138 research outputs found
Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment
Exploring and traversing extreme terrain with surface robots is difficult,
but highly desirable for many applications, including exploration of planetary
surfaces, search and rescue, among others. For these applications, to ensure
the robot can predictably locomote, the interaction between the terrain and
vehicle, terramechanics, must be incorporated into the model of the robot's
locomotion. Modeling terramechanic effects is difficult and may be impossible
in situations where the terrain is not known a priori. For these reasons,
learning a terramechanics model online is desirable to increase the
predictability of the robot's motion. A problem with previous implementations
of learning algorithms is that the terramechanics model and corresponding
generated control policies are not easily interpretable or extensible. If the
models were of interpretable form, designers could use the learned models to
inform vehicle and/or control design changes to refine the robot architecture
for future applications. This paper explores a new method for learning a
terramechanics model and a control policy using a model-based genetic
algorithm. The proposed method yields an interpretable model, which can be
analyzed using preexisting analysis methods. The paper provides simulation
results that show for a practical application, the genetic algorithm
performance is approximately equal to the performance of a state-of-the-art
neural network approach, which does not provide an easily interpretable model.Comment: Published in: 2019 IEEE Aerospace Conference Date of Conference: 2-9
March 2019 Date Added to IEEE Xplore: 20 June 201
Semantic Communication-Empowered Physical-layer Network Coding
In a two-way relay channel (TWRC), physical-layer network coding (PNC)
doubles the system throughput by turning superimposed signals transmitted
simultaneously by different end nodes into useful network-coded information
(known as PNC decoding). Prior works indicated that the PNC decoding
performance is affected by the relative phase offset between the received
signals from different nodes. In particular, some "bad" relative phase offsets
could lead to huge performance degradation. Previous solutions to mitigate the
relative phase offset effect were limited to the conventional bit-oriented
communication paradigm, aiming at delivering a given information stream as
quickly and reliably as possible. In contrast, this paper puts forth the first
semantic communication-empowered PNC-enabled TWRC to address the relative phase
offset issue, referred to as SC-PNC. Despite the bad relative phase offsets,
SC-PNC directly extracts the semantic meaning of transmitted messages rather
than ensuring accurate bit stream transmission. We jointly design deep neural
network (DNN)-based transceivers at the end nodes and propose a semantic PNC
decoder at the relay. Taking image delivery as an example, experimental results
show that the SC-PNC TWRC achieves high and stable reconstruction quality for
images under different channel conditions and relative phase offsets, compared
with the conventional bit-oriented counterparts
Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT
Abdominal organ and tumour segmentation has many important clinical
applications, such as organ quantification, surgical planning, and disease
diagnosis. However, manual assessment is inherently subjective with
considerable inter- and intra-expert variability. In the paper, we propose a
hybrid supervised framework, StMt, that integrates self-training and mean
teacher for the segmentation of abdominal organs and tumors using partially
labeled and unlabeled data. We introduce a two-stage segmentation pipeline and
whole-volume-based input strategy to maximize segmentation accuracy while
meeting the requirements of inference time and GPU memory usage. Experiments on
the validation set of FLARE2023 demonstrate that our method achieves excellent
segmentation performance as well as fast and low-resource model inference. Our
method achieved an average DSC score of 89.79\% and 45.55 \% for the organs and
lesions on the validation set and the average running time and area under GPU
memory-time cure are 11.25s and 9627.82MB, respectively
Clinical Study of Azithromycin in Treatment of Respiratory Tract Infections in Children
Objective: To observe the clinical efficacy and safety of azithromycin in the treatment of respiratory tract infections in children. Method: This study was select 110 cases of respiratory tract infection in our hospital from April 2013 to December 2014 as the research object. According to the random grouping method, the children were divided into two groups, 55 cases in the control group and 55 cases in the treatment group. On the basis of conventional treatment, the control group was treated with erythromycin 15 to 30 mg/kg per day for 1 week while for the treatment group was treated with Azithromycin 10 mg/kg per day by intravenous drip, and 8 mg/kg per day was administered orally for 4 days. To observe the clinical symptoms, signs, chest X-ray and adverse reactions of two groups before and after treatment. Results: The treatment group cure rate was significantly higher than that of the control group (p < 0.05), cough and fever disappearance time is shorter than that of the control group (p < 0.05), adverse reactions occurred rate of treatment group was lower than that of the control group (p < 0.05). Conclusion: The efficacy of azithromycin in the treatment of respiratory tract infections in children is reliable, less adverse reactions and it is worthy of promotion
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