The Improvement of Motor Cooling Through Stator Profile Optimization using CFD Analysis in Hermetic Scroll Compressors

With the increased pressure of cost, hermetic compressor sizing become an important part of the design optimization process, while maintain the same performance level in the same time. Hermetic compressor motor design also face the same challenge. However, reducing motor size for same compressor capacity will increase the motor power density, with the same motor cooling design as before, motor running temperature will be increased and this affect both motor life and reliability as well as passing UL certification requirement for compressors using OLP ( internal overload protector). Hermetic motor cooling improvement using thermal modelling has been investigated before by using thermal network method, with limited accuracy since this do not taken all thermal correlation between motor and compressors. (refer to Purdue paper: thermal modelling ro the motor in semi-hermetic screw refrigeration compressor under part load conditions) However, the real situation in terms of thermal and fluid distribution which affect motor cooling inside the compressor in much more complicated than a pure thermal lumped circuit can represent. With the usage of fluid and thermal coupled simulation method today, we can simulate and understand more accurately the correlation between motor and compressor heat transfer and fluid distribution to optimize motor cooling channel, both in static and dynamic stage, and keep motor temperature under the accepted level while main a good flow for the overall compressor performance. In this paper, different geometry of stator outer diameter profiles are investigated, to balance between the motor efficiency impacts versus the motor temperature increase, compressor and conclude the optimization in terms of stator outer profile for the compressor structure discussed in this paper. Motor efficiency results are calculated and also tested, motor cooling optimization also calculated and tested inside compressor. From the research work done here, we can see that by using CFD tool (ANSYS), compressor motor design engineers can find the optimal stator lamination design, and understand the biggest influence factor to motor cooling, but not important for motor electromagnetic design and performance. In future, if the design concentrated on the important factors, and optimize the motor cooling, compressor design in terms of sizing and cooling correlation would be well balanced between cost and performance

Masked Images Are Counterfactual Samples for Robust Fine-tuning

Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data demonstrate unprecedented robustness to various distribution shifts. However, fine-tuning on these models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. Existing methods for tackling this trade-off do not explicitly address the OOD robustness problem. In this paper, based on causal analysis on the aforementioned problems, we propose a novel fine-tuning method, which use masked images as counterfactual samples that help improving the robustness of the fine-tuning model. Specifically, we mask either the semantics-related or semantics-unrelated patches of the images based on class activation map to break the spurious correlation, and refill the masked patches with patches from other images. The resulting counterfactual samples are used in feature-based distillation with the pre-trained model. Extensive experiments verify that regularizing the fine-tuning with the proposed masked images can achieve a better trade-off between ID and OOD performance, surpassing previous methods on the OOD performance. Our code will be publicly available.Comment: Accepted by CVPR 2023 (v2: improve the clarity

Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection

We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge. Additionally, we developed a practical fingerprint recognition system with 94.68% accuracy, earning second place in LivDet 2023 Liveness Detection in Action. By investigating various methods, particularly style transfer, we demonstrate improvements in accuracy and generalization when faced with limited training data. As a result, our approach achieved state-of-the-art performance in LivDet 2023 Challenges.Comment: 1st Place in LivDet2023 Fingerprint Representation Challeng

Internet of vehicles for e-health applications : a potential game for optimal network capacity

Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective - the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments. © 2007-2012 IEEE

Lifting the Veil: Unlocking the Power of Depth in Q-learning

With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great success in numerous applications, including recommender systems, supply chains, games, and robotic manipulation. However, the success of deep Q-learning lacks solid theoretical verification and interpretability. The aim of this paper is to theoretically verify the power of depth in deep Q-learning. Within the framework of statistical learning theory, we rigorously prove that deep Q-learning outperforms its traditional version by demonstrating its good generalization error bound. Our results reveal that the main reason for the success of deep Q-learning is the excellent performance of deep neural networks (deep nets) in capturing the special properties of rewards namely, spatial sparseness and piecewise constancy, rather than their large capacities. In this paper, we make fundamental contributions to the field of reinforcement learning by answering to the following three questions: Why does deep Q-learning perform so well? When does deep Q-learning perform better than traditional Q-learning? How many samples are required to achieve a specific prediction accuracy for deep Q-learning? Our theoretical assertions are verified by applying deep Q-learning in the well-known beer game in supply chain management and a simulated recommender system