공항 출입국관리 구역 내에서 발생할 수 있는 다수의 움직이는 사람의 신원을 자동으로 식별하고, 위험 상황을 실시간으로 탐지하는 인공지능 시스템 구축,실증, 검증 지원 및 산업 육성 과제 인공지능(AI) 식별, 추적 시스템의 공항 출입국관리 실증 적용을 위한 자체 학습 데이터 가공 및 저작

Marker Beacon(마커 비컨) 은 항공기가 계기접근 절차 중 착륙을 위해 하강하는 과정에서, 지상에 설치된 특정 지점을 통과했음을 알려주는 무선 신호장치로, 항공기의 현재 위치를 확인하고 접근 정확도를 높이기 위한 보조 항행시설입니다.공항식별자, 공항명, 식별자 ,마커비콘명 ,좌표에 대한 정보를 제공하고 있습니다.식별자 : 마커 비컨 장비를 식별하기 위한 고유 코드입니다.좌표 표기 방식은 다음과 같습니다N (North): 북위를 의미하며, 위도 정보를 나타냅니다.E (East): 동경을 의미하며, 경도 정보를 나타냅니다.

Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation framework augmented with model-based fault identification that are applied to spacecraft power distribution systems. Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe the implementation of the two algorithms and compare their diagnosis results on a representative spacecraft power distribution system.

The databases of computational and experimental data from the first Aeroelastic Prediction Workshop are located here. The databases file names tell their contents by configuration, angle of attack, Mach number and Reynolds number where necessary. The experimental data sets are in files with _X in the name. Files without an _X are computational results. These are the files updated to include data received as of Sept 11, 2013. (JH)

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions. **Reference:** B. W. Ricks and O. J. Mengshoel, "Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study." In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 – October 1, 2009. **BibTex Reference:** @inproceedings{ricks09methods, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study}, booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)}, address = {San Diego, CA}, month = sep, year = {2009} }

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:,

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:,

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:,

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:,