• 한국측량학회지
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• 제39권5호
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• 정보보호학회논문지
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• 제18권3호
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• pp.3-8
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• 2008

세션상태 정보(session-state information)가 안전하지 않은 메모리에 저장되거나 또는 랜덤 난수 생성기 (random number generator)가 공격자에 의해 제어된다면 특정 세션에만 사용되는 난수 값과 같은 임시적인 데이터(ephemeral data)는 쉽게 노출될 수 있다. 본 논문에서는 Bresson과 그 외의 그룹 키 교환 스킴을 개선한Nam과 그 외의 그룹 키 교환 스킴이 세션상태 정보노출 공격에 안전하지 않음을 보인다. 그리고 이러한 안전성의 결함을 보완한 개선된 스킴을 제안한다.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.50-56
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• 2022

In order to process a vast amount of data, there is demand for a new system with higher processing speed and lower energy consumption. To prevent 'memory wall' in von Neumann architecture, RRAM, which is a neuromorphic device, has been researched. In this paper, we summarize the features of RRAM and propose the device structure for characteristic improvement. RRAM operates as a synapse device using a change of resistance. In general, the resistance characteristics of RRAM are nonlinear and random. As synapse device, linearity and uniformity improvement of RRAM is important to improve learning recognition rate because high linearity and uniformity characteristics can achieve high recognition rate. There are many method, such as TEL, barrier layer, NC, high oxidation properties, to improve linearity and uniformity. We proposed a new device structure of TiN/Al doped TaO_x/AlO_x/Pt that will achieve high recognition rate. Also, with simulation, we prove that the improved properties show a high learning recognition rate.

• Journal of Yeungnam Medical Science
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• 제14권1호
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• pp.220-226
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• 1997

저자들은 경련을 주소로 내원한 특발성 부갑상선 기능저하증에서 뇌기저핵, 소뇌, 뇌실 주위의 광범위한 석회침착을 보인 환자를 치험하여 방사선학적 뇌단층촬영상 특징적 소견과 그 병태생리 및 임상증상과의 연관관계를 문헌고찰과 함께 보고 한다.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.49-56
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• 2016

Increasing Internet traffic and forwarding table size need high performance IP address lookup engine which is a crucial function of routers. For finding the longest matching prefix, trie-based or its variant schemes have been widely researched in software-based IP lookup. As a software router, we enhance the IP address lookup engine using GPU which is a device widely used in high performance applications. We propose a data structure for multibit trie to exploit GPU hardware efficiently. Also, we devise a novel scheme that the root subtrie is loaded on Shared Memory which is specialized for fast access in GPU. Since the root subtrie is accessed on every IP address lookup, its fast access improves the lookup performance. By means of the performance evaluation, our implemented GPU-based lookup engine shows 17~23 times better performance than CPU-based engine. Also, the fast access technique for the root subtrie gives 10% more improvement.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.394-396
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• 2004

CE 제품에 리얼타임 OS를 도입하면서, 제품의 조건을 만족시키기 위한 기술에 대해 많은 연구가 진행되고 있다. 특히, CE 제품에 있어서 중요한 이슈인 부팅 시간은 펌웨어수준과 비교했을 때 코드사이즈나 OS 초기화 과정 때문에 다소 느려지는 경향을 보이고 있다. 본 논문은 이러한 CE 제품의 부팅 시간에 초점을 맞추고 리얼타임 OS 적용시의 부팅 시간을 개선하였다. 구현에 사용된 ARM920T Core는 32-비트 RISC 구조이며, 각 16KB의 인스트럭션 Cache와 데이터 Cache, 그리고 MMU(Memory Management Unit)로 구성되어 있으며, 리얼타임 OS는 선점형 방식의 커널로 구성된 OS를 사용하였다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2014년도 추계학술대회
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• pp.10-11
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• 2014

In a 2-dimensional (2D) Discrete Cosine Transform (DCT) hardware, a significant fraction of the total hardware area is contributed by the combinational logic used to perform 1-dimensional (2D) transform. The size of the non-combinational logic i.e. the transpose memory is dictated by the size of the largest transform supported. Hence, the optimization of hardware area is performed mainly for 1D-transform combinational logic. This paper demonstrates the use of Multiple Constant Multiplication (MCM) algorithm to reduce the combinational logic area. Partial optimizations are also described for the cases where the direct use of MCM algorithm doesn't meet the timing constraint. Experimental results show that 46% improvement in gate count is achieved for 32 point 1D DCT transform logic after using MCM optimization.

• International Journal of Internet, Broadcasting and Communication
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• 제5권2호
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• pp.5-10
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• 2013

We proposed a novel similarity measure using weighted difference entropy (WDE) to improve the performance of the CF system. The proposed similarity metric evaluates the entropy with a preference score difference between the common rated items of two users, and normalizes it based on the Gaussian, tanh and sigmoid function. We showed significant improvement of experimental results and environments. These experiments involved changing the number of nearest neighborhoods, and we presented experimental results for two data sets with different characteristics, and results for the quality of recommendation.