Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional IGBT-based Battery Fast Charger

This paper presents the development of a three-phase fast charger for electric vehicle batteries. Nominally this charger is designed to deliver up to 50kW and 100A nominal to the battery. The first charging stage consists of an AC/DC converter coupled to an LCL filter and connected to the power grid at 220V Phase-Phase. The second stage consists of a DC/DC interleaved converter, which is connected to the DC bus at 500V. The charger is coupled to the battery in order to validate charging through the CC-CV method (Constant Current - Constant Voltage). The operation of the converter and its control technique are validated through simulations in the Matlab software, using the Simulink tool. Then, the charger is built physically using the first stage AC/DC and its operation is demonstrated using a bank of batteries. The control strategy is implemented in the DSP TMS320F28379D. Satisfactory results were obtained in the battery charging control. Although the practical implementation is done on a reduced scale, the control system can be easily used at higher powers, just with adjustments in the control gains. The importance of compensating the dead-times that appear in the practical part is highlighted.
Manipulation of Deformable Linear Objects Enabled by Sound-event Classification in the Manufacturing Environment

Automated handling of deformable linear objects (DLO), specifically wires, is challenging due to their physical and geometric properties. Manufacturing companies handling wires mainly rely on manual work with operators to produce wiring systems. To address the research gap for automated wire handling solutions, this article addresses the human-robot co-manipulation of DLO and focuses on robot hearing for process monitoring. The focus is on the handling task of DLO insertion. Robot hearing in the manufacturing environment was realized through an acoustic sensor and sound-event classification. The paper outlines the collaborative robot system and data processing pipeline for audio data classification. The goal is to assess the process of DLO insertion regarding success and failure. The experiments were conducted for an automotive use case. Data collection, pre-processing, and processing are presented and experiments were conducted to evaluate the proposed solution. The experimental findings show that high accuracies for sound-event classification can be achieved and enable reliable and monitored robotic DLO handling.
State-of-Charge Balancing of Supercapacitors: A Consensus-Based Approach *

State-of-charge (SOC) balancing is crucial for the safety and lifetime of supercapacitors. However, the classical decentralized SOC balancing methods typically suffer from low energy efficiency. In this paper, we propose a consensus-based SOC balancing method for supercapacitors to synchronize cell SOC and improve the energy efficiency of the balancing system. Firstly, the switched system model is built by combining the equivalent circuit model of supercapacitors and the balancing circuit. Then, a sliding mode observer is designed to evaluate the SOC of supercapacitors. Thereafter, a consensus-based SOC balancing method is proposed to synchronize the SOC of supercapacitors during the charging process. An experimental platform is established, and the SOC estimation performance and effectiveness of proposed balancing method are verified with experiment results. Experiment results indicate that the sliding mode observer accurately estimate the SOC and the energy efficiency of proposed method improves 9.6% compared with the decentralized charging method.
Enhancing Holt-Winters Forecasting of PSEi Data With Genetic Algorithm and Cuckoo Search Algorithm: A Comparative Analysis

Accurate forecasting of stock market indices plays a crucial role in investment decision-making. The study investigates the application of the Holt-Winters forecasting method using two optimization algorithms, namely Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA), to enhance the forecasting performance of the Philippine Stock Exchange Index (PSEi). The Holt-Winters method is a popular technique for time series forecasting, capable of capturing level, trend, and seasonality components. GA and CSA are metaheuristic optimization techniques that can improve the accuracy of the Holt-Winters forecasts by optimizing the model parameters. The optimization models are assessed using historical PSEi data spanning several years. The experimental results show that GA outperformed CSA in the goal of increasing the Holt-Winters forecasting method's accuracy. The comparative analysis shows that both algorithms have promising results with little divergence in the outcomes, though there are differences in how well they can optimize the model parameters and recognize the intricate patterns in the PSEi data. The results imply that employing GA in the Holt-Winters forecasting model can be a useful strategy for stock market forecasting, empowering investors to make more informed choices.
Partial Replication-Enabled Cooperative Transmission for Distributed Edge Computing

