Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigate Networked Microgrids to Enhance Distribution Network System Resilience

Modern society is becoming more dependent on secure and reliable power energy and increasing consumption power energy simultaneously. Rising fuel prices and growing environmental damage motivate Generation companies to use renewable energy. In this regard, Microgrids are being considered to build a future power network system and are being implemented widely due to its various advantages. It is made more promising by connecting it in parallel with nonrenewable distributed generators (e.g. Micro turbine and Fuel cell). Modern society needs more reliable, secure, and safe power. A Rising number of natural disasters that caused large blackouts put change of structure of the current power system. This paper investigates the impact of Microgrids to enhance the power system resiliency. The impact of networked Microgrids and distributed generators to enhance resilience and using theirs effectively during and after outages are also considered in this paper.
A Data Science Approach to Football Team Player Selection

FIFA (Fédération Internationale de Football Association) is world football (soccer) league that is separate from Olympics. FIFA been largely instrumental for making soccer as the most popular game in the world. It has led to development of many private soccer clubs all over the world. Creating new clubs with young players, loaning players from other clubs, picking choice positions, determining wages and remuneration to players based on performance and international rankings is complicated decision process in terms of global business perspective. This paper presents a data science approach to minimize the time taken in selecting a player for a team by considering the cost and player's skills as constraints. Such an analysis will help an owner to maximize the profit and popularity of an existing club or to create a new club. We present statistical analysis of player performance based on abilities and skills for a new team using powerBI and Python Pandas by minimizing the cost. The results show that it leads to improved business profits through a systematic enhancement to football data sets. These kind of approaches and analytical results can be useful to franchisor of proprietary knowledge to form group of selected players as team.
Dynamic Economic Dispatch Considering Transmission-Distribution Coordination and Automatic Regulation effect of Transmission System

With the increase of proportion of renewable energy, the uncertainty in power system is significantly increased, and the security operation of power system is facing more challenges. In traditional economic dispatch model, the unit output and load are expressed by constant power, which ignores the automatic regulation effect during system operation. And the transmission system and distribution system are separated optimized, the coordination effect is not considered. In this paper, a new dynamic economic dispatch model is proposed and the automatic regulation effect and transmission-distribution coordination are considered, which can improve the renewable energy accommodation and enhance the system operation security and economics. The validity and effectiveness of the proposed model are illustrated by case studies.
A Decentralized Market Solver for Local Energy Communities

The progressive development of local energy communities requires the reorganization of the energy production and consumption, with a new energy system in which the technical and commercial decision-making process need to be decentralized from central authorities to distributed entities properly coordinated. This will be increasingly aided by the spread of IoT systems capable of interacting among distributed resources. The technical and commercial energy management burden will be then shared among cooperating IoT devices, which will perform the necessary optimization and control operations. In this context, a Decentralized Genetic Algorithm (DGA) methodology, able to perform a wide spectrum of power system optimizations in a fully decentralized fashion is introduced. This paper aim at developing a DGA management procedure, tested considering a model for a local energy market and an automated distributed resource scheduling in a local energy community. The testing is performed through a HIL experimental setup, which proves the effectiveness of the methodology proposed, as well as a Blockchain platform
High Isolation 8⨯8 MIMO Antenna Design for 5G Sub-6 GHz Smartphone Applications

In this paper, an $8\times 8$ Multiple Input Multiple Output (MIMO) antenna design for Fifth Generation (5G) sub-6GHz smartphone applications is presented. The antenna elements are based off a folded quarter wavelength monopole that operate at 3.4-3.8GHz. Isolation between antenna elements is provided through physical distancing. The fabricated antenna prototype outer casing is made from Rogers R04003C with dimensions based on future 5G enabled phones. Measured results show an operating bandwidth of 3.32 to 3. $925\mathrm{G}\mathrm{H}\mathrm{z}(\mathrm{S}_{11}\lt$ -6dB) with a transmission coefficient $\lt_{-}14.7\mathrm{d}\mathrm{B}$. A high total efficiency for an antenna array is also obtained at 70-85.6%. The design is suitable for MIMO communications exhibited by an Envelope Correlation Coefficient (ECC) $\lt0.014$. To conclude a Specific Absorption Rate (SAR) model has been constructed and presented showing the user’s effects on the antenna’s S-parameter results. Measurements of the amount of power absorbed by the head and hand during operation have also been simulated.
Humanoid Bipedal Balancing and Locomotion

