Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Operation Optimization of Desulfurization System Based on Improved Differential Evolution Algorithm

Aiming at the characteristics of non-linearity and complex lag of desulfurization system, an improved differential algorithm is proposed to optimize the parameters of desulfurization system. The pH value, slurry density and liquid level height are used as control targets and safety ranges, and emission indicators are used as constraints to make decisions. maximize economic benefits. An adaptive operator is introduced on the basis of the differential evolution algorithm to improve the global optimum. At the same time, the crossover operator is modified to improve the system diversity. The results show that, compared with GA algorithm, PSO algorithm and differential evolution algorithm, the improved differential evolution algorithm can meet the requirements of desulfurization system economy and energy-saving emission, and has guiding significance for desulfurization system decision-making.
Adaptive Fuzzy Control of Nonlinear Systems With Predefined Time and Accuracy

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy controller is developed by applying the backstepping technique, and the stability time and convergence accuracy of the tracking error can be set previously, which facilitates the engineering application with requirements for convergence time and accuracy. Especially, to reduce chattering and decrease energy consumptions on the premise of ensuring the stability of the system, an inequality is introduced to construct a continuous term which can replace the sign function. Finally, a practical example is presented to illustrate the feasibility of main results.
Comparison of 2D-CNN, LSTM, and GRU for cardiovascular disease diagnosis

There are various methods for classifying ECG signals related to cardiovascular diseases using deep learning methods. A representative method for applying a one-dimensional ECG signal to deep learning is an RNN-based method such as LSTM and GRU. Another representative method is to exploit 2D-CNN algorithms. In order to apply the ECG signal to the 2D-CNN algorithms, however, a one-dimensional signal must be converted into a two-dimensional image. In this paper, when converting a one-dimensional ECG signal to a two-dimensional image, two methods are considered: plotting the ECG signal as a grayscale image and generating a scalogram. Comparing the performance of 2D-CNN methods and RNN-based methods in this study, the 2D-CNN methods achieved better performance. In particular, comparing the diagnostic power of the ensemble model and the 12 single 2D-CNN models, the ensemble model showed the best performance measures.
RSSI Signal Processing for Short-Distances Estimation: Case Study Hand Movement Detection

Due to ongoing advancements in sensor and communication technology, wearable devices have been applied to various applications, including rehabilitation, health monitoring, and body poster supervision. With the help of signal transmission, wearable nodes can communicate and transmit data over wireless channels. In fact, when wireless signals are received or transmitted, the Received Signal Strength Indicator (RSSI) is generated, which aids in signal evaluation and distance estimation between wearable nodes. Despite the advantages that RSSI can provide, it can still be inaccurate and unstable in certain situations, which makes it necessary to revise RSSI measurements before putting them into the application. In this paper, there is a proposed solution for hand movement detection using RSSI measurement. For that different filter was tested from which Kalman filter was chosen for his better result than the average filter and Savitzky Golay in short distance measurement with real-time response. Then calibrated to achieve the desired result and have better results. The Kalman filter offers a stable measurement with a low error rate when applied for hand movement detection at different positions, with an error rate of +/-2
A Rule-Based Language for Configurable N-way Model Matching

To build complex software-intensive systems, different stakeholders from diverse domains must collaborate to create and modify models. Model matching is a fundamental precondition of collaborative development, which is concerned with identifying common elements in input models. When stakeholders work on multiple models, they need to simultaneously compare all models to better understand differences and similarities. However, the literature shows no consensus on how to specify comparison criteria for matching multiple models, especially in a form that is independent of modeling language, which hampers their reuse and adoption. In this paper, we present a rule-based formalism that enables the user to specify their comparison criteria for multiple models at a high level of abstraction. We also introduce an N-way matching algorithm for comparing both homogeneous and heterogeneous models. As the tool support, we implemented a syntax-aware editor and a parser for specifying comparison rules for EMF-based models. The evaluation of our formalism shows that it is applicable in real modeling scenarios.
Using Motion Sensors For Improved Beam Tracking in THz Communications with User Micromobility

The terahertz frequency band (0.3-300 THz) is expected to be used in sixth generation (6G) cellular systems providing enormous bandwidth at the air interface. The cellular systems utilizing this band will rely on extremely directional antennas at both base station (BS) and user equipment (UE) to compensate for path losses. However, this property makes links vulnerable to frequent antenna misalignment caused by micromobility, e.g., small shakes and rotations of UE even when a user is in a stationary position. In this paper, we propose a new scheme for efficient beamalignment to reduce the fraction of time in the outage and improve ergodic capacity of the link under UE micromobility. The main idea consists in tracking the displacement of the center of the antenna half-power beamwidth (HPBW) allowing to drastically decrease the search space during the beamalignment procedure. By utilizing real measurements of micromobility patterns, we demonstrated that the proposed the scheme allows reducing the fraction of time in outage by 10-30% and improve ergodic capacity by 5-20% depending on the type of application utilized by the user and the accuracy of utilized motion sensors at UE.
Open-Loop RHP Poles Issues in Online Stability Monitoring for Microgrid

