Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Course Text Entity Relationships Based on Improved Bert BiGRU Ratt Model

University courses have the characteristics of dispersed knowledge, low relevance, high text density, and long sentence lengths. Traditional relationship extraction methods cannot capture contextual semantic relationships well, resulting in unsatisfactory entity relationship extraction results. Propose an improved Bert BiGRU Ratt model based relationship extraction method. Firstly, use the Bert pre trained language model to obtain dynamic word vectors and obtain character features; Secondly, the obtained vectorized text is fed into the attention layer of relational words, and different weights are assigned to different characters; Afterwards, Bidirectional Gated Recurrent Unit is used to encode the information of different sentences to obtain sentence features. Finally, the attention mechanism is used to perform feature learning on all information again to obtain the final text information. After conducting experiments on a manually annotated dataset of the university course ‘Data Structure’, satisfactory results were obtained. Among them, the accuracy rate is 84.61%, the recall rate is 89.34%, and the F1 value reaches 84.35%. Compared with the Bert BiLSTM Att and Bert BiGRU Att models, the accuracy rate has increased by 3.4% and 2.1%, the recall rate has increased by 6.2% and 3.9%, and the F1 value has increased by 2.9% and 3.2%. The proposed model has certain outstanding effects and practical application significance.
Cognitive Workload and Usability of Virtual Reality Simulation for Prosthesis Training

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices.
Capacitor Coupled Voltage Transformer Defect Identification in the Presence of Tap Changer

This paper proposes an online non-intrusive method for capacitor coupled voltage transformer defect identification. This paper focuses on the challenge of distinguishing between normal power system events, such as sudden load changes or operation of an on load tap changer, and capacitor coupled voltage transformer defects. Two signal change point identification methods are used to achieve this goal and are compared in terms of their effectiveness. The proposed approach is non-intrusive, meaning that no additional equipment is needed, making it a cost-effective solution. Based on the obtained results, the proposed methods are capable of identifying the change points within measurement signals with a low false alarm rate. Overall, this study provides a valuable contribution towards enhancing the reliability of power systems by detecting capacitor coupled voltage transformer defects in a timely and accurate manner.
Improved RefineDet Network Assisted by Time Frequency Characteristics for UAV Signal Recognition

The unmanned aerial vehicle (UAV) signal detection method based on time-frequency spectrum has the problems of low success rate and difficulty in detecting small target signals in time-frequency spectrum. Combining theoretical knowledge related to deep learning, an intelligent perception and recognition method based on UAV telemetry command signals is proposed. Firstly, the linear STFT time-frequency analysis method and nonlinear SPWVD time-frequency analysis method are combined to balance the time-frequency resolution and avoid the cross terms. An improved RefineDet network with an Intersection over Union is proposed to solve the problem of mismatch between prediction box and classification. Finally, an UAV identification platform is built. Experiment results demonstrate that the proposed network can achieve high recognition accuracy in multi-objective UAV scenarios.
Multiple Hybrid AI Model-based Optimal Charging Profile of Lithium-ion Battery for SOH Enhancement

Battery is one of the major components of electric vehicles, which highly influences the performance of Electric Vehicles (EVs). However, enhancing the life of a Lithium-ion (Li-ion) battery is a challenging task because they have a high risk of fire hazards due to the electrochemical properties of Li-ion. Various key factors that have a significant influence on the health of the battery include a number of charge-discharge cycles, temperature, voltage, and current profiles. Hence, a highly efficient Battery Management System (BMS) with an optimal charging facility is needed. The main objective of the proposed work is to generate Multiple Hybrid Artificial Intelligence (MHAI)-based optimal charging current profiles for Li-ion batteries with minimum charging time and temperature rise in order to enhance the State Of Health (SOH). In this work, an open-source dataset of Li-ion-18650 from the National Aeronautics and Space Administration (NASA) was used. Firstly, the battery charging profile range from 0 to 100% is divided into 4 groups (0-25%, 26-50%, 51-75%,76-100%), and four hybrid AI models are developed and validated. to find the optimal charging current in each of the 4 regions instead of using a single AI model for the entire charging profile. For model development, temperature, maximum chargeable capacity, and charging time are considered as outputs, and charging voltage and current are taken as inputs. Long Short Term Memory (LSTM), Random Forest (RF), and Coulomb Counting (White Box Model) are used to develop models to predict temperature, maximum chargeable capacity, and charging time, respectively. Finally, Particle Swarm Optimization (PSO) is used to find the optimal current value for the developed models to minimize both the charging time and temperature rise using the weighted-sum method of the MultiObjective Particle Swarm Optimization (MOPSO) technique. The results show the feasibility of the proposed approach.
Measuring Emergency Department Resilience to Demand Surge: A Discrete-Event Simulation Framework

