SMART SENSOR AND TRACKING SYSTEM FOR UNDERGROUND MINING

The thesis predominantly discusses a smart sensor and tracking system for under- ground mining, as developed by the author. The tracking system is developed by two steps, the rst of which involves nding an e cient way to measure the distance, and the second of which involves localizing the positions of each miner in real-time. For the rst step, a Received Signal Strength Indicator (RSSI) is used to measure the distance between two points by indicating the amount of energy lost during the transmission. Due to environmental and human factors, errors exist when using RSSI to measure distance. Three methods are taken to reduce the error: Gaussian distribution, statistical average and preset points. It can be observed that the average error between actual distance and measured distance is only 0.1145 meters using the proposed model. In regards to the localization, the "3-point localization method" is considered rst. With the proposed method, the result of the localization is improved by 0.6 meters, as compared to the "2-point localization method". The transmission method for the project is then discussed. After comparing sev- eral transmission protocols in the market, ZigBee was chosen for the signal trans- mission. With the Zigbee protocol, up to 65000 nodes can be connected, which are suitable for many miners using the system at the same time. The power supply for the ZigBee protocol is only 1mW for each unit, thus potentially saving a great amount of energy during the transmission. To render the tracking system more powerful, two smart sensors are installed: an MQ-2 sensor and a temperature sensor. The MQ-2 sensor is used to detect the harmful gas and smoke. In the event that the sensor's detected value is beyond the threshold, it will provide a warning for the supervisor on the ground

Antheraea pernyi Silk Fiber: A Potential Resource for Artificially Biospinning Spider Dragline Silk

The outstanding properties of spider dragline silk are likely to be determined by a combination of the primary sequences and the secondary structure of the silk proteins. Antheraea pernyi silk has more similar sequences to spider dragline silk than the silk from its domestic counterpart, Bombyx mori. This makes it much potential as a resource for biospinning spider dragline silk. This paper further verified its possibility as the resource from the mechanical properties and the structures of the A. pernyi silks prepared by forcible reeling. It is surprising that the stress-strain curves of the A. pernyi fibers show similar sigmoidal shape to those of spider dragline silk. Under a controlled reeling speed of 95 mm/s, the breaking energy was 1.04 × 105 J/kg, the tensile strength was 639 MPa and the initial modulus was 9.9 GPa. It should be noted that this breaking energy of the A. pernyi silk approaches that of spider dragline silk. The tensile properties, the optical orientation and the β-sheet structure contents of the silk fibers are remarkably increased by raising the spinning speeds up to 95 mm/s

Failure Analysis

Two approaches to failure analysis are explained: analysis of individual failures and statistical analysis. Various criteria for failure sorting and classification are presented, as well as the main causes and mechanisms of failures. The text is accompanied by figures with characteristic fracture patterns. The chapter is complemented by an example of computer aided sorting of failures in railway driving vehicles

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.Comment: 0 pages, 5 figure

Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number of pixels occupied by a unit-length object in image as a metric of the sensing quality of the object, which is determined by the parameters of the camera, such as intrinsic, extrinsic, and distortional coefficients. Then, we form a single quantity that measures the sensing quality of the targets by the camera network. This quantity further serves as the objective function of our optimization problem to obtain the optimal camera configuration. We verify the effectiveness of our approach through extensive simulations and experiments, and the results reveal its improved performance on the AprilTag detection tasks. Codes and related utilities for this work are open-sourced and available at https://github.com/sszxc/MultiCam-Simulation.Comment: The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure

PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region

The Three-River-Source region is a highly significant natural reserve in China that harbors a plethora of untamed botanical resources. To meet the practical requirements of botanical research and intelligent plant management, we construct a large-scale dataset for Plant detection in the Three-River-Source region (PTRS). This dataset comprises 6965 high-resolution images of 2160*3840 pixels, captured by diverse sensors and platforms, and featuring objects of varying shapes and sizes. Subsequently, a team of botanical image interpretation experts annotated these images with 21 commonly occurring object categories. The fully annotated PTRS images contain 122, 300 instances of plant leaves, each labeled by a horizontal rectangle. The PTRS presents us with challenges such as dense occlusion, varying leaf resolutions, and high feature similarity among plants, prompting us to develop a novel object detection network named PlantDet. This network employs a window-based efficient self-attention module (ST block) to generate robust feature representation at multiple scales, improving the detection efficiency for small and densely-occluded objects. Our experimental results validate the efficacy of our proposed plant detection benchmark, with a precision of 88.1%, a mean average precision (mAP) of 77.6%, and a higher recall compared to the baseline. Additionally, our method effectively overcomes the issue of missing small objects. We intend to share our data and code with interested parties to advance further research in this field.Comment: 10 pages, 5 figure

LtpA, a CdnL-type CarD regulator, is important for the enzootic cycle of the Lyme disease pathogen

Little is known about how Borrelia burgdorferi, the Lyme disease pathogen, adapts and survives in the tick vector. We previously identified a bacterial CarD N-terminal-like (CdnL) protein, LtpA (BB0355), in B. burgdorferi that is preferably expressed at lower temperatures, which is a surrogate condition mimicking the tick portion of the enzootic cycle of B. burgdorferi. CdnL-family proteins, an emerging class of bacterial RNAP-interacting transcription factors, are essential for the viability of Mycobacterium tuberculosis and Myxococcus xanthus. Previous attempts to inactivate ltpA in B. burgdorferi have not been successful. In this study, we report the construction of a ltpA mutant in the infectious strain of B. burgdorferi, strain B31-5A4NP1. Unlike CdnL in M. tuberculosis and M. xanthus, LtpA is dispensable for the viability of B. burgdorferi. However, the ltpA mutant exhibits a reduced growth rate and a cold-sensitive phenotype. We demonstrate that LtpA positively regulates 16S rRNA expression, which contributes to the growth defects in the ltpA mutant. The ltpA mutant remains capable of infecting mice, albeit with delayed infection. Additionally, the ltpA mutant produces markedly reduced spirochetal loads in ticks and was not able to infect mice via tick infection. Overall, LtpA represents a novel regulator in the CdnL family that has an important role in the enzootic cycle of B. burgdorferi