Miracle-dz/UniFAR-FTUs huggingface dataset:
An Image segmentation method for Banana Leaf Disease image with complex background

In view of the complexity of original banana leaf disease image collected by smart phone, an image segmentation method is proposed. It combines with the color segmentation, Ostu segmentation, which uses the minium intra-cluster or the maximum inter-cluster gray variance to segment the image, and area threshold method. Firstly, the color segmentation method is used to remove the green background according to the color characteristics of banana leaf disease images. Secondly, converting the RGB image to YUV color space after color segmentation, in which U component image is segmented by the Ostu segmentation and area threshold method to remove the non green background. Area threshold is used to eliminate the background noise. Finally, after segmented by Ostu segmentation and area threshold method, the U image is processed by an “AND” operation with each component of the original RGB image, thus obtaining a complete disease spot target image. The proposed method is used to segment the test banana leaf disease images with complex background. The results shows that it has a better segmention performance with the averaged accuracy rate over 97% and averaged error rate merely 2.3 %, which can built a solid foundation for the subsequent feature extraction and banana leaf disease recognition.
Classification of Prehospital-Electrocardiograms taken in Ambulance According to Severity using Deep Learning Neural Network

The prehospital-electrocardiogram (PH-ECG) is an electrocardiogram (ECG) measurement performed by paramedics on a patient suspected of having a myocardial infarction. For example, in an ambulance and the data are transmitted to the hospital. A physician at the hospital can diagnose the condition of a patient based on the transmitted ECG, thus making efficient use of the time before the patient arrives and enabling an early start to treatment. The PH-ECG is particularly useful for patients who require immediate medical attention, such as those with ST-elevation myocardial infarction (STEMI). Multiple studies have shown that PH-ECG improves door-to-balloon time and in-hospital mortality. However, it is necessary to understand the various patterns of abnormal waveforms when analyzing PH-ECG, and it is difficult to make an accurate diagnosis quickly without a cardiologist. In areas where there is a shortage of hospitals and physicians, diagnosis is performed by non-cardiologists, and there is a need for an automated diagnosis system with performance similar to that of cardiologists. Recent studies on automated ECG diagnosis have focused on diagnosing specific abnormal findings, especially the classification and discrimination of myocardial infarction and arrhythmias. On the other hand, there are few studies on the classification of disease severity, independent of the types of abnormal findings. In this work, we analyzed a 12-lead ECG measured in an ambulance by using deep learning neural network to classify and evaluate the abnormal waveforms according to degrees of severity. For 88 cases of 12-lead ECG image data measured in the ambulance, each 12-lead waveform was divided into three parts, and 36 one-lead ECGs were extracted. An expert cardiologist annotated each image. The images were labeled in three classes according to the degree of severity, “normal,” “mild or moderate,” and “severe. ” Each image was thinned and binarized. Of 3,168 final images, 1,590 were normal waveforms, and 1,578 were abnormal waveforms. 80% of the images were used as training data and 20% of the images were used as test data. A total of 20% of the training data were used as validation data, five-fold cross validation was performed. EfficientnetB0 was used. The model was defined by using the network designer in MATLAB. The input image size was set to 224 × 224 pixels, and resizing was performed when no match was found. The optimization method was Adam, and the hyperparameters were set to $\alpha=0.0001, \beta_{-}1=0.9$, and $\beta_{-}2= 0.999$. We set the mini-batch size to 64 and the epoch to 100. We achieved a kappa coefficient of 0.810 and maximum classification accuracy of 86.6% for the test data. The result indicates the feasibility of an automatic diagnosis system using noisy ECGs measured in ambulances and is expected to provide a new research direction.
Scale Estimation for Monocular Visual Odometry Using Reliable Camera Height

The development of monocular visual simultaneous localization and mapping (VSLAM) has slowly begun in recent years. At present, the sensors used for VSLAM include monocular, binocular, or depth. For visual mapping, two problems will be encountered and the mapping cannot be performed. One is when there are not enough feature points, the camera's pose at the next moment cannot be estimated, such as a wall. The other is the VSLAM of dynamic environment changes may not be recognized as the same object because the feature matching needs to be coded through its surrounding environment, so it is easy to lose track when encountering changes in light. Compared with binocular and depth, monocular vision lacks depth information, but because it is cheap and easy to install, it needs to be used by multiple people. The current research proposes adding other information, including the camera height, the scene of the reference object and depth estimation by learning methods. The study uses the OpenVSLAM architecture to estimate the camera height scale, and proposes the mechanism be based on the change in the average scale of the first five key frames, with the average scale being updated at the same time to correct the current scale. Through this method, the drastic changes in scale are corrected, and the accuracy of trajectory positioning is improved. We also evaluate our proposed method on a real KITTI Dataset and demonstrate the proposed algorithm is effective and feasible for monocular visual SLAM.
Primitive permutation groups and their section-regular partitions

