Search

The Japanese space mission JAXA/ Global Change Observation Mission-Climate (GCOM-C) launched in 2017 carries a second-generation global imager (SGLI). The SGLI contains 19 channels from near-UV to thermal infrared (IR), including red (674 nm named PL1 band) and near-IR (869 nm; PL2 band) polarization channels. The SGLI's spatial resolution is fine as the instantaneous field-of-view is 250 m from the near-UV to short-IR wavelength range for radiometry and 1 km for the polarization measurements. This work presents the retrieval algorithms for biomass burning aerosol (BBA) using the advantages of the SGLI features. The BBA generated by wildfires causes long-range advection migration. Severe wildfires, which now frequently occur around the world, have become a major environmental problem. Using as an example the large scale wildfires in September of 2020 around western North America, we show that the SGLI's simultaneous observation of radiance and polarization is useful for the characterization of BBA. Furthermore, a regional numerical model scalable computing for advanced library and environment is used to consider meteorological conditions and/or topography. The objective of this study is to better understand the BBA events through the integration of space-borne and ground-based measurements as well as the regional meteorological model.

In avatar-mediated communication, the face-to-face interlocutor must sense the operator’s presence and emotions via an avatar. Although androids resembling humans have been developed to convey presence through appearance and movement, few studies have prioritized deepening the communication experience for operators and interlocutors using android robots as avatars. To address this gap, we introduced the “cybernetic avatar ‘Yui,’” featuring a human-like head unit with 28 actuation points, capable of expressing gaze, facial emotions, and speech-related mouth movements. Through an eye-tracking unit in a head-mounted display (HMD) and the degrees of freedom in both eyes of Yui, operators can naturally control the avatar’s gaze. Additionally, microphones embedded in Yui’s ears allowed operators to hear surrounding sounds in three dimensions, enabling them to discern the direction of calls based solely on auditory information. The HMD’s face-tracking unit synchronizes the facial movements of the avatar with those of the operator. This immersive interface, coupled with Yui’s human-like appearance, enabled real-time emotional transmission and communication, thus enhancing the sense of presence for both parties. Our experiments demonstrate Yui’s facial expression capabilities and validate the efficacy of the system through teleoperation trials, suggesting potential advancements in avatar technology.

The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.

Like most evolutionary algorithms, accuracy-based learning classifier systems (XCSs) use a fitness metric to recognize the superiority of rules, under a principle that a higher-quality rule has a higher fitness. However, XCS must learn the fitness values under a reinforcement learning scheme. This introduces uncertainty and asynchrony to the fitness estimation while no theoretical work formally guarantees that such a basic principle would hold. The goal of this paper is to complement this fundamental lack in the reliability of XCS by mathematically analyzing its fitness-update scheme. Our main assumption is that the fitness is updated with an absolute accuracy instead of its relative accuracy to sidestep unpredictable dynamics of XCS. Our theoretical conclusion is that the superiority of rules can be correctly recognized through the accuracy-based fitness under finite update times. We further show that recognizing the superiority among low-quality rules is a costly procedure that increases the number of necessary rule-trainings. This drawback indicates that XCS may struggle to identify good parent rules at early generations, degrading the efficiency of evolutionary propagation.

The Coronavirus disease 2019 (COVID-19) is still prevalent in the world. Exercise is important to maintain our health while dealing with infectious diseases. Social distancing is more important during exercise because we may not be able to wear masks to avoid breathing problems, heatstroke, etc. To maintain social distancing during exercise, we develop a close-contact detection system using a single camera especially for sports in schools and gyms. We rely on a single camera because of the deployment cost. The system recognizes people from a video and estimates the interpersonal distance for close-contact detection. The challenge is the occlusion of people, which leads to false negatives in close-contact detection. To solve the problem, we leverage the observation that most false negatives in human detection are caused by occlusion owing to other people. This is because there are few obstacles in sports facilities. Based on the above observation, we assume that a person still exists near the last detected position even when s/he disappears in the proximity of other people. For evaluation, we recorded 834 videos that were 112 min long in total including various scenarios with 2724 close-contacts. The results show that the F1-score of close-contact detection and tracking are 83.6% and 67.3%, respectively. We also confirmed that the start and end time errors are within 1 s for more than 80% of the close-contacts.

