How to Write a Dynamic Lesson Plan？ —Basis of Ignatian Pedagogical Paradigm

Future education should not allow teachers to become the protagonist of the classroom It should stimulate students self-efficacy make students the protagonist of the schoolroom and apply the knowledge they have learned in real life This article proposes the Ignatian Pedagogical Paradigm IPP introspective teaching method which first explains what the IPP is to apply this dynamic in the classroom secondly why the active lesson base on the IPP finally how to prepare the energetic lesson pla

Fractional Order Controller Designing with Firefly Algorithm and Parameter Optimization for Hydroturbine Governing System

A fractional order PID (FOPID) controller, which is suitable for control system designing for being insensitive to the variation in system parameter, is proposed for hydroturbine governing system in the paper. The simultaneous optimization for several parameters of controller, that is, Ki, Kd, Kp, λ, and μ, is done by a recently developed metaheuristic nature-inspired algorithm, namely, the firefly algorithm (FA), for the first time, where the selecting, moving, attractiveness behavior between fireflies and updating of brightness, and decision range are studied in detail to simulate the optimization process. Investigation clearly reveals the advantages of the FOPID controller over the integer controllers in terms of reduced oscillations and settling time. The present work also explores the superiority of FA based optimization technique in finding optimal parameters of the controller. Further, convergence characteristics of the FA are compared with optimum integer order PID (IOPID) controller to justify its efficiency. What is more, analysis confirms the robustness of FOPID controller under isolated load operation conditions

How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Three Essays on the Interrelationships Among Climate, Conflict and Economics

Conflict in a country is socially expensive and many are trying to understand what factors stimulate it in an effort to figure out ways to lessen its incidence. In this work three essays are presented on factors that drive conflict. The factors examined are: 1) the interrelationship between climate and conflict, 2) the causality between commodity prices and conflict, 3) the ways cereal demand affects and is affected by terrorism. In the first essay, we use a global dataset to econometrically explore whether the probability of conflict is affected by climate. We find that precipitation variation does have a statistically significant effect. That is, the less precipitation this year relative to the last, the more likely the country is to suffer from civil conflict. Methodologically the best predictions are obtained from a semiparametric estimation technique. In the second essay, we econometrically investigate the dynamic relationship between commodity prices and the onset of conflict in Sudan. Applying Structure Vector Autoregression (SVAR) and Linear Non-Gaussian Acyclic Model (LiNGAM), we find that wheat price is a cause of conflict events in Sudan. However, we find no feedback from conflict to commodity prices. In the third essay, we examine the extent that demand for three main cereals in Sudan (sorghum, millet, and wheat) is altered by the incidence of terrorism plus the effect of terrorism events on cereal demand. This is done by using an Almost Ideal Demand System (AIDS) and a Directed Acyclic Graph (DAG) approach. The results show terrorist attacks do cause changes in commodity demand for wheat. The DAG analysis also tentatively suggests that wheat demand is both marginally affected by and directly affecting the incidence of terrorism (conflict) in Sudan. Subsequently, we generate forecasts for the three commodities shares with the AIDS and DAG models, incorporating the effects of terrorist attacks. Examining those results independently and jointly, we find that a composite forecast of the two generates better forecasts

CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments. Most existing LPR methods use mundane representations of the input point cloud without considering different views, which may not fully exploit the information from LiDAR sensors. In this paper, we propose a cross-view transformer-based network, dubbed CVTNet, to fuse the range image views (RIVs) and bird's eye views (BEVs) generated from the LiDAR data. It extracts correlations within the views themselves using intra-transformers and between the two different views using inter-transformers. Based on that, our proposed CVTNet generates a yaw-angle-invariant global descriptor for each laser scan end-to-end online and retrieves previously seen places by descriptor matching between the current query scan and the pre-built database. We evaluate our approach on three datasets collected with different sensor setups and environmental conditions. The experimental results show that our method outperforms the state-of-the-art LPR methods with strong robustness to viewpoint changes and long-time spans. Furthermore, our approach has a good real-time performance that can run faster than the typical LiDAR frame rate. The implementation of our method is released as open source at: https://github.com/BIT-MJY/CVTNet.Comment: accepted by IEEE Transactions on Industrial Informatics 202

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.Comment: Submitted to IEEE Transactions on Industrial Electronic