Evaluation of carbon dioxide absorption by amine based absorbent

AbstractToshiba has developed amine based aqueous solution (Toshiba solvent 1, TS-1) that can significantly reduce CO2 regeneration energy compared with general 30 wt% monoethanolamine (MEA) aqueous solution and reported the results of the pilot plant of 10 t- CO2/day recovery from the flue gas of a coal- fired power plant. In order to reduce the CO2 regeneration energy further, we have developed new hindered amine based absorbent, Absorbent-A.In the present work, Absorbent-A was evaluated for CO2 absorption properties by laboratory scale apparatus. Absorbent-A was found to have the high CO2 absorption capacity and the low reaction heat. Furthermore, the CO2 regeneration energy of Absorbent-A was 45% less than that of general 30 wt% MEA aqueous solution.In future, we will additionally evaluate Absorbent-A in order to test in the pilot plant

Cross-View Gait Recognition Using View-Dependent Discriminative Analysis

Abstrac

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network

Modification of Intersession Variability in On-Line Signature Verifier

Abstract. For Pen-input on-line signature verification algorithms, the influence of intersession variability is a considerable problem because hand-written signatures change with time, causing performance degradation. In our previous work, we proposed a user-generic model using AdaBoost. However, this model did not allow for the fact that features of signatures change over time. In this paper, we propose a template renewal method to reduce the performance degradation caused by signature changes over time. In our proposed method, the oldest template is replaced with a new one if the new signature data gives rise to an index which exceeds a threshold value. No further learning is necessary. A preliminary experiment was conducted on a subset of the MCYT database

A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers