Intelligent Black Box Verification, Validation, and Accreditation for Rotorcraft Performance Modeling

2021 ◽  
Author(s):  
William B. McCandless, Jr. ◽  
Ian D. Dettwiller ◽  
Glover George
Keyword(s):  
Data Science ◽  
Black Box ◽  
Performance Model ◽  
Learning Performance ◽  
Performance Models ◽  
New Approach ◽  
Box Models ◽  
Rotorcraft Performance ◽  
New Applications ◽  
Intensive Work

The paper addresses process and tool development in support of qualification assessments of performance models. Details are provided regarding developing enablers in the fields of data science, uncertainty quantification, and machine learning. Performance models are delivered for assessment in all different shapes, for different purposes, with different pedigrees. All or parts of these models may be proprietary. Some models are delivered without documentation and pedigree, referred to as black box models. Traditionally these gaps in information, if possible, are filled by time-consuming, resource-intensive work of subject matter experts (SMEs). New processes and tools are proposed for reducing this burden and providing direction for SME efforts. The new approach is exercised for validation of an AH-64 performance model. Efforts contrast new applications of high-powered computational tools with the long-accepted SME-driven methods to establish value and increase capabilities. Results indicate that emerging methodologies can provide valuable guidance without SME involvement.

scholarly journals Explanation Generation Mechanism for Black Box Recommendation Model

2020 ◽  
Vol 9 (8) ◽  
pp. 275-279
Keyword(s):  
Recommender System ◽  
Semantic Network ◽  
Black Box ◽  
Machine Learning Techniques ◽  
New Approach ◽  
Semantic Web Technology ◽  
Box Models ◽  
Learning Techniques ◽  
Available Information ◽  
Black Box Models

The recommender system is everywhere, and even streaming platform they have been looking for a maze of user available information handling products and services. Unfortunately, these black box systems do not have sufficient transparency, as they provide littlie description about the their prediction. In contrast, the white box system by its nature can produce a brief description. However, their predictions are less accurate than complex black box models. Recent research has shown that explanations are an important component in bringing powerful big data predictions and machine learning techniques to a mass audience without compromising trust.This paper proposes a new approach using semantic web technology to generate an explanation for the output of a black box recommender system. The developed model is trained to make predictions accompanied by explanations that are automatically extracted from the semantic network.

The principles of duality in the light of the system economic theory

2019 ◽  
pp. 127-149
Author(s):  
George B. Kleiner
Keyword(s):  
Economic Theory ◽  
Economic System ◽  
External Environment ◽  
Black Box ◽  
Space Time ◽  
Production Cycle ◽  
Economic Systems ◽  
New Approach ◽  
Reproduction Cycle ◽  
Basic Concepts

This paper shows the diversity and significance of relations of duality among different economic systems. The composition of the principles underlying the system economic theory used for the analysis of duality in the economy is investigated. The concept of the economic system is clarified and the equivalence of three basic concepts of the economic system is shown: a) as a space-time volume (“black box”); b) as a complex of elements and connections among them; c) as a tetrad, including object, project, process and environment components. In a new way, the concept of the tetrad is revealed. The actual interpretation of the interrelationships of its components, based on the mechanisms of intersystem circulation of spatial and temporal resources and the transmission of abilities from one economic system to another, is proposed. On the basis of the obtained results, the most essential aspects of duality in the theory of economic systems are considered. It is shown that the interaction of internal content and the nearest external environment of economic systems lies in the nature of the relations of duality. A new approach to modeling the structure and to functioning of the economic system, based on the description of its activities in the form of two interconnected tetrads (the first tetrad reflects the intrasystem production cycle and the second one — the external realization-reproduction cycle) is put forward. It is shown that the concept of duality in a system economy creates prerequisites for adapting the functioning of local economic systems (objects, projects, etc.) in a market, administrative and functional environments and, as a result, harmonizing the economy as a whole.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

Validating Human Performance Models: Building a House without Bricks

2020 ◽  
Vol 64 (1) ◽  
pp. 1511-1514
Author(s):  
Richard Steinberg ◽  
Raytheon Company ◽  
Alice Diggs ◽  
Raytheon Company ◽  
Jade Driggs
Keyword(s):  
Air Force ◽  
Human Performance ◽  
Control Program ◽  
Real Data ◽  
Performance Model ◽  
Force Model ◽  
Performance Models ◽  
Actual System ◽  
Support Design ◽  
Trade Offs

Verification and validation (V&V) for human performance models (HPMs) can be likened to building a house with no bricks, since it is difficult to obtain metrics to validate a model when the system is still in development. HPMs are effective for performing trade-offs between the human system designs factors including number of operators needed, the role of automated tasks versus operator tasks, and member task responsibilities required to operate a system. On a recent government contract, our team used a human performance model to provide additional analysis beyond traditional trade studies. Our team verified the contractually mandated staff size for using the system. This task demanded that the model have sufficient fidelity to provide information for high confidence staffing decisions. It required a method for verifying and validating the model and its results to ensure that it accurately reflected the real world. The situation caused a dilemma because there was no actual system to gather real data to use to validate the model. It is a challenge to validate human performance models, since they support design decisions prior to system. For example, crew models are typically inform the design, staffing needs, and the requirements for each operator’s user interface prior to development. This paper discusses a successful case study for how our team met the V&V challenges with the US Air Force model accreditation authority and successfully accredited our human performance model with enough fidelity for requirements testing on an Air Force Command and Control program.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas Wan ◽  
Hamid R. Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

Creating Dynamic Pretrade Models: Beyond the Black Box

2018 ◽  
Keyword(s):  
Functional Form ◽  
Information Leakage ◽  
Black Box ◽  
Sensitivity Analyses ◽  
Stock Selection ◽  
Market Participants ◽  
Box Models ◽  
Portfolio Managers ◽  
Black Box Models ◽  
Shed Light

We provide a framework for investment managers to create dynamic pretrade models. The approach helps market participants shed light on vendor black-box models that often do not provide any transparency into the model’s functional form or working mechanics. In addition, this allows portfolio managers to create consensus estimates based on their own expectations, such as forecasted liquidity and volatility, and to incorporate firm proprietary alpha estimates into the solution. These techniques allow managers to reduce overdependency on any one black-box model, incorporate costs into the stock selection and portfolio optimization phase of the investment cycle, and perform “what-if” and sensitivity analyses without the risk of information leakage to any outside party or vendor.

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