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14 pages, 4331 KiB

Open AccessArticle

Yield Visualization Based on Farm Work Information Measured by Smart Devices

by Yoshiki Hashimoto, Daisaku Arita, Atsushi Shimada, Takashi Yoshinaga, Takashi Okayasu, Hideaki Uchiyama and Rin-Ichiro Taniguchi

Sensors 2018, 18(11), 3906; https://doi.org/10.3390/s18113906 - 13 Nov 2018

Cited by 1 | Viewed by 2337

Abstract

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based [...] Read more.

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2018)

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12 pages, 9817 KiB

Open AccessArticle

Sloshing Measurements inside a Liquid Hydrogen Tank with External-Heating-Type MgB₂ Level Sensors during Marine Transportation by the Training Ship Fukae-Maru

by Kazuma Maekawa, Minoru Takeda, Yuuki Miyake and Hiroaki Kumakura

Sensors 2018, 18(11), 3694; https://doi.org/10.3390/s18113694 - 30 Oct 2018

Cited by 10 | Viewed by 4799

Abstract

Recently, a project was initiated in Japan to transport a large amount of liquid hydrogen (LH₂) from Australia to Japan by sea. It is important to understand the sloshing and boil-off that are likely to occur inside an LH₂ tank [...] Read more.

Recently, a project was initiated in Japan to transport a large amount of liquid hydrogen (LH₂) from Australia to Japan by sea. It is important to understand the sloshing and boil-off that are likely to occur inside an LH₂ tank during marine transportation by ship, but such characteristics are yet to be experimentally clarified. To do so, we combined the liquid level detected by five 500 mm long external-heating-type magnesium diboride (MgB₂) level sensors with synchronous measurements of temperature, pressure, ship motion, and acceleration during a zigzag maneuver. During this zigzag maneuver, the pressure of gaseous hydrogen (GH₂) in the small LH₂ tank increased to roughly 0.67 MPaG/h, and the temperature of the GH₂ in the small LH₂ tank increased at the position of gaseous hydrogen at roughly 1.0 K/min when the maximum rolling angle was 5°; the average rolling and liquid-oscillation periods were 114 and 118 s, respectively, as detected by the MgB₂ level sensors, which therefore detected a long-period LH₂ wave due to the ship’s motion. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2018)

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16 pages, 2524 KiB

Open AccessArticle

Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm

by Fadilla Zennifa, Sho Ageno, Shota Hatano and Keiji Iramina

Sensors 2018, 18(11), 3691; https://doi.org/10.3390/s18113691 - 30 Oct 2018

Cited by 10 | Viewed by 3857

Abstract

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for [...] Read more.

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2018)

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