83 research outputs found
Direct and moderating effects of experience and leadership in relation to occupying a critical network position
No full textThis study draws on social network theory to develop a new theoretical model to explain how experience and leadership influence critical network position. Broad analyses of the mediating role of leadership between experience and critical network position calls attention to the need to investigate the direct relationship between leadership and critical network position. Empirical examinations of the roles of leadership and experience within the social network context are lacking. We seek to fill this gap by testing our model in the knowledge-intensive sector. We conclude that both experience and leadership have positive direct and moderating effects on critical position occupation. Our findings have significant career implications for persons occupying critical network position
Spatial-Temporal Heatmap Construction Algorithms
No full textDescription:
We develop a spatial-temporal heatmap algorithm to extract features from raw mmWave data. The heatmap integrates multiple activity features, including the range of movement, velocity, and time duration of each activity repetition. You can generate the heatmaps as we published in the datasets we shared using this algorithm.
Algorithm description:
We first perform range-FFT and doppler-FFT signal processing on the raw data to derive distance and velocity measurements, respectively.We normalize the derived velocity information and present the velocity-distance relationship in time dimension.
To mitigate the environmental interference, we propose an environmental impact mitigation method by filtering out non-moving objects in doppler-range domain.
To integrate multi-dimensional features including velocity, distance, and temporal information, we propose to construct spatial-temporal heatmaps by accumulate the velocity of every distance in every doppler-range heatmap together as follows:
\begin{equation}\label{equ:locations} V_{q,t}=\sum_{p=1}^{D} (I_{p,q,t})\times v_{p,t}, p\in [1, D], q\in [1,R], \end{equation}
where Ip,q,t is the intensity of a frequency response in the doppler-range heatmap, p is the doppler index, q represents the range index, and t is the frame index. vp,t is the velocity corresponding to a doppler index p in frame t.
We normalize the derived V q,t and transfer the original instantaneous velocity-distance relationship to a more comprehensive spatial-temporal heatmap which describes the process of a workout as shown in the attachment.
We determines the 2D window size of each repetition according to its time duration and range of movement in the spatial-temporal heatmap.
For more detailed information about the spatial-temporal heatmap construction algorithm, please refer the following papers:
If your paper is related to our works, please cite our papers as follows.
Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave." In 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2022.
Bibtex:
@inproceedings{xie2022mmfit,
title={mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave},
author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},
booktitle={2022 International Conference on Computer Communications and Networks (ICCCN)},
pages={1--10},
year={2022},
organization={IEEE}
}
Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmEat: Millimeter wave-enabled environment-invariant eating behavior monitoring." Smart Health 23 (2022): 100236.
Bibtex:
@article{xie2022mmeat,
title={mmEat: Millimeter wave-enabled environment-invariant eating behavior monitoring},
author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},
journal={Smart Health},
volume={23},
pages={100236},
year={2022},
publisher={Elsevier}
Automatic Polyp and InstrumentSegmentation in MedAI-2021
No full textPolyp and instrument segmentation plays a vital role in the early diagnosis of colorectal cancer (CRC) in that physicians visually inspect the bowel with an endoscope to identify polyps. However, recent works only focus on the accuracy of prediction in the positive samples while omitting the False-Positive (FP) predictions in the negative samples that might mislead the physicians. Here, we propose a novel Dual Model Filtering (DMF) strategy, which efficiently removes FP predictions in negative samples with metrics based threshold setting. To better adapt high-resolution input with various distributions, we embed the PVTv2 backbone to the framework SINetV2 as our model since the polyp segmentation is one of the downstream tasks of camouflaged object detection (COD). Experiments on challenging MedAI datasets demonstrate our method achieves excellent performance. We also conduct extensive experiments to study the effectiveness of the DMF.Polyp and instrument segmentation plays a vital role in the early diagnosis of colorectal cancer (CRC) in that physicians visually inspect the bowel with an endoscope to identify polyps. However, recent works only focus on the accuracy of prediction in the positive samples while omitting the False-Positive (FP) predictions in the negative samples that might mislead the physicians. Here, we propose a novel Dual Model Filtering (DMF) strategy, which efficiently removes FP predictions in negative samples with metrics based threshold setting. To better adapting high-resolution input with various distributions, we embed the PVTv2~\cite{wang2021pvtv2} backbone to the framework SINetV2~\cite{fan2021concealed} as our model since the polyp segmentation is one of the downstream tasks of camouflaged object detection (COD). Experiments on challenging MedAI~\cite{MediAI2021} datasets demonstrate our method achieves excellent performance. We also conduct extensive experiments to study the effectiveness of the DMF
WiFi-based Eating Activity Recognition Dataset
No full textDescription:
The wifi-based eating activity recognition dataset is collected leveraging commodity WiFi. The dataset contains the extracted CSI features from 5 eating activities from 7 volunteers . We hope this dataset will help researchers to reproduce the former work of user eating activity recognition through WiFi sensing.