This research explores the resilience components in emergency departments (EDs) during surges through discrete-event simulation (DES). By focusing on the resistance and recoverability components, the resilience of the ED is analyzed, as well as the flow of the patient and the resources required at each step. A simulation is developed to model an ED in the UAE and validated through collected timestamps. The results demonstrate the ordinary conditions of the ED and its calculated resilience, recoverability, and resistance, as well as its strength under conditions of surge demand. To investigate the impact of resources on the ED’s resilience, the resilience triangle is analyzed, and different interventions are applied by adding physicians, nurses, and beds and their effects. The methodology and simulation model provide significant insights to ED managers to evaluate and improve their department’s resilience during surges and emergencies.
Multi-Modal ASD Screening System: A Preliminary Study

Early screening for autism spectrum disorder (ASD) can significantly improve intervention outcomes. However, acknowledging the diverse nature of ASD and the variety of its symptoms, a multi-modal approach becomes necessary for effective autism screening. In this study, we propose a framework powered by the Robotic Operating System (ROS) to integrate various ASD screening tools as separate modalities seamlessly. With a focus on behavioral data, our preliminary study involves the analysis of three distinct ASD symptom groups using three modalities: the sensorized toy car for assessing repetitive behaviors, the Lightwheel for analyzing sensory input sensitivity, and the Roboparrot for simulating social interaction initiation. By fusing the results of each modality, we achieved enhanced accuracy and reliability in ASD screening, surpassing the individual modality accuracy by more than 10% on the average.
Combining biosignals to assess and monitor VR-assisted rehabilitation of children with Cerebral Palsy: a machine learning approach*

Cerebral palsy (CP) is one of the most frequent causes of motor disability in children. Physical therapy, coupled with sensing technology, assesses the integrity of the nervous system and muscular activity and eventually adapts the therapy to the patient’s abilities. In the TELOS Project, we carried out a single-blind controlled trial involving children with CP aged 6 to 16 years for VR-assisted rehabilitation of upper limbs and stability with tailored Serious Game.In collaboration with healthcare professionals, we used validated clinical scales and wearable sensors for surface electromyography (sEMG), electrocardiography (ECG), and motion capture to collect data from children before, and after therapeutic sessions. We aimed to integrate the acquired data to obtain a series of predictors to describe the therapy progression. We modelled the data using linear regressions with LASSO regularization to extract the relevant features. Although preliminary, our findings indicate heart activity as crucial information, suggesting the importance of emotions in the success of rehabilitation.Clinical Relevance: Gathering physiological data could allow therapists to perform tailored and adapted rehabilitation to the real skills and performance of the children.
A Multiload WPT System with CC/CV Output Based on LCC-S Topology for UAV Clusters

Recently, with the widespread application of UAVs in multiple fields, a single UAV can no longer meet complex tasks, and UAV cluster that collaborate to complete tasks are becoming increasingly popular. Wireless power transfer (WPT) technology offers a power supply approach with high reliability and safety that could effectively improving the endurance of UAV clusters. In this paper, a multiload WPT system with constant current/voltage (CC/CV) charging mode based on LCC-S topology for UAV clusters is proposed. The method for implementing CC and CV charging mode is derived with theoretical analysis, which can be accomplished by switching the operating resonant frequency. Besides, a transmitter coil with uniform magnetic field distribution is designed in this manuscript, which can effectively guarantee that each UAV in the cluster receives the equivalent power. Subsequently, a WPT charging platform suitable for two UAVs is established. It has been proven by experiment that the proposed WPT system can concurrently supply power to two UAVs, while the two UAVs could simultaneously achieve CC and CV charging modes independent of load resistance variations. The variation ratio of charging current and voltage with load resistance change in CC and CV stage can be maintained below 4.5% and 5.2%, respectively.
Entrepreneurial Mindset Learning (EML) in Simulation Education

