386 research outputs found
Biomechanical analysis of asymmetric and dynamic lifting task
Lifting tasks is one of the leading causes of occupational lower back disorders (LBD). Aimed at deriving internal forces of human musculoskeletal system during lifting, biomechanical models are utilized to address this problem. This thesis provides an indepth literature review of such modeling, and the results of experiments used to address LBD issues.
An isometric pulling experiment was conducted to study the correlation between electromyography (EMG) and predicted muscle forces by AnyBody Modeling System™ with increasing hand loads. An infinite order polynomial (min/max) optimization criterion predicted percentage of maximum muscle forces, which achieved 98% correlation with normalized EMG. In a separate study, motion data during lifting of 13.6 kg (30 lb) weight at 0°, 30° and 60° asymmetry was collected by the OptiTrack™ sixcamera motion capture system to drive the AnyBody™ model. Erector spinae was the most activated muscle during lifting. When the lifting origin became more asymmetric toward the right direction, the right external oblique was more activated, and complementarily the right Internal oblique was less activated. Since oblique muscles can support an external moment more efficiently, and in addition the subject squatted more as the lifting origin became more asymmetric, L5/S1 joint forces decreased.
This study contributes to the design and evaluation of lifting tasks to minimize LBD
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning
Federated Learning (FL) is a privacy-preserving distributed deep learning
paradigm that involves substantial communication and computation effort, which
is a problem for resource-constrained mobile and IoT devices. Model
pruning/sparsification develops sparse models that could solve this problem,
but existing sparsification solutions cannot satisfy at the same time the
requirements for low bidirectional communication overhead between the server
and the clients, low computation overhead at the clients, and good model
accuracy, under the FL assumption that the server does not have access to raw
data to fine-tune the pruned models. We propose Complement Sparsification (CS),
a pruning mechanism that satisfies all these requirements through a
complementary and collaborative pruning done at the server and the clients. At
each round, CS creates a global sparse model that contains the weights that
capture the general data distribution of all clients, while the clients create
local sparse models with the weights pruned from the global model to capture
the local trends. For improved model performance, these two types of
complementary sparse models are aggregated into a dense model in each round,
which is subsequently pruned in an iterative process. CS requires little
computation overhead on the top of vanilla FL for both the server and the
clients. We demonstrate that CS is an approximation of vanilla FL and, thus,
its models perform well. We evaluate CS experimentally with two popular FL
benchmark datasets. CS achieves substantial reduction in bidirectional
communication, while achieving performance comparable with vanilla FL. In
addition, CS outperforms baseline pruning mechanisms for FL
Concept Matching: Clustering-based Federated Continual Learning
Federated Continual Learning (FCL) has emerged as a promising paradigm that
combines Federated Learning (FL) and Continual Learning (CL). To achieve good
model accuracy, FCL needs to tackle catastrophic forgetting due to concept
drift over time in CL, and to overcome the potential interference among clients
in FL. We propose Concept Matching (CM), a clustering-based framework for FCL
to address these challenges. The CM framework groups the client models into
concept model clusters, and then builds different global models to capture
different concepts in FL over time. In each round, the server sends the global
concept models to the clients. To avoid catastrophic forgetting, each client
selects the concept model best-matching the concept of the current data for
further fine-tuning. To avoid interference among client models with different
concepts, the server clusters the models representing the same concept,
aggregates the model weights in each cluster, and updates the global concept
model with the cluster model of the same concept. Since the server does not
know the concepts captured by the aggregated cluster models, we propose a novel
server concept matching algorithm that effectively updates a global concept
model with a matching cluster model. The CM framework provides flexibility to
use different clustering, aggregation, and concept matching algorithms. The
evaluation demonstrates that CM outperforms state-of-the-art systems and scales
well with the number of clients and the model size
High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization
In this paper, we propose a novel image interpolation algorithm, which is
formulated via combining both the local autoregressive (AR) model and the
nonlocal adaptive 3-D sparse model as regularized constraints under the
regularization framework. Estimating the high-resolution image by the local AR
regularization is different from these conventional AR models, which weighted
calculates the interpolation coefficients without considering the rough
structural similarity between the low-resolution (LR) and high-resolution (HR)
images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize
the interpolated HR image, which provides a way to modify these pixels with the
problem of numerical stability caused by AR model. In addition, a new
Split-Bregman based iterative algorithm is developed to solve the above
optimization problem iteratively. Experiment results demonstrate that the
proposed algorithm achieves significant performance improvements over the
traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual
Communications and Image Processing (VCIP) 201
Decoding Attentional State to Faces and Scenes Using EEG Brainwaves
Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future
Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study
Brain computer interface (BCI) provides promising applications in
neuroprosthesis and neurorehabilitation by controlling computers and robotic
devices based on the patient's intentions. Here, we have developed a novel BCI
platform that controls a personalized social robot using noninvasively acquired
brain signals. Scalp electroencephalogram (EEG) signals are collected from a
user in real-time during tasks of imaginary movements. The imagined body
kinematics are decoded using a regression model to calculate the user-intended
velocity. Then, the decoded kinematic information is mapped to control the
gestures of a social robot. The platform here may be utilized as a
human-robot-interaction framework by combining with neurofeedback mechanisms to
enhance the cognitive capability of persons with dementia.Comment: Presented in: 25th Iranian Conference on Electrical Engineering
(ICEE
A study on the impact of pre-trained model on Just-In-Time defect prediction
Previous researchers conducting Just-In-Time (JIT) defect prediction tasks
have primarily focused on the performance of individual pre-trained models,
without exploring the relationship between different pre-trained models as
backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT,
BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained
model as its backbone. We systematically explore the differences and
connections between these models. Specifically, we investigate the performance
of the models when using Commit code and Commit message as inputs, as well as
the relationship between training efficiency and model distribution among these
six models. Additionally, we conduct an ablation experiment to explore the
sensitivity of each model to inputs. Furthermore, we investigate how the models
perform in zero-shot and few-shot scenarios. Our findings indicate that each
model based on different backbones shows improvements, and when the backbone's
pre-training model is similar, the training resources that need to be consumed
are much more closer. We also observe that Commit code plays a significant role
in defect detection, and different pre-trained models demonstrate better defect
detection ability with a balanced dataset under few-shot scenarios. These
results provide new insights for optimizing JIT defect prediction tasks using
pre-trained models and highlight the factors that require more attention when
constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better
performance than DeepJIT and CC2Vec on the two datasets respectively under 2000
training samples. These findings emphasize the effectiveness of
transformer-based pre-trained models in JIT defect prediction tasks, especially
in scenarios with limited training data
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