arXiv subjects

Lei Fan

Publications and source records attributed to Lei Fan.

At least 109 records · Page 6Linked to original sources

arXiv2023

Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection

Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain quality and facilitate grain circulation and processing. However, GAI is routinely performed manually by inspectors with cumbersome procedures, which poses a significant bottleneck in smart agriculture. In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp. By analyzing the distinctive characteristics of grain kernels, we formulate GAI as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible kernels are considered normal samples while damaged grains or unknown objects are regarded as anomalies. We further propose an AD model, called AD-GAI, which is trained using only normal samples yet can identify anomalies during inference. Moreover, we customize a prototype device for data acquisition and create a large-scale dataset including 220K high-quality images of wheat and maize kernels. Through extensive experiments, AD-GAI achieves considerable performance in comparison with advanced AD methods, and AI4GrainInsp has highly consistent performance compared to human experts and excels at inspection efficiency over 20x speedup. The dataset, code and models will be released at https://github.com/hellodfan/AI4GrainInsp.

cs.CV↗

arXiv2023

PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models

Privacy policies serve as the primary conduit through which online service providers inform users about their data collection and usage procedures. However, in a bid to be comprehensive and mitigate legal risks, these policy documents are often quite verbose. In practical use, users tend to click the Agree button directly rather than reading them carefully. This practice exposes users to risks of privacy leakage and legal issues. Recently, the advent of Large Language Models (LLM) such as ChatGPT and GPT-4 has opened new possibilities for text analysis, especially for lengthy documents like privacy policies. In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM. This framework was tested using two datasets. The first dataset comprises of privacy policies from 115 websites, which were meticulously annotated by legal experts, categorizing each segment into one of 10 classes. The second dataset consists of privacy policies from 304 popular mobile applications, with each sentence manually annotated and classified into one of another 10 categories. Under zero-shot learning conditions, PolicyGPT demonstrated robust performance. For the first dataset, it achieved an accuracy rate of 97%, while for the second dataset, it attained an 87% accuracy rate, surpassing that of the baseline machine learning and neural network models.

cs.CL↗

arXiv2023

Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance

In real-world scenarios, typical visual recognition systems could fail under two major causes, i.e., the misclassification between known classes and the excusable misbehavior on unknown-class images. To tackle these deficiencies, flexible visual recognition should dynamically predict multiple classes when they are unconfident between choices and reject making predictions when the input is entirely out of the training distribution. Two challenges emerge along with this novel task. First, prediction uncertainty should be separately quantified as confusion depicting inter-class uncertainties and ignorance identifying out-of-distribution samples. Second, both confusion and ignorance should be comparable between samples to enable effective decision-making. In this paper, we propose to model these two sources of uncertainty explicitly with the theory of Subjective Logic. Regarding recognition as an evidence-collecting process, confusion is then defined as conflicting evidence, while ignorance is the absence of evidence. By predicting Dirichlet concentration parameters for singletons, comprehensive subjective opinions, including confusion and ignorance, could be achieved via further evidence combinations. Through a series of experiments on synthetic data analysis, visual recognition, and open-set detection, we demonstrate the effectiveness of our methods in quantifying two sources of uncertainties and dealing with flexible recognition.

cs.CV↗

arXiv2023

Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models

Landslides are a common natural disaster that can cause casualties, property safety threats and economic losses. Therefore, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. A commonly used means is to carry out a landslide susceptibility assessment based on a landslide inventory and a set of landslide contributing factors. This can be readily achieved using machine learning (ML) models such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (Xgboost), or deep learning (DL) models such as convolutional neural network (CNN) and long short time memory (LSTM). As the input data for these models, landslide contributing factors have varying influences on landslide occurrence. Therefore, it is logically feasible to select more important contributing factors and eliminate less relevant ones, with the aim of increasing the prediction accuracy of these models. However, selecting more important factors is still a challenging task and there is no generally accepted method. Furthermore, the effects of factor selection using various methods on the prediction accuracy of ML and DL models are unclear. In this study, the impact of the selection of contributing factors on the accuracy of landslide susceptibility predictions using ML and DL models was investigated. Four methods for selecting contributing factors were considered for all the aforementioned ML and DL models, which included Information Gain Ratio (IGR), Recursive Feature Elimination (RFE), Particle Swarm Optimization (PSO), Least Absolute Shrinkage and Selection Operators (LASSO) and Harris Hawk Optimization (HHO). In addition, autoencoder-based factor selection methods for DL models were also investigated. To assess their performances, an exhaustive approach was adopted,...

cs.LG↗

arXiv2023

Strategic Investments of Large Scale Battery Energy Storage Systems in the Wholesale Electricity Market

In this paper, we study the strategic investment problem of battery energy storage systems (BESSs) in the wholesale electricity market from the perspective of BESSs owners. Large-scale BESSs planning without considering the possible wholesale market price change may result in possible locational marginal price (LMP) changes. To overcome such limits, we propose a three-phase approach for the BESS investment problem. In Phase-1, we conduct a search for the optimal BESS configurations via a congestion-based heuristics and Bayesian optimization. In Phases 2 and 3, we alternatively dispatch optimization tasks to optimize the wholesale market clearing for LMPs and identify the optimal schedule for BESSs operations. We validate the model using a ten-year simulation on the Electric Reliability Council of Texas (ERCOT) market. Experimental results show that the Bayesian optimization model runs 16 times faster than the grid search model in achieving the same solution quality. Further, the iterative method demonstrates that the model without considering possible LMP changes makes a 21% profit overestimation.