With the proliferation of delay-sensitive and computation-intensive mobile applications, recent years have witnessed a transition towards more advanced distributed edge computing. However, the diverse computation results among different edge nodes (ENs) in conventional distributed edge computing present challenges to cooperative transmission and may result in prolonged downlink latency. In this regard, replicated edge computing has emerged as a promising solution to perform computations repeatedly across multiple ENs, thereby facilitating cooperative transmission in the downlink. Nonetheless, this approach inevitably leads to additional computation cost. To encompass the advantages of both distributed edge computing and replicated edge computing, a novel partial replication based distributed edge computing is proposed in this work. Specifically, by partitioning the original task into a common part computed at all ENs and multiple private parts, with each computed by one EN, cooperative downlink transmission can be enabled without incurring excessive computation cost. To minimize the overall processing delay, a task partition and downlink transmit power allocation algorithm is developed based on alternating optimization. Simulation results are presented to validate the superiority of the proposed scheme.
Managing Accessibility Requirements in Web Application Development Projects: The Perspectives from Research and the Industry

This paper focuses on the management of accessibility requirements in web application development projects. First, it presents a map that integrates the methods for assessing web accessibility, the factors contributing to accessibility barriers, the consequences of accessibility barriers and the possible solutions for enhancing the accessibility of web applications. Second, it provides insights into industry practices related to every theme in that map and the working knowledge that can help improve the accessibility of web applications. Findings show that applying accessibility standards and using effective evaluation methods and tools help better manage accessibility requirements in web application development projects. Implications for practice are discussed.
Study on the Spatiotemporal Distribution Characteristics of Offshore Wind Power Output in East China

With the continuous growth of renewable energy demand, offshore wind power, as a clean and pollution-free form of energy, has gradually gained attention and adoption. However, the intermittency and randomness of offshore wind power output may lead to the abandonment of wind and solar power, resulting in the instability of power system and the waste of resources in society. To deal with this issue, this article adopts an autoregressive moving average model and a fast forward generation elimination technique based on probability distance for scenario reduction, and obtains the characteristic curves of offshore wind power output on typical days in each quarter of the province in East China. This method utilizes the measure of probability distance to select representative scenarios and reduces the complexity of computation by reducing scenarios similar to representative ones. In addition, this article conducts a statistical analysis of the volatility, complementarity, and correlation of offshore wind power in East China. The results obtained in this article accurately describe the spatiotemporal distribution characteristics of offshore wind power resources in East China, providing a scientific basis for various offshore wind power projects.
Loss Minimization of Dual Active Bridge Converter Through Design Optimization in CC-CV Mode for Electric Vehicle Battery Charging Applications

The dual active bridge (DAB) converter is the most widely adopted topology for the dc-dc power conversion stage in electric vehicle battery charging owing to its inherent capabilities such as soft switching and galvanic isolation. However, the performance of DAB is compromised when it's operating under a constant-current constant-voltage (CC-CV) charging process. Therefore, the optimal design and operation of the DAB converter become of paramount importance to achieve its efficient operation during CC-CV charging. The existing design methods optimize the converter, particularly in CC mode, and take no notice of the CV mode in the design optimization process. Furthermore, the DAB converter's zero voltage switching (ZVS) operation range is limited during the light-load conditions in CV mode. This article overcomes the aforementioned concerns by proposing a loss minimization approach for the DAB converter through design optimization throughout CC-CV mode. Also, a smooth mode transition from CC to CV is achieved for the DAB converter through the control mechanism. The proposed method is formulated analytically and validated with simulation results.
CSP- LSTM Based Emotion Recognition from EEG Signals

Emotion recognition from the electroencephalogram (EEG) signals is a growing area of research in the field of neuroscience and medical technology. The ability to accurately identify and classify emotions can have significant implications for various applications, including the diagnosis and treatment of emotional disorders, human-computer interaction, etc. A deep learning-based emotion identification method is proposed here. The EEG data is divided into four frequency bands (alpha, beta, lower gamma and upper gamma) using a temporal domain filter, and then the common spatial pattern (CSP) feature extraction is applied to obtain a set of discriminative features that are used to train the long short-term memory (LSTM) for emotion classification. The LSTM was trained for each band and the predictions of each of these models (same architecture with different weights) were combined using a majority voting classifier (MVC) method. All subjects achieved accuracy values exceeding 90%, resulting in a mean accuracy of 97.34% when considering the average across all subjects. The utilisation of this method for bio-signal modeling and interpretation has the capability to enhance the identification and management of emotional disorders. etc.
Modulated Model Predictive Current Control (M2PCC) Applied to Induction Machine Drive Systems Operating Under Single-Phase Open-Circuit Fault

