• Journal of Digital Convergence
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• v.12 no.1
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• pp.555-561
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• 2014

Artificial intelligence in the game is mainly used to determine patterns of behavior of NPC (Non Player Character) and the enemy, path finding. These artificial intelligence is implemented by FSM (Finite State Machine) or Flocking method. The number of NPC behavior in FSM method is limited by the number of FSM states. If the number of states is too small, then NPC player can know the behavior patterns easily. On the other hand, too many implementation cases make it complicated. The NPC behaviors in Flocking method are determined by the leader's decision. Therefore, players can know easily direction of movement patterns or attack pattern of NPCs. To overcome these problem, this paper proposes agonistic behaviors(attacks, threats, showing courtesy, avoidance, submission)in animals to apply for the NPC, and implements agonistic behaviors using Unity3D engine. This paper can help developing a real sense of the NPC artificial intelligence.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.597-598
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• 2016

행동 인식을 위한 기존의 심층신경망은 행동 패턴 모델링과 행동 인식 성능 향상에 큰 기여를 하였다. 그러나 이 신경망은 영상 전체를 하나의 행동 인식 대상으로 보기 때문에 다중 객체의 개별적인 행동 인식에는 한계가 있다. 이에 본 논문에서는 R-CNN과 LSTM을 융합한 RC-LSTM 심층신경망을 통해 다중 객체의 행동 인식을 위한 방법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.598-600
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• 2001

본 논문에서는 양상 뮤 논리를 위한 속성 명세 패턴 연구를 통해 시제 논리에 대한 패턴 기반의 단일한 프레임워크를 제시한다. 본 연구에서는 Dwyer의 속성 명세 패턴 분류를 상태(S)와 행동(A)으로 세분화하고 이를 다시 강함(A)와 약함(E)으로 다시 세분했다. 이러한 의미 기반의 계층적 패턴 분류 체계를 통해 양상 뮤 논리의 속성 명세 패턴을 분석했으며 실제 모형 검사기에서 사용된 예제들의 패턴 분류에 적용했다. 그 결과 기존의 분류 체계보다 더 정확한 분류가 가능했을 뿐만 아니라, 속성 명세의 작성 및 이해가 용이하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.535-542
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• 2017

The ubiquitous computing made it happen to easily take cognizance of context, which includes user's location, status, behavior patterns and surrounding places. And it allows providing the catered service, designed to improve the quality and the interaction between the provider and its customers. The personalized recommendation service needs to obtain logical reasoning to interpret the context information based on user's interests. We researched a model that connects to the practical value to users for their daily life; information about restaurants, based on several mobile contexts that conveys the weather, time, day and location information. We also have made various approaches including the accurate rating data review, the equation of Naïve Bayes to infer user's behavior-patterns, and the recommendable places pre-selected by preference predictive algorithm. This paper joins a vibrant conversation to demonstrate the excellence of this approach that may prevail other previous rating method systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1361-1364
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• 2015

본 연구에서는 노인들이 일상에서 많이 행동하는 7가지 유형의 행동의 특징을 추출하고, 총 7가지 분류 알고리즘에 적용하여 가장 인식률이 높은 알고리즘을 도출하고자 하였다. 행동패턴은 정상보행, 절름발이, 지팡이, 느린 보행, 허리가 굽은 상태에서 보행, 스스로 휠체어 끌 때 그리고 누군가가 휠체어를 끌어줄 때 총 7가지로 구성하였다. 행동패턴의 특징은 3축 가속도 센서의 값, 평균, 표준편차, 수직 및 수평축의 데이터를 사용하였다. 분류 알고리즘은 Naive Bayes, Bayes Net, k-NN, SVM, Decision Tree, Multilayer perception, Logistic regression을 사용하였다. 연구결과 k-NN 알고리즘의 인식률이 98.7%로 다른 분류알고리즘에 비해 인식률이 높게 나타났다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.190-195
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• 2007

As Ubiquitous era comes, it became necessary to construct environment which can provide more useful information to human in the spaces where people live like homes or offices. On this account, network of the peripheral devices of Ubiquitous should constitute efficiently. For it, this paper researched human pattern by classified motion recognition using sensors module data. (This data processing by Neural network and fuzzy algorithm.) This pattern classification can help control home network system communication. I suggest the system which can control home network system more easily through patterned movement, and control Ubiquitous environment by grasp human's movement and condition.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.2
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• pp.106-112
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• 2007

Recently the crowd environment in the department of the animation is necessary to the digital industry. The goal of researching a proper crowd animation is to design character animation that is defined by the reality of scenes, performance of system and interaction with users to show the crowd vividly and effectively in cyber underwater. It is important to set up the crowd behavior patterns to represent for moving crowd naturally in cyber space. In the paper, we expressed the behavior patterns for flocks of fish in cyber underwater, and compared with the number of mesh, the number of fish, the number of frame, elapsed time, and resolution and analyzed them with the fish behavior simulating system.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.945-952
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• 2001

In consequence of expansion of internet users, electronic commerce is becoming a new prototype for marketing and sales, arid most of electronic commerce sites or internet shopping malls provide a rich source of information and convenient user interfaces about the organizations customers to maintain their patrons. One of the convenient interfaces for users is service to recommend products. To do this, they must exploit methods to extract and analysis specific patterns from purchasing information, behavior and market basket about customers. The methods are association rules and sequential patterns, which are widely used to extract correlation among products, and in most of on-line electronic commerce sites are executed with users information and purchased history by category-oriented. But these can't represent the diverse correlation among products and also hardly reflect users' buying patterns precisely, since the results are simple set of relations for single purchased pattern. In this paper, we propose an efficient mining technique, which allows for multiple purchased patterns that are category-independent and have relationship among items in the linked structure of single pattern items.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.245-250
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• 2008

최근 동작 및 행동 인식에 대한 연구가 활발하다. 특히, 센서가 소형화되고 저렴해지면서 그 활용을 위한 관심이 증가하고 있다. 기존의 많은 행동 인식 연구에서 사용되어 온 정적 분류 기술 기반 동작 인식 방법은 연속적인 데이터 분류 기술에 비해 유연성 및 활용성이 부족할 수 있다. 본 논문에서는 연속적인 데이터의 패턴 분류 및 인식에 효과적인 확률적 추론 기법인 은닉 마르코프 모델(Hidden Markov Model)과 사전 지식 없이도 자동 학습이 가능하며 의미 깊은 궤적 패턴을 클러스터링하고 효과적인 양자화가 가능한 자기구성지도(Self Organizing Map)를 이용한 동작 인식 기술을 소개한다. 또한, 그 유용성을 입증하기 위해 실제 가속도 센서를 이용하여 다양한 동작에 대한 데이터를 수집하고 분류 성능을 분석 및 평가한다. 실험에서는 실제 가속도 센서를 통해 수집된 숫자를 그리는 동작의 성능 평가 결과를 보이고, 행동 인식기 별 성능과 전체 인식기별 성능을 비교한다.