Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generalized Extended MIMO Array Design Strategy for DOA Estimation

With the rapid development of new-generation communication technologies, Multiple-input multiple-output (MIMO) radar has attracted the attention of researchers in the field of array signal processing due to its unique signal model. In traditional array signal processing, most of the attention has been focused on the problem of increasing the degrees of freedom (DOFs) of difference virtual arrays by improving the array structure in passive sensing. In MIMO-based active sensing system, the DOFs offered by existing array structures are limited. To address this issue, we propose a generalized extended MIMO array design strategy which introduces a two-layer difference based on the signal characteristics of MIMO. The array construction in this strategy is flexible, and due to the inter-element spacing is extended, higher DOFs are obtained. Simulation results demonstrate the superiority of the proposed array design strategy,
Health-Conscious Charging of Lithium-Ion Battery Cells: Using Physics-Based Models to Minimize Calendar and Cyclic Ageing Effects

Lithium-ion batteries are currently a viable alternative as energy source in vehicles, instead of fossil fuels. However, the usage of such batteries faces multiple challenges such as long charging time and faster degradation. To mitigate these challenges, this paper presents a charging strategy based on Physics-Based Models (PBMs). PBMs can capture calendar and cyclic degradation effects, by modelling lithium plating and SEI growth. Simulation results show that this approach reduces degradation compared to common charging strategies. Additionally, simulations also show that charging in the latter part of the charging window produces the least degradation, because of calendar ageing effects.
Flexibility Management For Enhanced Distribution Grid Control And Operation - The Case of FEVER Project

The constant increase of renewable energy sources, microgrids, non-linear loads and electronic devices imposed a new set of challenges for distribution system operators to maintain operational stability. To address these challenges, the FEVER project was conceived to propose solutions based on energy flexibility. In this paper, the authors introduce three applications developed within the project. First, the energy forecaster uses smart-meter data and machine learning to provide demand and generation forecast. The voltage compensation application runs time-series power flows to detect voltage deviations, also performing a sensitivity analysis to assess the impact of flexibility of available assets to voltage correction. Lastly, the power quality service uses machine learning to forecast the state of the grid in terms of harmonics and phase unbalances, with the purpose of estimate a power capacity reserve of flexible assets.
ROC-Score-Based Ensemble Training for Multiple Deep Learning Modules in Classification Between Polyps and Non-Polyps in CT Colonography

We developed an automatic ensemble training method for fusing massive-training artificial neural network (MTANN) deep-learning modules in classification between polyps and non-polyps in CT colonography. We started from an initial MTANN module that had been trained with an initial set of polyps and non-polyps. We applied the trained initial module to polyps and non-polyps to analyze the weakness of the initial module. We arranged the output scores of the initial module to form a score scale in receiver-operating-characteristic (ROC) space, representing the “degree of difficulty” in distinction between polyps and non-polyps by the initial module. Based on the score-space, several sets of training polyps and non-polyps with different degrees of difficulties were determined. We trained several MTANN modules with the several sets of training samples so that each module became an expert at a certain level of difficulty. We then combined expert modules with a mixing module to form a “mixture of expert” MTANNs. Our database consisted of CT colonography datasets acquired from 100 patients, including 26 polyps. The mixture of expert MTANNs with the ensemble training method distinguished all polyps correctly from more than 50% of the non-polyps. We compared the effectiveness of the ensemble training with that of training with manually selected cases. The performance of the mixture of expert MTANNs with ensemble training method was superior to that of the “reference-standard” MTANNs trained with manually selected cases, which could reduce the cost of the manual selection.
Upper-Confidence-Bound Procedure for Robust Selection of The Best

Robust selection of the best (RSB) is an important problem in the simulation area, when there exists input uncertainty in the underlying simulation model. RSB models this input uncertainty by a discrete ambiguity set and then proposes a two-layer framework under which the best alternative is defined to have the best worst-case mean performance over the ambiguity set. In this paper, we adopt a fixed-budget framework to address the RSB problem. Specifically, in contrast with existing procedures, we develop a new robust upper-confidence-bound (UCB) procedure, named as R-UCB. We can show that, the R-UCB procedure successfully inherits the simplicity and convergence guarantee of the traditional UCB procedure. Furthermore, simulation experiments demonstrate that the R-UCB procedure numerically outperforms the existing RSB procedures.
Pedestrian and Vehicle Detection Using the YOLO Framework and Multi-Model Approaches