The study reported here arises out of a question asked by João Araújo (Lisbon) in an e-mail message of 19 October 2006. Let X be a finite set and let G be a group of permutations ofX, that is, a subgroup of Sym(X). A partition ofX as a disjoint union of non-empty pairwise disjoint subsets corresponds to an equivalence relation, and we shall move freely between the two concepts, using ρ to stand either for the relation or for the partition, as context demands. A section (or transversal) of ρ is then a subset S ofX that contains precisely one element from each class of ρ. Given a relation ρ and one of its sections S, the two conditions
Cosmological constraints on Newton's constant

We present cosmological constraints on deviations of Newton's constant at large scales without specifying a model, analyzing latest cosmic microwave background (CMB) anisotropies and primordial abundances of light elements synthesized by big bang nucleosynthesis (BBN). BBN limits the possible deviation at typical scales of BBN epoch, say at 10{sup 8}-10{sup 12} m, to lie between -5% and +1% of the experimental value, and CMB restricts the deviation at larger scales 10{sup 2}-10{sup 9} pc to be between -26% and +66% at the 2{sigma} confidence level. The cosmological constraints are compared with the astronomical one from the evolution of isochrone of globular clusters.
Series Capacitor Converter with MHz Switching Frequency Based on GaN HEMT

This paper applies GaN HEMT to series capacitor buck converters and increases the switching frequency to 1MHz, greatly improving the power density of the converter. The advantages brought by the new wide-bandgap semiconductor device were analyzed, the driving circuit was optimized, and a 1MHz improved two-phase Buck high-frequency converter with 48V input, 12V/240W output and peak efficiency of 98.5% was designed and built. The effective power density reaches 218.5 W/in3.In the rated state, the efficiency of the prototype is 93.4%.
Value-Based Transmission Planning Considering Load Duration Curve

Benefit and cost should be considered in planning method under power market environment, which requires a reasonable method to measure the value of invested equipment. Value-based transmission planning recognizes the reliability needs of traditional planning while providing long-term extensions that address economic value. In this paper, the annual value of lines is evaluated with the help of load duration curve, and value indexes are established based on the line's mitigation of grid congestion and line power flow. Furthermore, based on the value indexes and investment cost, a multi-objective transmission planning model is proposed, and an improved genetic algorithm is used to solve the model. The effectiveness of the proposed method is verified through the case study based on the modified IEEE39 bus system.
A Study on the Driver-Vehicle Interaction System in Autonomous Vehicles Considering Driver’s Attention Status

Before fully autonomous driving technology is developed, drivers are not free from the responsibility of Take-Over Request (TOR). Even with level 5 automation, a driver still can play a role as a final decision maker for various driving and non-driving functions, even though most of the tasks will be conducted by the vehicle. In this regard, the performance of the driver’s reaction to the request from the car is crucial in autonomous driving throughout and it is highly dependent on the driver’s attention. It is important to understand the state of the driver during autonomous driving and utilize this information in the driver-vehicle interaction system to enhance safety. Accordingly, it is important to extract features from information about the driver and the surrounding environment in order to increase the accuracy of the model that predicts the driver’s state. This paper aims to examine a method of predicting an attentional status based on the driver’s emotional state and explore the possibility of applying brain-computer interaction (BCI) with consideration of the driver’s type to increase accuracy. As a result, the prediction model for attentional state with emotional information was developed, and the driver’s characteristics and types were investigated. Based on the results, a driver-vehicle interaction system was proposed for the context of the autonomous vehicle in the future. Although the prediction model for attentional status was not powerful, it can be developed by considering more critical features such as driver’s characteristics and types. In addition, it can be used as a constraint for effective interaction when a driver uses the BCI system to deliver his/her decision to a vehicle.
Macroscopic realism of quantum work fluctuations