Impedance-based stability analysis has been widely adopted for ac grid. Online stability monitoring, which requires a real-time assessment of the system stability margin, is advantageous to perform crucial tasks in the grid. Most related studies assumed no right-half-plane (RHP) poles in the minor loop gain to apply many popular criteria such as the forbidden-region-based criterion. However, this paper found that RHP poles may appear in a microgrid's open-loop impedance if its PV power or PV converters' phase-locked-loop (PLL) bandwidth exceeds a certain value. With or without open-loop RHP poles changes the characteristic loci dramatically in the complex plane, as such, many past stability-margin criteria become not applicable. This paper thus proposes other potential stability margin indexes for online stability monitoring applications.
Hip Joint Trajectory Generation Based on Human Limb Motion Synergy

Lower limb exoskeleton robot can help hemiplegic patients with rehabilitation training and enhance human movement ability. Lower limb trajectory generation is an indispensable part of lower limb exoskeleton robot control, which can better realize human-machine collaboration and significantly improve the quality of life of patients. One of the main challenges in this field is to predict the wearer’s hip joint trajectory in advance. In this study, we analyzed the synergy law between limbs in the process of human walking, and studied how to use the shoulder motion data to predict the hip joint trajectory based on the synergy law. We recruited four subjects, and measured their three-dimensional coordinate motion trajectories data of 8 markers at the left and right shoulders, elbows, hip joints, and knee joints by the optical motion capture system OptiTrack. The non-linear mapping model between the shoulder and the hip joint is established using the least square (LS) method. An error correction model is also proposed based on long short term memory (LSTM) to reduce the hip joint trajectory prediction error. Based on the proposed synergy law of upper and lower limbs, the hip joint trajectory can be predicted in advance only by using the shoulder motion information. The predicted hip joint trajectory error can reach as low as 2.1°.
A Blockchain-based Profiling System for Exploring Human Factors in Cloud-Edge-End Orchestration

In mobile edge computing (MEC), application partitioning is one of the most effective measures to leverage computing resources. Due to the user's unpredictable behavior pattern, which is an indispensable factor affecting the performance of an offloading system, traditional partitioning algorithms, considering only purely technical QoS, are no longer enough to meet the increasing concern for the user experience of mobile applications. In this paper, in order to explore human factors in modeling partitioning algorithms for cloud-edge-end orchestration under a safe and trusted environment, we present a blockchain-based profiling system to collect behavioral data from several invited subjects. For discovering user-driven relations of method-level components, we propose a clustering algorithm framework to process each subject's data. Based on the disparate results, we illustrate a case study to prove the usefulness of the system and the data for the orchestration by analyzing the variance of user behavior and the feasibility of applying human factors to the partitioning algorithm.
Dual-circulant Modulation for Inherent Submodule Voltage Balancing in MMDC

The modular multilevel dc-dc converter (MMDC) has attracted much interest in medium voltage dc applications, but it has to face the main issue of balancing submodule (SM) capacitor voltages. This paper proposes a dual-circulant modulation and mathematically proves it possessing the inherent balancing capability for arbitrary operation cases. Two sets of circular switching patterns are preset and combined to complete the full constrains on SM voltages for balancing, and then reallocated to keep the advantage of SM uniformity. The associated polynomial of circulant matrix is introduced and the coprime of polynomials demonstrates the full-rank feature of the extended switching matrix that promises the inherent balancing. Experiments are conducted on prototype to validate the inherent voltage balancing capability of the dual-circulant modulation.
DMP-Based Motion Generation for a Walking Exoskeleton Robot Using Divergent Component of Motion

The planning of footsteps has proven to be a difficult task for researchers working on walking exoskeleton robots. Over the past years, researchers have proposed many methods. In addition, Some researchers have payed attention to linear inverted pendulum (LIP) model and its divergent component of motion (DCM). In the meantime, dynamic movement primitives (DMPs) has also been applied to solve this motion planning problem and attracting a lot of attention. This paper describes a gait planning strategy using DCM and DMPs. We design a two-level planning in the proposed strategy. In the first level, we calculate the center of mass(COM) using LIP Model and DCM. Then we use the inverse kinematics of the walking exoskeleton robot to solve the joint trajectories. In the second level, we use gaussian mixture regression(GMR) represent the joint trajectories. And we learn and reproduce the joint trajectory with DMPs. Finally, we validate the feasibility of our approach on walking exoskeleton robot.
Biomimetic peptide-conjugated membranes for developing an artificial cornea

