Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based DOA Estimation with Single Vector Hydrophone in Shallow Water

Deep learning method based on residual neural network and deep transfer learning is proposed for direction of arrival (DOA) estimation with a single vector hydrophone in shallow water. The data received by a single vector hydrophone is simulated by normal wave theory of acoustic propagation model. We propose a residual neural network that contains 50 layers network (Resnet50), which is trained with high-quality pre-processing data. A fine-tuned training strategy is proposed to transfer Resnet50 to a new acoustic environment. Convolutional layer parameters are adjusted freely with a lower learning rate in transfer learning, while the fully connected network layer is initialized. The simulation and the experiment results show that the deep learning method has excellent performance compared with the active intensity method and convolutional neural network (CNN) with the different environment conditions or the source signal-to-noise ratio (SNR) decreases.
Bootstrap Confidence Intervals for Simulation Output Parameters

Bootstrapping has been used to characterize the impact on discrete-event simulation output arising from input model uncertainty for thirty years. The distribution of simulation output statistics can be very non-normal, especially in simulation of heavily loaded queueing systems, and systems operating at a near optimal value of the output measure. This paper presents issues facing simulationists in using bootstrapping to provide confidence intervals for parameters related to the distribution of simulation output statistics, and identifies appropriate alternatives to the basic and percentile bootstrap methods. Both input uncertainty and ordinary output analysis settings are included.
Evaluation of Dual-Chemistry Battery Storage System for Electric Vehicles Charging Stations

The use of stationary batteries in electric vehicle fast-charging stations is a well-known solution to alleviate stress on the grid. However, the rapid variations in charging demand pose a challenge to battery lifetime due to cycle aging. In this framework, the paper aims to evaluate the adoption of two different battery chemistries within the same storage system to cope with different demand dynamics (fast and slow power variations). The comparison is done by finding the optimal sizing for both the single and hybrid chemistry solutions and comparing their costs.
Intelligent Energy Management for Fuel Cell Bus Based on Enhanced Soft Actor-Critic Algorithm

Energy management strategies (EMSs) directly affect the fuel economy of hybrid electric vehicles, and deep reinforcement learning (DRL) has become the mainstream method for energy management in recent years. This paper proposes an intelligent DRL-based EMS for an urban fuel cell bus (FCB) powered by both fuel cell and battery to improve the energy efficiency of the FCB. To begin, an enhanced soft actor-critic (SAC) algorithm integrating the standard SAC algorithm with the prioritized experience replay (PER) mechanism is innovatively formulated. Then, an intelligent EMS based on the enhanced SAC algorithm is developed. After that, the superiority of the proposed EMS is evaluated in detail by comprehensive comparisons. Simulation results indicate that the proposed EMS accelerates the convergence speed by 62.79% while improving the fuel economy by 5.18% in comparison with the EMS based on standard SAC.
Modeling Reactive Game Agents Using The Cell-Devs Modeling Formalism

Intelligent game agents are vital to modern games as they add life, story, and immersion to the game environment. The requests in the gaming industry for more realism have made intelligent agents more important than ever. Modeling and simulation of game agents and their surrounding environment provide an alternate setting to study dynamic agent behavior before integration into the game engine. The Cell-DEVS formalism, an extension of Cellular Automata, allows modeling such behaviors using the rigorously formalized Discrete Event Systems Specification (DEVS) formalism. This paper explains how to model and test reactive game agents using the Cell-DEVS formalism and the CD++ toolkit. To analyze the dynamic behavior of such agents, we perform several experiments in varying system configurations. Our experimental results confirm the versatility of Cell-DEVS and the functionalities in the CD++ toolkit to model comfort-driven, exploratory, and desire-driven game agents.
Are Deep Point Cloud Classifiers Suffer From Out-of-distribution Overconfidence Issue?

3D point cloud perception using deep neural networks (DNNs) has been a trend for various application scenarios. However, the black-box nature of DNNs will bring many hidden risks as in the 2D image field. In this paper, we present a preliminary evaluation on the out-of-distribution (OOD) overconfidence issue of deep point cloud classifiers, which has been proven to exist in deep 2D image classifiers, i.e., OOD inputs will lead to overconfident predictions on predefined categories. We also investigate whether a simple thresholding baseline and two modern OOD detection solutions can handle the issue by detecting OOD samples. Extensive experiments with four representative deep point cloud classifiers train/evaluate on different in/out-of-distribution point clouds validate the severity and knottiness of the OOD overconfidence issue. Our investigation will provide the groundwork for future studies on handling the OOD overconfidence issue of DNN classifiers for 3D point clouds.
Incorporation of Military Doctrines and Objectives into an AI Agent Via Natural Language and Reward in Reinforcement Learning

