We explore the class of trilevel equilibrium problems with a focus on energy-environmental applications and present a novel single-level reformulation for such problems, based on strong duality. To the best of our kno...
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The scope of this research is delimited by the study of not-for-profit organizations performance through operations management lens. According to a systematic literature review, the performance measurement frameworks ...
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Deep reinforcement learning (DRL) achieved significant progress in several areas enabling computers to perform complex decision-making tasks. Applied to quantitative trading, DRL trading agents can optimize their deci...
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To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work...
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We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of...
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Currently, changes in the customer’s mindset and increased flexibility in manufacturing shifted competition between companies to focus on the product’s value rather than its cost. In this context, Product-Service Sy...
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Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem...
Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.
In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically ind...
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In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.
This paper presents the context of the Ubiquitous computing course carried out in an industrialengineering undergraduateprogram throughout 2020 and the first semester of 2021. This course took advantage of the Insti...
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ISBN:
(纸本)9781665424899
This paper presents the context of the Ubiquitous computing course carried out in an industrialengineering undergraduateprogram throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduateprogram, which consists in the modernization of undergraduateengineeringprograms at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.
LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these...
LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.
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