Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposition-based Multi-Agent Distributional Reinforcement Learning for Task-Oriented UAV Collaboration with Noisy Rewards

Collaborated unmanned aerial vehicles (UAVs) are often deployed to perform complex tasks on the basis of multi-agent reinforcement learning (MARL). However, the environmental disturbance, commonly leading to noisy observations (e.g., rewards, states), could significantly shape the performance of task-oriented UAV collaboration. Therefore, it becomes imperative to revolutionize the design of MARL, so as to capably ameliorate the annoying impact of noisy rewards. In this paper, we propose a novel decomposition-based multi-agent distributional RL method by approximating the globally shared noisy reward by a Gaussian mixture model and decomposing it into the combination of individual distributional local rewards, with which each agent can be updated locally through distributional RL. Besides, the optimality of the distributional decomposition is theoretically validated, while the design of loss functions is carefully calibrated to tackle the decomposition ambiguity. We also verify the effectiveness of the proposed method through extensive simulation experiments with noisy rewards.
Effects of Calendar and Cycle Ageing on Battery Scheduling for Optimal Energy Management: A Case Study of HSB Living Lab

This paper deals with the optimal scheduling of a building microgrid coupled with solar photovoltaic and battery energy storage (BES) considering battery degradation. The aim is to minimize the operation cost of the microgrid which includes the cost of imported electricity from the grid, the degradation cost of the battery, the cost for the peak power drawn from the grid, and the revenue from selling electricity to the grid. The nonlinear models of calendar and cycle ageing are linearized to solve the optimal scheduling as a mixed-integer linear programming (MILP) problem. The developed model is examined for a real residential building microgrid (HSB Living Lab) in Gothenburg, Sweden. The results show that if the degradation of the BES is ignored, the operation cost of the microgrid will increase by 1,394 SEK per year, and the ageing cost of the BES will also rise by 42.27%.
Quadratic Gain-Based Boost Converter: Reduced Switch Current and Component Voltage Stress

The requirement for high-performance, high-gain DC-DC converters is ever on the rise in the fields of electric transportation, renewable energy integration, and biomedical, military, and satellite applications. A novel active switched-inductor-capacitor Quadratic Gain -based Boost Converter (QGBC) is proposed in this manuscript. The converter produces a very high gain in voltage at relatively lower duty ratio values, with reduced current stress on the MOSFETs and reduced voltage stress on the semiconductor devices and the capacitors. This paper construes the principle of operation in continuous and discontinuous conduction modes, the boundary condition, and the design methodology of the converter. In addition, the voltage and current stress of components and loss analysis are studied. The analytical findings are verified using a 200 W prototype that boosts an input of 48 V to 650 V. Further, an extensive comparison of the proposed converter with the state-of-the-art is carried out to prove the potential merits of the converter
A Multi-Channel Deep-Learning Prediction Algorithm for Battery State-of-Health Indicator

Lithium batteries are an essential part of many modern technologies. In most mission-critical applications it is essential to evaluate the current state-of-health (SOH) of the battery during its operating life using adequate condition monitoring tools. The acquired diagnostic data can then be used to forecast the future degradation trend of the battery, and thus to predict the remaining useful life of the device. In this work, a multi-channel deep-learning algorithm based on a Long Short-Term Memory Neural Network is presented in order to predict the future degradation trend of a set of battery's parameters. In order to test and validate the performances of the algorithm, a battery degradation dataset provided by the Toyota Research Institute has been used. The results illustrated the ability of the proposed method to predict the future degradation of several parameters, like charge and discharge capacity, internal resistance, required charging time and maximum overheating of the batteries. Using this approach, it is possible to correctly predict the degradation trend of battery SOH and the remaining useful life even after only few cycles of the battery life.
An Assistive Wrist-Elbow Exoskeleton (WE-Exo) Driven by Variable Stiffness Actuators

Exoskeletons have been developed to assist the rehabilitation of stroke patients in the recent years. However, the safety of the physical human-robot interaction (pHRI) of the exoskeleton still need to be further improved. In this study, a 2-degree-of-freedom (2-DoF) rehabilitation exoskeleton is designed for the assistance of elbow flexion/extension (FE) and wrist supination/pronation (SP). The joints are powered by two independent variable stiffness actuators (VSAs), which ensures the intrinsic safety of pHRI of the exoskeleton, and enhances its adaptability to environmental changes, e.g., collisions. Each VSA has two independent DC motors to adjust the output torque and stiffness, respectively. To reduce the moment inertia of the exoskeleton, the wrist actuator is coupled with its main drive motor through a cable-driven system. A feedback linearization controller of the exoskeleton is developed to achieve position tracking of the joints. A prototype of the exoskeleton is developed to evaluate its performance. The result finds that the proposed exoskeleton can assist the movement of users sufficiently with the controller.
Photoluminescence Imaging for Monitoring the Electrical Health of Photovoltaic Assemblies Throughout the Production of Spacecraft Solar Arrays

