• 제목/요약/키워드: Sequential effectiveness

검색결과 181건 처리시간 0.027초

Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

  • Siva S;Shilpa Chaudhari
    • Journal of information and communication convergence engineering
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    • 제22권1호
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    • pp.44-55
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    • 2024
  • High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

결합 조정에 기반한 연속 항공삼각측량 알고리즘 (A Sequential AT Algorithm based on Combined Adjustment)

  • 최경아;이임평
    • 한국측량학회지
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    • 제27권6호
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    • pp.669-678
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    • 2009
  • 멀티센서시스템으로부터 획득된 영상으로부터 공간정보를 신속하게 생성하기 위하여 영상의 실시간 절대좌표화가 요구된다. 이에 본 연구에서는 결합 조정(combined adjustment) 모델에 기반하여 연속(sequential) 조정 알고리즘을 유도하고 이를 항공삼각측량(Aerial Triangulation : AT)에 적용하여 시스템으로부터 획득되는 영상을 실시간으로 절대좌표화하기 위한 연속 AT 알고리즘을 제안한다. 제안된 방법을 통해 새로운 영상이 획득될 때마다 이전 단계의 AT 수행 결과를 최대한 활용하고 최소한의 추가적인 연산을 통해 AT를 신속하게 다시 수행할 수 있다. 시뮬레이션 데이터를 이용하여 검증한 결과 영상 1장이 추가될 때마다 매우 짧은 연산 시간을 통해, 기존의 일괄(simultaneous) AT 결과와 비교하여 지상좌표값을 기준으로 ${\pm}4cm$ 이내의 정확도를 확보할 수 있었다.

위성궤도의 추정기법에 관한 연구

  • 최철환;조겸래;박수홍
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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    • pp.65-70
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    • 1989
  • Lately, at an epock of a full-scale satellite launching plan of Korea, T.T.C(Tracking, Telemetery & Command) is a indispensable part. In this paper, particular attention is given to orbit determination problem of the role of T.T.C. A near-earth satellite is modeled, batch and extended sequential estimation algorithm (ESEA) are compared using range data. As a result, ESEA show effectiveness.

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홴형상 막냉각홀의 신경회로망 기법을 이용한 최적설계 (Design Optimization of a Fan-Shaped Film-Cooling Hole Using a Radial Basis Neural Network Technique)

  • 이기돈;김광용
    • 한국유체기계학회 논문집
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    • 제12권4호
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    • pp.44-53
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    • 2009
  • Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.

Elastic modulus in large concrete structures by a sequential hypothesis testing procedure applied to impulse method data

  • Antonaci, Paola;Bocca, Pietro G.;Sellone, Fabrizio
    • Structural Engineering and Mechanics
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    • 제26권5호
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    • pp.499-516
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    • 2007
  • An experimental method denoted as Impulse Method is proposed as a cost-effective non-destructive technique for the on-site evaluation of concrete elastic modulus in existing structures: on the basis of Hertz's quasi-static theory of elastic impact and with the aid of a simple portable testing equipment, it makes it possible to collect series of local measurements of the elastic modulus in an easy way and in a very short time. A Hypothesis Testing procedure is developed in order to provide a statistical tool for processing the data collected by means of the Impulse Method and assessing the possible occurrence of significant variations in the elastic modulus without exceeding some prescribed error probabilities. It is based on a particular formulation of the renowned sequential probability ratio test and reveals to be optimal with respect to the error probabilities and the required number of observations, thus further improving the time-effectiveness of the Impulse Method. The results of an experimental investigation on different types of plain concrete prove the validity of the Impulse Method in estimating the unknown value of the elastic modulus and attest the effectiveness of the proposed Hypothesis Testing procedure in identifying significant variations in the elastic modulus.

Efficacy of Using Sequential Primary Circulating Prostate Cell Detection for Initial Prostate Biopsy in Men Suspected of Prostate Cancer

  • Murray, Nigel P;Reyes, Eduardo;Fuentealba, Cynthia;Jacob, Omar
    • Asian Pacific Journal of Cancer Prevention
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    • 제17권7호
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    • pp.3385-3390
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    • 2016
  • Background: Sequential use of circulating prostate cell (CPC) detection has been reported to potentially decrease the number of unnecessary prostate biopsies in men suspected of prostate cancer. In order to determine the real world effectiveness of the test, we present a prospective study of men referred to two hospitals from primary care physicians, one using CPC detection to determine the necessity of prostate biopsy the other not doing so. Materials and Methods: Men with a suspicion of prostate cancer because of elevated PSA >4.0ng/ml or abnormal DRE were referred to Hospitals A or B. In Hospital A all underwent 12 core TRUS biopsy, in Hospital B only men CPC (+), with mononuclear cells obtained by differential gel centrifugation identified using double immunomarking with anti-PSA and anti-P504S, were recommended to undergo TRUS biopsy. Biopsies were classifed as cancer or no-cancer. Diagnostic yields were calculated, including the number of posible biopsies that could be avoided and the number of clinically significant cancers that would be missed. Results: Totals of 649 men attended Hospital A, and 552 men attended Hospital B; there were no significant differences in age or serum PSA levels. In Hospital A, 228 (35.1%) men had prostate cancer detected, CPC detection had a sensitivity of 80.7%, a specificity of 88.6%, and a negative predictive value of 89.5%. Some 39/44 men CPC negative with a positive biopsy had low grade small volume tumors. In Hospital B, 316 (57.2%) underwent biopsy. There were no significant differences between populations in terms of CPC and biopsy results. The reduction in the number of biopsies was 40%. Conclusions: The use of sequential CPC testing in the real world gives a clear decision structure for patient management and can reduce the number of biopsies considerably.