This paper discusses the use of the Modulated Model Predictive Current Control (M2PCC) method in two fault-tolerant induction machine drive systems operating under a single-phase open circuit fault. By means of M2PCC simulation, it is shown that employed method is applicable for postfault operation, providing fault-tolerant behavior. Furthermore, a comparison between FOC and M2PCC is conducted for the discussed drive systems, demonstrating that M2PCC provides better transient response, with smoother and faster migration from healthy to postfault operation.
How Does Experience Influence Usability Expectations in Human-Robot Interaction? Results of a Worker Evaluation in an Industrial Setting

This paper presents results of a worker evaluation in three industrial use cases, focusing on the assessment of expected usability aspects within human-robot interaction and their relation to the level of experience. 27 workers from three different European companies were asked about their expectations of working with an interactive robotic system, using statements about the dialogue principles based on ISO standard 9241–110:2019. The results show a significant difference between the two groups of experienced and inexperienced workers for multiple principles. Learnability and Controllability are rated less important with more experience while Suitability for the task and Individualization grow in importance with the level of experience. Overall, the results show that it is most important for the users that the functionalities of the system meet their expectations. Limitations regarding sample size and allocation are discussed. Nevertheless, the presented results and interpretations underline the importance of considering workers' expectations during the technology development process
Effect of Maize –Legume Intercrop and Fertilizer on Weed Suppression and Maize Performance in South Eastern, Nigeria

Maize is one of the most commonly cultivated arable crops in the rain forest zone of South Eastern Nigeria. Globally soil fertility and weed pressure are the most important constraints limiting increase productivity of Maize especially in Sub Saharan Africa (SSA). Unavailability and cost of inorganic fertilizer as well as cost of labour for weeding have engendered low productivity of maize. Hence this trial was conducted to evaluate the efficacy of maize-legume systems on weed suppression and maize performance. The trial was carried out at the Teaching and Research Farm of Faculty of Agriculture, University of Port Harcourt, Nigeria located within latitude 04°54’N and longitude 6°55’ E). The trial was conducted between April 4th and July 5th, 2017. The experiment was a 3 x 3 factorial arrangement fitted into a randomized complete block design (RCBD) consisting of 3 types of legume systems (Mucuna pruriens, Lablab purpurens and No legume) and three levels of NPK 15:15:15 fertilizer (0, 15, and 30 kg NPK/ha). The 9 treatment combinations were replicated thrice to give 27 plots. Data collected were on maize yield and yield components, weed and legume parameters at 4, 8 and 16 weeks after sowing (WAS). Result showed that legume significantly reduced weed biomass when compared to the natural fallow. The effect of weed biomass reduction was Mucuna 34.8% >Lablab 29.2%. The legume system significantly suppressed weed compared to natural fallow and the weed suppression ability average 56% and 30% respectively for Mucuna and Lablab whether or not they received NPK. Result of this trial also revealed that within 8 weeks after sowing legumes (8 WASL) 26% N and 22% N can be harvested by integrating this legume cover in cropping system and that NPK application has little or no effect in the performance of these legumes. Mucuna was not sensitive to fertilizer application while Lablab responded to fertilizer application. Maize was sensitive to Mucuna due to early integration; hence, it is recommended that these legumes be integrated at six weeks after sowing maize.
Experimental Identification of Mechanical Parameters for Speed Controller Design of Switched Reluctance Machine Drive

The identification of mechanical parameters is important in the design of an efficient speed controller for electric drives. In this paper, an experimental method is proposed to determine the moment of inertia and frictional torque characteristics of a Switched Reluctance Machine (SRM) connected to a load. In this method, the no-load steady-state frictional torque is estimated from the SRM’s static-torque characteristics while maintaining constant speeds. Subsequently, to calculate the moment of inertia, the SRM drive is accelerated till a peak speed using a low-torque ripple torque controller, and decelerated through the loss torque. The moment of inertia is then determined from the measured speed responses, during the acceleration and deceleration periods by employing two different numerical methods: the area method and least square error fitting. Furthermore, using the experimentally determined mechanical parameters, this paper presents the design of a PI-based-speed controller for SRM. The validity and effectiveness of this method are confirmed through simulations and experiments conducted on an 8/6 pole 4-phase, 4kW SRM connected to a DC shunt generator.
The Value of Immersive Communication Systems in Online Meetings: A Problem Statement and Literature Review