This article is about humanoid robot bipedal balancing and locomotion. There is a description of bipedal robots and main concepts. The Linear Inverted Pendulum Mode and its main advantages are presented. In general, the main identified problem about humanoid robot locomotion is the collecting and analyzing information about the near environment, particularly about the terrain. The proposed method assumes implementing, firstly, the improved vision system that consists of LIDAR and an additional camera that allows to identify object and surface properties and, secondly, design a pressure-sensitive system for getting a pressure map of the surface under the robot feet. Finally, both of these sensor systems should have a unified database of the object and surface types to verify the surface properties by various methods.
Analysis and Control of PV Cascaded H-Bridge Multilevel Inverter With Failed Cells and Changing Meteorological Conditions

This article proposes an approach to control a photovoltaic cascaded H-bridge multilevel inverter with failed cells and changing meteorological conditions for large-scale grid-connected applications. The controller development is based on an analysis of the interaction between the inverter common-mode and differential-mode quantities, which is done in the time domain, supported by a space vector representation analysis. The proposed approach is able to produce balanced three-phase line-to-line voltages and currents even if there is a failed cell or if the fluctuating meteorological conditions cause uneven power distribution among the bridges. This is achieved through the modification of the pulsewidth modulation reference phase voltage angles combined to the injection of a dynamic homopolar component. Such an approach avoids tripping the system due to the protective functions #25 and #87 for per grid applicable codes and standards. Numerical simulations and laboratory experiments performed on a seven-level converter with different abnormal conditions to confirm the effectiveness of the suggested control strategy.
Study on Time-varying Resistance Model of Arc Furnace Based on Arc Length Modulation and PSO Algorithm

Electric arc furnace(EAF) is one of the important equipments of modern iron and steel enterprises. As a typical nonlinear load, the power factor of arc furnace is low, and the reactive load fluctuates greatly. When connected to the power grid, it may cause the increase of harmonic content, voltage flicker, three-phase imbalance and other power quality problems. Therefore, the accurate modeling of arc furnace is of great practical significance. In this paper, starting with the nonlinear time-varying resistance model of the ac arc furnace, PSO(Particle Swarm optimization) algorithm is used for parameter identification, and the parameter setting method of arc furnace model with strong universality is obtained. In order to make the model reflect the characteristics of chaos and randomness in the operation of the arc furnace, three kinds of random signals are used to modulate the arc length, and the equivalent model of the arc furnace which is closer to the actual operation is obtained. Based on the above analysis, the accuracy of the model is proved by simulation analysis, which provides an effective simulation model for the study of harmonic management of electric arc furnace power grid, and has strong practical value.
Combining High Throughput and Low Migration Latency for Consistent Data Storage on the Edge

Today, many applications offload computation and storage to the cloud. Unfortunately, the high network latency between clients and datacenters can impair novel, latency-constrained, applications such as augmented reality. Edge computing has emerged as a potential solution to circumvent this problem. To unleash its full potential, the edge must cache data that is frequently used. However, building a storage service that is able to maintain many (partial) replicas while providing meaningful consistency guarantees to clients that migrate among multiple edge caches is an open challenge. In this paper, we present Gesto, a data storage architecture that enables scalable causal consistency for edge networks. Gesto integrates a novel causality tracking mechanism that relies on multi-part timestamps of constant size, independently on the number of edge caches. As our evaluation shows, this mechanism enables Gesto to simultaneously offer scalability, low read/write latency, high throughput, and, unlike previous work, fast client migrations.
The Role of Good Human Uniqueness in Social Robot Anthropomorphism Influenced by Chronic Loneliness

Social robot companion will become pervasive in the near future as a result of technological development and social work robotization to confront long-time loneliness. Chronic loneliness is not only a painful individual experience but an increasingly serious social problem. It is thus crucial to drill down the relationship between people’s chronic loneliness and how they see humans in this artificial intelligent agent. Based on the designed human-robot interactions, we measured participants’ anthropomorphic tendency on our social robot prototype as well as their chronic loneliness, and found that chronic loneliness — permanent disposition caused by person, not easily relieved — deterred participants from making anthropomorphic inference of the robot (i.e., to perceive human essence, warmth and competence, from the robot). In particular, mediation analysis revealed that reduced attribution of positive human uniqueness traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) to the robot explained the negative relation between being chronically lonely and anthropomorphizing the robot. We further corroborated this result by enabling the robot to conversationally manifest the five traits and successfully verified a substantial reduction in the negative effect of chronic loneliness on robot anthropomorphism. The findings suggest good human uniqueness, exemplifying secondary emotions, is vital not only in interpersonal contexts but in establishing connections with social robots.
Long-Prediction-Horizon Near-Optimal Model Predictive Grid Current Control for PWM-Driven VSIs With LCL Filters

