• International Journal of Control, Automation, and Systems
• /
• 제3권4호
• /
• pp.509-523
• /
• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• Journal of Information Processing Systems
• /
• 제18권6호
• /
• pp.719-728
• /
• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• 한국전자통신학회논문지
• /
• 제16권5호
• /
• pp.859-866
• /
• 2021

영농형 태양광 발전은 농지 상부에 태양광 발전 설비를 설치하는 방식으로 농작물과 전기를 동시에 생산함으로써 농가 소득을 증대시키는 새로운 모델이다. 최근 영농형 태양광 발전을 활용하는 다양한 시도들이 이루어지고 있다. 영농형 태양광 발전은 기존의 태양광 발전과는 달리 비교적 높은 구조물 상부에 설치하게 되므로 유지 보수가 상대적으로 어렵다는 단점이 있다. 이러한 문제를 해결하기 위해 지능적이고 효율적인 운용 및 진단 기능이 요구된다. 본 논문에서는 영농형 태양광 발전 설비의 전력 생산량을 수집, 저장하여 지능적인 예측 모델을 구현하기 위한 예측 및 진단 시스템의 설계 및 구현에 대해 논한다. 제안된 시스템은 태양광 발전량과 환경 센서 데이터를 기반으로 발전량을 예측하여 설비의 이상 유무를 판별하며 설비의 노화 정도를 산출하여 사용자에게 제공한다.

• International Journal of Computer Science & Network Security
• /
• 제21권4호
• /
• pp.65-74
• /
• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• 전자통신동향분석
• /
• 제34권1호
• /
• pp.111-122
• /
• 2019

As society develops, the demand for safety and security services increases. Developed nations such as the United States use advanced technology to lower crime rate and promote intelligent security services. First, this article examines intelligent systems that are used for monitoring and detecting crimes and dangerous situations. Recently, we have been studying technologies that enable preemptive responses through prediction of crime and hazardous situations. In this paper, we examine the cases of security services based on a crime/risk prediction model and explain the structure and major technologies of an intelligent security system. In addition, we propose a direction for technological development for achieving future security services.

• 지능정보연구
• /
• 제28권1호
• /
• pp.217-242
• /
• 2022

AI 기술이 결합된 지능형 제품은 기술적 차별화를 실현하며 시장 경쟁력을 높일 수 있는 잠재성을 지닌다. 하지만 시장 수용도를 극대화 할 수 있는 AI 기반의 신제품 개발 방법론은 부재하다. 본 연구는 AI 기반의 지능형 제품 개발에 대한 방법론으로서 KANO-QFD 통합 모델을 제안한다. 실증적인 분석을 위한 구체적 사례로 탈모 예측 및 치료 기기에 대한 소비자 요구조건(Customer Requirements)의 유형을 분류하고, 이를 구현하기 위한 기술적 요구사항(Engineering Characteristics)의 상대적 중요도 및 우선순위를 도출하여 지능형 메디컬 신제품 개발의 방향을 제시하였다. 소비자 130명을 대상으로 실시한 설문조사 분석 결과, KANO 카테고리 중 매력적 품질(Attractive Quality) 요소로 미래 탈모 진행 상황에 대한 정확한 예측, 미래 탈모 모습 및 치료 후 개선된 미래 모습을 실물화하여 스마트폰으로 보고, 세련된 디자인, 레이저와 LED 빛 복합 에너지를 이용한 치료 등이 도출되었다. QFD의 품질의 집(House of Quality)을 기반으로 분석한 결과, 탈모 진단 및 예측을 위한 학습 데이터, 두피 스캔용 Micro 카메라 해상도, 탈모 유형 분류 모델, 맞춤화를 위한 개인별 계정 관리, 탈모 진행상황 진단 모델 순으로 상대적 중요도 및 우선순위가 도출되었다. 본 연구는 기존에 선행되지 않았던 AI 기반의 지능형 메디컬 제품 개발에 대한 방향을 제시하였다는 면에서 의의를 지닌다.

• 한국지능시스템학회논문지
• /
• 제11권4호
• /
• pp.336-339
• /
• 2001

오존농도 메커니즘은 매우 복잡하고, 비선형성과 비정상성이 강하기 때문에 오존 예보시스템들은 많은 문제점을 가지고 있다. 특히 고농도 오존에 있어서 예측결과들이 성능이 좋지 않다. 본 논문은 뉴로-퍼지기법과 퍼지 클러스터링을 이용한 오존 예측시스템의 모델링 방법을 설명하고자 한다. GMDH의 전형적인 알고리즘에 기초한 동적 다항식 신경망은 데이터 분석, 비선형적이고 복잡한 시스템의 검증 그리고 동적 시스템의 예측을 위한 유용한 방법이다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.67-70
• /
• 1998

There were many cases to apply artificial intelligence to medicine. In this paper, we present the prediction model of the development of the NIDDM(noninsulin-dependent diabetes mellitus). It is not difficult that doctor diagnose patient as DM(diabetes mellitus). However NIDDM is usually developmented later on 40 years old and symptom appeares gradually. So screening test or prediction model is needed absolutely. Our model predicts development of NIDDM with still normal data 2 year ago. Prediction models developed are both MLP(multilayer perceptron) with backpropagation training and RBFN(radial basis function network). Performance of both models were evaluated with likelihood ratio. MLP was about two and RBFN was about three. We expect that models developed can prevent development of DM and utilize normal data.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제9권4호
• /
• pp.327-332
• /
• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
• /
• pp.1013-1016
• /
• 1993

This paper describes a new type of neuron model, the inputs of which are interfered with one another. It has a high mapping ability with only single unit. The learning speed is considerably improved compared with the conventional linear type neural networks. The proposed neuron model was successfully applied to the prediction problem of chaotic time series signal.