This paper emphasizes the integration of sound tactical behavior in the generation of realistic military simulations, which includes the definition of combat tactics, doctrine, rules of engagement, and concepts of operations. Recent advances in reinforcement learning (RL) enable RL agents to generate a wide range of tactical actions. A multi-agent ground combat scenario is used in this paper to demonstrate how a machine learning (ML) application generates strategies and issues commands while following a given objective. Natural language is used to issue doctrines and objectives to improve communication between the human advisor and the ML agent. This allows us to embed objectives and existing doctrines into the reasoning of an artificial intelligence (AI). The research demonstrates the successful integration of natural language to enable an agent to achieve different objectives. This groundwork will enhance RL agents' ability in the future to uphold the doctrines and rules of military operations.
Research and Design of an IoT Face Recognition System Based on MobileNet-V3 and ArcFace

With the burgeoning advancements in Internet of Things (IoT) technology, the imperative for efficient and precise facial recognition methods at the network edge has come to the fore. Yet, optimizing these methods for real-time utility at the network's edge remains a persistent challenge. This research delves deeply into the fusion of deep learning with facial recognition, elaborating meticulously on network training and its application. Employing MobileNet-V3for feature extraction and the ArcFace loss function for model refinement, this study delineates edge device design based on NVIDIA Jetson Nano, detailing the system's communication and operational design intricacies. In essence, this article proffers a novel perspective for the design of facial recognition systems in IoT environments. Keywords-Internet of Things, Deep Learning, Facial Recognition, MobileNet-V3, ArcFace
A 250pA, Gate-Leakage Based Trimming Free Current Reference, from −55°C to 150°C for Lower Power IoT Applications

This paper presents a fully integrated ultra-low power trim-free current reference for wide-temperature applications. The proposed circuit doesn't incorporate resistors, amplifiers, and start-up circuitry. A sub-threshold current-based curvature compensation is utilized to extend the temperature range. A process calibration technique is included to calibrate against process variations. Designed in a 55nm CMOS process for a temperature range of −55°C to 150°C, the current reference occupies an active area of 0.00458mm2• It achieves a line sensitivity of 0.06%/V for a supply range of 1.2-3.6V. The current produced is 250pA, with a temperature coefficient of 70ppm/°C at the typical corner.
Harmonic Analysis of Laboratory-Based Power System Utilizing Passive Filter for High-Speed Railway Traction Applications

Railway electrification has posed a challenge for power quality in railway networks due to their unique characteristics. This research paper explores the impact of an inductor filter on high-order harmonic currents generated by the pulse-width modulation (PWM) converter in the traction drive of high-speed railway (HSR) systems. The paper begins by providing an overview of the traction power transfer system (TPTS) in a HSR and models a permanent magnet synchronous motor (PMSM) for traction applications. Then, a laboratory-based system (LBS) HSR is built to replicate the energy flow characteristics observed in a real-world HSR system. The LBS-HSR is modeled in Matlab/Simulink and is utilized to analyze the harmonic spectrum of current on the grid and motor terminal under various operating conditions. Furthermore, the paper investigates the impact of the inductor filter on other system components such as the input current of the converter and the voltage on the DC capacitor. Finally, a laboratory-based HSR system is developed on experiment and validated against the results obtained from the simulation model.
Comparison of different types of users in Sweden mitigating energy poverty and utilizing green technologies

To mitigate the ongoing climate changes and promote a power grid that widely utilizes the production of renewable electricity and flexibility services, synergy with the users is of interest. However, it is not certain that critical actors such as governmental bodies or the industry perceive the users and their motivations correctly, and misconceptions might hinder the implementation of green technologies in society. In this paper, we investigate how different types of users in Sweden differ in their ability to create revenues using green technologies and flexibility. We investigate if views held by, e.g., governmental bodies or the industry could be detrimental to necessary future policy changes
Identification and Comparison of Spine MRI Image with Soft Computing Approaches

The procedure for identifying and identifying spinal tumors involves sophisticated segmentation techniques. Image processing is not limited to tumors but can also classify various parts of the human body. Spinal tumors typically originate from other affected organs rather than the spine itself. Magnetic Resonance Imaging (MRI) utilizes strong radio waves to create detailed internal body images, particularly of tumors. This study involves reducing noise in the images and applying numerous soft computing methods for segmentation. These techniques are then compared to enhance image processing. The outcomes show approximately 99% accuracy and specificity in tumor detection using Otsu and Fuzzy entropy methods combined with active contour techniques. This investigation is valuable in determining the most effective segmentation method for further extraction and classification of features in spinal tumor images.
Quantitation of Bacopaside I in Rat Biological Samples by LC-QTOF-MS/MS and Its Pharmacokinetic Application

