Design of Feedforward Controller to Reduce Force Ripple for Linear Motor using Halbach Magnet Array with T Shape Magnet

AbstractRecently, in micro/nano fabrication equipments, linear motors are widely used as an actuator to position workpiece, machining tool and measurement head. To control them faster and more precise, the motor should have high actuating force and small force ripple. High actuating force enable us to more workpiece with high acceleration. Eventually, it may provide higher throughput. Force ripple gives detrimental effect on the precision and tracking performance of the equipments. In order to accomplish more precise motion, it is important to make lower the force ripple. Force ripple is categorized into cogging and mutual ripple. First is dependent on the shape of magnets and/or core. The second is not dependent on them but dependent on current commutation. In this work, coreless mover i.e. coil winding is applied to the linear motor to avoid the cogging ripple. Therefore, the mutual ripple is only considered to be minimized. Ideal Halbach magnet array has continuously varying magnetization. The THMA (Halbach magnet array with T shape magnets) is proposed to approximate the ideal one. The THMA can not produce ideal sinusoidal flux, therefore, the linear motor with THMA and sinusoidal commutation of current generates the mutual force ripple. In this paper, in order to compensate mutual force ripple by feedforward(FF) controller, we calculate the optimized commutation of input current. The ripple is lower than 1.17% of actuating force if the commutation current agree with the magnetic flux from THMA. The performance of feedforward(FF) controller is verified by experiment

Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network

Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes.Comment: 9 pages; AAAI 202

Misdiagnosis in occupational and environmental medicine: a scoping review

Introduction There has been no comprehensive review for misdiagnosis in Occupational and Environmental Medicine (OEM). The possible ramifications of an occupational disease (OD) or an environmental disease (ED) misdiagnosis are not just confined to the individual case but may extend to others exposed to the occupational or environmental hazard. Therefore, a comprehensive scoping review of published literature is imperative for understanding the nature of misdiagnoses in OEM. Methods A medical librarian searched MEDLINE (PubMed), EMBASE, and the Cochrane Library (on 06 November 2020). All collected OEM misdiagnoses were classified based on 2 conceptual frameworks, the typical framework, and the causation model. The distribution of misdiagnosis across each medical specialty, each diagnostic step of the typical framework and the causation model, and false-negative and false-positive were summarized. Results A total of 79 articles were included in the scoping review. For clinical specialty, pulmonology (30 articles) and dermatology or allergy (13 articles) was most frequent and second-most frequent, respectively. For each disease, occupational and environmental interstitial lung diseases, misdiagnosed as sarcoidosis (8 articles), and other lung diseases (8 articles) were most frequent. For the typical framework, the most vulnerable step was the first step, evidence of a disease (38 articles). For the causation model, the first step, knowledge base, was the most vulnerable step (42 articles). For reported articles, the frequency of false-negative (55 articles) outnumbered the frequency of false-positive (15 articles). Discussion In OEM, compared to general medicine, causal misdiagnosis associated with the probability of causation is also important. For making a diagnosis in OEM, a knowledge base about possible ODs and EDs is essential. Because of this reason, the education and training of treating physicians for common ODs and EDs are important. For ODs and EDs, various intentional behaviors of stakeholders should be considered. This scoping review might contribute to the improvement of understanding for misdiagnosis in OEM.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector

Learning to Discriminate Information for Online Action Detection

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202

Does Increasing Model Resolution Improve the Real-Time Forecasts of Western North Pacific Tropical Cyclones?

In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The experiment, with a coarse resolution of 12 km, could not significantly capture the intensification process, especially for maximum intensities (>60 m s(-1)). The intense TCs were better predicted by experiments using a moving nesting domain with a horizontal resolution of 4 km. The forecast errors for maximum wind speed and minimum sea-level pressure decreased in the experiment with higher resolution; the forecast of lifetime maximum intensity was improved. For the track forecast, the experiment with a coarser resolution tended to simulate TC tracks deviating rightward to the TC motions in the best-track data; this erroneous deflection was reduced in the experiment with a higher resolution. In particular, the track forecast in the experiment with a higher resolution improved more frequently for intense TCs that were generally distributed at relatively lower latitudes among the test cases. The sensitivity of the track forecast to the model resolution was relatively significant for lower-latitude TCs. On the other hand, the track forecasts of TCs moving to the mid-latitudes, which were primarily influenced by large-scale features, were not sensitive to the resolution

