Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Reinforcement Learning Framework for Capacitated Facility Location Problems with Discrete Expansion Sizes

Capacitated facility location problem (CFLP) is a classical combinatorial optimization problem widely applied in the domains of distribution, transportation planning, and telecommunication. As a typical NP-hard optimization problem, CFLPs featured by combinatorially high-dimensional decision spaces are not easily solved by most conventional methods. To appropriately handle the hard nature of CFLPs, we propose a deep reinforcement learning (DRL)-based framework to address CFLPs with discrete expansion sizes. Since a solution to the investigated CFLP can be sequentially constructed by partial solutions, we reformulated the CFLP as a Markov decision process with an unfixed and discrete time horizon. A deep Q-network (DQN)-based framework is adopted to learn the policy parameters and location solution. We experimentally demonstrate that our proposed approach can effectively find near-optimal solutions for CFLPs.
Optimal Design of Smart Grid Information Scheduling Algorithm Based on Deep Learning

Due to the rapid growth of smart grid, the application of power grid dispatching system has gradually developed from basic dispatching business to information business applications such as data fusion, heterogeneous system interaction and big data analysis. The overall development direction of power grid now and in the future is smart grid, and the application of relatively emerging cloud technology to power grid system can provide technical support for comprehensive and in-depth innovation of business model and operation mode of power grid enterprises to a certain extent. However, the current power grid dispatching system can not meet the requirements of big data analysis and other information services. Based on the theory of complex science, aiming at the development and preliminary planning of smart grid, this article analyzes the complex characteristics of smart grid management information system (MIS), and proposes a smart grid information scheduling algorithm based on deep learning. The results show that the system has good performance, and can effectively improve the business application level of the system and complete the corresponding scheduling.
OD-REM: On-Demand Regular Expression Matching on FPGAs for Efficient Deep Packet Inspection

regex (regular expression) matching is one of the most demanding stages of deep packet inspection process. Numerous studies explored FPGA-based acceleration techniques for regex matching. Existing FPGA-based regex matching engines, such as Grapefruit, assume that each packet potentially match all rules and employ a pipelined structure where each packet traverses each automata sequentially. However, in practical DPI systems, it is often the case that only a small subset of rules needs to be applied to a given packet. Consequently, subjecting every packet to processing by all automata incurs significant throughput wastage. In fact, prior to regex matching, a fast pattern matching stage can ascertain the rules that a packet may satisfy (these rules are referred to as ‘candidate rules’ for the packet). Based on this observation, we propose OD-REM, an On-Demand Regex Matching architecture on FPGAs. Within OD-REM, each packet is processed by automata corresponding to its candidate rules only, with these automata being dynamically generated as needed. We design a simple yet efficient scheduler to allocate packets to their candidate automata. We implemented ODREM containing 64 automata on Xilinx U200 smart NIC. ODREM obtained a throughput of 27. 6Gbps (13.8times faster than Grapefruit), and consumed 6.06% logic resources and 10.28% BRAMs of the on-board FPGA (Xilinx VU9P), respectively. It is worth pointing out that OD-REM only need more UltraRAMs to accommodate more regex rules, leaving logic resource and BRAM consumption unchanged.
Suki: A Feasibility Study on Developing a Platform Application for Local Public Markets

Suki Application serves as a platform that makes it easier for people to shop for groceries online, offering convenience to both customers and store owners while supporting local public markets. The study intended to assess the advantages of the mobile application. The Suki Application offers various features designed to enhance the grocery shopping experience. With an end view of offering a convenient way to meet the shopping needs of the public such as students, professionals, and persons always on the go, the Suki Mobile Application was evaluated in terms of market, technical, and financial feasibility. The product development involved three phases: Need analysis, technology development, and validation from the experts, and potential users. Quantitative and qualitative techniques of data gathering were used to assess and evaluate the viability of mass-producing the application. The Suki Application makes it easier for customers to access a variety of products by partnering with local public markets. The application also makes it easy to compare prices, giving users the power to choose products wisely. The research emphasizes how online grocery shopping can increase store sales and foster price competition. A model for a start-up company can be implicit which begins with small earnings and sales but gradually expands over time, resulting in increased sales, profits, and positive cash flow.
Stochastic Geometry Analysis of Air-to-Ground Communication Networks With Circular Trajectory