Object detection is a significant problem in the field of computer vision, and it has widespread applications in areas such as autonomous driving, security surveillance, and medical imaging. This paper delves into an in-depth study based on the You Only Look Once (YOLO) object detection framework. By conducting experiments on multiple commonly used object detection datasets. We evaluated the performance of the YOLO model in terms of both accuracy and real-time capability. Through experimental validation, the design of the YOLO model enables simultaneous object detection and localization, significantly improving detection efficiency.
Warm Start Fitted Q Reinforcement Learning for Electric Vehicle Depot Charging

Electric Vehicle (EV) charging coordination is gaining more interest for better integrating EV depots into the electrical grid. Several solutions exist, ranging from rule-based control to, more recently, Reinforcement Learning (RL) techniques. Batch RL approaches are particularly interesting for their ability to use past data for training the controller. However, such solutions typically work with action space discretization, which does leverage continuous charging set-points of Electric Vehicle Supply Equipments (EVSEs), hindering the scalability of solutions. In this paper, we leverage dual annealing global optimization algorithm to pick continuous actions from a neural network RL agent trained with fitted Q-iteration with synthetic data coming from a custom depot simulator. Results for one year simulation of a 10 EVSEs depot are reported and compared with good results with random policy over several criteria.
Torque Ripple Analysis of Five-phase Induction Motor Drive with Pentacle Connected Stator Windings

This paper presents a method of analysis of torque ripple in a pulse width modulated five-phase induction motor (IM) drive whose windings are connected in pentacle mode. The proposed method is based on the notion of stator flux ripple. The torque ripple induced by switching harmonics in two distinct space vector PWM (SVPWM) techniques that utilize different sets of active vectors during the modulation process is investigated. The first PWM technique employs two large and two medium vectors, while the second technique employs four large vectors, in addition to zero vectors. The analyses demonstrate that, in a five-phase induction motor drive, the PWM method utilizing four large vectors generates higher torque ripple when compared to the method using two large and two medium vectors. The mathematical analysis is verified by simulation using the Matlab-Simulink software.
CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images.
Autoencoder-Based Learning of Transmission Parameters in Fast Pulse-Echo Ultrasound Imaging Employing Sparse Recovery

There is recently a notable rise in the exploration of pulse-echo ultrasound image reconstruction techniques that address the inverse problem employing sparse signal and rely on a single measurement cycle. Nevertheless, these techniques continue to pose significant challenges with regard to accuracy of estimations. Previous studies have endeavored to decrease the correlation between received samples in each transducer array in order to enhance accuracy of sparsely approximated solutions to inverse problems. In this paper, our objective is to learn the transmission parameters within a parametric measurement matrix by employing an autoencoder, which encodes sparse spatial data with a parametric measurement matrix and subsequently decodes it using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Outcomes exhibit superior performance in comparison to both state-of-art random selection of the parameters and conventional plane wave imaging (PWI) scenarios in terms of reconstruction accuracy.
The Manyas fault zone (southern Marmara region, NW Turkey): active tectonics and paleoseismology

The Manyas fault zone (MFZ) is a splay fault of the Yenice Gönen Fault, which is located on the southern branch of the North Anatolian Fault System. The MFZ is a 38 km long, WNW–ESE-trending and normal fault zone comprised of three en-echelon segments. On 6 October 1964, an earthquake (Ms = 6.9) occurred on the Salur segment. In this study, paleoseismic trench studies were performed along the Salur segment. Based on these paleoseismic trench studies, at least three earthquakes resulting in a surface rupture within the last 4000 years, including the 1964 earthquake have been identified and dated. The penultimate event can be correlated with the AD 1323 earthquake. There is no archaeological and/or historical record that can be associated with the oldest earthquake dated between BP 3800 ± 600 and BP 2300 ± 200 years. Additionally, the trench study performed to the north of the Salur segment demonstrates paleoliquefaction structures crossing each other. The surface deformation that occurred during the 1964 earthquake is determined primarily to be the consequence of liquefaction. According to the fault plane slip data, the MFZ is a purely normal fault demonstrating a listric geometry with a dip of 64°–74° to the NNE.
Malicious network traffic detection method based on traffic behavior characteristics and machine learning

