579 research outputs found
A study for the influence of human behavior on a safety marking of electrical machines
Manufacturers aim to design electric machines so that users will not get injured if they misuse them. However, manufacturers are not able to consider all types of misuse that may occur. Therefore, an instruction marking that instructs users regarding actions that prevent misuse of the machine in question must be effective, in particular because it is easy to add an instruction marking to a machine. In this study, we have tried to ascertain what types of instruction markings are effective in terms of preventing users from misusing a machine. For a marking to be effective, it may be important that users can easily notice the marking as then they are more likely to heed the related warning. We have carried out experiments on human behavior focusing on the differences in the position of a marking, whether or not a marking includes an illustration, and whether or not a marking has a flashing light in close proximity
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning
Self-supervised learning (SSL) using mixed images has been studied to learn
various image representations. Existing methods using mixed images learn a
representation by maximizing the similarity between the representation of the
mixed image and the synthesized representation of the original images. However,
few methods consider the synthesis of representations from the perspective of
mathematical logic. In this study, we focused on a synthesis method of
representations. We proposed a new SSL with mixed images and a new
representation format based on many-valued logic. This format can indicate the
feature-possession degree, that is, how much of each image feature is possessed
by a representation. This representation format and representation synthesis by
logic operation realize that the synthesized representation preserves the
remarkable characteristics of the original representations. Our method
performed competitively with previous representation synthesis methods for
image classification tasks. We also examined the relationship between the
feature-possession degree and the number of classes of images in the multilabel
image classification dataset to verify that the intended learning was achieved.
In addition, we discussed image retrieval, which is an application of our
proposed representation format using many-valued logic.Comment: This work has been submitted to the IEEE for possible publication.
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Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
Learning Compliant Stiffness by Impedance Control-Aware Task Segmentation and Multi-objective Bayesian Optimization with Priors
Rather than traditional position control, impedance control is preferred to
ensure the safe operation of industrial robots programmed from demonstrations.
However, variable stiffness learning studies have focused on task performance
rather than safety (or compliance). Thus, this paper proposes a novel stiffness
learning method to satisfy both task performance and compliance requirements.
The proposed method optimizes the task and compliance objectives (T/C
objectives) simultaneously via multi-objective Bayesian optimization. We define
the stiffness search space by segmenting a demonstration into task phases, each
with constant responsible stiffness. The segmentation is performed by
identifying impedance control-aware switching linear dynamics (IC-SLD) from the
demonstration. We also utilize the stiffness obtained by proposed IC-SLD as
priors for efficient optimization. Experiments on simulated tasks and a real
robot demonstrate that IC-SLD-based segmentation and the use of priors improve
the optimization efficiency compared to existing baseline methods.Comment: Accepted to IROS202
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