• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권5호
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• pp.462-466
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• 2008

본 논문에서는 동적 애드 혹 네트워크(MA-NET)상에서 효율적인 라우팅을 위해 대표적인 군집단지능 알고리즘인 Ant Colony Optimization 알고리즘에 기반을 둔 정보 속성을 고려한 Data Dependent Swarm Intelligence Routing Algorithm(DSRA)을 제안한다. 제안된 알고리즘은 정보를 Realtime 정보와 Non-Realtime 정보로 분류하여 이 두 가지 속성에 의존적인 전송 알고리즘을 적용함으로써 첫째, Realtime 정보의 지연시간을 감소시켜 보다 효율적인 라우팅 경로를 구성하고 둘째, Non-Realtime 정보와 Realtime 정보의 경로 분산 효과를 통해 전체적인 네트워크의 lifetime을 증대시킨다. AODV[1], DSR[2], AntHocNet[3]과 비교를 통해 지연시간과 lifetime 모두에서 DSRA가 더 나은 성능을 보인다는 것을 실험적으로 확인한다.

• 한국인공지능학회지
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• 제9권1호
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• ETRI Journal
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• 제44권6호
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• ETRI Journal
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• 제45권5호
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• pp.822-835
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• 2023

Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.33-40
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• 2017

Internet of Vehicles (IoV) is a subset of Internet of Things (IoT) and it is an infrastructure for vehicles. Therefore, IoV consists of three main network including inter-vehicle network, intra-vehicle network, and vehicular mobile internet. IoV mainly used in urban traffic environment to provide network access for drivers, passengers and traffic management. Accordingly, many research works have focused on network technology. But, recent concerted efforts in academia and industry point to paradigm shift in IoV system. In this paper, we proposed a knowledge base for intelligence service in IoV. A detailed design and implementation of the proposed knowledged base is illustrated. We hope this work will show power of IoV as a disruptive technology.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권11호
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• 인터넷정보학회논문지
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• 제13권2호
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• pp.109-118
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• 2012

본 논문에서는 워크플로우 협력네트워크 지식의 발견 알고리즘을 제안한다. 즉, 워크플로우 인텔리전스 (또는 비즈니스 프로세스 인텔리전스) 기술은 워크플로우 모델들과 그의 실행이력으로부터 일련의 지식을 발견, 분석, 모니터링 및 제어, 그리고 예측하는 세부기법들로 구성되는데, 본 논문에서는 워크플로우 모델을 구성하는 액티버티들과 그들의 수행자들간의 협력네트워크 지식을 "워크 플로우 협력네크워크 지식"라고 정의하고, 그의 발견기법인 정보제어넷(ICN, information control net)기반 워크플로우 협력네트워크 지식 발견 알고리즘을 제안한다. 특히, 제안한 알고리즘의 적용 사례를 통해 특정 워크플로우 모델로부터 해당 워크플로우 협력네트워크 지식을 성공적으로 생성할 수 있음을 증명함으로써 본 논문에서 제안한 알고리즘의 정확성 및 적합성을 검증한다.

• 예술경영연구
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• 제59호
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• pp.5-31
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• 2021

본 연구는 인공지능 시대의 미술창작에 관한 사회적 인식 및 주요 담론을 분석하여, 인공지능 등장에 따른 예술계의 대응 방안을 모색하는 것에 그 목적이 있다. 이에 본 논문은 인공지능을 통한 창작원리와 한계를 개념적으로 이해하고, 예술사회학적 관점을 바탕으로 인공지능 미술창작을 사회적 맥락에서 해석했다. 본고는 인공지능 미술창작 관련 기사 472건을 주요 자료로 삼고 언어 네트워크 분석을 진행하였다. 연구결과, 인공지능 미술창작의 주체에 대한 혼재된 관점이 언어 네트워크상에서 나타났다. 그러나 지식재산권의 인정을 표상하는 단어군집의 지배적 영향력을 미루어보아, 인공지능을 미술창작의 주체로서 간주하는 관점 중심으로 사회적 인식이 형성됨을 포착하였다. 또한 해당 군집과 제도적 지원을 반영하는 군집의 밀접한 관계를 바탕으로 인공지능 미술에 대한 핵심 담론이 기술 발전과 법적 체제 정비에 한정되어 있음을 확인하였다. 이에, 본 연구는 매체로서의 인공지능의 규정 및 장르로서의 인공지능 미술에 대한 정책적 담론 형성의 필요성을 시사한다.