Assuring the electrical health of solar cells is critical in the entire product cycle of a photovoltaic assembly. Continuous inspection and monitoring are essential from the qualification process throughout manufacturing, assembly, and final integration on the spacecraft. Electroluminescence imaging is one of the standard techniques in the space industry for this purpose. While photoluminescence imaging has several advantages compared to electroluminescence inspection and is a well-known technique in academia and research, it is still widely unused in the industrial production of solar arrays for space applications. The present publication wants to provide detailed information on the techniques and apparatus suitable for testing the electrical health of space-grade solar cells and photovoltaic assemblies using photoluminescence imaging. Exemplary part lists for building a bare eye inspection visor and photographic documentation set-up based on commercially available low-cost parts are included.
Inductance Estimation Method for Inductors Used in DC-DC Power Electronic Converters: Theory and Application

The information of an inductor’s inductance is required for many applications of DC-DC converters, such as controller tuning, non-invasive current estimation, power sharing between different converters, etc. Alteration in operating conditions, such as temperature, aging, possible deformation in the inductor core, and path length of the printed circuit board affects the value of effective inductance. Change in the inductance changes the peak-to-peak current ripple of the inductor current, which affects the closed-loop system behavior of the dc-dc converter. On account of different converter-related constraints (like inaccessibility of measuring points, converter packaging, etc.) the value of the inductance can not be measured directly. An indirect method of estimation of inductance is one of the solutions to the above-mentioned problem. The inductance of the inductor in a dc-dc converter can be indirectly estimated by employing a series arrangement consisting of a switch and a predetermined resistance (meter resistance) value connected in parallel with the load. The theory of the inductance estimation method and its implementation aspects are presented in this paper. The theory of estimation has been validated using a non-invasive current estimator for a dc-dc boost converter.
Improving ANC Computational Efficiency: A Novel Fx LMS Algorithm with Dynamic Response Control

This paper presents a novel variant of the Fx LMS algorithm. This variant optimizes computational efficiency by utilizing a calculated imperfect secondary path model with a pure-delay impulse response, simplifying the filtered reference signal calculation and enhancing the convergence rate. Additionally, the algorithm includes a control parameter to adjust its dynamic response, further improving its convergence behaviour. We provide a theoretical framework based on root locus theory to optimize this parameter and the algorithm's performance. The theoretical contributions are substantiated through computer simulations, validating the enhanced effectiveness of the proposed approach in ANC applications.
SAR onboard Broadband C-band Circularly Polarized Antenna for In situ Volcanic Lava Observation

Estimating the volcanic eruption scale is important for mitigating eruption hazards. While the eruption scale can be explained by the eruption mode and its temporal variation, the dominant factor is expected to be the formation reaction of volcanic products, but there are still many unknowns, including the relationship with the thermal history. To solve this problem, we proposed a broadband circularly polarized microwave antenna for a synthetic aperture radar (SAR) sensor that is suitable for in situ observation of volcanic materials such as lava. As a result of fabricating the proposed antenna, the antenna characteristics were almost the same as the simulation one, with a center frequency of 5.394 GHz, IBW=32.6%, ARBW=23.7%, and Gain=12.0dBic. The measurement results indicate that the proposed antenna has loss and radiation characteristics suitable for the purpose.
On Learning of Inverse Kinematics for Highly Redundant Robots with Neural Networks

The inverse kinematic problem for redundant robots is still difficult to solve. One approach is learning the inverse kinematic model with artificial neural networks, while the key challenge is the ambiguity of solutions. Due to the redundancy in the robot's degrees of freedom, there are multiple or even unlimited valid joint states bringing the end effector to a desired position. We show to what extent this problem influences the achievable accuracy of supervised training approaches depending on the number of degrees of freedom. To overcome the difficulties, a new training scheme is proposed, which uses the analytically solvable forward kinematics model. The new unsupervised training method uses random sampling in the joint state space and is not dependent on ambiguous tuples of joint values and end effector poses. We analyze the effect of the sample density on the remaining position error and show that additional soft constraints can easily be integrated in the training scheme, which offers the possibility to consider obstacle avoidance directly in the inverse kinematic model. Evaluations have been done using different robot models with up to 20 degrees of freedom, while not only position, but also the end effector's orientation at the goal point is considered.
An Adaptive Successive Cancellation Priority Decoder with Iteratively-increased Searching Width for Polar Codes