The corneal endothelium is composed of a single layer of specialized endothelial cells, protecting, and nourishing the inner surface of the cornea. Corneal endothelial cells do not proliferate after birth and their number decrease with age. Trauma, inflammation, or surgical intervention can cause cell loss. When damage is extensive and the density of corneal endothelial cells decreases to a critical level, it results in corneal edema and vision loss. Besides them, when corneal endothelium has irreversible damage, the only treatment way is corneal transplantation. But there are some drawbacks such as finding donors, immune reactions, and the number of patients waiting on the transplantation lists for years. Tissue engineering approaches can provide promising alternatives for the regeneration of corneal endothelium tissue. Peptides can be used to modify and functionalize the scaffolds, allowing for the production of bioactive and biomimetic surfaces. Peptide-modified scaffold surfaces might direct and enhance the behaviors of cells. In this study, the aim was to functionalize the polycaprolactone (PCL) membranes with tissue-specific peptides and to characterize the peptide-conjugated membranes by Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and X-ray Photoelectron Spectroscopy (XPS) analysis. The synthesized peptides were successfully conjugated on the PCL biomembranes.
On the Performance of GLRT-LTD CFAR Processor in Correlated Pareto Clutter Under Different Estimators

Pareto type II distribution is a class of high-resolution sea-reverberation data models. Application of the GLRT-LTD (Generalized Likelihood Ratio Test Linear Threshold Detector) algorithm requires an accurate estimation of the clutter parameters. Under the assumption of correlated Pareto clutter, several estimators could be applied. In this work, we investigate the effect of the MLE (Maximum likelihood Estimation), Integer order moments, fractional-order moments, and zlog(z) estimators on the detection performance of the GLRT-LTD procedure. From simulated datasets, it is shown that approximate results are obtained by MLE and zlog(z) methods. Moreover, the zlog(z) approach is advantageous when complicated parameter estimation scenarios occur (i.e., correlation coefficient tends to one).
An Evaluation of the Participants' Perception of a Gamification to solve Problems of Software Process Improvement in the Educational Context

This Research to Practice Full Paper presents that Software Process Improvement (SPI) is seen as the fundamental approach to improving software products in software development organizations, being used to improve software quality and reliability, employee and customer satisfaction and return on investment, among other factors. Specialized studies report that organizations face several problems and difficulties in conducting software process improvement initiatives. Among the existing factors are issues related to the attitudes of individuals, for example, resistance to change, lack of motivation, support and commitment of those involved in the initiatives. In this context, it is important that organizations adopt approaches and strategies to facilitate the implementation of the SPI initiatives, since the lack of adequate treatment and consequently the occurrence of problems is something that leads to the failure experienced in the improvement initiatives. Many companies have used game strategies to motivate and engage the employee, not only in productivity and fun, which inevitably improves the work environment, but also to encourage innovation and development of their tasks. Thus, the use of gamification in the context addressed can stimulate people's motivation and commitment to effectively join and participate in SPI initiatives. Gamification has been used to assist in the teaching-learning process, and can be applied in the educational area or in companies, to stimulate a learning and work climate through the motivation of the people involved. Thus, this paper considered the context of problems and difficulties that occur in the software process pmprovement. To evaluate the strategies developed to minimize or treat these problems, a dynamics with gamification elements was applied in a research laboratory. It is noteworthy that gamification has been used to assist in the teaching-learning process, and can be applied in the educational area or in companies, to stimulate a learning and work climate through the motivation of the people involved. In view of the above, this paper aims to evaluate the perception of the participants who acted in the dynamics, in order to obtain feedback regarding the strategies applied to the problems, if they promoted the learning and engagement necessary to develop the activities necessary to achieve the results of the improvement. Thus, in the participants' perception, the use of gamification elements contributes as a mechanism to stimulate people's motivation and commitment to adhere and effectively participate in SPI initiatives.
Teaching and Learning Cybersecurity Awareness with Gamification in Smaller Universities and Colleges