An entrepreneurial mindset is associated with recognizing and seeking opportunity that can result in societal benefits. Entrepreneurial mindset learning (EML) is a pedagogy that has gained increasing attention in science, technology, engineering, and math education. In this paper, we present a set of examples to illustrate how EML methods can be applied in simulation courses to foster the development of the entrepreneurial mindset of students. In addition, we discuss some of the opportunities and challenges for adoption of EML in simulation education.
Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar

We present a novel joint localization and tracking algorithm for multiple-input multiple-output active radars. The proposed algorithm, which we dub Bayesian localization and tracking (BLaT), relies on approximate Bayesian inference using the mean field approach and processes all available received data to jointly estimate the target's track and location. This approach makes it possible to take advantage of the inherent synergy between the tracking and localization tasks. BLaT is shown by simulation to outperform a classical sequential processing baseline in terms of its ability to track targets in low signal-to-noise ratio conditions as well as superior tracking of manoeuvring targets.
A High-Speed Low-Power Two-Stage Comparator with Regeneration Enhancement and Through Current Suppression Techniques

This paper presents a high-speed low-power two-stage dynamic comparator for SAR ADC. The pre-amplifier of the proposed comparator uses the dynamic bias technique to save power. To increase the output voltage difference of the pre-amplifier, the StrongARM latch is inserted. The proposed latch keeps a cross-coupled inverter to ensure good positive feedback ability. The tail current source is replaced by the input pair to save power. The auxiliary input pair speeds up the regeneration of the latch. Simulation results demonstrate that the proposed comparator with $V_{DD}=1.8\mathrm{V}$ achieved a clock-to-Q delay of 228 ps and an input-referred noise of 479 $\mu \mathrm{V}$ at $V_{id}=1\text{mV}$ and $V_{CM}{=}$ 0.9 V. The proposed comparator consumes 196.3 $\text{fJ}$ per comparison with 1.024 GHz.
Data-Driven Identification and Optimal Control of a Biomechanical Triple-Link Inverted Pendulum for Sit-to-Stand Movement

Data-driven methods are becoming popular to identify large, complex, and nonlinear systems and replacing equation-based methods. Dynamic mode decomposition methods are utilized to identify the linear dynamics of an unforced reference generator system and a highly nonlinear triple-link inverted pendulum biomechanical model around an equilibrium point. The identification methods successfully revealed the underlying linear dynamics of the systems. Feedforward and feedback optimal control policies are designed using a policy iteration method based on adaptive dynamic programming. The designed controllers exhibited satisfactory simulation performance in tracking the sit-to-stand movement of the nonlinear model.
Securing the Skies: Enhancing Communication Security for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have dynamic topology and communication of UAVs among themselves and Ground Control Station (GCS) takes place over a wireless communication link placing them at risk of a cyber-attack. MAVLink is the most commonly used lightweight and open-source communication protocol used by UAVs in their communication with GCS and other UAVs. The communication may involve the sharing of sensitive information that needs to be protected. However, the MAVLink protocol offers no built-in security mechanism like encryption which means that the communication takes place in plain text format without any encryption mechanism used which places the communication at the risk of an eavesdropping attack hence revealing sensitive information to unauthorized individuals. The already proposed security solutions by the authors have used various encryption algorithms including AES, ChaCha20, RC4 etc but all of them lack the secure key management part thus placing the encrypted communication at risk of being decrypted by the attacker if the encryption key is compromised. In light of this issue in this paper, we proposed an improvement for the secure version of MAVLink with unmanaged security keys using a lightweight symmetric cryptographic algorithm ChaCha20 along with the secure key management algorithm Diffie-Hellman to protect the UAVs from eavesdropping attacks. In the end, we evaluated the proposed method in a simulated environment using ArduPilot for UAV simulation and Mission Planner for GCS simulation. We compared the results of our proposed method with already existing methods proposed by the authors and original unencrypted MAVLink in terms of security, packet transfer rate, CPU usage and memory consumption. By conducting experiments for evaluation purposes we showed the robustness of the proposed method against eavesdropping attacks without affecting the system’s performance.
Discovering Vital Nodes in Complex Networks Using Isolating Extended Coreness Score