In this work, we demonstrate the effectiveness of nitrogen plasma treatment on the formation of low-resistive source/drain (S/D) in self-aligned (SA) oxide thin-film transistors (TFTs) using a high-mobility oxide semiconductor (OS), In-Ga-Zn-Sn-O (IGZTO). The nitrogen plasma treatment was more effective at reducing the sheet resistance ( ${R} _{\mathrm{ sheet}}$ ) of IGZTO films than the commonly used argon plasma treatment. Furthermore, ${R} _{\mathrm{ sheet}}$ for nitrogen-plasma-treated IGZTO films remained low, even when the RF power and radiation time during the plasma treatment were increased when the minimum ${R} _{\mathrm{ sheet}}$ was achieved. The same trends were also observed in OS films with different compositions, such as In-Ga-Zn-O and In-Sn-Zn-O. These results indicate that nitrogen plasma treatment is effective for achieving a reduction of ${R} _{\mathrm{ sheet}}$ for various OS films with a wide process window regarding plasma processing parameters. The advantages could be attributed to the smaller sputtering effect on the OS films due to the lower mass of nitrogen ions than argon ions, which was verified by X-ray reflectivity and X-ray photoelectron spectroscopy analyses. For further validation, SA IGZTO TFTs with a channel length ( ${L}$ ) of 3 to 100 $\mu \text{m}$ were fabricated with nitrogen or argon plasma treatment. The width-normalized parasitic SD resistance ( $R_{\mathrm{ SD}} {W}$ ) with the nitrogen plasma treatment was determined to be 11.3 $\Omega \cdot $ cm, which was ca. 40% lower than that with the argon plasma treatment. This improvement in $R_{\mathrm{ SD}} {W}$ resulted in higher mobility ( $\mu $ ) in the nitrogen-plasma-treated SA IGZTO TFTs. A nitrogen-plasma-treated SA IGZTO TFT with $L=10\,\,\mu \text{m}$ exhibited a high $\mu $ of 27.2 cm2/Vs.

We propose a new wearable agent, “FinU (Friend in You),” based on the hypothesis that inducing social facilitation effect, i.e., the effect of having other agents around to increase the task performance, is possible, even for wearable agents with a single sensory modality, by establishing beliefs about the agent in advance. Our agent system, FinU, consists of two devices: FinU-Box, a box-shaped device that synchronizes visual, auditory, and tactile stimuli to represent an agent living on the user’s hand, and the FinU-Band, a wearable device worn on the hand with a sensory modality limited to tactile stimuli. In this study, in addition to the development of the system, we also conducted preliminarily evaluation by dividing the participants into two experimental conditions: one in which subjects interacted with the agent using the FinU-Box beforehand, and one in which participants did not use the FinU-Band. By having the user learn beliefs about the agent from the FinU-Box, it was expected that the user would attribute higher presence to the agent than if they had not learned beliefs. Participants were then tested on a continuous performance task to measure the effect of social facilitation, which was expected to improve performance, and their performance was compared across conditions. We assumed that higher performance indicated a stronger sense of agent presence. The results showed that the social facilitation effect was stronger for participants who had experienced the FinU-Box beforehand, as they responded more accurately to the task stimuli. This suggests that our system, which combining a device that presents a rich sensory experience with a wearable device that presents a single sensory experience would allow the agent’s presence to be more strongly felt by the user.

With increasing threats to information security, information security education through practical exercises specifically cyber range has attracted attention. However, the use of a cyber range is not widespread because of the high initial and maintenance cost and difficulty of developing new scenarios. Because many virtual instances are executed in the cyber range, the advantage of container type virtualization, which can provide a lightweight execution environment, is expected to increase efficient hardware utilization and decrease the total cost. On the other hand, containers pose challenges in scalability and scenario development when it comes to their use in cyber ranges because their performance advantages and vulnerability reproducibility have not been reported. In this paper, we conducted an exhaustive experiment to compare the performance and reproducibility of container-type virtualization with other virtualization types. The results show that containers can provide a more efficient execution environment than the other types, with almost perfect vulnerability reproducibility of more than 99% while reducing memory consumption by half and storage consumption to 1/60. The container’s high performance and reproducibility enabled us to develop CyExec*, a cyber range system with DAG-based scenario randomization technology. CyExec* can increase educational effectiveness by automatically generating multiple scenarios with the same learning objective. Compared with a random scenario generator for CTF using another virtualization type, CyExec* shows more than three times higher performance. CyExec* can solve existing cyber range issues.

Humans can express their emotions with not only facial expressions or gestures but also whole-body motion, even while walking, which are essential for our interpersonal relationships and our interaction with others. Communication with people is also considered an important task for robots, however emotional expressions by wheeled mobile humanoid robots when moving has not been well studied. In this paper, we implemented emotional human-like gait-induced upper body motion in a mobile android ibuki. We hypothesize that the gait-induced upper body motion with vertical oscillation enhances the human perception of a mobile robot’s emotional expressions. ibuki is a mobile robot, which has a vertical oscillation mechanism, which enables the robot to move its body with a human gait-induced upper body motion. We ran two experiments in which participants watched videos of ibuki moving in a room expressing different emotions - anger, happiness, and sadness–, before answering how they perceived the emotion and their respective confidence level in their answer. Our results show that for motions with vertical oscillation, recognition rates were: 56.0 % for anger, 77.7 % for happiness, and 97.0 % for sadness. We also found that the recognition rate and confidence level of motions expressing happiness with vertical oscillation were higher. Our results play an important role in opening up avenues for more natural ways of interaction between humans and robots. For example, by approaching humans closer while expressing emotions, a robot can naturally provide an opportunity to get started conversation with them.