Addressing Negative Transfer in Diffusion Models

Diffusion-based generative models have achieved remarkable success in various domains. It trains a model on denoising tasks that encompass different noise levels simultaneously, representing a form of multi-task learning (MTL). However, analyzing and improving diffusion models from an MTL perspective remains under-explored. In particular, MTL can sometimes lead to the well-known phenomenon of $\textit{negative transfer}$, which results in the performance degradation of certain tasks due to conflicts between tasks. In this paper, we aim to analyze diffusion training from an MTL standpoint, presenting two key observations: $\textbf{(O1)}$ the task affinity between denoising tasks diminishes as the gap between noise levels widens, and $\textbf{(O2)}$ negative transfer can arise even in the context of diffusion training. Building upon these observations, our objective is to enhance diffusion training by mitigating negative transfer. To achieve this, we propose leveraging existing MTL methods, but the presence of a huge number of denoising tasks makes this computationally expensive to calculate the necessary per-task loss or gradient. To address this challenge, we propose clustering the denoising tasks into small task clusters and applying MTL methods to them. Specifically, based on $\textbf{(O2)}$, we employ interval clustering to enforce temporal proximity among denoising tasks within clusters. We show that interval clustering can be solved with dynamic programming and utilize signal-to-noise ratio, timestep, and task affinity for clustering objectives. Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods. We validate the proposed clustering and its integration with MTL methods through various experiments, demonstrating improved sample quality of diffusion models.Comment: 22 pages, 12 figures, under revie

Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed-source LLMs (i.e., ChatGPT and GPT-4) are capable of generating corrective feedback to edit erroneous inputs. However, it remains challenging for open-source code LLMs to generate feedback for code editing, since these models tend to adhere to the superficial formats of feedback and provide feedback with misleading information. Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing. To this end, we present Coffee, a collected dataset specifically designed for code fixing with feedback. Using this dataset, we construct CoffeePots, a framework for COde Fixing with FEEdback via Preference-Optimized Tuning and Selection. The proposed framework aims to automatically generate helpful feedback for code editing while minimizing the potential risk of superficial feedback. The combination of Coffee and CoffeePots marks a significant advancement, achieving state-of-the-art performance on HumanEvalFix benchmark. Codes and model checkpoints are publicly available at https://github.com/Lune-Blue/COFFEE.Comment: Work in progres

Effects of topography and sea surface temperature anomalies on heavy rainfall induced by Typhoon Chaba in 2016

Typhoon Chaba made landfall on the Korean Peninsula in the fall of 2016, resulting in record-breaking rainfall in southeastern Korea. In particular, the Ulsan metropolitan region experienced the most severe floods due to heavy rainfall of 319 mm for just 3 h. The heavy rainfall was possibly associated with the mountainous southeastern Korea topography and the warm sea surface temperature (SST) anomaly in the East China Sea. In this study, the Weather Research and Forecasting (WRF) model was used to investigate the effects of topography and SST anomalies through high-resolution numerical experiments. Simulation using original topography showed more rainfall on the windward and less on the leeward slope compared to the experiment with reduced topography around Ulsan. The moist flow in the typhoon was raised by orographic uplift, enhancing precipitation on the windward side and summits of the mountains. The orographically induced updraft extended to the mid-troposphere and contributed to the upward vertical moisture flux associated with rainfall. Therefore, the mountainous topography around Ulsan affected the local change in rainfall induced by the simulated typhoon. In addition, SST on the track of the typhoon controlled storm intensity and caused extreme precipitation changes. The experiment using the original SST in the East China Sea simulated less decayed typhoons and produced more precipitation compared to the experiment wherein the positive SST anomaly in the East China Sea was removed. The warm SST anomaly hindered the weakening of the typhoon moving northward to the mid-latitudes. At landfall, the stronger typhoon contained more water vapor, transported more moisture inland, and generated increased precipitation