math.OC↗

arXiv2023

SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening

Pansharpening is a process of fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image to create a high-resolution multispectral image. A novel single-branch, single-scale lightweight convolutional neural network, named SDRCNN, is developed in this study. By using a novel dense residual connected structure and convolution block, SDRCNN achieved a better trade-off between accuracy and efficiency. The performance of SDRCNN was tested using four datasets from the WorldView-3, WorldView-2 and QuickBird satellites. The compared methods include eight traditional methods (i.e., GS, GSA, PRACS, BDSD, SFIM, GLP-CBD, CDIF and LRTCFPan) and five lightweight deep learning methods (i.e., PNN, PanNet, BayesianNet, DMDNet and FusionNet). Based on a visual inspection of the pansharpened images created and the associated absolute residual maps, SDRCNN exhibited least spatial detail blurring and spectral distortion, amongst all the methods considered. The values of the quantitative evaluation metrics were closest to their ideal values when SDRCNN was used. The processing time of SDRCNN was also the shortest among all methods tested. Finally, the effectiveness of each component in the SDRCNN was demonstrated in ablation experiments. All of these confirmed the superiority of SDRCNN.

cs.CV↗

arXiv2023

Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study

The heart's contraction is caused by electrical excitation which propagates through the heart muscle. It was recently shown that the electrical excitation can be computed from the contractile motion of a simulated piece of heart muscle tissue using deep learning. In cardiac electrophysiology, a primary diagnostic goal is to identify electrical triggers or drivers of heart rhythm disorders. However, using electrical mapping techniques, it is currently impossible to map the three-dimensional morphology of the electrical waves throughout the entire heart muscle, especially during ventricular arrhythmias. Therefore, the approach to calculate or predict electrical excitation from the hearts motion could be a promising alternative diagnostic approach. Here, we demonstrate in computer simulations that it is possible to predict three-dimensional electrical wave dynamics from ventricular deformation mechanics using deep learning. We performed thousands of simulations of electromechanical activation dynamics in ventricular geometries and used the data to train a neural network which subsequently predicts the three-dimensional electrical wave pattern that caused the deformation. We demonstrate that, next to focal wave patterns, even complicated three-dimensional electrical wave patterns can be reconstructed, even if the network has never seen the particular arrhythmia. We show that the deep learning model has the ability to generalize by training it on data generated with the smoothed particle hydrodynamics (SPH) method and subsequently applying it to data generated with the finite element method (FEM). Predictions can be performed in the presence of scars and with significant heterogeneity. Our results suggest that, deep neural networks could be used to calculate intramural action potential wave patterns from imaging data of the motion of the heart muscle.

physics.med-ph↗

arXiv2023

OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images

Semantic segmentation of multichannel images is a fundamental task for many applications. Selecting an appropriate channel combination from the original multichannel image can improve the accuracy of semantic segmentation and reduce the cost of data storage, processing and future acquisition. Existing channel selection methods typically use a reasonable selection procedure to determine a desirable channel combination, and then train a semantic segmentation network using that combination. In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network. Based on this concept, a One-Shot Task-Adaptive (OSTA) channel selection method is proposed for the semantic segmentation of multichannel images. OSTA has three stages, namely the supernet training stage, the pruning stage and the fine-tuning stage. The outcomes of six groups of experiments (L7Irish3C, L7Irish2C, L8Biome3C, L8Biome2C, RIT-18 and Semantic3D) demonstrated the effectiveness and efficiency of OSTA. OSTA achieved the highest segmentation accuracies in all tests (62.49% (mIoU), 75.40% (mIoU), 68.38% (mIoU), 87.63% (mIoU), 66.53% (mA) and 70.86% (mIoU), respectively). It even exceeded the highest accuracies of exhaustive tests (61.54% (mIoU), 74.91% (mIoU), 67.94% (mIoU), 87.32% (mIoU), 65.32% (mA) and 70.27% (mIoU), respectively), where all possible channel combinations were tested. All of this can be accomplished within a predictable and relatively efficient timeframe, ranging from 101.71% to 298.1% times the time required to train the segmentation network alone. In addition, there were interesting findings that were deemed valuable for several fields.

cs.CV↗

arXiv2023

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics.

physics.ins-det↗

arXiv2023

Deformation measurement of a soil mixing retaining wall using terrestrial laser scanning

Retaining walls are often built to prevent excessive lateral movements of the ground surrounding an excavation site. During an excavation, failure of retaining walls could cause catastrophic accidents and hence their lateral deformations are monitored regularly. Laser scanning can rapidly acquire the spatial data of a relatively large area at fine spatial resolutions, which is ideal for monitoring retaining walls' deformations. This paper attempts to apply laser scanning to measurements of the lateral deformations of a soil mixing retaining wall at an ongoing excavation site. Reference measurements by total station and inclinometer were also conducted to verify those from the laser scanning. The deformations derived using laser scanning data were consistent with the reference measurements at the top part of the retaining wall (i.e., mainly the ring beam of the wall). This research also shows that the multi-scale-model-to-model method was the most accurate deformation estimation method on the research data.

