FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization

As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating serving a robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.Comment: 13 pages, 9 figure

Drivers of brand loyalty in the chain coffee shop industry

The present study aimed to examine patrons’ loyalty generation process for a chain coffee shop brand by considering the role of cognitive drivers, affective drivers, brand satisfaction, and relationship commitment. A field survey was conducted in chain coffee shops located in the popular shopping districts of a metropolitan city in South Korea. The proposed model was evaluated by using a structural equation analysis. The results revealed that cognitive and affective factors were in general significantly interrelated; such associations along with brand satisfaction and relationship commitment significantly influenced brand loyalty; and, the brand satisfaction was the most important contributor to building brand loyalty. In addition, the mediating role of study variables was identified. Overall, the proposed theoretical framework contained a sufficient level of explanatory power for brand loyalty. With a lack of research about coffee shop customers’ purchasing behavior, the findings can be meaningfully used for the enhancement of customer loyalty

Impact of functional/cognitive and emotional advertisements on image and repurchase intention

In this study, we attempted to explore the role of chain steakhouse image (functional, symbolic, and experiential), pleasure, arousal, and brand satisfaction in building customer repurchase intention and to uncover the moderating impact of functional/cognitive and emotional advertisements (ads). A field survey was conducted for data collection. A quantitative approach was employed to analyze the data. Findings from the structural analysis showed that image, emotions, and satisfaction played a significant role in generating intention, both pleasure and satisfaction acted as mediators, and hypothesized associations were mostly supported. Moreover, the relationships among functional image, pleasure, and brand satisfaction within the proposed theoretical framework were significantly moderated by functional/cognitive and emotional ads. Implications for researchers and practitioners are discussed. Current research provides chain steakhouse management a better understanding of the underlying complicated mechanism of customers’ repurchase decision generation process

Lightweight user authentication scheme for roaming service in GLOMONET with privacy preserving.

With the development of information technology and the Internet, users can conveniently use roaming services without time and space restrictions. This roaming service is initiated by establishing a session key between a home node, which exists in a home network, and a mobile node, which exists in a foreign network. However, in the process of verifying a legitimate user and establishing a session key, various security threats and privacy exposure issues can arise. This study demonstrates that the authentication scheme for the roaming service proposed in the existing Global Mobility Network (GLOMONET) environment has several vulnerabilities and, hence, is impractical. In addition, the scheme does not satisfy the privacy of the session key or user's identity or password. Accordingly, we propose a new lightweight authentication scheme to compensate for these vulnerabilities and secure a high level of privacy, such as non-traceability. In addition, formal and informal analyses are conducted to examine the safety of the proposed scheme. Based on the results of our analyses, we prove that the proposed scheme is highly secure and applicable to the actual GLOMONET environment

Energy Consumption Analysis of Downward-Tethered Quadcopter

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Real-Time Optimal State Estimation Scheme With Delayed and Periodic Measurements

This paper proposes an efficient real-time optimal estimation scheme that uses accurate but delayed measurements obtained periodically from high-performance sensing devices. For real-time optimal estimation, we employ two Kalman filters: one to conventionally estimate the current state and the other to precompute for the future state estimation to be carried out, when a new, accurate but delayed measurement arrives. The precomputing Kalman filter does the necessary computation in advance, for the future state estimation, from the available measurements for distributing the computational burden over time, thereby obtaining an optimal estimate in real time. By optimally incorporating accurate but delayed measurements, the optimality is preserved at all times, without imposing a heavy computational burden in a short sampling time interval. It is demonstrated through experiments that the proposed scheme can significantly improve the estimation performance with the least detriment to the real-time computation and memory size, when delayed and periodic measurements are available.11Nsciescopu

Smart Count System Based on Object Detection Using Deep Learning

Object counting is an indispensable task in manufacturing and management. Recently, the development of image-processing techniques and deep learning object detection has achieved excellent performance in object-counting tasks. Accordingly, we propose a novel small-size smart counting system composed of a low-cost hardware device and a cloud-based object-counting software server to implement an accurate counting function and overcome the trade-off presented by the computing power of local hardware. The cloud-based object-counting software consists of a model adapted to the object-counting task through a novel DBC-NMS (our own technique) and hyperparameter tuning of deep-learning-based object-detection methods. With the power of DBC-NMS and hyperparameter tuning, the performance of the cloud-based object-counting software is competitive over commonly used public datasets (CARPK and SKU110K) and our custom dataset of small pills. Our cloud-based object-counting software achieves an mean absolute error (MAE) of 1.03 and a root mean squared error (RMSE) of 1.20 on the Pill dataset. These results demonstrate that the proposed smart counting system accurately detects and counts densely distributed object scenes. In addition, the proposed system shows a reasonable and efficient cost–performance ratio by converging low-cost hardware and cloud-based software

Comparison of Wear of Interim Crowns in Accordance with the Build Angle of Digital Light Processing 3D Printing: A Preliminary In Vivo Study

The aim of this study is to evaluate the wear volume of interim crowns fabricated using digital light processing 3D printing according to the printing angle. A total of five patients undergoing the placement of a single crown on the mandibular molar were included. Interim crowns were fabricated directly in the oral cavity using the conventional method. A digital light processing 3D printer was then used to fabricate crowns with build angles of 0, 45, and 90 degrees. Therefore, four fabricated interim crowns were randomly delivered to the patients, and each was used for one week. Before and after use, the intaglio surfaces of the interim crowns were scanned using a 3D scanner. The volume changes before and after use were measured, and changes in the height of the occlusal surface were evaluated using the root mean square value. Data normality was verified by statistical analysis, and the wear volume in each group was evaluated using a one-way analysis of variance and Tukey’s honestly significant difference test (α = 0.05). Compared with the RMS values of the conventional method (11.88 ± 2.69 µm) and the 3D-printing method at 0 degrees (12.14 ± 2.38 µm), the RMS values were significantly high at 90 degrees (16.46 ± 2.39 µm) (p p = 0.002), with a significantly higher volume change value at 90 degrees (1.74 ± 0.41 mm3) than in the conventional method (0.70 ± 0.15 mm3) (p < 0.05). A printing angle of 90 degrees is not recommended when interim crowns are fabricated using digital light processing 3D printing

A Novel Omnidirectional Depth Perception Method for Multi-rotor Micro Aerial Vehicles

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