This article proposed and investigated a near-optimal finite-control-set model-predictive grid current control (NOP-MPGCC) for voltage source inverters (VSIs) with actively damped LCL filters. It carries three advantages: (i) the constant-frequency pulsewidth modulator produces regular switching spectrum—which can ease the LCL filter design process in grid current control applications with medium-to-high carrier ratio; (ii) it avoids the use of the usual first-order assumption in most PI-/PR-based control and the common simplifications adopted in some existing FCS-MPC schemes. This is achieved by considering the third-order behavior using long prediction horizon; and (iii) it can operate near and across the critical frequency, defined using the established classical control definition. Moreover, it retains the intuitive enumeration structure and maximizes the dc-bus utilization through the consideration of entire hexagonal vector space at every control cycle. The theoretical derivation, simulation, and experiment results verify that NOP-MPGCC is potentially a viable direct grid current control scheme for PWM-VSIs with actively damped LCL filters.
A Periodically Refreshed Capacitive Floating Level Shifter for Conditional Switching Applications

This letter presents a periodically refreshed capacitive floating level shifter (CFLS) for applications that require a conditional switching signal. The proposed structure can generate a floating signal without additional bias voltage, and all coupling capacitors (CCs) can be simultaneously refreshed. Digital low-dropout (DLDOs) regulators are integrated to show how the leakage current from a CC affects the DLDOs that are driven by conventional and proposed CFLSs. Results are measured according to the temperature and supply voltage. With a 28-nm CMOS process, the proposed CFLS achieves a delay of 8.9 ns including the I/O buffer delay and an energy consumption of 788 fJ per cycle at VDD = 0.9 V.
A Laboratory Experiment with a Flexible Surface: Hardware, Software, and Control

This paper describes the design, the construction and the control of a flexible surface that is able to shape arbitrary forms. From a hardware point of view, the surface is realized by fifty linear motors that are mounted beneath a latex foil and are capable of extending or retracting a piston. Through the correct position of each piston, the overall latex surface forms the desired shape. From the software side, three type of controllers are used: Synchronizing controllers that reduce the stress on the material, feedforward controllers that enable each motor to track a reference trajectory, and proportional-integral controllers to compensate for model uncertainties and mismatched initial conditions. The paper shows experiments in which the surface should form a travelling wave. The results demonstrate the effectiveness of the chosen controllers as the overall surface forms the desired shape with practically no tracking error.
Almost-common denominators

I developed the prompts in preparation for a lesson with preservice elementary school teachers (PSETs) about comparing the sizes of fractions, a topic typically included in standards from the third to fifth grade. Determining which fraction is larger builds toward a foundational conception that fractions have magnitudes (Siegler et al., 2011), which students should explore before working with fraction operations (Bruce et al., n.d.).
A Program Logic for Reasoning About C11 Programs With Release-Sequences

With the popularity of weak/relaxed memory models widely used in modern hardware architectures, the C11 standard introduced a language level weak memory model, A.K. A the C11 memory model, that allows C/C++ programs to exploit the optimisation provided by the hardware platform in memory ordering and gain benefits in efficiency. On the other hand, with the weakened memory ordering allowed, more program behaviours are introduced, among which some are counterintuitive and make it even more challenging for programmers to understand or to formally reason about C11 multithread programs. To support the formal verification of the C11 weak memory programs, several program logics, e.g. RSL, GPS, FSL, and GPS+, have been developed during the last few years. However, due to the complexity of the weakened memory model, some intricate C11 features still cannot be handled in these logics. A notable example is the lack of supporting to the reasoning about a highly flexible C11 synchronisation mechanism, the release-sequence. Recently, the FSL++ logic proposed by Doko and Vafeiadis moves one step forward to address this problem, but FSL++ only considers the scenarios with atomic update operations in a release-sequence. In this article, we propose a new program logic, GPS++, that supports the reasoning about C11 programs with fully featured release-sequences. We also introduce fractional read permissions to GPS++, which are essential to the reasoning about a large number of real-world concurrent programs. GPS++ is a successor of our previous program logic GPS+, but it comes with much finer control over the resource transmission with the newly introduced restricted-shareable assertions and an enhanced protocol system. A more sophisticated resource model is devised to support the soundness proof of our new program logic. We also demonstrate GPS++ in action by verifying C11 programs with release-sequences that could not be handled by existing program logics.
Analysis of Electrical Impedance Myography Electrodes Configuration for Local Muscle Fatigue Evaluation Based on Finite Element Method