Bacopaside I is a triterpenoid saponin, a major compound in Bacopa monnieri, which exhibits several potentially useful clinical activities including memory enhancement. Accordingly, a sensitive liquid chromatography tandem mass spectrometric method was developed and validated to determine bacopaside I in rat serum, urine, and feces. Digitoxin was the internal standard. Simple protein precipitation was used in the sample preparation. The separation was performed using a C18 column with gradient elution, and electrospray ionization in the negative ion mode was selected. The method proved to be precise (both intra- and inter-day CV <13.2% in serum, urine, and feces), accurate (intra- and inter-day recovery within 93.1–112.0 %), and selective, while providing high sensitivity (LLODs were 0.13 ng for rat serum and urine and 0.25 ng for rat feces and LLOQs were 0.13 ng for rat serum and urine and 0.25 ng for rat feces). We successfully applied this method to the pharmacokinetics of bacopaside I in rats. This report describes mean residence time and elimination of bacopaside I.
Design, modeling and control of a soft gripper with TCP actuator using image processing

Artificial muscles have gained importance due to their high deformability, which is used in soft robots. In this article, to further exploit and understand the behavior of TCP muscles, as well as to control these muscles more easily, we mathematically model the stimulus, which includes a model to understand the temperature behavior of muscles with changes in flow and amount of displacement due to changes in temperature and amount of external force. is. By designing a soft gripper and applying the necessary restrictions, we get the force needed to move this gripper. Then we obtain the kinematic model of the finger to obtain the bending angle due to the displacement of the muscle which is connected to the fingers and gripper by the cable. We also check the muscle’s ability to perform this movement. In the next step, programming and implementation of a simple model of turning on and off the heating and cooling system to create expansion and contraction in the muscle is done by image processing on the Arduino controller to control the system. Using the obtained models and taking into account different and important values in the models, we extracted the related diagrams and checked and compared them. From these results, we can point out the changes in the temperature behavior and movement of the muscle with the change in the input current as well as the type of alloy used, the controllability of the muscle, and the ability of the muscle to move the gripper.
Selective ensemble random convolutional neural network for power load prediction

In the increasingly open power market environment, power load prediction can ensure the safe and stable operation of the power grid, and provide technical support for demand-side response. In this paper, a novel random convolutional neural network called ROCKET is adopted and improved to establish the power load prediction model. We integrate the proposed ROCKET† with multiple-input multiple-output strategy for multi-step-ahead prediction task. In addition, a selective ensemble approach which uses part of the selected ROCKETs† rather than all of the ROCKETs† is designed to further improve the generalization ability of our model. For verification and testing, three real world power load datasets are presented to test the effectiveness of our model. Application results demonstrate that the proposed se-ROCKET† has a higher accuracy than the comparison models.
Experimental Studies with Real-Time Hardware-in-Loop Microgrid Structure and its Components

High penetration of renewable energy resources in microgrid (MG) makes the control process complex. Simulation studies are often undertaken with MG components for designing suitable control algorithms. Complexity in MG control actions increases due to intermittent nature of sources. Software validation is often useful before implementation of the physical structure. Simulation studies enable transient and others abnormal phenomenon to be identified before actual hardware implementation. In offline simulation, different MATLAB-Simulink blocks are utilized to identify MG behavior under different operating states. Analysis of signals at key locations of the MG can be done effectively through graphical representation in simulation environment. However, direct implementation of certain control blocks from MATLABSimulink to real hardware may cause troublesome situation. Real-time (RT) simulator OpalRT adds a new dimension to hardware control from MATLAB-Simulink model. RT simulator eliminates the need for intermediate validations of simulated controller blocks through hardware prototypes. The MATLAB-Simulink model can be directly utilized to run the hardware emulator system through suitable communicating device of RT simulator. Validation of control techniques thus become more robust and realistic for practical application approach. This article introduces different types of RT emulators of a MG that are controlled and coordinated through suitable communicating protocols of RT simulator. Steps for executing the MATLAB-Simulink model in OpalRT environment is also introduced. As an application example, inverter control using synchronous reference frame (SRF), and phase locked loop (PLL) have been demonstrated in real time environment.
Test-Stand Experimental Analysis of RoboWalk Weight Assisting Performance

The increment in elder population has been the main cause of exoskeletons and rehabilitation robots development. Studies have proven that assistive robots are more effective in limb empowerment and rehabilitation than traditional ways. A lower-limb assistive robot called RoboWalk has been designed in ARAS lab at K. N. Toosi University of Technology to help compensate user weight when moving. This study reviews the RoboWalk weight assisting robot and illustrates its Test-Stand more thoroughly with updated modifications and parts. Test-Stand system features and operational specifications are then described. Then, system kinetics and kinematics are also explained. Finally, the weight assisting performance of RoboWalk is analyzed. Results show that the gait generating mechanism creates a singularity in Test-Stand system that causes a decrease in the assisting force. It is concluded that this issue is only related to the Test-Stand and does not appear in the actual RoboWalk robot. Nevertheless, Solutions to avoid this in Test-Stand including back and forth simulation of the gait and maximum motor torque limitation are also suggested.
Design And Implementation Of Wearable Antenna For WBAN Applications