cs.CV↗

arXiv2023

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

physics.ins-det↗

arXiv2023

Comparisons of five indices for estimating local terrain surface roughness using LiDAR point clouds

Terrain surface roughness is an abstract concept, and its quantitative description is often vague. As such, there are various roughness indices used in the literature, the selection of which is often challenging in applications. This study compared the terrain surface roughness maps quantified by five commonly used roughness indices, and explored their correlations for four terrain surfaces of distinct surface complexities. These surfaces were represented by digital elevation models (DEMs) constructed using airborne LiDAR (Light Detection and Ranging) data. The results of this study reveal the similarity in the global patterns of the local surface roughness maps derived, and the distinctions in their local patterns. The latter suggests the importance of considering multiple indices in the studies where local roughness values are the critical inputs to subsequent analyses.

math.NA↗

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image

Semantic segmentation of Very High Resolution (VHR) remote sensing images is a fundamental task for many applications. However, large variations in the scales of objects in those VHR images pose a challenge for performing accurate semantic segmentation. Existing semantic segmentation networks are able to analyse an input image at up to four resizing scales, but this may be insufficient given the diversity of object scales. Therefore, Multi Scale (MS) test-time data augmentation is often used in practice to obtain more accurate segmentation results, which makes equal use of the segmentation results obtained at the different resizing scales. However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation. Based on this behaviour, a Stacking-Based Semantic Segmentation (SBSS) framework is proposed to improve the segmentation results by learning this behaviour, which contains a learnable Error Correction Module (ECM) for segmentation result fusion and an Error Correction Scheme (ECS) for computational complexity control. Two ECS, i.e., ECS-MS and ECS-SS, are proposed and investigated in this study. The Floating-point operations (Flops) required for ECS-MS and ECS-SS are similar to the commonly used MS test and the Single-Scale (SS) test, respectively. Extensive experiments on four datasets (i.e., Cityscapes, UAVid, LoveDA and Potsdam) show that SBSS is an effective and flexible framework. It achieved higher accuracy than MS when using ECS-MS, and similar accuracy as SS with a quarter of the memory footprint when using ECS-SS.

cs.CV↗

arXiv2022

Prospects for Detecting the Diffuse Supernova Neutrino Background with JUNO

We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged \textit{in situ} measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3$σ$ for 3 years of data taking, and achieve better than 5$σ$ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space.

hep-ex↗

arXiv2022

Check on the features of potted 20-inch PMTs with 1F3 electronics prototype at Pan-Asia

The Jiangmen underground neutrino observatory (JUNO) is a neutrino project with a 20-kton liquid scintillator detector located at 700-m underground. The large 20-inch PMTs are one of the crucial components of the JUNO experiment aiming to precision neutrino measurements with better than 3% energy resolution at 1 MeV. The excellent energy resolution and a large fiducial volume provide many exciting opportunities for addressing important topics in neutrino and astro-particle physics. With the container #D at JUNO Pan-Asia PMT testing and potting station, the features of waterproof potted 20-inch PMTs were measured with JUNO 1F3 electronics prototype in waveform and charge, which are valuable for better understanding on the performance of the waterproof potted PMTs and the JUNO 1F3 electronics. In this paper, basic features of JUNO 1F3 electronics prototype run at Pan-Asia will be introduced, followed by an analysis of the waterproof potted 20-inch PMTs and a comparison with the results from commercial electronics used by the container #A and #B.

physics.ins-det↗

arXiv2022

Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon

Spatial-temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate, and biodiversity in the Brazilian Amazon. Here we investigate inter-annual changes of AGB and forest area by analyzing satellite-based annual AGB and forest area datasets. We found the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, net AGB loss was three times smaller in 2019 than in 2015. During 2010-2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.

physics.bio-ph↗

arXiv2022

GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains

Cereal grains are a vital part of human diets and are important commodities for people's livelihood and international trade. Grain Appearance Inspection (GAI) serves as one of the crucial steps for the determination of grain quality and grain stratification for proper circulation, storage and food processing, etc. GAI is routinely performed manually by qualified inspectors with the aid of some hand tools. Automated GAI has the benefit of greatly assisting inspectors with their jobs but has been limited due to the lack of datasets and clear definitions of the tasks. In this paper we formulate GAI as three ubiquitous computer vision tasks: fine-grained recognition, domain adaptation and out-of-distribution recognition. We present a large-scale and publicly available cereal grains dataset called GrainSpace. Specifically, we construct three types of device prototypes for data acquisition, and a total of 5.25 million images determined by professional inspectors. The grain samples including wheat, maize and rice are collected from five countries and more than 30 regions. We also develop a comprehensive benchmark based on semi-supervised learning and self-supervised learning techniques. To the best of our knowledge, GrainSpace is the first publicly released dataset for cereal grain inspection.

cs.CV↗