We present a set of novel algorithms which we call sequenceMiner, that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms we present are general and domain-independent, we focus on a specific problem that is critical to determining system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of he longest common subsequence (nLCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from a cluster. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithm provides a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. The final section of the paper demonstrates the effectiveness of sequenceMiner for anomaly detection on a real set of discrete sequence data from a fleet of commercial airliners. We show that sequenceMiner discovers actionable and operationally significant safety events. We also compare our innovations with standard HiddenMarkov Models, and show that our methods are superior

Our primary goal is to automatically analyze textual reports from the Aviation Safety Reporting System (ASRS) database to detect/discover the anomaly categories reported by the pilots, and to assign each report to the appropriate category/categories. We have used two state-of-the-art models for text analysis: (i) mixture of von Mises Fisher (movMF) distributions, and (ii) latent Dirichlet allocation (LDA) on a subset of all ASRS reports. The models achieve a reasonably high performance in discovering anomaly categories and clustering reports. Each category is represented by the most representative words with the highest probability in this category. In addition, since the inference algorithm for LDA was somewhat slow, we have developed a new fast LDA algorithm which is 5-10 times more efficient than the original one, therefore more applicable for the practical use. Further, we have developed a simple visualization tool based on non-linear manifold embedding (ISOMAP) to generate a 2-d visual representation of each report based on its content/topics, which gives a direct view of the structure of the whole dataset as well as the outliers.

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.

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.

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 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)

We detail all of the facets of adapting classical model checking to a real aerospace system, in- cluding deriving the formal model and a set of specifications from natural language descriptions. To ensure the model checking results are meaningful, we have to ensure that both the model and specifications correctly reflect the intentions of the designers, thus we employ model validation and property debugging techniques. We demonstrate the utility of enhancing LTL satisfiability checking by taking the fairness constraints of the system model into consideration. We argue that specification debugging in real applications deserves more attention in future research efforts, and the utility of a system formalization, model and specification debugging, and verification trilogy for model checking real systems under development. In this paper we assume there are no hardware failures or lost messages. As the AAC design develops and hardware details are decided by AAC designers, we plan to take the failure rates of the chosen components into consideration, i.e. by extending our work to probabilistic model checking using PRISM [19]. Previous work has reported on analyzing the safety of air traffic control systems using simulation [3] or fault trees [1]. By extending the model we designed in this paper, we can carry out safety analysis using PRISM to capture the dynamic interactions in the AAC.