대용량 순차 데이터베이스에서 근사 순차패턴 탐색 (Mining Approximate Sequential Patterns in a Large Sequence Database)

  • 금혜정;장중혁
    • 정보처리학회논문지D
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    • 제13D권2호
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    • pp.199-206
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    • 2006
  • 순차패턴 탐색은 다양한 응용 분야에서 매우 중요한 데이터 마이닝 작업으로 간주된다. 그러나 기존의 순차패턴 탐색 방법들은 길이가 긴 순차패턴이나 노이즈 정보를 다수 포함한 데이터베이스에 대한 마이닝에서는 한계가 있다. 해당 방법들은 매우 짧고 사소한 패턴들은 탐색하지만 다수의 순차 정보들에서 공유되는 중요 패턴들을 분석하는데 어려움을 겪는다. 본 논문에서는 이러한 문제를 해결하기 위한 방법으로 대용량 데이터베이스에 대한 근사 순차패턴 탐색 방법을 제안한다. 근사 순차패턴은 다수의 순차 정보들에서 근사적으로 공유되는 순차패턴을 의미한다. 제안된 방법은 두 과정으로 구분된다. 하나는 유사도에 따라 분석 대상 순차 정보들을 몇 개의 군집으로 나누는 과정이며, 다른 하나는 다중 정렬 방식을 적용하여 각 군집으로부터 대표 패턴을 찾는 과정이다. 이를 위해서 다수의 순차 정보들을 하나로 표현할 수 있는 가중치 순차패턴을 제시하며, 다수의 순차 정보들은 가중치 순차패턴 형태로 통합된다. 이렇게 통합된 정보를 가진 각 가중치 순차패턴을 이용하여 여러 순차 정보와 근사한 하나의 대표 패턴을 생성한다. 끝으로, 다양한 실험을 통해서 제안된 방법의 유용성을 검증한다.

장소 추천을 위한 방문 간격 보정 (Temporal Interval Refinement for Point-of-Interest Recommendation)

  • 김민석;이재길
    • 데이타베이스연구회지:데이타베이스연구
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    • 제34권3호
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    • pp.86-98
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    • 2018
  • 장소추천시스템은 시간과 장소가 주어졌을 때, 사용자에게 가장 흥미로운 장소를 추천해주는 시스템을 말한다. 스마트폰과 사물인터넷(IoT), 장소기반 소셜네트워크(LBSN)의 발달에 힘입어 사용자들의 방대한 양의 장소 방문 데이터를 축적하게 되었고, 이를 통해 특정한 시점에 사용자들이 원하는 장소를 적절히 추천해줄 수 있는 장소추천시스템의 중요성이 부각되었다. 장소추천시스템은 사용자의 방문(Check-in) 횟수라는 암시적 피드백(Implicit feedback) 데이터에서 사용자의 시퀀스 선호(Sequential preference)를 이끌어내어 높은 성능을 내기 위한 연구들이 제안되었다. 하지만 시퀀스 선호 정보를 활용하여 모델을 구성하는 경우, 데이터의 밀도가 더욱 희박해지고 이에 따라 적은 수의 데이터에 기반하여 구축되는 모델의 성능이 왜곡될 가능성이 존재한다. 본 연구에서는 신뢰도(Confidence)에 기반하여 방문 주기를 보정하는 방법론을 제안한다. 사용자의 시퀀스 선호 정보로부터 도출된 장소 간 방문 시간전이간격(temporal transition interval)을 활용하여 추천시스템을 구성할 때, 해당 방법론을 통하여 데이터의 왜곡을 보정함으로써 추천시스템의 성능을 향상하였다. 제안하는 방법의 효과를 검증하기 위하여, Foursquare와 Gowalla의 데이터셋을 이용한 비교실험을 통해 제안하는 방법론의 우수성을 보였다.

노이즈 필터링을 적용한 반응표면 기반 순차적 근사 최적화 (Sequential Approximate Optimization Based on a Pure Quadratic Response Surface Method with Noise Filtering)

  • 이용빈;이호준;김민수;최동훈
    • 대한기계학회논문집A
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    • 제29권6호
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    • pp.842-851
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    • 2005
  • In this paper, a new method for constrained optimization of noisy functions is proposed. In approximate optimization using response surface methods, if constraints have severe noise, the approximate feasible region defined by approximate constraints is apt to include some of the infeasible region defined by actual constraints. This can cause the approximate optimum to converge into the infeasible region. In the proposed method, the approximate optimization is performed with the approximate constraints shifted by their deviations, which are calculated using a diagonal quadratic response surface method. This can prevent the approximate optimum from converging into the infeasible region. To fit the objective and constraints into diagonal quadratic models, we select the center and 4 additional points along each axis of design variables as experimental points. The deviation of each function is calculated using the differences between the real and approximate function values at the experimental points. A sequential approximate optimization technique based on the trust region algorithm is adopted to manage approximate models. The proposed approach is validated by solving some design problems. The results of the problems show the effectiveness of the proposed method.

임상적 의사결정지원시스템에서 순차신경망 분류기를 이용한 급성백혈병 분류기법 (Acute Leukemia Classification Using Sequential Neural Network Classifier in Clinical Decision Support System)

  • 임선자;이반빈센트;권기룡;윤성대
    • 한국멀티미디어학회논문지
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    • 제23권2호
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    • pp.174-185
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    • 2020
  • Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only cover the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.