Dataset Format:
.dat files
Section 1: Device Configuration:
For the transmitter. we use a Nexus 6 smartphone powered by a 2.7 GHz quad-core Snapdragon 805 processor with 3 GB of RAM.
For the receiver, we use a Dell E6430 equipped with Intel 5300 802.11n WiFi wireless card and 6dBi rubber ducky external omni-directional antennas for extracting CSI readings.
The laptop which serves as the access point is configured to run in the netlink mode. Internet Control Message Protocol (ICMP) echo is sent from the laptop and replied by the smartphone to collect the CSI data.
The WiFi packet rate is 1000 pkts/s.
The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
Section 2: Data Format
We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:
5 eating activities are collected from 7 participants.
Each data file contains 20 rounds of one type of eating activity from each participant.
The dataset file name is presented as "User_Day_Action_Location". The detailed information as:
User: The participants that CSI was collected from.
Day: The date this data was collected.
Action: The specific eating activity performed.
Location: The specific location the experiment was conducted.
Section 3: Experimental Setup
Section 4: Data Description
Section 5: Citations
If your paper is related to our works, please cite our papers as follows.
https://eudl.eu/pdf/10.1007/978-3-030-42029-1_6
Lin, Zhenzhe, Yucheng Xie, Xiaonan Guo, Chen Wang, Yanzhi Ren, and Yingying Chen. "Wi-fi-enabled automatic eating moment monitoring using smartphones." In IoT Technologies for HealthCare: 6th EAI International Conference, HealthyIoT 2019, Braga, Portugal, December 4–6, 2019, Proceedings 6, pp. 77-91. Springer International Publishing, 2020.
Bibtex:
@inproceedings{lin2020wi,
title={Wi-fi-enabled automatic eating moment monitoring using smartphones},
author={Lin, Zhenzhe and Xie, Yucheng and Guo, Xiaonan and Wang, Chen and Ren, Yanzhi and Chen, Yingying},
booktitle={IoT Technologies for HealthCare: 6th EAI International Conference, HealthyIoT 2019, Braga, Portugal, December 4--6, 2019, Proceedings 6},
pages={77--91},
year={2020},
organization={Springer}
Special Issue: Advanced Modeling and Design for Composite Materials and Structures
No full textGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Structural Integrity & Composite
WiFi-based Eating Activity Recognition Dataset
No full textDescription:
The wifi-based eating activity recognition dataset is collected leveraging commodity WiFi. The dataset contains the extracted CSI features from 5 eating activities from 7 volunteers . We hope this dataset will help researchers to reproduce the former work of user eating activity recognition through WiFi sensing.
Dataset Format:
.dat files
Section 1: Device Configuration:
For the transmitter. we use a Nexus 6 smartphone powered by a 2.7 GHz quad-core Snapdragon 805 processor with 3 GB of RAM.
For the receiver, we use a Dell E6430 equipped with Intel 5300 802.11n WiFi wireless card and 6dBi rubber ducky external omni-directional antennas for extracting CSI readings.