The successive cancellation list (SCL) decoding of polar codes can achieve a performance close to the maximum-likelihood (ML) decoding while large list size results in high computational complexity. To this end, successive cancellation priority (SCP) decoding is proposed to reduce the complexity of the SCL decoding with acceptable space complexity. In this paper, an adaptive SCP (ADSCP) decoder with iteratively increased searching width is proposed. Two priority queues are applied to store all the priority nodes during the decoding. When the correct decoding path cannot be obtained with the current searching width, the decoding continues with a larger searching width rather than be restarted. Furthermore, an early termination criterion is applied to quit the decoding when too many inefficient path extensions are performed based on a preset threshold. The early termination criterion can reduce the worst-case complexity of the decoder. Simulation results show that given the same searching width (list size), the proposed ADSCP decoder can achieve the same decoding performance as the conventional adaptive SCL (ADSCL) decoder while significantly reducing the complexity.
Integrating Structural Priors into Transformer for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) field. Recently, owing to the superiority to parallelize and model long-term dependencies, Transformer has been widely used in various NLP tasks. However, such position-independent model is weak at capturing the relative position between sequences of words which leads to the performance drop when directly applied to NER. In this paper, we propose a straightforward yet effective multi-mask attention, aiming at integrating structural priors such as relative distance and dependency structure into Transformer to better model sequential order. Experiments on four NER benchmarks demonstrate the effectiveness of our method against several strong baselines.
Cable Sizing Flowchart - A Tool To Help Reinforce The State of Electrical Engineering Workforce

In the last decade, maybe longer, the oil, gas and chemical (OGC) industry has seen a decline in the electric power engineering ranks and knowledge base. With the advent of the pandemic and the great resignation that followed, the industry has seen an acceleration of this knowledge drain. As experienced workers retire, knowledge around engineering and design of electrical systems and the application of various products is leaving with them, and hence engineers are being tasked with doing more sooner. Many universities have shifted the focus of their curriculums to cater to the field of high tech, leaving only a handful of institutions with strong programs in electric power engineering and subfields such as electric machines, power electronics, and power systems. Addressing skill gaps has become a point of discussion from break rooms to board rooms. One clear example is the sizing of Medium and Low Voltage power cables, that are critical to the supply of electric power in all industries. Selecting and sizing cables can have a material impact on the overall reliability, safety, and the CAPEX cost of a project.This paper discusses the state of engineers retiring and resigning in the industry causing knowledge and training gaps and give examples of a cable selection tool. The tool brings relevant knowledge that resides in IEEE, and the National Electrical Code (NEC) around cables and selecting the right size of cable using Low Voltage Cable Sizing Flowchart in appendix A, based on 2023 NEC. The goals of the paper are to expose the knowledge and training crisis to a widespread audience and then to provide a handy reference cable sizing guide for the early career professional who is asked to make cable selection, and to serve as a springboard for further reference depending on the specific application that the engineer might be tasked with.
Online Labs in Modern Engineering Education: Global Reality or Restricted Concept?

This paper briefly addresses the terms Online, Virtual and Remote Laboratories in modern engineering education and evaluates online labs from different angles. The main contribution of this work is collecting the feedback of both the instructors and students and comparing their responses for the purpose of evaluating the different approaches of lab work delivery mode and effectiveness in the context of electrical engineering education. Despite their advantages, the results attained from this work show that online labs do not easily replace face-to-face, whether for the hands-on and in-lab work experience or the soft skills of teamwork and effective communication.
Load Frequency Control Based on Sliding Mode Control and Disturbance Rejection Method

A novel control strategy is suggested for regulating frequency deviation, merging sliding mode control and observer-based control. The approach involves two main steps. Initially, disturbance rejection gains are utilized to create a unique sliding surface that generates an additional signal aiding in the correction of estimation errors. Subsequently, linear matrix inequality (LMI) techniques are employed to determine feedback control gains, ensuring improved dynamic performance of the load frequency control system by minimizing disturbance effects at the output. The outcomes demonstrate the effectiveness of the proposed techniques in mitigating estimation errors, outperforming traditional methods in terms of performance.
High Efficiency GaN Based Resonant Reset Forward Converter with Synchronous Rectification for Space Applications

In order to satisfy more stringent demands from the telecommunication sector, satellite manufacturers are inclined to provide novel and flexible solutions. GaN technology provides an opportunity to drastically improve power processing functions. This study presents a Forward converter with resonant reset used in the Space Inspire platform by Thales Alenia Space. The converter supplies a high power consumption processing module at 5V, +/−18V from a 100V primary, with maximal power of 60W. Additional improvement is achieved by using synchronous rectification on the high current output. The converter achieves 91% peak efficiency, gaining a 10% in comparison with commercial solutions, as validated experimentally
Torque Ripple Suppression Strategy of Switched Reluctance Machine Based on Phase Current Harmonic Optimization

