• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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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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• v.21 no.8
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.3-8
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• 2008

Ephemeral data are easily revealed if state specific information is stored in insecure memory or a random number generator is corrupted. In this letter, we show that Nam et al.'s group key agreement scheme, which is an improvement of Bresson et al.'s scheme, is not secure against session-state reveal attacks. We then propose an improvement to fix the security flaw.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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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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• v.14 no.1
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• pp.220-226
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• 1997

Idiopathic hypoparathyroidism is frequently associated with intracranial calcification and neuropsychiatric abnormalities. The most commonly recognized central nervous system manifestations of chronic hypoparathyroidism are seizure, alteration of mental function and extrapyramidal signs. We present a case of hypoparathyroidism, demonstrating extensive intracranial calcification, not only basal ganglia, but also outside the extrapyramidal system. An 58-year-old woman presented with 30 year history of seizure and memory disturbance. The physical examination and several laboratory studies disclosed normal. However hypocalcemia, hyperphosphatemia with hypocalciuria and decreased parathormone level were demonstrated. Clinical symptoms and signs showed improvement after supplementary calcium and Vit $D_3$ therapy.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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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.

• Proceedings of the KIEE Conference
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• 2004.11c
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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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• 2005.04a
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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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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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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• v.5 no.2
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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.