The laptop which serves as the access point is configured to run in the netlink mode. Internet Control Message Protocol (ICMP) echo is sent from the laptop and replied by the smartphone to collect the CSI data.
The WiFi packet rate is 1000 pkts/s.
The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
Section 2: Data Format
We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:
5 eating activities are collected from 7 participants.
Each data file contains 20 rounds of one type of eating activity from each participant.
The dataset file name is presented as "User_Day_Action_Location". The detailed information as:
User: The participants that CSI was collected from.
Day: The date this data was collected.
Action: The specific eating activity performed.
Location: The specific location the experiment was conducted.
Section 3: Experimental Setup
Our system aims to imitate the real scenario when people are eating and placing their smartphones on the dining table as shown in the figure attached.
The user’s smartphone will send WiFi signals to the access point and sense different eating activities.
The participants are asked to eat with different utensils (i.e., fork, fork&knife, spoon, chopsticks, bare hand).
The distance between the laptop and the smartphone is 80 cm.
The data are collected at an lab with a size of (5.0m×3.0m).
Section 4: Data Description
We separate our raw data into different folders based on different eating activity types. In each activity type, data are further distributed in terms of users. All data files are in .dat format.
We create one zip files to store this dataset. There are 5 folders inside starting with "EA", each contains repetitions with different utensils.
5 eating activities and their corresponding files
File Name
Activity Type
EA1
Eating with chopsticks
EA2
Eating with fork
EA3
Eating with bare hand
EA4
Eating with fork&knife
EA5
Eating with spoon
Section 5: Citations
If your paper is related to our works, please cite our papers as follows.
https://eudl.eu/pdf/10.1007/978-3-030-42029-1_6
Lin, Zhenzhe, Yucheng Xie, Xiaonan Guo, Chen Wang, Yanzhi Ren, and Yingying Chen. "Wi-fi-enabled automatic eating moment monitoring using smartphones." In IoT Technologies for HealthCare: 6th EAI International Conference, HealthyIoT 2019, Braga, Portugal, December 4–6, 2019, Proceedings 6, pp. 77-91. Springer International Publishing, 2020.
Bibtex:
@inproceedings{lin2020wi,
title={Wi-fi-enabled automatic eating moment monitoring using smartphones},
author={Lin, Zhenzhe and Xie, Yucheng and Guo, Xiaonan and Wang, Chen and Ren, Yanzhi and Chen, Yingying},
booktitle={IoT Technologies for HealthCare: 6th EAI International Conference, HealthyIoT 2019, Braga, Portugal, December 4--6, 2019, Proceedings 6},
pages={77--91},
year={2020},
organization={Springer}
Vortex Analysis – Clustering and Temporal Tracking of Vortices
No full textMASTER OF SCIENCE (2024) (School of Computational Science and Engineering)
McMaster University Hamilton, Ontario, Canada
TITLE: Vortex Analysis – Clustering and Temporal Tracking of Vortices
AUTHOR: Yucheng Feng
M.Eng. (Electrical Engineering)
Xi’an Jiaotong University, Xi'an, Shaanxi, China
B.Eng. (Electrical Engineering)
Shandong University, Jinan, Shandong, China
SUPERVISOR: Dr. Li Xi
NUMBER OF PAGES: xix, 75The vortex is a fundamental concept in fluid dynamics, and analyzing it is crucial for explaining and predicting the behavior of fluids in practical applications. In this thesis, two techniques that can lead to a deeper understanding of vortices will be proposed and verified by applying them to Newtonian turbulence and polymer-added flow. The first technique is vortex clustering. By doing dimension reduction and clustering simultaneously, the performance of vortex clustering is notably improved since the hidden features that are immersed in the original input features but can efficiently distinguish different types of vortices can now be extracted objectively. Then, the reliability of the clustering technique is verified in various Newtonian flows. The second technique is vortex tracking based on vortex axis lines, which can efficiently provide complete evolving routines of each vortex over time. With this tracking method, temporal information of vortices, such as their detailed evolving routines and temporal drift positions, can be fully observed and recorded for a future study. The mechanisms and details of this tracking method will first be illustrated and verified using Newtonian flow. Finally, since these two techniques for vortex analysis are solely developed for Newtonian turbulence, a polymer-added flow, where a small amount of polymer can notably modify the behaviour of vortices in Newtonian turbulence, is introduced to check to which level these two techniques are still