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To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch. This study demonstrates the scaling law of simulation-to-real (Sim2Real) transfer learning for several machine learning tasks in materials science. Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases. Observing the scaling behavior offers various insights for database development, such as determining the sample size necessary to achieve a desired performance, identifying equivalent sample sizes for physical and computational experiments, and guiding the design of data production protocols for downstream real-world tasks., Comment: 22 pages, 6 figures

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap. First, it defines the SOTIF Temporal Error and Failure Model (STEAM) as a refinement of the SOTIF cause-and-effect model, offering a comprehensive system-design perspective. STEAM refines error definitions, introduces error sequences, and classifies them as error sequence patterns, providing particular relevance to systems employing advanced sensors and AI. Second, this paper proposes the Model-based SOTIF Analysis of Failures and Errors (MoSAFE) method, which allows instantiating STEAM based on system-design models by deriving hazardous error sequence patterns at module level from hazardous behaviors at vehicle level via weakest precondition reasoning. Finally, the paper presents a case study centered on an automated speed-control feature, illustrating the practical applicability of the refined model and the MoSAFE method in addressing complex safety challenges in DAS., Comment: 23 pages, 13 figures, 5 tables

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels. We demonstrate the applicability of ISCaP with a detailed case study and discuss its use as a tool to support incremental development of perception components.

More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and $\textit{Ethics guidelines for trustworthy AI}$ from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical system, it is necessary to demonstrate the safety and security of the system through engineering processes. However, thus far, no such widely accepted engineering concepts or frameworks have been established for these systems. The key to using a machine learning model in a deductively engineered system is decomposing the data-driven training of machine learning models into requirement, design, and verification, particularly for machine learning models used in safety-critical systems. Simultaneously, open problems and relevant technical fields are not organized in a manner that enables researchers to select a theme and work on it. In this study, we identify, classify, and explore the open problems in engineering (safety-critical) machine learning systems --- that is, in terms of requirement, design, and verification of machine learning models and systems --- as well as discuss related works and research directions, using automated driving vehicles as an example. Our results show that machine learning models are characterized by a lack of requirements specification, lack of design specification, lack of interpretability, and lack of robustness. We also perform a gap analysis on a conventional system quality standard SQuARE with the characteristics of machine learning models to study quality models for machine learning systems. We find that a lack of requirements specification and lack of robustness have the greatest impact on conventional quality models., Comment: 20 pages, 4 figures, 4 tables

Deep learning techniques are rapidly advanced recently, and becoming a necessity component for widespread systems. However, the inference process of deep learning is black-box, and not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we develop a simple analysis method which consists of 1) structural feature analysis: lists of the features contributing to inference process, 2) linguistic feature analysis: lists of the natural language labels describing the visual attributes for each feature contributing to inference process, and 3) consistency analysis: measuring consistency among input data, inference (label), and the result of our structural and linguistic feature analysis. Our analysis is simplified to reflect the actual inference process for high transparency, whereas it does not include any additional black-box mechanisms such as LSTM for highly human readable results. We conduct experiments and discuss the results of our analysis qualitatively and quantitatively, and come to believe that our work improves the transparency of neural networks. Evaluated through 12,800 human tasks, 75% workers answer that input data and result of our feature analysis are consistent, and 70% workers answer that inference (label) and result of our feature analysis are consistent. In addition to the evaluation of the proposed analysis, we find that our analysis also provide suggestions, or possible next actions such as expanding neural network complexity or collecting training data to improve a neural network., Comment: 11 pages, 14 figures, 1 table. This is a pre-print of an article accepted in "Progress in Artificial Intelligence" on 26 Feb 2019. The final authenticated version will be available online soon

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance concepts and techniques. At the same time, open problems and the relevant technical fields are not organized. To establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions. In the present study, we identify, classify, and explore the open problems in quality assurance of safety-critical machine-learning systems, and their relevant corresponding industry and technological trends, using automated-driving vehicles as an example. Our results show that addressing these open problems requires incorporating knowledge from several different technological and industrial fields, including the automobile industry, statistics, software engineering, and machine learning., Comment: DISE1: Joint Workshop on Deep (or Machine) Learning for Safety-Critical Applications in Engineering

Safety critical systems strongly require the quality aspects of artificial intelligence including explainability. In this paper, we analyzed a trained network to extract features which mainly contribute the inference. Based on the analysis, we developed a simple solution to generate explanations of the inference processes.

The Advanced Earth Observing Satellite (ADEOS) is developed with the aim of establishment of platform technology for future spacecraft and inter-orbit communication technology for the transmission of earth observation data. ADEOS uses 5 batteries, consists of two packs. This paper describes, using graphs and tables, the ground simulation tests and results that are carried to determine the performance of the ADEOS batteries.

ETS-6 satellite's Ni-Cd and NI-H2 battery performance is evaluated. Flight data and ground data are compared.

This paper presents in viewgraph format an overview of NASDA's evaluation of commercial nickel metal-hydride (Ni-MH) cells and the development and testing of Ni-MH cells for use in space. The commercial cells are concluded to be feasible and suitable for use in LEO; for GEO, the durability for overcharge is needed because long-term charge retention is required. For the aerospace Ni-MH cell design, two activation procedures are applied to evaluate the effect of the difference in the amount of overcharge protection and precharge. Specific energy of the Ni-MH cell is nearly accomplished at 50 Wh/kg. Initial characteristics indicate the effect derived from precharge. Thirty-five amp-hour class Ni-MH cells have good performance for LEO cycle of 25 and 40 percent DOD up to 3000 cycles as similar to commercial cells. The effect of the difference in the amount of overcharge protection will appear in life test.

The topics covered are presented in viewgraph form and include the following: development schedule; main specification; cell design; production flow; acceptance test (1); acceptance test (2); cell weight; 20 C performance; capacity; overcharge pressure; end of charge voltage; -5 C performance; ETS-VI simulation cycle test; and battery storage.

National Space Development Agency of Japan (NASDA) has conducted the research and development (R&D) of battery cells for space use. A new R&D program about a Nickel-Metal Hydride (Ni-MH) cell for space use from this year, based on good results in evaluations of commercial Ni-MH cells in Tsukuba Space Center (TKSC), was started. The results of those commercial Ni-MH cell's evaluations and recent status about the development of Ni-MH cells for space use are described.

The National Space Development Agency of Japan (NASDA) began developing Nickel-Cadmium (Ni-Cd) cells for space use in 1985. Sanyo Electric was responsible for the cell design, manufacturing, and initial testing. The cell life was evaluated by NASDA in the Tsukuba Space Center (TKSC). The development is presently in the qualification Test (QT) phase. Recent life-cycle data for 35 Ah Ni-Cd cells are presented.