In this paper, a torque ripple suppression method based on phase current harmonic optimization is proposed. In order to analyze the effect of phase current harmonics on the torque of switched reluctance machine and its ripple, the basic law of phase current harmonics affecting the torque ripple is obtained by modeling and analyzing the torque using magnetic co-energy. On this basis, in order to better suppress torque ripple, based on the traditional CCC control, different orders of current harmonics are injected to achieve online optimization of the current reference. The results prove that the proposed control method effectively suppresses torque ripple and is implemented in a simple and effective way.
Adaptative Termination and Control Algorithm for Resonant Arrays Used in Inductive Wireless Power Transfer Systems for Vehicle Charging

This paper proposes the implementation of an adaptative termination impedance and respective control algorithm for resonant arrays, used in Inductive Wireless Power Transfer (IWPT) Systems for Vehicle Charging. The use of an adaptative termination impedance on the last cell of the array, significantly increases the amount of energy transferred between the transmitter array and the receiver as well as the efficiency of the transfer process. The proposed algorithm uses the value of the current on the first cell which is connected to the power source to estimate the position of the receiver in relation to the array, thus eliminating the need for additional position sensors.
Design of Variable Stiffness Load-Reducing Suspended Backpack

Long time walking with heavy load can cause irre-versible muscle damage. The research shows that spring damping systems can effectively change the phase difference between the suspended backpack and the wearer. In this paper, the mechanism of phase difference change is analysed and a method for selecting appropriate spring damping coefficient is proposed. Based on the requirement of stiffness transformation, a variable stiffness load reduction suspended backpack that can adaptively change for the size of load weight is designed. And the load reduction test was carried out using this backpack prototype to verify the correctness of the load reduction theory.
Sentiment Analysis of Semester Learning Essays in Design Education

AME4163: Principles of Engineering Design is a design, build and test course offered at the University of Oklahoma, Norman, USA. Throughout the semester students are expected to reflect on authentic and immersive experiences and write Take-aways and Learning Statements. At the end of the semester each student submits a summative Semester Learning Essay. Between 2019–2021 we collected about 10,000 Take-aways and Learning Statements from the Semester Learning Essays of nearly 400 students. In this paper, we attempt to help instructors gain insight of what students have learned through these materials and thence improve the delivery of the course in the future. We analyze their Take-aways from summative Semester Learning Essays by using dictionary-based sentiment analysis to assess students' subjective feelings toward what they have learned. The results prove that we gain insight into their learning thereby providing instructors with evidence-based guidance on modifying the course from different perspectives. The method proposed is generalizable to courses that involve authentic immersive experiences.
Control Oriented Modeling of a Three-Phase Interleaved T-Type Converter

Multilevel converters have been gaining significant interest due to the increasingly demand for high power density and efficient converters. In particular, there has been a growing focus on the three-level T-Type (3LT2) converter. However, when multiple 3LT2 modules are interleaved, it adds complexity to the model and design. The paper approaches this issue and proposes a control-oriented dynamic average-value model (AVM) for the interleaved 3LT2 converter considering rectifier operation. The obtained model was developed in dq0 frame and its validation through PLECS simulations was considered satisfactory for the purposes of closed-loop control
Research on Intelligent Vulnerability Identification Method for Power Information System

With the rapid development of informatization in power systems, the network security threats are increasing day by day. Manual penetration testing is inefficient, while vulnerability scanning tools lack targeting. Therefore, an intelligent, automatic method is urgently needed to identify vulnerabilities. In this paper, an intelligent vulnerability identification method for power information system is proposed. Aiming at the characteristics of power systems, this paper constructs a dedicated vulnerability knowledge database. An end-to-end solution is proposed, extracting features from heterogeneous vulnerability data and modeling them via convolutional and recurrent neural networks to classify Web, system, protocol and firmware vulnerabilities. Experiments show this intelligent solution greatly improves efficiency over manual penetration by promptly discovering vulnerabilities, boosting security of power information system.
Treatment Efficiency Study of Nizwa New Sewage Treatment Plant

Water is life. There is a huge gap between supply and demand of safe drinking water due to population explosion. There is a need to recycle and reuse waste water to meet the growing demand for water. In some developed countries like Malaysia, waste water is being converted in to drinking water. Nizwa new sewage treatment plant has a modified activated sludge process to improve treatment efficiency. The sewage after removing suspended solids enters aeration tank. High capacity surface aerators are supplying oxygen to activate the sludge. In secondary treatment coagulation is by-passed. Aeration tank outlet enters clarifier. The clarifier outlet water is passed through membranes. The waste water from membrane filter is recycled back in to process. Sewage treatment plant BOD and COD removal efficiency is 99%, Nitrate-nitrogen is 80% and TDS efficiency is 50%. The treatment efficiency of Nizwa new STP is rated as excellent.