Unmanned Ground Vehicle navigation and coverage hole patching in Wireless Sensor Networks

This dissertation presents a study of an Unmanned Ground Vehicle (UGV) navigation and coverage hole patching in coordinate-free and localization-free Wireless Sensor Networks (WSNs). Navigation and coverage maintenance are related problems since coverage hole patching requires effective navigation in the sensor network environment. A coordinate-free and localization-free WSN that is deployed in an ad-hoc fashion and does not assume the availability of GPS information is considered. The system considered is decentralized and can be self-organized in an event-driven manner where no central controller or global map is required. A single-UGV, single-destination navigation problem is addressed first. The UGV is equipped with a set of wireless listeners that determine the slope of a navigation potential field generated by the wireless sensor and actuator network. The navigation algorithm consists of sensor node level-number assignment that is determined based on a hop-distance from the network destination node and UGV navigation through the potential field created by triplets of actuators in the network. A multi-UGV, multi-destination navigation problem requires a path-planning and task allocation process. UGVs inform the network about their proposed destinations, and the network provides feedback if conflicts are found. Sensor nodes store, share, and communicate to UGVs in order to allocate the navigation tasks. A special case of a single-UGV, multi-destination navigation problem that is equivalent to the well-known Traveling Salesman Problem is discussed. The coverage hole patching process starts after a UGV reaches the hole boundary. For each hole boundary edge, a new node is added along its perpendicular bisector, and the entire hole is patched by adding nodes around the hole boundary edges. The communication complexity and present simulation examples and experimental results are analyzed. Then, a Java-based simulation testbed that is capable of simulating both the centralized and distributed sensor and actuator network algorithms is developed. The laboratory experiment demonstrates the navigation algorithm (single-UGV, single-destination) using Cricket wireless sensors and an actuator network and Pioneer 3-DX robot

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a $31$-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks. Code is available at: https://tinyurl.com/ShowAttendReadComment: Accepted to Proc. AAAI Conference on Artificial Intelligence 201

Resource levelling in repetitive construction projects with interruptions: an integrated approach

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result

Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation

Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an existing predictor from the formula. Since the computation of IPM only involves two distributions, this generalization term is independent with specific classifiers. With popular learning models, the empirical risk minimization is expressed as a general convex optimization problem and thus can be solved effectively by existing tools. Empirical studies on synthetic data for regression and real-world data for classification show the effectiveness of this method

Link Prediction via Sparse Gaussian Graphical Model

Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC) value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods

Sulfonated poly(arylene thioether phosphine oxide)s copolymers for proton exchange membrane fuel cells

Abstract High molecular weight sulfonated poly(arylene thioether phosphine oxide)s (sPATPO) with various sulfonation degrees were prepared directly by aromatic nucleophilic polycondensation of 4,4 -thiobisbenzenethiol with sulfonated bis(4-fluorophenyl) phenyl phosphine oxide and bis(4-fluorophenyl) phenyl phosphine oxide. sPATPO in the acid form with sulfonation degrees of 60-100% exhibits a glass transition temperature higher than 230 • C and a 5% weight loss temperature above 400 • C, indicating high thermal stability. sPATPO with a high sulfonation degree shows high proton conductivity and good resistance to swelling as well. For instance, sPATPO-70 displays the conductivity of 0.0783 S/cm and a swelling ratio of 11.6% at 90 • C. TEM micrographs showed that sPATPO membranes with a high sulfonation degree could form continuous ion channels, which are favorable for improving the proton conductivity but harmful to remaining the mechanical property. The membranes are expected to show good performances in fuel cell applications

The ocean's ultimate trashcan: Hadal trenches as major depositories for plastic pollution

Embargo until 24.09.2021Plastic debris and marine microplastics are being discharged into the ocean at an alarming scale and have been observed throughout the marine environment. Here we report microplastic in sediments of the Challenger Deep, the deepest known region on the planet, abyssal plains and hadal trenches located in the Pacific Ocean (4900 m–10,890 m). Microplastic abundance reached 71.1 items per kg dry weight sediment. That high concentrations are found at such remote depths, knowing the very slow sinking speed of microplastics, suggests that supporting mechanisms must be at-play. We discuss cascading processes that transport microplastics on their journey from land and oceanic gyres through intermediate waters to the deepest corners of the ocean. We propose that hadal trenches will be the ultimate sink for a significant proportion of the microplastics disposed in the ocean. The build-up of microplastics in hadal trenches could have large consequences for fragile deep-sea ecosystems.acceptedVersio

The prognosis impact of hyperthermic intraperitoneal chemotherapy (HIPEC) plus cytoreductive surgery (CRS) in advanced ovarian cancer: the meta-analysis

Abstract Background and objective Previous studies about the prognostic value of the HIPEC have yielded controversial results. Therefore, this study aims to assess the impact of HIPEC on patients with ovarian cancer. Results We included 13 comparative studies, and found that the overall survival (OS) and progression-free survival (PFS) in HIPEC groups were superior to groups without HIPEC treatment in the all total population (HR = 0.54,95% CI:0.45 to 0.66, HR = 0.45, 95% CI: 0.32 to 0.62). Additionally, the subgroup analysis showed that patients with advanced primary ovarian cancers also gained improved OS and PFS benefit from HIPEC (HR = 0.59,95% CI:0.46 to 0.75, HR = 0.41,95% CI:0.32 to 0.54). With regard to recurrent ovarian cancer, HIPEC was associated with improved OS (HR = 0.45,95% CI:0.24 to 0.83), but for the PFS, no correlation was observed between HIPC group and the non-HIPEC group (HR = 0.55,95% CI:0.27 to 1.11). HIPEC also led to favorable clinical outcome (HR = 0.64,95% CI:0.50 to 0.82, HR = 0.36,95% CI:0.20 to 0.65) for stage III or IV ovarian cancer with initial diagnosis. Conclusion The review indicated that HIPEC-based regimens was correlated with better clinical prognosis for patients with primary ovarian cancers. For recurrent ovarian cancers, HIPEC only improved the OS but did not elicit significant value on the PFS