Traditional online meeting platforms present several limitations, including lower engagement and productivity, worse social interactions and communication, and technical challenges. TNO believes that Immersive Communication Systems (ICSs), thanks to their higher level of social presence, can overcome the drawbacks of non-immersive meetings and improve the overall experience of the users. In this paper, we first present a literature review showing how the social presence of ICSs leads to various added values, such as enhanced effectiveness, increased creative ideas, and better communication. The findings suggest that it is best to evaluate the Quality of Experience (QoE) of ICSs compared to traditional meeting platforms relying on subjective measures of usability, perceived effectiveness, and social presence, while objective measurements should be seen as complementary. Such QoE evaluations are crucial to also ensure the readiness and feasibility of a system's deployment. We proceed by describing the ICS developed by TNO that makes use of photo-realistic user representations given by the 3D point cloud technology. We briefly discuss the advantages and disadvantages between the state of the art and the TNO ICS, while we also bring important system aspects that should be considered for improved user experience. Lastly, we propose three research questions to guide future evaluation of the TNO system based on the most prominent subjective factors that were identified by our literature review.
Exploring the Use of Deep Reinforcement Learning Algorithms for Wound-Approaching Trajectories in Robot-Assisted Minimally Invasive Surgery

Robot-assisted minimally invasive surgery (RAMIS) has improved the quality of operations, execution time, and patient recovery time. However, suturing remains a tedious, complex, and time-consuming task. To automate this procedure, we have investigated the use of Deep Deterministic Policy Gradient (DDPG) to generate a trajectory for Wound-Approaching (WA). The trajectory must satisfy surgical constraints, be neither too long nor too wide, and minimize recovery time and side effects. We compare standard DDPG, DDPG from Demonstration (DDPGfD), and we have developed a new algorithm that learns from success epochs, DDPG from Success (DDPGfS). An ad hoc reward function was developed to minimize the number of actions and avoid undesirable behaviour in the neighbourhood of the wound. Among the algorithms, DDPGfS proves to be the best choice in terms of reliability and robustness with a percentage of success equal to 74% respect to 64% of DDPGfD. The new dense reward function enhanche the performance, either in low support situation, preventing the agent to oscillate around the entry point.
Utilizing Deep Learning for Semi-Automatic Conversation Analysis During Recruitment and Employee Education in the Seed Phase of High-Tech Startups

In high-tech startups in the seed stage, grappling with limited staffing resources, the expeditious discernment of an individual's potential as a pivotal element of the institutional structure is of paramount importance. The present research enabled a substantial diminution in evaluation duration in contrast to traditional methods by harnessing the power of Deep Learning for transcription, automatic speaker differentiation, and extraction of speech attributes during the Deep Learning procedure. Furthermore, utilizing the attributes excavated via Deep Learning for textual analysis through Principal Component Analysis (PCA), it became feasible to glean quantitative propositions from a broader range of perspectives hitherto unobtainable. Despite the concentration of this study on attributes pertinent to dialogic velocity, it elucidates the feasibility of accruing more multifaceted suggestions through the extraction of additional attributes, such as vocal pitch, from the internal output of Deep Learning and associating them with analysis findings of PCA.
Data Model Using Graph DB to Integrate Data from Multi-Field Sources for Service Utilization

To achieve Digital Transformation, companies are required to create new value and deploy solutions by using multi-field data, not just data from one domain. In recent years, data sharing platforms such as FIWARE and GAIA-X are being developed to integrate and manage multi-field data stored in various locations. However, while these platforms provide everything from data storage to data provision, we need to implement data-to-data association and data linkage processing through programming for building services after extracting data from these platforms. Therefore, in this paper, focusing on building services and applications, we propose a data model that can define data-to-data association and linkage processing. The proposed model can be stored and managed by Graph Database, allowing retrieval and extraction of data across multiple fields. We also developed an application that lists related data on a map using the proposed model.
An Influential Node Identification Framework in the Aircraft Assembly Network Based on the Community Structure

Improving the reliability and flexibility of key process assembly schemes is a critical factor in the intelligent management of aircraft production. However, the complexity of aircraft assembly operations, large-scale production, and fierce competition for resources aggravate the difficulty of key process identification in the aircraft assembly manufacturing system. To efficiently recognize the key processes and facilitate the management of the aircraft assembly, this study proposes an adaptive influential node identification framework. The aircraft assembly manufacturing system is transformed into an assembly complex network (ACN) according to the technology. Then, the problem of community detection is addressed by applying the Louvain algorithm to partition ACN into several communities. Moreover, within each community, the structure characteristics and physical information are combined to evaluate the influence of the node. Finally, the experimental results demonstrate the efficiency of the proposed framework in adaptively analyzing ACN and identifying key processes in the aircraft assembly manufacturing system. This study provides an efficient and convenient solution for key process identification in aircraft assembly system management.