Smart Road Danger Detection and Warning

Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information. To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy. Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. The EcRD is proposed to improve latency and reduce labeling cost, which is an Edge-cloud-based Road Damage detection and warning framework that leverages the fast-responding advantage of edges and the large storage and computation resources advantages of the cloud. In EcRD, a simple yet efficient road segmentation algorithm is introduced for fast and accurate road area detection by filtering out noisy backgrounds. Additionally, a light-weighted road damage detector is developed based on Gray Level Co-occurrence Matrix (GLCM) features on edges for rapid hazardous road damage detection and warning. Further, a multi-types road damage detection model is proposed for long-term road management on the cloud, embedded with a novel image-label generator based on Cycle-Consistent Adversarial Networks, which automatically generates images with corresponding labels to improve road damage detection accuracy further. EcRD achieves 91.96% accuracy with only 0.0043s latency, which is around 579 times faster than cloud-based approaches without affecting users' experience while requiring very low storage and labeling cost. Secondly, although EcRD relieves the problem of high latency by edge computing techniques, road users can only achieve warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' personal information. A novel FedRD named FedRD is developed to improve the coverage range of warning information and protect data privacy. In FedRD, a new hazardous road damage detection model is proposed leveraging the advantages of feature fusion. A novel adaptive federated learning strategy is designed for high-performance model learning from different edges. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results show that FedRD achieves similar high detection performance (i.e., 90.32% accuracy) but with more than 1000 times wider coverage than the state-of-the-art, and works well when some edges only have limited samples; besides, it largely preserves users' privacy. Finally, despite the success of EcRD and FedRD in improving latency and protecting privacy, they are only based on a single modality (i.e., image/video) while nowadays, different modalities data becomes ubiquitous. Also, the communication cost of EcRD and FedRD are very high due to undifferentiated data transmission (both normal and dangerous data) and frequent model exchanges in its federated learning setting, respectively. A novel edge-cloud-based privacy-preserving Federated Multimodal learning framework for Road Danger detection and warning named FedMRD is introduced to leverage the multi-modality data in the real-world and reduce communication costs. In FedMRD, a novel multimodal road danger detection model considering both inter-and intra-class relations is developed. A communication-efficient federated learning strategy is proposed for collaborative model learning from edges with non-iid and imbalanced data. Further, a new multimodal differential privacy technique for high dimensional multimodal data with multiple attributes is introduced to protect data privacy directly on users' devices before uploading to edges. Experimental results demonstrate that FedMRD achieves around 96.42% higher accuracy with only 0.0351s latency and up to 250 times less communication cost compared with the state-of-the-art, and enables collaborative learning from multiple edges with non-iid and imbalanced data in different modalities while preservers users' privacy.2021-11-2

The noncompact Schauder fixed point theorem in random normed modules

Random normed modules ($RN$ modules) are a random generalization of ordinary normed spaces, which are usually endowed with the two kinds of topologies -- the $(\varepsilon,\lambda)$-topology and the locally $L^0$-convex topology. The purpose of this paper is to give a noncompact generalization of the classical Schauder fixed point theorem for the development and financial applications of $RN$ modules. Motivated by the randomized version of the classical Bolzano-Weierstrauss theorem, we first introduce the two notions of a random sequentially compact set and a random sequentially continuous mapping under the $(\varepsilon,\lambda)$-topology and further establish their corresponding characterizations under the locally $L^0$-convex topology so that we can treat the fixed point problems under the two kinds of topologies in an unified way. Then we prove our desired Schauder fixed point theorem that in a $\sigma$-stable $RN$ module every continuous (under either topology) $\sigma$-stable mapping $T$ from a random sequentially compact closed $L^0$-convex subset $G$ to $G$ has a fixed point. The whole idea to prove the fixed point theorem is to find an approximate fixed point of $T$, but, since $G$ is not compact in general, realizing such an idea in the random setting forces us to construct the corresponding Schauder projection in a subtle way and carry out countably many decompositions for $T$ so that we can first obtain an approximate fixed point for each decomposition and eventually one for $T$ by the countable concatenation skill. Besides, the new fixed point theorem not only includes as a special case Bharucha-Reid and Mukherjea's famous random version of the classical Schauder fixed point theorem but also implies the corresponding Krasnoselskii fixed point theorem in $RN$ modules.Comment: 37 page

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}

Combustion Performance of Various Polylactic Acid Plastics with Different Porous Structures Constructed by 3D Printing

Polylactic acid (PLA) has intrigued widespread attention as a biodegradable and environmentally friendly polymer, and recent research has revealed that the use of porous PLA in heat sinks for thermal management materials offers promising development potential. However, the heat transfer performance is closely related to its structure theoretically, whether it is virgin, and how the pore structure affects its heat transfer. Therefore, a novel approach is proposed to address this issue by preparing porous PLA through 3D printing at low complexity and cost, the combustion performance is employed to evaluate the heat transfer indirectly, and the higher burning speed represents higher efficient heat transfer. A new framework is developed to investigate combustion performance and three series of PLA with different pore structures in pore shape, size, and interval are studied by combining experimental tests, respectively. It demonstrates that adjusting the pore structure of PLA significantly alters its combustion performance, evidenced by significant variations in flame growth index, which are 83% better for the 2 mm holes than the largest holes and 71% better for the 2 mm interval than for the sparsest pore structure. Generally, it provides some experimental basis for designing porous thermal management materials; the various pore structures generate different combustion performances, corresponding to various heat transfer

PrSLoc : Sybil attack detection for localization with private observers using differential privacy

During the localization process in wireless networks, risks such as Sybil attacks and nodes’ location privacy leakage can exist due to the open and shared nature of wireless networks. However, it is challenging to obtain accurate location for sensor nodes while preserving the privacy of observer nodes who assist in sensor node localization while in the presence of Sybil attacks. Therefore, in this paper, we propose a secure and private localization algorithm, PrSLoc, based on Differential Privacy and Approximate Point-In-Triangulation (APIT, a range-free positioning algorithm). Specifically, APIT is applied for sensor nodes’ localization based on the received signal strength (RSS) from observer nodes as APIT doesn't require special hardware or additional devices to estimate node positions. Besides, Differential Privacy technique is utilized to protect the privacy of observer nodes’ location information (i.e., nodes’ identity, location, and transmission power). Additionally, we mitigate Sybil attacks using a novel lightweight Sybil detection approach introduced in this paper that is based on the twice difference of the perceived RSS. Simulation results demonstrate that PrSLoc can efficiently eliminate Sybil nodes while preserving the observers’ privacy during wireless localization processes. Furthermore, the computation overhead and storage cost of the proposed PrSLoc are competitive compared with existing work. Our code can be found here: https://github.com/learning-lemon/PrSLoc-Sybil-Attack-Detection-for-Localization-with-Private-Observers-using-Differential-Privacy.git

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost

LbSP : Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization

Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users' privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users' privacy