Robbie Nakatsu

Bio: Robbie Nakatsu is an academic researcher. The author has contributed to research in topics: Legal expert system & Rule-based system. The author has an hindex of 1, co-authored 1 publications receiving 127 citations.

Rule‐Based Expert Systems

Abstract: What is knowledge? § is a theoretical or practical understanding of a subject or a domain. § is also the sim of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts. § Anyone can be considered a domain expert if he or she has deep knowledge and strong practical experience in a particular domain. § The human mental process is internal, and it is too complex to be represented as an algorithm § However, most experts are capable of expressing their knowledge in the form of rules for problem solving.

Ensemble-based classifiers

Abstract: The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.

A Survey on Deep Learning for Data-Driven Soft Sensors

Abstract: Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.

An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

Abstract: This paper aims to develop a novel model to assess the risk factors of maritime supply chains by incorporating a fuzzy belief rule approach with Bayesian networks. The new model, compared to traditional risk analysis methods, has the capability of improving result accuracy under a high uncertainty in risk data. A real case of a world leading container shipping company is investigated, and the research results reveal that among the most significant risk factors are transportation of dangerous goods, fluctuation of fuel price, fierce competition, unattractive markets, and change of exchange rates in sequence. Such findings will provide useful insights for accident prevention.

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors.

Abstract: There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

Sensitivity Analysis, a Powerful System Validation Technique

Abstract: A sensitivity analysis is a powerful technique for understanding systems. This heuristic paper shows how to overcome some of the difficulties of performing sensitivity analyses. It draws examples from a broad range of fields: bioengineering, process control, decision making and system design. In particular, it examines sensitivity analyses of tradeoff studies. This paper generalizes the important points that can be extracted from the literature covering diverse fields and long time spans. Sensitivity analyses are particularly helpful for modeling systems with uncertainty.