reliable. Moreover, these two techniques can be compatibly embedded into existing vortex analyzing tools. By doing this, the interested types of vortices can be found and isolated from others, and their specific features and routines can thus be thoroughly studied.ThesisMaster of Science (MSc)In turbulence research, efficient clustering and tracking of vortices are appealing. Hence, the fundamental motivation of this research is to investigate vortex clustering techniques and vortex tracking techniques to analyze vortices in turbulent flows automatically and objectively. With the proposed vortex clustering technique, the hidden features immersed in input data space that can efficiently distinguish different types of vortices can be extracted objectively to classify vortices into various groups. With the proposed vortex tracking technique, the temporal behaviours of vortices, such as their detailed developing routines, can be fully tracked, and recorded in a simple but efficient way. With these two techniques, our understanding of the differences between various types of vortices, the ways vortices evolve under different conditions, etc., can be further improved. Besides, embedding these two techniques in existing vortex analyzing tools makes them more powerful
Post-digital museum in transformation
No full textArchitecture, Urbanism and Building Sciences | Complex Project
Effects of Personal Characteristics on Music Recommender Systems with Different Levels of Controllability
No full textPrevious research has found that enabling users to control the recommendation process increases user satisfaction. However, providing additional controls also increases cognitive load, and different users have different needs for control. Therefore, in this study, we investigate the effect of two personal characteristics: musical sophistication and visual memory capacity. We designed a visual user interface, on top of a commercial music recommender, with different controls: interactions with recommendations (i.e., the output of a recommender system), the user profile (i.e., the top listened songs), and algorithm parameters (i.e., weights in an algorithm). We created eight experimental settings with combinations of these three user controls and conducted a between-subjects study (N=240), to explore the effect on cognitive load and recommendation acceptance for different personal characteristics. We found that controlling recommendations is the most favorable single control element. In addition, controlling user profile and algorithm parameters was the most beneficial setting with multiple controls. Moreover, the participants with high musical sophistication perceived recommendations to be of higher quality, which in turn lead to higher recommendation acceptance. However, we found no effect of visual working memory on either cognitive load or recommendation acceptance. This work contributes an understanding of how to design control that hits the sweet spot between the perceived quality of recommendations and acceptable cognitive load.Accepted author manuscriptWeb Information System
Monitoring Techniques in Modern Industrial Systems: Fault detection and non-intrusive load monitoring
No full textThe Monitoring technique plays a vital role in ensuring the proper functioning of modern industrial systems that are highly sophisticated and automated. On two different applications, this thesis investigates two major categories of information redundancy monitoring techniques, model-based and data-driven.The first application focuses on ground fault detection in microgrid systems. Leveraging the model information of the system, we propose a design approach for the fault detection filter by creating a linear programming problem. This design ensures the complete decoupling of the disturbance and guarantees fault sensitivity. Recognizing that decoupling is not always feasible, we create a new optimization problem by exploiting available disturbance patterns, so that the filter suppresses the impact of the disturbances while ensuring the fault sensitivity. Simulation studies validate the effectiveness of the proposed designs. The second application deals with non-intrusive load monitoring (NILM) in building systems. Our approach involves a two-stage process that utilizes data to perform NILM. In the first stage, events are identified from the aggregate load measurement. In the second stage, an integer programming problem is formulated to estimate the load for each appliance. The effectiveness of our method is evaluated on a real-world dataset and compared with several other NILM approaches, demonstrating competitive performance in terms of accuracy and computational complexity.Mechanical Engineering | Systems and Contro
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