This paper first analyzes the research background and current situation of unknown network threat detection, points out the main problems existing in the current method, and clarifies the research scope and basic ideas of this paper. On this basis, the main research contents and innovations of this paper are as follows: 1) In response to the lack of implementation ideas for detecting unknown network threats in large-scale network systems, a coarse-grained extraction approach for network traffic load characteristics is proposed. Based on the load, it provides the most valuable feature information for subsequent machine learning models, reduces learning costs, and improves model fitting ability; 2) For coarse-grained load information, we use fine-grained text2vec statistical feature construction techniques for processing; 3) Based on the text2vec feature vectors, we select the optimal dimension; 4) Using machine learning models based on high-dimensional statistical features to perform complex calculations and output classification results.
Effective Power Management and Stability Analysis of MIMO EV Charging Station

This paper presents the design of a Multi-Input Multi-Output Bidirectional Grid and Renewable Energy Source(RES) Integrated Electric Vehicle Charging Station. The converter integrates conventional and non-conventional energy sources with EV batteries requiring different voltage levels for their operation via a common DC bus. The controller of the proposed system is designed to extract maximum energy from the renewable energy source, reduce the stress on the battery and integrate the grid during shortage of power. The stability analysis of the proposed MIMO system was performed. The MIMO system can be considered to be a superimposition of multiple SISO systems. These SISO systems in the form of cascaded DC-DC converters can be analyzed to judge the stability of the MIMO system. Therefore, the highly accurate modelling and stability analysis of cascaded DC-DC converters is fundamental and significant
Exploration-Exploitation Balancing in Reinforcement Learning for Microgrid Scheduling: A Systematic Approach for Fixing Cooling Schedules

Reinforcement learning is being applied to solve several microgrid scheduling problems. However, appropriately balancing exploration and exploitation is a major difficulty in using RL. One popular method is epsilon-greedy, in which the learning agent explores initially, but as the episodes progress, exploration is reduced. In the epsilon-greedy algorithm, this is achieved by gradually reducing the value of epsilon. This gradual reduction of epsilon is known as a cooling schedule. For simple problems, any cooling schedule will work. However for real-life problems, finding an appropriate cooling schedule is a challenging task. None of the existing literature on microgrid scheduling discusses the type of cooling schedule they have used. In many cases, a cooling schedule may involve multiple parameters, and finding the best one becomes challenging. In this paper, we discuss various cooling schedules we have tried and suggest a systematic way of fixing a cooling schedule with only one hyperparameter.
Simulating the Impact of Forecast Related Overbooking and Underbooking Behavior on MRP Planning and a Reorder Point System

Production Planning and its parameterization is critical to fulfil customer demands and to successfully react on changes in high volatile markets. Therefore, demand updates should be considered to improve production planning. In this paper the performance of two production planning methods MRP (Material Requirements Planning) and RPS (Reorder Point System) are compared in a multi-item single stage system where customer orders are updated in a rolling horizon manner. Applying a simulation study, we investigate the performance of MRP and RPS for biased and unbiased forecast information and discuss the difference in the optimal planning parameters. The study shows that for a production system with underbooking and low demand uncertainty, RPS method is superior, in all other scenarios MRP outperforms RPS. For overbooking scenarios, the results show MRP leads to overall cost improvements ranging from 8 to 30 %.
A Multi-retail Bi-level Supply Chain Game Based on Nested and Iterative Heuristic Algorithm

The bi-level game problem in supply chain management has been extensively studied by scholars and practitioners. However, there is a gap when it comes to combining pricing decisions and inventory management problems within the bi-level game. To address this challenge, a supply chain scheme is proposed to balance the trade-off between manufacturers and retailers, where a joint price and inventory management game is played. Specifically, the manufacturer, as the leader, maximizes its profit by determining the wholesale price, while the retailers, as followers, maximize their profits through pricing and inventory management. Then, two heuristic algorithms are designed to solve this bi-level optimization model. Both algorithms incorporate an improved threshold acceptance strategy, which can highly improve convergence accuracy and speed. Finally, a numerical simulation is performed to demonstrate the effectiveness of the supply chain for market coordination.
A Baseline Comparison of Linear and Exponential Charge Pumps

This paper analyzes two types of integrated charge pumps: linear and exponential. The two circuits are compared in static and dynamic conditions and at equal area occupation. Analytic models are presented for optimizing the component sizes, estimating the start-up time, and revealing design trade-offs. Simulations with parasitic effects and a case study highlight the conditions in which the exponential charge pump has better performance to facilitate the choices of the designer.
Advancements in Computer Vision for Automated Fruit Quality Inspection: A Focus on Apple Detection and Grading