Identifying vital nodes involves the task of pinpointing the most essential nodes within intricate networks. This challenge holds significant implications across different domains, including areas like viral marketing and managing the spread of viruses or rumors within real-world networks. Numerous techniques have been proposed for ranking influential nodes in complex networks, spanning from node centrality to diffusion-based processes. K-shell coreness centrality is employed in network analysis to evaluate the structural significance of nodes. The process in-volves k-shell decomposition, which categorizes nodes into shells according to their connectivity patterns. However, these measures are based on the coreness of direct nodes. We proposed an extended coreness score for finding the vital nodes based on the coreness of a node and its neighbors along with the degree. The foundation of degree centrality lies in the principle that the highly connected node is also the most central within the network. With the combination of the degree and isolating centralities, we proposed the isolating extended coreness score. We employ the SIR (Susceptible-Infected-Recovered) model to analyze the maximum information spread achieved by the proposed measure in comparison to conventional centralities. We apply the proposed centrality measure to various real-world networks to identify vital nodes. Additionally, we compare these results with existing basic centrality measures.
A Single-Event Transient (SET) Tolerant Dynamic Bias Comparator in 65-nm CMOS

A single-event transient (SET) tolerant dynamic bias comparator leveraging path-splitting is presented in this paper. The dynamic bias structure with a tail capacitor is found to be vulnerable to positive and negative SET transients. A negative transient on the tail transistor doubles the energy per conversion. A positive transient on the tail MOS capacitor reduces the preamplifier gain by 35.7%, which increases the metastability error rate in the subsequent latch. A path splitting technique is applied to mitigate the positive SET. Simulations in a 65-nm CMOS process showed that a two path split can recover the comparator output voltage swing by 12.7%. Compared with the state-of-the-art CMOS radiation-hardened by design (RHBD) dynamic comparators, this circuit achieved the lowest energy per comparison while maintaining low input referred noise and gaining SET tolerance in the evaluation phase.
A Simple Analytical Expression of Average AUC with Diversity over Composite Fading

In the realm of cognitive radio networks, spectrum sensing stands out as a practical approach to efficiently utilize unoccupied portions of the spectrum. Among the various methods within cognitive radio, energy detection emerges as a straightforward technique to detect primary user signals in the spectrum. However, this method encounters challenges in scenarios involving composite fading effects on the communication channel. The mathematical derivation of the average area under the receiver operating characteristic curve (AUC) is achieved by employing Gaussian-Hermite integration (GH-I) over a shadowed Weibull distribution combined with maximum ratio combining reception (MRC). The accuracy of the derived mathematical expressions is verified through comparison with simulation results.
Automation Data Acquisition and Shortening Training Time for Surgical Instrument Detection System in Total Knee Arthroplasty

In total knee arthroplasty, nearly 100 types of surgical instruments are prepared in addition to the usual surgical instruments. Since there are many types of surgical instruments used and their shapes and sizes are similar, accidents due to incorrect selection of implants have occurred. In addition, there is concern that the shortage of nurses will accelerate around the world in the future. For these reasons, we developed a surgical instrument detection system for total knee arthroplasty to reduce the incidence of accidents and the burden on scrub nurses. The surgical instrument detection system in the previous study required a lot of labor and time to create training data. Also, when increasing the number of surgical instruments to be detected, it was necessary to retrain all the surgical instruments. To solve these problems, we developed an automatic annotation system using a turntable in this study. In addition, we develop learning and inference methods using standard objects. In the experiment, we verified the accuracy of the learning data created by the automatic annotation system and verified the object detection accuracy for five types of surgical instruments. As a result, all experiments showed the effectiveness of this system.
Virtual Agents in Internet-Based Cognitive Behavioral Therapy: Enhancing Engagement and Alleviating Depression

With the rise of digital therapy environments, Cognitive Behavioral Therapy (CBT) has evolved into an internet-based form, iCBT. However, iCBT is often criticized for its high dropout rates and reduced therapeutic effectiveness compared to traditional methods. This study suggests the integration of virtual agents to address these issues, hypothesizing that such agents can improve the therapeutic alliance—a crucial element for successful iCBT. To empirically test this, we conducted an experiment with 80 participants divided into two groups. The intervention group interacted with iCBT content incorporating a virtual agent for two weeks, while the control group accessed identical content without the agent. Results showed a significant reduction in PHQ-9 (Patient Health Questionnaire-9) scores for the intervention group, a change not seen in the control group. Sentiment analysis via zero-shot classification further confirmed a more positive sentiment in the intervention group's responses. Notably, the former reported increased engagement, potentially addressing iCBT's high dropout concern. In conclusion, while virtual agents might not resolve all iCBT challenges, they do appear to enhance their efficacy. This study provides a significant step towards optimizing the incorporation of virtual agents in therapeutic settings, showcasing a novel contribution to the field of digital mental health interventions.