The PASCAL Visual Object Classes Challenge

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

Applications of machine learning to machine fault diagnosis: A review and roadmap

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

Regular inspection of nuclear power plant components is important to guarantee safe operations. However, current practice is time consuming, tedious, and subjective, which involves human technicians reviewing the inspection videos and identifying cracks on reactors. A few vision-based crack detection approaches have been developed for metallic surfaces, and they typically perform poorly when used for analyzing nuclear inspection videos. Detecting these cracks is a challenging task since they are tiny, and noisy patterns exist on the components’ surfaces. This study proposes a deep learning framework, based on a convolutional neural network (CNN) and a Naive Bayes data fusion scheme, called NB-CNN, to analyze individual video frames for crack detection while a novel data fusion scheme is proposed to aggregate the information extracted from each video frame to enhance the overall performance and robustness of the system. To this end, a CNN is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naive Bayes decision making discards false positives effectively. The proposed framework achieves a 98.3% hit rate against 0.1 false positives per frame that is significantly higher than state-of-the-art approaches as presented in this paper.

NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

Space layout planning using an evolutionary approach

As data visibility in factories has increased with the deployment of sensors, data-driven maintenance has become popular in industries. Machine learning has been a promising tool for fault detection, but the problem is that the amount of fault data is much less than that of normal data which causes a data imbalance. In this study, we designed a deep neural network for fault detection and diagnosis, and compared the oversampling by a generative adversarial network to standard oversampling techniques. Simulation results indicate that oversampling by the generative adversarial network performs well under the given condition and the deep neural network designed is capable of classifying the faults of an induction motor with high accuracy.

Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

An emotion-based vehicle service system for safe driving includes a multi-emotion sensor node configured to sense emotion information of a driver and passenger in a vehicle and condition information of the vehicle. The emotion-based vehicle service system further includes an emotion cognition processing apparatus configured to analyze sensing information provided from the multi-emotion sensor node to generate emotion cognition information, and a safe driving service apparatus configured to provide an emotion-based safe driving service depending on the emotion cognition information.

Emotion-based vehicle service system, emotion cognition processing apparatus, safe driving service apparatus, and emotion-based safe driving service method

Visual condition inspections remain paramount to assessing the current deterioration status of a
bridge and assigning remediation or maintenance tasks so as to ensure the ongoing serviceability of the
structure. However, in recent years, there has been an increasing backlog of maintenance activities. Existing
research reveals that this is attributable to the labour-intensive, subjective and disruptive nature of the current
bridge inspection method. Current processes ultimately require lane closures, traffic guidance schemes and
inspection equipment. This not only increases the whole-of-life costs of the bridge, but also increases the risk
to the travelling public as issues affecting the structural integrity may go unaddressed. As a tool for bridge
condition inspections, Unmanned Aerial Vehicles (UAVs) or, drones, offer considerable potential, allowing a
bridge to be visually assessed without the need for inspectors to walk across the deck or utilise under-bridge
inspection units. With current inspection processes placing additional strain on the existing bridge
maintenance resources, the technology has the potential to significantly reduce the overall inspection costs
and disruption caused to the travelling public. In addition to this, the use of automated aerial image capture
enables engineers to better understand a situation through the 3D spatial context offered by UAV systems.
However, the use of UAV for bridge inspection involves a number of critical issues to be resolved, including
stability and accuracy of control, and safety to people. SLAM (Simultaneous Localisation and Mapping) is a
technique that could be used by a UAV to build a map of the bridge underneath, while simultaneously
determining its location on the constructed map. While there are considerable economic and risk-related
benefits created through introducing entirely new ways of inspecting bridges and visualising information,
there also remain hindrances to the wider deployment of UAVs. This study is to provide a context for use of
UAVs for conducting visual bridge inspections, in addition to addressing the obstacles that are required to be
overcome in order for the technology to be integrated into current practice.

Towards UAV-based bridge inspection systems: a review and an application perspective

This paper presents the components and overall architecture of the ubiquitous robot (Ubibot) system developed to demonstrate ubiquitous robotics, a new paradigm for integrated services. The system has been developed on the basis of the definition of the ubiquitous robot as that of encompassing the software robot Sobot, embedded robot Embot and the mobile robot Mobot. This tripartite partition, which independently manifests intelligence, perception and action, enables the abstraction of intelligence through the standardization of sensory data and motor or action commands. The Ubibot system itself is introduced along with its component subsystems of Embots, the position Embot, vision Embot and sound Embot, the Mobots of Mybot and HSR, the Sobot, Rity, a virtual pet modeled as an artificial creature, and finally the middleware which seamlessly enables interconnection between other components. Three kinds of experiments are devised to demonstrate the fundamental features, of calm sensing, context awareness and seamless service transcending the spatial limitations in the abilities of earlier generation personal robots. The experiments demonstrate the proof of concept of this powerful new paradigm which shows great promise.

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Jun Jo

Papers

Space layout planning using an evolutionary approach

Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

Emotion-based vehicle service system, emotion cognition processing apparatus, safe driving service apparatus, and emotion-based safe driving service method

Towards UAV-based bridge inspection systems: a review and an application perspective

Ubiquitous Robot: A New Paradigm for Integrated Services