This research to practice full paper presents our investigation into the use of gamification in teaching cybersecurity awareness to students. Although there are evidences of increasing research activities in cybersecurity, cyberattacks continue to be pervasive. Academic institutions are largely responsible for educating and producing skilled professionals with cybersecurity competences. However, cybersecurity education can be challenging, especially to smaller institutions, usually characterized by meagre resources. In literature, one of the beneficial pedagogical methods for teaching cybersecurity awareness is gamification, which is based on constructivist theoretical framework. While this method has proved very useful, one major challenge is that gamification platforms can be costly to develop and difficult to maintain. This may discourage smaller institutions from using gamification to teach cybersecurity awareness. Freemium gamified platforms e.g., Kahoot! can offer an alternative that is affordable, easy to use and requires very little to no overhead cost. The research questions under investigation are: what is the impact of gamification (as an instructional method) on students’ learning of cybersecurity awareness? , which game elements and what aspect of gamification best motivate students? Using questionnaire, we asked the students, from a small university in Northwestern Pennsylvania, USA, to rate their knowledge and awareness of cyberattacks. Afterwards, we taught a cyber awareness module with 5 learning objectives to these students using gamification in Kahoot! platform. At the end of the class, we administered another questionnaire to the students and asked them to rate their knowledge and awareness of those same cyberattacks. Our analysis and statistical results show that gamification is an effective technique for knowledge acquisition in cybersecurity awareness. Furthermore, students are mostly motivated by game elements that give them a sense of achievement. Finally we found that students are more interested in the knowledge acquisition aspect of gamification rather than the entertainment and winning aspects. The results of our study may be beneficial for instructional design of introductory cybersecurity awareness courses.
Reduced Order Small Signal Modeling of Parallel Resonant Converter based on State-plane Analysis

Parallel resonant DC-DC converter operating in continuous conduction mode with variable frequency modulation is considered in this work. Existing techniques to arrive at the plant model of the converter relies on extended describing functions based approach which ignores the effect of harmonics and considers only the fundamental component. The proposed small signal modeling approach is based on the state plane analysis of the resonant converter which is exact and considers all the operating modes of the converter. In conjunction with basic averaging techniques used in Discontinuous Conduction Mode (DCM) of PWM converters, closed form expressions for the open loop control to output transfer function of the converter is derived. The closed form expressions are shown to be accurate upto (1/20)th of the switching frequency. Simulation and experimental data on a 5 kW prototype is presented to validate the analysis.
Hyperverse: A High Throughput Pattern Matching Engine for Metaverse

Cyberspace has continued to evolve since the In-ternet became widespread in the 1990s. A variety of computer-mediated virtual environments have been created, including social networks, video conferencing, virtual 3D worlds (e.g., VR chat), augmented reality applications (e.g., Ingress), and non-fungible token games. Such virtual environments, while not permanent and incoherent, have brought us varying degrees of digital transformation. The term “metaverse” was devised to further facilitate the digital transformation of all aspects of our physical lives. With the increasing number of devices and users such as loT, augmented reality and virtual reality glass connected to the metaverse, huge amounts of data need to be filtered or captured for metaverse security or user behavior analysis by utilizing pattern matching. However, directly utilizing the existing pattern matching engine is impossible since it cannot achieve the throughput that is required by the metaverse, where low throughput could cause poor user experiences. Thus, in this paper, we propose a new pattern matching design called Hyperverse. Hyperverse can significantly increase the throughput of pattern matching by designing a new algorithm that is based on instruction-level parallelism. We implement Hyperverse in Hyperscan, which is the fastest regular expression engine in the world. Compared with the existing solution, Hyperverse can achieve a throughput of up to 10.4Gbps per core, which is a 3.83x boost than the existing solution. Thus, the significantly increased throughput will prevent a negative impact on the user experience in the metaverse.
Small-Signal Analysis of a Microgrid with Secondary Control Including the Dynamics of Primary Control and Communication Delays

Microgrids (MGs) facilitate the integration of distributed energy resources (DERs) in electrical grids. To coordinate the operation of these DERs, a three-level hierarchical control is commonly used. In general, different control layers are designed with sufficient bandwidth separation between them to avoid unwanted dynamic interactions, resulting in slow outer controllers. However, the interactions between primary and secondary controllers when their bandwidths are close has not been addressed in detail in the literature. In addition, communication latency represents an important bottleneck for the controller speed, and its modelling and impact on the control are both of interest for the scientific community. In this paper, possible interactions between primary and secondary controllers in an islanded MG are studied as well as the role of communication delays. For that purpose, participation factor and eigenvalue analyses are used. Results show that primary and secondary controllers are engaged in a slight interaction linked with the weighting factors of the secondary controller. It is also shown that the sensitivity of secondary control eigenvalues to communication delays is similar for different designs of the primary control.