Local muscle fatigue (LMF) is a common physiological phenomenon that occurs in daily exercise training and medical rehabilitation. Without timely treatment it can easily lead to muscle spasm, ligament rupture, and even stress fractures. Electrical impedance myography (EIM) is a noninvasive bioelectrical impedance technique suitable for the wearable LMF monitoring anytime and anywhere. In this paper, a novel EIM electrode configuration was proposed by establishing a four-layer simulation model of the human upper arm in FEM software. Sensitivity parameters were introduced to optimize muscle selectivity. The effect of fat thickness on impedance change rate was explored to reduce the influence of individual fat differences on EIM results. Dynamic and static contraction experiments of muscle fatigue were performed on the biceps brachii muscles of 10 volunteers to verify the effectiveness and feasibility of the proposed electrode configuration. The proposed electrode configuration reduced the measurement area by 25%, whereas the impedance amplitude and sensitivity remained the same. The influence of individual fat differences on EIM results was significantly reduced. When the fat thickness increased from 6 mm to 18 mm, the impedance change decreased by 31.78% compared with the traditional electrode configuration. When the muscles were extremely exhausted, the decrease in resistance varied around $10~\Omega $ and within 10−1 order of magnitude in different volunteers. In a word, the proposed electrode configuration effectively evaluated the degree of LMF, providing more feasibility for the design of wearable devices.
Robustness and Reliability of Model-based Sensor Data Fusion in a Lithium-Ion Battery System

In recent years, battery monitoring systems with sensors on cell level have been introduced to enhance robustness and safety. With model-based sensor data fusion the uncertainty of cell voltage and current measurement is diminished. Within this contribution approaches are explored to strengthen the robustness of the sensor data fusion method. In particular, two Kalman filters are investigated that are based on Student's t instead of Gaussian noise. Furthermore, the underlying parameter estimation is improved by adaption of measurement noise and taking the estimator windup into account. Simulation results show that each of the presented methods increases the robustness of the sensor data fusion framework and may be combined for best performance. Moreover, with sensor data fusion the reliability of each cell measurement is enhanced, which is also investigated within this contribution.
Deep Learning-Based Hybrid Model for Forecasting Locational Marginal Prices

It has generally become a challenging task to accurately forecast locational marginal prices (LMPs) due to its strong volatility originated from transmission congestion and extreme weather. Most of existing literature fails to predict both price spikes and normal prices with equally satisfying effectiveness. In fact, normal price prediction mostly relies on the strong correlation with demand, which proves to have a high Pearson correlation coefficient with load changes. However, price spikes prediction is associated with network congestion resulting from features with high Maximal Information Coefficient (MIC). In this paper, we propose a deep learning-based hybrid model for LMPs prediction, considering the discrepancy of two kinds of price. Based on a support vector machine (SVM) and a given threshold, we firstly distinguish and label normal prices and price spikes. For normal prices, a long-short term memory (LSTM) neural network can uncover the trend of time series and bring out an accurate prediction. For price spikes, a light gradient boosting machine (LightGBM) is adopted to address those extreme jumps by analyzing relevant features in tabular format. Case studies based on realistic datasets from the PJM market demonstrate the efficiency and effectiveness of our proposed hybrid model.
Monitoring and verification of event-driven transportation systems in discrete manufacturing

Reliable, yet flexible operation of manufacturing systems is important for efficient and economically viable production. Model-based analysis and verification methods are becoming increasingly important to achieve such an operation. In this work, we outline a model-based approach to monitor and verify transportation systems commonly employed in discrete manufacturing. To provide guaranteed verification and monitoring results despite uncertainties and the event-driven nature of the considered transportation systems, we combine tailored first principle models with suitable set-based feasibility formulations. Simulation examples and results from an industrial test plant underline the performance and real-time capability of the presented approach.
Valve Power Loss Fast Evaluation Method for Modular Multi-level Converter Operating at High-Frequency

The standard IEC 62751 proposed a valve power loss evaluation method combing analytical formulae and numerical simulation for modular multilevel converter (MMC). However, the illustration of building a MMC model and processing simulation results have not been addressed. And also, obtaining solutions from a diodes and IGBTs based simulation model can cost excessively long time. A fast power loss evaluation method is proposed in this paper by linking the PSCAD/EMTDC and Matlab files. Detailed valve power loss analysis of a 201-level 500MW MMC operating at 50Hz$\sim$1000Hz is then presented. Relationship between the valve power loss, power transfer level and operating frequencies are presented and fitted. Research results of this paper can be used as guidance for the application of medium and high frequency MMC.