2012

HIGHT는 국내에서 개발된 초경량 블록 암호로서, 정보통신단체(TTA) 표준과 국제표준화기구(ISO/IEC) 18033-3 표준으로 제정되었다. 본 논문에서는 블록암호 HIGHT에 대한 차분 오류 주입 공격을 제안한다. 제안하는 공격에서 공격자는 암호화 과정에서 라운드 28의 입력값에 임의의 1-바이트 오류를 주입할 수 있다고 가정한다. 이러한 가정에서 오류 주입을 통해 얻어진 암호문과 정상적으로 얻어진 암호문의 차분 특성을 이용하여 비밀키를 복구한다. 12개의 오류를 주입할 경우에는 88%의 성공 확률, 7개의 오류를 주입하는 경우에는 51%의 성공 확률로 수초내에 HIGHT의 비밀키를 복구한다.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.79-85
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• 2021

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of "numbers" and secondly, the recognition rate of "Ga Na Da Hangul" are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1114-1121
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• 2021

In this paper, we implement a recipe recommendation system using ingredient harmonization analysis based on recipe data. The proposed system receives an image of a food ingredient purchase receipt to recommend ingredients and recipes to the user. Moreover, it performs preprocessing of the receipt images and text extraction using the OCR algorithm. The proposed system can recommend recipes based on the combined data of ingredients. It collects recipe data to calculate the combination for each food ingredient and extracts the food ingredients of the collected recipe as training data. And then, it acquires vector data by learning with a natural language processing algorithm. Moreover, it can recommend recipes based on ingredients with high similarity. Also, the proposed system can recommend recipes using replaceable ingredients to improve the accuracy of the result through preprocessing and postprocessing. For our evaluation, we created a random input dataset to evaluate the proposed recipe recommendation system's performance and calculated the accuracy for each algorithm. As a result of performance evaluation, the accuracy of the Word2Vec algorithm was the highest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.