Wearable antennas are rapidly growing in today’s world, being used in various fields. A monopole antenna backed with a 4*6 Artificial magnetic conductor (AMC) is designed for Wireless Body Area Network (WBAN) Applications. The proposed antenna with dimensions of 68*102 mm2 operates at Industrial, scientific, medical 5.8 GHz band with gain 7.14dB and efficiency 0.91. There is an improvement in front to back ratio and Specific Absorption Rate (SAR) after placing the AMC backside. The designed antenna is placed on a human tissue modal and simulated. The simulated specific absorption rate is 0.5 W/Kg which satisfies both the US and EUR standards.
Optimal Targeted Attacks Against Gait-Based Identity Recognition

To adequately understand endogenetic vulnerabilities of gait-based identity recognition, we propose an optimal generation strategy for launching the targeted attacks. Specifically, we formulate a bilevel optimization problem to model a Stackelberg game, during which an adversarial interaction process is activated between the leader, i.e., attacker, and the follower, i.e., recognizer. The former intends to undermine the gait signal features for fooling the recognizer, resulting that an unauthenticated user can be misrecognized as a specific authenticated victim. The latter usually adapts a common sparse presentation optimization to exactly match the similar gait signals for recognizing the genuine identities. Further, to tractably solve this NP-hard bilevel problem, we absorb the duality theory to reformulate a calculable second-order cone program. Finally, we perform experiments on public datasets to verify the validity of our strategy in adversarial effectiveness and attack costs. Empirical results shed light on huge potential of our model in the vulnerability assessments of gait-based identity recognition.
ChatGPT Tweets Sentiment Analysis Using Machine Learning

Nowadays, ChatGPT attracts the attention of people due to its ability to perform various tasks impressively. Recently, ChatGPT has become a very popular tool, and many people are keen to know about its performance as well as responses or opinions about ChatGPT from its users. This paper can determine whether or not people like the ChatGPT tool by learning their opinions about it. In this paper, a dataset has been used that contains 50,000 tweets related to ChatGPT. The main goal is to build, train, test, and deploy a model to predict sentiment from thousands of tweets. In this in-depth sentiment analysis of ChatGPT responses, the polarity of each tweet has been found and labelled either positive or negative. In this, the analysis of the distribution of sentiment in the ChatGPT Kaggle dataset as well as how to prepare the Twitter data to prepare it for sentiment analysis have been studied. After that, the accuracy of different machine learning models has been found out. Find out which one has the highest accuracy as compared to others.
Improving Fitting CAD to 3D Point Cloud Acquired with Line-of-Sight Sensor

Due to self-occlusions in 3D point clouds acquired with line-of-sight sensors, an incomplete representation of an object’s surface is used in fitting a CAD (computer-aided design) model to the data. For certain types of object geometry, CAD pose fitted using the Iterative Closest Point (ICP) procedure is systematically misaligned, even when the selected initial pose is very close to the expected, correct pose. We demonstrate on experimental data obtained from manufacturing-relevant parts that the final CAD alignment can be greatly improved if only a part of the CAD surface is used in the ICP registration. The resulting residual ICP error is then reduced by three to four times.
Electronic Evidence Preservation and Traceability Method Based on Blockchain Smart Contract

With the popularization of computer and network technology, data with proof and legal effect has gradually changed from physical to electronic, and electronic documents have gradually become an important carrier and tool for transmitting information and recording facts. Electronic evidence has the characteristics of easy storage and high security, which also imposes high requirements on network security and other aspects. How to effectively preserve electronic evidence and fully trace its source when necessary has become an important research issue. This paper proposes a method to achieve efficient preservation and traceability of electronic evidence through the combination of blockchain, Image-Caption and word segmentation technology. When using blockchain technology to ensure the non-tampering and anti-repudiation of electronic evidence, ensure that electronic evidence While improving the authenticity and integrity of electronic evidence, it improves the traceability and accuracy of electronic evidence.
Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks

Brain-inspired neuromorphic computation can be extremely efficient at very large scales due to inherent parallelism, scalability, and fault and failure tolerance. One widely used, biologically plausible synaptic learning mechanism is spike-timing-dependent plasticity (STDP). The proposed generic model of time-varying resistance, or R(t) elements in this work, can produce classical and beyond classical STDP in electronic spiking neural networks with memristive synapses. Hebbian and Anti-Hebbian STDP is verified with the proposed generic R(t) model by tuning the R(t) function. By appropriately placing R(t) functions with selective resistance values, symmetric or non-classical STDP learning behavior is achieved.