The sense of agency (SoA) is part of psychophysiological modules related to the self. Disturbed SoA is found in several clinical conditions, hence understanding the neural correlates of the SoA is useful for the diagnosis and determining the proper treatment strategies. Although there are several neuroimaging studies on SoA, it is desirable to translate the knowledge to more accessible and inexpensive EEG-based biomarkers for the sake of applicability. However, SoA has not been widely investigated using EEG. To address this issue, we designed an EEG experiment on healthy adults (n = 15) to determine the sensitivity of EEG on the SoA paradigm using hand movement with parametrically delayed visual feedback. We calculated the power spectral density over the traditional EEG frequency bands for ten delay conditions relative to no delay condition. Independent component analysis and equivalent current dipole modeling were applied to address artifact rejection, volume conduction, and source localization to determine the effect of interest. The results revealed that the alpha and low-beta EEG power increased in the parieto-occipital regions in proportion to the reduced SoA reported by the subjects. We conclude that the parieto-occipital alpha and low-beta EEG power reflect the sense of agency.

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.

In large-scale engineering plants like nuclear power plants, various anomalies and accidents are usually predicted and efficient measures to minimize the damage from them have been implemented. However, when an unexpected anomaly or accident occurs, operators are asked to take flexible responsive actions to avoid its development to a catastrophic situation and to minimize the influence and damage by the accident. Considering the lessons learned from Fukushima Daiichi accidents, it is important to develop a system to support counter activities by operators and plant staff in an accidental plant condition that is not covered by emergency operating procedures prepared. The purpose of this study is to develop a technique for generating plausible operation procedures under such unexpected emergency situations. The multilevel flow modeling (MFM), a method to represent the functions of components and their relations, is applied as a framework to model functions of components. The technique uses some additional data and knowledge for deriving operation procedures. The applicability of the technique is confirmed by trial operation procedure generations for pressurized water reactor (PWR) loss-of-coolant accident cases with the conditions of some failures of safety systems.

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs’ manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells’ existence.

This paper applies our developed novice users oriented force feedback steering wheel interface and mouse interface to navigating a tank type rescue robot. By analyzing merits and limitation of operating each interface, we propose a combined navigation strategy by the two interfaces. The steering wheel interface consists of a force feedback steering control and a six monitors’ wall. Through this interface, users can navigate the tank robot like driving cars, while watching incoming videos. It provides a daily life operation method for novice users to navigate the tank rescue robot. The steering wheel interface is efficient in exploring open areas. For complex disaster fields, this interface requires users have skillful operation experiences, which take them more attention. The mouse-screen interface consists of a mouse and a camera’s view displayed in a computer screen. Through this interface, users can navigate the tank robot just by mouse clicking. Path planning and low-level controlling are realized by system automatically. The mouse-screen interface can realize exact navigation, especially needed in complex structures, without taking much attention. It gives users more time to care incoming information. The two interfaces can shift into each other at any time. The combined navigation strategy adopts merits of the two interfaces and compensates limitation of each of them. It provides an efficient operation method for novice users to navigate rescue robots.