Trajectory Control via Reinforcement Learning with RSS Model

Various researchers proposed several types of methods, algorithms, and simulator to control bottom hole assembly (BHA) while drilling a deviated well. These works consist of drilling, control, mechanical and electrical engineering knowledge. The last year at University of Stavanger, Jerez established a work for this purpose. The aim of this work developing a physical method to control bit directions through the well path inside RSS simulator environment. This study structured upon RSS simulator developed at University of Stavanger and propose a different perspective on trajectory control. After a serious struggle on RSS model to shape for reinforcement learning, managed to have a new environment for the trajectory control. This new environment cleaned all possible errors of RSS model. On the other hand, make possible to control path by weight on bit and rotational speed. Also, the observation parameters selected as coordinates, measured depth, and drilling time. Adding tool face angle and dog leg severity values to observation caused bad training for the agents. Afterward, according to discrete observations and actions there was two RL agent options given by MATLAB reinforcement learning toolbox. The first one is proximity policy optimization agent, and the other one is deep q-network agent. After countless training sessions on J shape well, managed to create significant reward functions to test the environment on different well shapes. First try made on J shape well with both RL agents offered by MATLAB and results were satisfactory. However, simulations attempt for S and complex shape wells were not precise and needs more development. Therefore, utilization of RL environment, reward function and optimization of time demand became crucial outputs of these attempts

A Simple Converse of Burnashev's Reliability

In a remarkable paper published in 1976, Burnashev determined the reliability function of variable-length block codes over discrete memoryless channels with feedback. Subsequently, an alternative achievability proof was obtained by Yamamoto and Itoh via a particularly simple and instructive scheme. Their idea is to alternate between a communication and a confirmation phase until the receiver detects the codeword used by the sender to acknowledge that the message is correct. We provide a converse that parallels the Yamamoto-Itoh achievability construction. Besides being simpler than the original, the proposed converse suggests that a communication and a confirmation phase are implicit in any scheme for which the probability of error decreases with the largest possible exponent. The proposed converse also makes it intuitively clear why the terms that appear in Burnashev's exponent are necessary.Comment: 10 pages, 1 figure, updated missing referenc

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP) Loss extends precision to a ranking-based loss function for classification (Chen et al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking-based loss function for both classification and localisation tasks. (ii) Thanks to using ranking for both tasks, aLRP naturally enforces high-quality localisation for high-precision classification. (iii) aLRP provides provable balance between positives and negatives. (iv) Compared to on average $\sim$6 hyperparameters in the loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP points, achieves $48.9$ AP without test time augmentation and outperforms all one-stage detectors. Code available at: https://github.com/kemaloksuz/aLRPLoss .Comment: NeurIPS 2020 spotlight pape

Generating Positive Bounding Boxes for Balanced Training of Object Detectors

Two-stage deep object detectors generate a set of regions-of-interest (RoI) in the first stage, then, in the second stage, identify objects among the proposed RoIs that sufficiently overlap with a ground truth (GT) box. The second stage is known to suffer from a bias towards RoIs that have low intersection-over-union (IoU) with the associated GT boxes. To address this issue, we first propose a sampling method to generate bounding boxes (BB) that overlap with a given reference box more than a given IoU threshold. Then, we use this BB generation method to develop a positive RoI (pRoI) generator that produces RoIs following any desired spatial or IoU distribution, for the second-stage. We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling. Using our generator as an analysis tool, we show that (i) IoU imbalance has an adverse effect on performance, (ii) hard positive example mining improves the performance only for certain input IoU distributions, and (iii) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level. Finally, we train Faster R-CNN using our pRoI generator and, compared to conventional training, obtain better or on-par performance for low IoUs and significant improvements when trained for higher IoUs for Pascal VOC and MS COCO datasets. The code is available at: https://github.com/kemaloksuz/BoundingBoxGenerator.Comment: To appear in WACV 2

Florid cemento-osseous dysplasia: report of a case documented with clinical,radiographic, biochemical and histological findings

Florid cemento-osseous dysplasia (FCOD) has been described as a condition that characteristically affects the jaws of middle-aged black women. This condition has also been classified as gigantiform cementoma, chronic sclerosing osteomyelitis, sclerosing osteitis, multiple estenosis and sclerotic cemental masses. It usually exhibits as multiple radiopaque cementum-like masses distributed throughout the jaws. Radiographically, FCOD appears as dense, lobulated masses, often symmetrically located in various regions of the jaws. Computed tomography, because of its ability to give axial, sagittal, and frontal views, is useful in the evaluation of these lesions. This article reports the case of a 45-year-old white man who was diagnosed with FCOD on the basis of clinical, radiographic, biochemical and histological findings. It is of major importance to realize that all dentists have a unique opportunity as well as ethical obligation to assist in the struggle against wrong dental treatments that might save patients dental health. This case report illustrates the point that periapical radiolucencies may represent benign fibro-osseous lesions that may be overlooked or result in unnecessary endodontic treatment