We study the fluctuations of the work performed on a driven quantum system, defined as the difference between subsequent measurements of energy eigenvalues. These work fluctuations are governed by statistical theorems with similar expressions in classical and quantum physics. In this article we show that we can distinguish quantum and classical work fluctuations, as the latter can be described by a macrorealistic theory and hence obey Leggett-Garg inequalities. We show that these inequalities are violated by quantum processes in a driven two-level system and in a harmonic oscillator subject to a squeezing transformation.
ASAN: An Extendable Approach for Automatic Step-sizes Adjustment for Newton-Raphson Consensus Optimization Algorithms

We propose a novel approach for solving the Automatic Step-size Adjustment problem in Newtown-Raphson Consensus based distributed optimization algorithms (abbreviated as ASAN). The approach leads the agents in the network to autonomously, continuously and automatically choose their local stepsizes as the distributed optimization process unfolds. In practice, it is based on first evaluating the reliability of the next predicted local optimum by using the information collected at each node as the optimization process is being executed, and then using this reliability assessment to locally adjust the step-size accordingly. By letting each node adjust its own step-size separately by means of local inspection of local variables, the approach does not add communication overheads (a feature that is beneficial especially for systems with limited communication possibilities). Moreover, the strategy does not require information about the current topology of the communication network, nor information about the local cost functions, serving thus situations for which both the network structure and the local cost functions are time-varying. Besides the overall concept, the paper introduces different heuristic reliability evaluation algorithms to analyse the temporal dynamics of the local data, and compares the approach against an oracle-based implementation that selects the best constant step-size for each specific network and set of local costs by means of Monte Carlo analyses. The paper statistically shows that the proposed heuristic leads to convergence rates that are most often better (and in general not worse) than the ones of the oracle-based optimization scheme, without though the need for knowing information about the communication topology or local costs.
Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies; 2) There are complex nonlinear relations in water quality time series; and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy.
Control and Dynamic Proportional Integral Observer co-Design for Uncertain Linear Systems

This paper presents a new approach to co-design of state feedback and dynamic proportional observer for a class of uncertain linear systems with disturbances and faults. Our objective is to simultaneously estimate the state, the faults. The system is controlled with the state estimate. An H∞ index is used in order to minimize the effect of the disturbance input on the state and fault estimation errors. The state feedback and observer gains are obtained by solving an optimization problem under LMI constraints. The procedure is applied to the dynamics of a 2DOF helicopter. It shows its ability to well handle the system.
A Method for Evaluation and Optimization of Automotive Camera Systems based on Simulated Raw Sensor Data

Automated driving functions make mobility safer and more comfortable. The perception of these functions relies on sensors that capture the vehicle’s environment. A sound configuration of the sensor’s intrinsic and extrinsic parameters is crucial for a high perception performance. Simulation is a method of increasing importance for the evaluation and optimization of sensor sets since it enables fast and cost-effective procedures. This paper presents a method for evaluating and optimizing cameras during traffic scenarios in a simulation based on their raw sensor data quality. We present a framework using the CARLA simulator and evaluate the coverage and visibility of the surrounding objects during virtual traffic scenarios. For this purpose, we propose a general metric to measure the acquired information content of camera systems. We compare our metric to the performance of a state-of the-art object detector to investigate the ability to represent detection performance. The suitability of the metric for the optimization of intrinsic hardware parameters and extrinsic mounting parameters is explored. We show in our experiments that the metric correlates strongly with a benchmark perception function’s performance. Furthermore, we prove that sensor configurations optimized by our metric outperform a baseline sensor set in terms of object detection performance.
Frequency-Locked Loop based New Automatic Phase-Shift Method for Active Islanding Detection of Three-Phase MicroGrid

This paper proposes an active Islanding Detection Method (IDM) integrating Frequency Locked-Loop (FLL) for detection of voltage phase and frequency at the point of common coupling (PCC) and modified Auto Phase Shift (APS) method for three phase Distributed Energy Resource (DER) serving a local RLC load. The FLL is composed of a double second-order generalized integrator (DSOGI) to attenuate the harmonics and pass the fundamental positive sequence components in a frequency adaptive manner. Different from traditional APS methods, a new variable frequency term with an exponential factor is injected into the phase shift algorithm to reduce the Non-detection Zone (NDZ) during the case of DER power matching with the local load demand. This modified approach enables the decentralized power generation system to detect the islanding phenomenon more quickly. Simulations and experimental tests conducted on a DG system on OPALRT (OP5600) and hardware test-bed demonstrate the behaviour of the proposed model within IEEE Standards.
De-COP: A Decentralized Community Convergence Approach for Message Forwarding in Pocket Switched Networks

