Prediction System of Cloud Distribution Image Using Fully Convolutional Networks

In this paper, we propose a cloud distribution prediction model in which fully convolutional networks are used to improve the prediction accuracy for photovoltaic power generation systems. The model learns the cloud distribution from meteorological satellite images and predicts the cloud image 60 min later. We examined the applicability of Day Microphysics RGB as input to the cloud image prediction model. Day Microphysics RGB is a type of RGB composite image based on the observation image of Himawari-8. It is used for daytime cloud analysis and can perform detailed cloud analysis, for example, the discrimination of cloud areas such as upper and lower clouds. The performance of the proposed method is evaluated on the basis of the root mean square error of the prediction and ground truth images

Thermal transformation and ionic conductivity of ammonium ion-exchanger prepared from Na-4-mica

Ammonium ion exchange of Na-4-mica and thermal transformation of the ion-exchanger were investigated and the conductivities of the heated ion-exchangers were estimated. More than 95.4% of Na+ ions in the interlayer of the Na-4-mica could be substituted by NH4+ ions after repeating ion exchange four times. Almost of NH4+ ions remained in the interlayer at 700 degrees C and they were completely decomposed into protons at 900 degrees C. The conductivities of the ion-exchangers heated at 700-900 degrees C were 10(-7)-10(-6) S/cm at 700 degrees C. The conduction mechanism of the ion-exchangers heated at 700 and 800 degrees C was NH4+ ion migration in the interlayer and that of the ion-exchanger heated at 900 degrees C was proton migration. However, the contribution of residual Na+ ions to the conduction cannot be excluded. (C) 2010 Elsevier B.V. All rights reserved.ArticleMaterials Science and Engineering: B. 173(1-3):271-274 (2010)journal articl

Multiple Vehicles Semi-Self-driving System Using GNSS Coordinate Tracking under Relative Position with Correction Algorithm

This paper describes a simple and low-cost semi-self-driving system which is constructed without cameras or image processing. In addition, a position correction method is presented by using a vehicle dynamics. Conventionally, self-driving vehicle is operated by various expensive environmental recognition sensors. It results in rise in prices of the vehicle, and also the complicated system with various sensors tends to be a high possibility of malfunction. Therefore, we propose the semi-self-driving system with a single type of global navigation satellite system (GNSS) receiver and a digital compass, which is based on a concept of a preceding vehicle controlled by a human manually and following vehicles which track to the preceding vehicle automatically. Each vehicle corrects coordinate using current velocity and heading angle from sensors. Several experimental and simulation results using our developed small-scale vehicles demonstrate the validity of the proposed system and correction method

Object Conveyance Algorithm for Multiple Mobile Robots based on Object Shape and Size

This paper describes a determination method of a number of a team for multiple mobile robot object conveyance. The number of robot on multiple mobile robot systems is the factor of complexity on robots formation and motion control. In our previous research, we verified the use of the complex-valued neural network for controlling multiple mobile robots in object conveyance problem. Though it is a significant issue to develop effective determination team member for multiple mobile robot object conveyance, few studies have been done on it. Therefore, we propose an algorithm for determining the number of the team member on multiple mobile robot object conveyance with grasping push. The team member is determined based on object weight to obtain appropriate formation. First, the object shape and size measurement is carried out by a surveyor robot that approaches and surrounds the object. During surrounding the object, the surveyor robot measures its distance to the object and records for estimating the object shape and size. Since the object shape and size are estimated, the surveyor robot makes initial push position on the estimated push point and calls additional robots for cooperative push. The algorithm is validated in several computer simulations with varying object shape and size. As a result, the proposed algorithm is promising for minimizing the number of the robot on multiple mobile robot object conveyance

Measurement of Tomato Leaf Area Using Depth Camera

We describe a leaf region detection and leaf area calculation algorithm for estimating the growing conditions of small tomatoes. In leaf region detection, a color image taken by a depth camera is analyzed by image processing. The leaf region is divided into more than one hundred quadrangle meshs, and the spatial coordinates of the vertices of the mesh are calculated from the depth information obtained by the depth camera. To reduce the effect of measurement noise, an approximate surface expressed by a two-variable quadratic function is obtained, and the leaf area is calculated as the sum of the area of the meshs on the approximate surface. In the experiment, the algorithm will be evaluated using leaf measurements of small tomatoes in a greenhouse

TRPC6 counteracts TRPC3-Nox2 protein complex leading to attenuation of hyperglycemia-induced heart failure in mice

Excess production of reactive oxygen species (ROS) caused by hyperglycemia is a major risk factor for heart failure. We previously reported that transient receptor potential canonical 3 (TRPC3) channel mediates pressure overload-induced maladaptive cardiac fibrosis by forming stably functional complex with NADPH oxidase 2 (Nox2). Although TRPC3 has been long suggested to form hetero-multimer channels with TRPC6 and function as diacylglycerol-activated cation channels coordinately, the role of TRPC6 in heart is still obscure. We here demonstrated that deletion of TRPC6 had no impact on pressure overload-induced heart failure despite inhibiting interstitial fibrosis in mice. TRPC6-deficient mouse hearts 1 week after transverse aortic constriction showed comparable increases in fibrotic gene expressions and ROS production but promoted inductions of inflammatory cytokines, compared to wild type hearts. Treatment of TRPC6-deficient mice with streptozotocin caused severe reduction of cardiac contractility with enhancing urinary and cardiac lipid peroxide levels, compared to wild type and TRPC3-deficient mice. Knockdown of TRPC6, but not TRPC3, enhanced basal expression levels of cytokines in rat cardiomyocytes. TRPC6 could interact with Nox2, but the abundance of TRPC6 was inversely correlated with that of Nox2. These results strongly suggest that Nox2 destabilization through disrupting TRPC3-Nox2 complex underlies attenuation of hyperglycemia-induced heart failure by TRPC6.Fil: Oda, Sayaka. Okazaki Institute for Integrative Bioscience; Japón. SOKENDAI; JapónFil: Numaga Tomita, Takuro. Okazaki Institute for Integrative Bioscience; Japón. SOKENDAI; JapónFil: Kitajima, Naoyuki. Okazaki Institute for Integrative Bioscience; Japón. Kyushu University; JapónFil: Tomizaki, Takashi. Okazaki Institute for Integrative Bioscience; Japón. Kyushu University; Japón. University of Tsukuba; JapónFil: Harada, Eri. Ajinomoto Co.; Japón. EA Pharma Co.; JapónFil: Shimauchi, Tsukasa. Okazaki Institute for Integrative Bioscience; Japón. Kyushu University; JapónFil: Nishimura, Akiyuki. Okazaki Institute for Integrative Bioscience; Japón. SOKENDAI; Japón. Ajinomoto Co.; JapónFil: Ishikawa, Tatsuya. Kyushu University; Japón. Ajinomoto Co.; Japón. EA Pharma Co.; JapónFil: Kumagai, Yoshito. University of Tsukuba; JapónFil: Birnbaumer, Lutz. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; ArgentinaFil: Nishida, Motohiro. Okazaki Institute for Integrative Bioscience; Japón. SOKENDAI; Japón. Kyushu University; Japón. PRESTO; Japó