• Title/Summary/Keyword: complex factorization

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COMPLEX FACTORIZATIONS OF THE GENERALIZED FIBONACCI SEQUENCES {qn}

  • JUN, SANG PYO
    • Korean Journal of Mathematics
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    • v.23 no.3
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    • pp.371-377
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    • 2015
  • In this note, we consider a generalized Fibonacci sequence {$q_n$}. Then give a connection between the sequence {$q_n$} and the Chebyshev polynomials of the second kind $U_n(x)$. With the aid of factorization of Chebyshev polynomials of the second kind $U_n(x)$, we derive the complex factorizations of the sequence {$q_n$}.

Hybrid Approach of Texture and Connected Component Methods for Text Extraction in Complex Images (복잡한 영상 내의 문자영역 추출을 위한 텍스춰와 연결성분 방법의 결합)

  • 정기철
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.175-186
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    • 2004
  • We present a hybrid approach of texture-based method and connected component (CC)-based method for text extraction in complex images. Two primary methods, which are mainly utilized in this area, are sequentially merged for compensating for their weak points. An automatically constructed MLP-based texture classifier can increase recall rates for complex images with small amount of user intervention and without explicit feature extraction. CC-based filtering based on the shape information using NMF enhances the precision rate without affecting overall performance. As a result, a combination of texture and CC-based methods leads to not only robust but also efficient text extraction. We also enhance the processing speed by adopting appropriate region marking methods for each input image category.

Simplification of Linear Time-Invariant Systems by Least Squares Method (최소자승법을 이용한 선형시불변시스템의 간소화)

  • 추연석;문환영
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.5
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    • pp.339-344
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    • 2000
  • This paper is concerned with the simplification of complex linear time-invariant systems. A simple technique is suggested using the well-known least squares method in the frequency domain. Given a high-order transfer function in the s- or z-domain, the squared-gain function corresponding to a low-order model is computed by the least squares method. Then, the low-order transfer function is obtained through the factorization. Three examples are given to illustrate the efficiency of the proposed method.

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A Block-Based Volume Rendering Algorithm Using Shear-Warp factorization (쉬어-왑 분해를 이용한 블록 기반의 볼륨 렌더링 기법)

  • 권성민;김진국;박현욱;나종범
    • Journal of Biomedical Engineering Research
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    • v.21 no.4
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    • pp.433-439
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    • 2000
  • Volume rendering is a powerful tool for visualizing sampled scalar values from 3D data without modeling geometric primitives to the data. The volume rendering can describe the surface-detail of a complex object. Owing to this characteristic. volume rendering has been used to visualize medical data. The size of volume data is usually too big to handle in real time. Recently, various volume rendering algorithms have been proposed in order to reduce the rendering time. However, most of the proposed algorithms are not proper for fast rendering of large non-coded volume data. In this paper, we propose a block-based fast volume rendering algorithm using a shear-warp factorization for non-coded volume data. The algorithm performs volume rendering by using the organ segmentation data as well as block-based 3D volume data, and increases the rendering speed for large non-coded volume data. The proposed algorithm is evaluated by rendering 3D X-ray CT body images and MR head images.

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Exercise Recommendation System Using Deep Neural Collaborative Filtering (신경망 협업 필터링을 이용한 운동 추천시스템)

  • Jung, Wooyong;Kyeong, Chanuk;Lee, Seongwoo;Kim, Soo-Hyun;Sun, Young-Ghyu;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.22 no.6
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    • pp.173-178
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    • 2022
  • Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

Positive and Negative Covariation Mechanism of Multiple Muscle Activities During Human Walking (보행 과정에서 발생하는 복합 근육 활성의 양성 및 음성 공변 메커니즘)

  • Kim, Yushin;Hong, Youngki
    • The Journal of the Korea Contents Association
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    • v.18 no.1
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    • pp.173-184
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    • 2018
  • In human walking, muscle co-contraction which produces simultaneous activities of multiple muscles is important in motor control mechanism of the central nervous system. This study aims to understand positive and negative covariation mechanism of inter-muscle activities during walking. In this study, we measured electromyography (EMG) in leg muscles. To identify motor modules, we recored EMG from 4 leg muscles bilaterally (the tibialis anterior, medial gastrocnemius, rectus femoris and medial hamstring muscles) and performed non-negative matrix factorization (NMF) and principa component analysis (PCA). Then, we computed covariation values from various combinations between muscles or motor modules and used two-way repeated measures analysis of variance to identify significantly different covariation patterns between muscle combinations. As the results, we found significant differences between covariation values of muscle combinations (p < 0.05). muscle groups within the same motor modules produced the positive covariations. However, there were strong negative covariation between motor modules. There was negative covariation in all muscle combination. Stable inter-module negative covariation suggests that motor modules may be the control unit in the complex motor coordination.

Well-Defined series and parallel D-spectra for preparation for linear time-varying systems (선형 시변 시스템에 대한 잘 정의된 (well-defined) 직렬 및 병렬 D-스펙트럼)

  • Zhu, j.jim;Lee, Ho-Cheol;Choe, Jae-Won
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.5
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    • pp.521-528
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    • 1999
  • The nth-order, scalar, linear time-varying (LTV) systems can be dealt with operators on a differential ring. Using this differential algebraic structure and a classical result on differential operator factorizaitons developed by Floquet, a novel eigenstructure(eigenvalues, eigenvectors) concepts for linear time0varying systems are proposed. In this paper, Necessary and sufficient conditions for the existence of well-defined(free of finite-time singularities) SD- and PD- spectra for SPDOs with complex- and real-valued coefficients are also presented. Three numerical examples are presented to illustrate the proposed concepts.

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Performance Evaluation and Numerical Calculation of Flows through a Vaned Diffuser for Centrifugal Compressor (원심압축기용 베인 디퓨저 내부유동의 수치해석 및 성능평가)

  • Choi, Yun-Ho;Kang, Shin-Hyoung;Lee, Jang-Chun
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.23 no.10
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    • pp.1296-1309
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    • 1999
  • A three dimensional compressible Navier-Stokes code is developed to analyze flowfields and performance of a vaned diffuser in a centrifugal compressor. It employs scalar implicit approximate factorization, finite volume formulation, second order upwind differencing and a two-equation $q-{\omega}$ turbulence model based on the integration to the wall. Pressure recovery and loss coefficients of a vaned diffuser are evaluated using a developed computer code. The simulated three dimensional flows show how through flow structure affects pressure recovery performance and loss coefficients of a vane for design and off-design inlet flow angles. Development of complex three dimensional flow over the inlet region and leading edge are very influential to the overall flow and performance.

Recommender system using BERT sentiment analysis (BERT 기반 감성분석을 이용한 추천시스템)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.27 no.2
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    • pp.1-15
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    • 2021
  • If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.