• 한국실내디자인학회논문집
• /
• 제18권5호
• /
• pp.49-58
• /
• 2009

Robert Venturi's theories like 'Complexity and Contradiction in Architecture' and 'Symbolism of Architecture' had a major impact on architects in postmodern culture and we could have his contextual understandings in those theories. In his early books, "Complexity and Contradiction in Architecture" and "Learning from Las Vegas", Robert Venturi showed theories related to context several times. But with looking at existing books or papers, we could barely see well-organized studies about his contextual understandings. So this study shows contextual approaches and thoughts with those theories, 'Complex and contradictory architecture', 'Architectural order and conventional architecture', 'Discontinuity in internal and external architecture', and 'Symbolism of architecture' in his two books. In those four theories, Venturi's contextual understandings are as fellows. To begin with, he developed contextual theories in architecture, understanding a whole building embracing each architectural factor, with architectural thoughts of complexity and contradiction. Second, he stressed architectural order to link each contradictory factor and used conventional architecture, as for existing common and ordinary things, to make available communication. Conventional factors were applied to urban viewpoints. Given the fact that contemporaries shared those factors, we could see contextual understandings in his approach. On top of that, unlike modern architects, he understood that functions of the inside and the outside were two different things. Based on contextual thoughts, he tried applying 'facade' that is one side providing an interface between in and out of a building to surroundings. Last, he wanted to express any meaningful connection between present and past, using symbolism in architecture. Presented by symbolism of architecture, architectural functions, architectural uses, historical meaning, ordinary factors, or something were also based on sharing in contemporary people. As the methodology to show these contextual factors, Venturi used an approach of symbolism.

• 스마트미디어저널
• /
• 제11권9호
• /
• pp.9-20
• /
• 2022

심층강화학습은 자율주행 도메인에서 널리 사용되는 end-to-end 데이터 기반 제어 방법이다. 그러나 기존의 강화학습 접근 방식은 자율주행 과제에 적용하기에는 비효율성, 불안정성, 불확실성 등의 문제로 어려움이 존재한다. 이러한 문제들은 자율주행 도메인에서 중요하게 작용한다. 최근의 연구들은 이런 문제를 해결하고자 많은 시도가 이루어지고 있지만 계산 비용이 많고 특별한 가정에 의존한다. 본 논문에서는 자율주행 도메인에 불확실성 순차 모델링이라는 방법을 도입하여 비효율성, 불안정성, 불확실성을 모두 고려한 새로운 알고리즘 MCDT를 제안한다. 강화학습을 높은 보상을 얻기 위한 의사 결정 생성 문제로 바라보는 순차 모델링 방식은 기존 연구의 단점을 회피하고 효율성과 안정성을 보장하며, 여기에 불확실성 추정 기법을 융합해 안전성까지 고려한다. 제안 방법은 OpenAI Gym CarRacing 환경을 통해 실험하였고 실험 결과는 MCDT 알고리즘이 기존의 강화학습 방법에 비해 효율적이고 안정적이며 안전한 성능을 내는 것을 보인다.

• 공학교육연구
• /
• 제19권3호
• /
• pp.23-34
• /
• 2016

Teaching orientations represent teachers' general way of conceptualizing their teaching. The orientations are regarded as a very important factor in developing teachers' pedagogical content knowledge because they often guide their instructional decisions such as the selection of contents and teaching strategies, the use of curricula materials, and the evaluation of learning. Thus, understanding teachers' orientations can provide meaningful suggestions to understand their instructional approaches and furthermore to enhance the quality of engineering education in college. The research question for this present study was what kinds of teaching orientations engineering professors possessed in teaching engineering courses and how the orientations were represented in their teaching. Six engineering professors, particularly interested in instructional approaches, participated in the research. The data sources included in-depth interviews with individual professors, classroom observations with field notes, and related documents. In results, four teaching orientations toward engineering courses were identified: 1) expert knowledge in engineering, 2) engineering practice, 3) social practice, and 4) interdisciplinary design. Individual professors had between one to three different teaching orientations. Even though the professors had similar orientations but their instructional strategies somewhat varied based on the disciplines.