The paper discusses the most current advancements in image analysis and computer vision systems and how they are being used to assess the grade of food products. Computer vision is a quick, reliable, and objective examination method that has spread throughout many different sectors. Its efficiency and precision enable the creation of fully automated systems by meeting the growing needs for quality and output. With a focus on monochrome image processing, colour images, and multispectral imaging for contemporary sorting and grading systems, the needs and most current advancements in hardware and software for machine vision systems are reviewed. The suggested method utilises the SVM algorithm to learn and identify fruit quality while using processing of images to extract features. Research experiments show how accurate the system is. Also included are demonstrations of uses for spotting faults, pollution, and illness on vegetables and fruits.
A 15μW Low Cost CMOS Smart Temperature Sensor

A CMOS-based simple, low cost, low power, integrated smart temperature sensor with inaccuracy of less than ±1.8°C with 7 output bits in the wide temperature range of -40°C to 125°C is presented. The proposed design uses only MOS transistors and hence can be realized in any standard CMOS process without the need for bipolar transistors. The proposed design produces a digital representation of temperature using a successive approximation algorithm without the need of an explicit analog to digital converter and hence saves on power and area and reduces design complexity. The average power consumption of the sensor is 15uW in less than 20us conversion time and occupies a chip area of less than 0.083mm2 in a standard TSMC 180 nm CMOS process.
MONITORING OF SEASONAL DYNAMICS IN TWO AGE-DIFFERENT EUROPEAN BEECH STANDS

This study focused on the wood formation process on cellular basis, comparing seasonal cambium dynamics and differentiation phases between two age-different (45- and 130-year-old) European beech ( Fagus sylvatica L.) stands, during 2013, and thereafter, to infer the influence of weather conditions (temperature, precipitation). Microcores were taken from 12 trees at weekly intervals from April till November, by using Trephor tool. The average temperature during the growth period was 12.7°C and the warmest month of the year was July (18.8°C). According the Standardized Precipitation Index, the year was classified as near normal (SPI = −0.19). The results showed that onset, cessation and duration of the phenological phases of wood formation (i.e. cambial cell production, cell enlargement, secondary wall thickening and lignification) occurred almost simultaneously in both European beech stands. The final tree-ring width was measured 1655 μm in the 45-year-old and 1571 μm in the 130-year-old European beech stand. The timing of wood formation phases and the width of xylem increment was influenced by temperature and precipitation.
Behavioural SiC IGBT Modelling Using Non-Linear Voltage and Current Dependent Capacitances

This paper presents a behavioural silicon carbide (SiC) IGBT model that utilizes voltage and current dependent capacitances to simulate its switching characteristics, and a voltage dependent current source to simulate the static characteristics. The non-linear capacitances are extracted from dynamic Current-Voltage (IV) measurements, eliminating the need for non-standard Capacitance-Voltage (C-V) characterization methods under high voltage and high current. The accuracy of the compact model is compared with previously validated numerical Technology Computer Aided Design (TCAD) simulation results across a wide range of operational conditions. The model performance is demonstrated by accurately predicting the unique characteristics of a 27kV SiC IGBT, including dV/dt, dI/dt and losses, while significantly reducing the simulation time by 4–5 orders of magnitude. Additionally, the model convergence is tested using a buck converter topology with non-ideal parasitic elements.
Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty Applications

This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, which arise frequently in data-driven simulation analyses under input uncertainty. When facing these models, it can be difficult to run repeated evaluations due to computation cost, and also to obtain auxiliary information such as gradients due to analytical intractability, thus rendering many traditional statistical approaches challenging to apply. We describe several lines of approaches to resolve these challenges, including data-splitting methods based on batching variants, a recent so-called cheap bootstrap approach, and subsampling schemes. We discuss the applications of these approaches to simulation, including problems suffering from both aleatory error exhibited via Monte Carlo noises and epistemic error stemming from the input uncertainty.
A High-Speed Comparator Using a New Regeneration Latch

This paper presents a high-speed comparator which employs a novel regeneration latch to enhance the comparison process. The regeneration stage employs an innovative mechanism that reduces the RC constant at the output while avoiding static power consumption. Furthermore, the proposed comparator is capable of operating seamlessly with a rail-to-rail input common-mode voltage. This is made possible by utilizing two preamplifiers working in parallel. To partially cancel out kickback noise, the proposed comparator also benefits from an intrinsic neutralization technique. The design was simulated in a 22-nm FD-SOI CMOS technology with a supply voltage of 0.8 V showing that the proposed comparator achieves a 17% decrease in delay compared to the fastest conventional topology simulated in this paper, while consuming the same amount of power.