Pocket Switched Networks (PSNs) are an evolution of mobile ad-hoc and Delay Tolerant Networks in which there is no assumption made about the existence of a complete path between two nodes wishing to communicate, thus making routing even more challenging. Various routing algorithms have been proposed over the years like Epidemic, Spray and Wait, ProPHET, etc. There is a separate class of routing algorithms that exploit social structuralism to selectively forward messages to the best candidates. One such algorithm is BUBBLE Rap, which takes into account the notion of popularity and communities to make forwarding decisions. However, popularity as a sole deal-breaker is a rigid policy. We propose a decentralized community convergence-based message forwarding approach viz., De-COP that makes use of the familiarity metric which is dictated by the characteristic of a community to make message forwarding decisions. When familiarity is used for forwarding messages in converged communities, the messages are delivered with low latency and high probability. A community is defined as converged when the change in its membership is gradual. The forwarding node also takes into account the butter availability at the target node to reduce delay and message loss probability. The results were obtained using ONE simulator on two popular datasets, i.e., Infocomm06 and Cambridge to demonstrate the efficacy of the proposed De-COP approach in terms of improved delivery probability and message overhead ratio compared to ProPHET and BUBBLE Rap.
Angular Domain Control Design for Single-Phase Inverter to Enhance Distribution Grid Power Quality

The United States has seen an enormous influx of single-phase behind-the-meter installations in the last decade. With the latest Federal Energy Regulatory Commission (FERC) order 2222, these behind-the-meter installations can be aggregated and participate in the power markets by providing ancillary services. Hence, controlling smart inverters to maintain power quality in the distribution grid remains challenging. With multiple smaller units forming an aggregation and responding to market signals, nearby standalone single-phase behind-the-meter smart inverters would need better controls to react to frequency excursions. This paper presents an Angular Space Repetitive Control (ASRC)-based Active Power Filter (APF) for a single-phase smart inverter used predominantly in behind-the-meter installations. The proposed control guarantees power quality during large-scale frequency excursions, well above and beyond the stipulated frequency range (59.5-61 Hz) strictly enforced throughout North America. The proposed controller is modeled in Simulink, and the response to input frequency deviations shows a current Total Harmonic Distortion (THD) of less than 3% even under extreme frequency excursions.
Tensor Voting Based Similarity Matching of Wafer Bin Maps in Semiconductor Manufacturing

In semiconductor manufactuing, wafer bin maps (WBMs) with specific defect patterns provide valuable information on the root causes of process abnormality. The identification of these patterns is thus of crucial importance for yield enhancement and quality improvement. Existing studies have made great efforts to automatically classify the WBMs into predefined patterns. However, rarely occurred and mixed-type defect patterns are also generated due to the increasing complexity of the manufacturing processes. In this case, the conventional classification based method has its limitations. to solve this problem, this study aims to develop a novel approach to search similar WBMs for the identification of defect patterns and their potential causes. Tensor voting, which is a perceptual grouping approach, is considered to extract structural information from the spatial patterns. The modified best-buddies similarity (BBS) is applied to measure the similarity level. Experimental results demonstrate the effectiveness of the proposed approach in the similarity searching and identification of defect patterns.
Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings

Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.
Rapid Betweenness Centrality Estimates for Transportation Networks using Capsule Networks

Measuring importance of nodes in a graph is one of the key aspects in graph analysis. Betweenness centrality (BC) measures the amount of influence that a node has over the flow of information in a graph. However, the computation complexity of calculating BC is extremely high with large-scale graphs. This is especially true when analyzing the road networks with millions of nodes and edges. In this study, we propose a deep learning architecture RoadCaps to estimate BC with sub-second latencies. RoadCaps aggregates features from neighbor nodes using Graph Convolutional Networks and estimates the node level BC by mapping low-level concept to high-level information using Capsule Networks. Our empirical benchmarks demonstrates that RoadCaps outperforms base models such as GCN and GCNFCL in both accuracy and robustness. On average, RoadCaps generates a node’s BC value in 7.5 milliseconds.