• 산경연구논집
• /
• 제7권4호
• /
• pp.11-16
• /
• 2016

Purpose - There are numerous theories for retail trade area analysis which are designed to select candidate locations for new stores. In this study, comparative analysis on the characteristics from those of the theories are shown, and the explanation for the power in consumers' store-choice behaviors and their limitations are examined. Also, plans for improving commercial sphere analysis are explored. Research design, data, and methodology - This study is based on literature reviews with normative research methodology. Among many researches regarding the analysis on the location and commercial sphere for launching a new store, researches relying on statistics are excluded in this study since they belong to the marketing research area,. Results - In the Law of retail gravitation, Huff's model multinomial logit model and etc. are mutual complementary mathematical techniques for analyzing commercial spheres and each of them has its own characteristics. These theories rely on the same hypothesis in which consumers are all believed to be behaving rationally under a similar behavioral system. However, the trial in explaining or estimating behavior of choosing a store with only a select size of the population that is objectively estimated by some major properties has limits in its credibility. Conclusion - Research on consumer's spatial behaviors can be fully illustrative and explainable when it has both quantitative approaches such as 'law of retail gravitation', 'logit model' and etc., and qualitative approaches like consumer's 'cognitive structure', 'learning status', 'image formation', 'attitude' and etc.

• Journal of Communications and Networks
• /
• 제17권6호
• /
• pp.568-581
• /
• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 한국초등수학교육학회지
• /
• 제6권1호
• /
• pp.23-40
• /
• 2002

수학은 계통성이 강하기 때문에 고학년의 수학 학습 부진은 저학년에서의 수 계산 학습 부진에 그 원인을 찾을 수 있다. 가감승제의 기본적인 계산 원리를 이해하지 못한 일부 학생들은 아무리 반복해서 알고리즘 연습을 하더라도 수학 불안으로부터 벗어날 수 없고 따라서 실제 문제 상황에서 방해를 받기 때문이다. 본 연구에서는 영상적(iconic) 표상 활동을 강화차기 위하여 2학년 학생을 대상으로 웨일의 언덕도를 도입하고 그 효과를 알아보았다. 이를 위하여 연구반과 비교반을 선정하고 실험 가설을 적용한 후, 수학에 대한 지필 평가지와 수학에 대한 설문지 조사를 시행한 결과 다음을 알 수 있었다. 첫째, 문장제 해결 능력에서 두 집단 사이에는 의미 있는 차이를 발견할 수 없었다. 그러나 시암산 능력과 추론 능력 면에서는 유의 수준 5%에서 연구반이 비교반보다 우수하였다. 둘째, 언덕도 학습을 통해서 연구반 학생들은 수 계산의 중요성을 의식하고, 계산의 즐거움, 수학에 대한 자신감이 증진되었다.

• International Journal of Computer Science & Network Security
• /
• 제23권9호
• /
• pp.1-7
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
• /
• 제23권8호
• /
• pp.210-216
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Smart Structures and Systems
• /
• 제33권1호
• /
• pp.55-64
• /
• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• 예술인문사회 융합 멀티미디어 논문지
• /
• 제9권11호
• /
• pp.1133-1144
• /
• 2019

최근 스마트 기기의 발전으로 인터넷상에 존재하는 이미지 데이터의 양이 급속하게 증가하는 상황에서 효과적인 이미지 검색을 위한 다양한 방법들이 연구되고 있다. 기존의 이미지 검색 방법들은 이미지에 존재하는 물체들을 단순하게 검출하여 각 물체들의 라벨 정보에 근거한 검색을 수행하기 때문에 사용자가 원하는 이미지와 검색 결과로 얻은 이미지 간에 의미적 차이인 시맨틱 갭(Semantic Gap)이 발생된다. 이미지 검색에서 발생하는 시맨틱 갭을 줄이기 위해, 본 논문에서는 딥러닝 기반의 다중 객체 분류 모듈과 사람의 행위를 분류하는 모듈을 연결하고, 이 모듈들에 행위 온톨로지를 결합하였다. 즉, 딥러닝과 행위 온톨로지의 결합을 기반으로 객체들 간의 연관성을 고려한 이미지 검색 시스템을 제안한다. 이미지에 포함된 동적인 행위를 고려하기 위해 Walking과 Running 데이터를 이용하여 실험한 결과를 분석하였다. 제안한 방법은 향후 이미지 검색 결과의 정확도를 높일 수 있는 영상의 자동 주석 생성 연구에 확장하여 적용할 수 있다.