Measurement of Radius of a Metallic Ball Using Eddy Current Testing Based on Peak Frequency Difference Feature

This paper proposes a linear eddy-current feature to determine the radius of a metallic ball in a non-contact manner. An electromagnetic eddy-current sensor with two coils is placed co-axially to the metal ball during measurement. It is well known that the distance between the sensor and test piece (i.e. lift-off) affects eddy-current signals. In this paper, it is found that the peak frequency feature of inductance spectrum is linear to the lift-off spacing between the centre of coil and ball. Besides, the slope of peak frequencies versus lift-offs is linked to the radius of ball. The radius of metallic balls is retrieved from the experimental and embedded analytical result of the slope. Measurements have been carried out on 6 metallic balls with different radii. The radius of the metallic ball can be retrieved with an error of less than 2 %.

Measurement of Radius of a Metallic Ball Using Eddy Current Testing Based on Peak Frequency Difference Feature

This paper proposes a linear eddy-current feature to determine the radius of a metallic ball in a non-contact manner. An electromagnetic eddy-current sensor with two coils is placed co-axially to the metal ball during measurement. It is well known that the distance between the sensor and test piece (i.e. lift-off) affects eddy-current signals. In this paper, it is found that the peak frequency feature of inductance spectrum is linear to the lift-off spacing between the centre of coil and ball. Besides, the slope of peak frequencies versus lift-offs is linked to the radius of ball. The radius of metallic balls is retrieved from the experimental and embedded analytical result of the slope. Measurements have been carried out on 6 metallic balls with different radii. The radius of the metallic ball can be retrieved with an error of less than 2 %.

Multilevel Methods and Statistics: The Next Frontier

The purpose of this article is to take stock of extant multilevel methodological and statistical work and highlight needed areas for future research. A basic overview of the history and progression of multilevel methods and statistics in the organizational sciences is provided, as well as a discussion of recent developments to summarize the current state of the science. The eight articles in the current feature topic are also summarized and integrated to depict several themes and directions for the next wave of multilevel methods and statistics. Last, to highlight what still needs to be accomplished in the field, several unresolved issues and future research topics are noted and an agenda related to future multilevel work is discussed.

Feature Selection in Big Image Datasets

In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level features. However, the resulting datasets have grown in the number of features, leading into long training times due to the curse of dimensionality. In this research, some feature selection methods were applied to these image features through big data technologies. Additionally, we analyzed how image resolutions may affect to extracted features and the impact of applying a selection of the most relevant features. Experimental results show that making an important reduction of the extracted features provides classification results similar to those obtained with the full set of features and, in some cases, outperforms the results achieved using broad feature vectors.

Unsupervised Feature Selection Methodology for Clustering in High Dimensionality Datasets

Feature selection is an important research area that seeks to eliminate unwanted features from datasets. Many feature selection methods are suggested in the literature, but the evaluation of the best set of features is usually performed using supervised metrics, where labels are required. In this work we propose a methodology that tries to aid data specialists to answer simple but important questions, such as: (1) do current feature selection methods give similar results? (2) is there is a consistently better method ? (3) how to select the m-best features? (4) as the methods are not parameter-free, how to choose the best parameters in the unsupervised scenario? and (5) given different options of selection, could we get better results if we fusion the results of the methods? If yes, how can we combine the results? We analyze these issues and propose a methodology that, based on some unsupervised methods, will make feature selection using strategies that turn the execution of the process fully automatic and unsupervised, in high-dimensional datasets. After, we evaluate the obtained results, when we see that they are better than those obtained by using the selection methods at standard configurations. In the end, we also list some further improvements that can be made in future works.

SEBUAH KARYA FEATURE WISATA DAN PERJALANAN “TABLE STORY”

Current feature programs broadcast on Indonesian television have begun to be of little interest to the public, because they have begun to be displaced by other television programs whose content is merely entertainment. Though the community needs various programs that are able to provide information, inspiration and education in order to provide and increase awareness of the potential of their own people. The creator of the work wants to create a program that has a lot of information, inspiration and education about culinary from both the dish and the place. This program is a culinary travel feature titled "TABLE STORY", a program that packages a variety of unique culinary found in Indonesia, this program not only discusses my food but also provides in-depth information about the theme of the food and the place itself which has the desires to be the hallmark of the place itself and also a place that provides education for its customers. This program has a total duration of 15 minutes. This program will be aired once a week on Saturdays at 09.30 WIB, on the private television station SCTV (Surya Citra Televisi). The creator of the work has consideration of choosing the time and placing this program on the SCTV television station, because the television station does not have a culinary program that discusses in depth the presentation and design of the culinary place, so that this program can provide information, inspiration, and education to audience at the station.

Memory-Augmented Temporal Dynamic Learning for Action Recognition

Human actions captured in video sequences contain two crucial factors for action recognition, i.e., visual appearance and motion dynamics. To model these two aspects, Convolutional and Recurrent Neural Networks (CNNs and RNNs) are adopted in most existing successful methods for recognizing actions. However, CNN based methods are limited in modeling long-term motion dynamics. RNNs are able to learn temporal motion dynamics but lack effective ways to tackle unsteady dynamics in long-duration motion. In this work, we propose a memory-augmented temporal dynamic learning network, which learns to write the most evident information into an external memory module and ignore irrelevant ones. In particular, we present a differential memory controller to make a discrete decision on whether the external memory module should be updated with current feature. The discrete memory controller takes in the memory history, context embedding and current feature as inputs and controls information flow into the external memory module. Additionally, we train this discrete memory controller using straight-through estimator. We evaluate this end-to-end system on benchmark datasets (UCF101 and HMDB51) of human action recognition. The experimental results show consistent improvements on both datasets over prior works and our baselines.