In this work, we utilize stochastic geometry to investigate the performance of mobile unmanned aerial vehicle (UAV) networks providing ground coverage service in post-disaster scenarios or coverage blind areas. The location of ground users is modeled as a Poisson cluster process (PCP) to represent the distribution clustering of ground users in these scenarios, i.e., node clusters in internet of things (IoT). A flying UAV serving as aerial base station (ABS) is forced to move around a cluster center with circular trajectory to provide information coverage service for that user cluster. By considering the periodic fluctuation of network topology caused by the movement of UAVs, we derive the success probability (SP) with Type-I hybrid automatic repeat request (HARQ) retransmission scheme. The simulation results verify the correctness of theoretical results and demonstrate the effect of key parameters on transmission performance.
An Efficient Attention-Based Network for Screening Major Depressive Disorder with sMRI

Major Depressive Disorder (MDD) is a prevalent psychiatric disorder that adversely affects the quality of life of those affected. Artificial intelligence (AI) has emerged as a promising tool for computer-aided diagnosis of MDD based on magnetic resonance imaging (MRI) technology. In this study, we propose a novel deep learning framework that utilizes the SlowFast network to analyze structural MRI (sMRI) for screening MDD. We incorporate a multi-scale feature fusion mechanism that integrates attentional mechanisms with path information to enhance the channel attentional features of gray matter images. Furthermore, we explore the correlation between regional features of sMRI slices and the spatial information our classification model focuses on. We evaluated our proposed method on MDD datasets generated from multiple research centers with varying scanning parameters and scanner field strengths, achieving a classification accuracy of 93.12%. Our method outperforms existing sMRI-based unimodal methods and provides clinicians with a reliable method for MDD diagnosis. During the investigation of regional spatial dependency, an impressive classification accuracy of 94.38% was achieved. By introducing the attention mechanism in the multi-scale feature fusion process, we can effectively focus on channel features, achieve more accurate MDD classification, and provide a more precise and reliable method for sMRI-based MDD diagnosis,
A Text Matching Model Based on the Combination of ERNIE and Composite Graph Attention Network

In the text matching task, we are faced with the problems of how to better obtain the contextual semantic information of the text and the difficulty of extracting word meaning features, which often lead to unsatisfactory matching results. Based on this, this paper proposes a text matching model based on the combination of ERNIE network and composite graph attention network. First, the pre-trained network ERNIE based on knowledge graph is used as the embedding layer to obtain a more accurate word vector representation. Secondly, a composite graph attention network is designed. First, a two-layer graph attention network is used to model the text to obtain more comprehensive context information and overall relationships. Then, DPCNN is used to extract and integrate model features. The deep pyramid structure makes The network can effectively learn semantic feature representations of different granularities, enhance the receptive field and local continuity, and extract features better. Finally, the hidden output of ERNIE is fused with the extracted feature information, that is, the important information of the text itself is preserved and relatively complete interaction information is obtained. The experimental results show that on the two text matching task datasets of LCQMC and BQ, the improvement effect is significant, and the two indicators of accuracy and F1 value have achieved a good improvement effect.
Towards Personalized Myoelectric Control Strategies

This work aims to revise and pilot test some of the most popular myoelectric control approaches for assistive robotic devices, with the objective of showing that, when it comes to the impaired population, different myoelectric control strategies may result in different behaviours. In particular, we show that the way we use myoelectric signals to provide commands to the robot plays a fundamental role: different strategies may lead to statistically different behaviors of the coupled human-robot system. These results open up the perspective of investigating optimal patient-specific myoelectric control strategies to make myoelectric interfaces actually usable and acceptable by a greater number of patients, including subjects with muscular dystrophy.
An Approach for Combined AC-DC On-Board Fast Charging using Three-Port Partial Power Conversion

This paper proposes a new three-port partial power DC-DC converter (PPC), which allows the combined ACDC on-board fast charging from grid and battery energy storage system, simultaneously. The concept of partial power conversion reduces the power processed by the grid port and also supports the higher charging rate, as the converter power density and efficiency are considerably improved over the conventional full power processing converter (F - PPC). The proposed topology is derived using a triple active half bridge converter (TAHB), which integrates the functions of multiple AC-DC and DC-DC converters in conventional fast charging station into one threeport converter. The proposed topology has minimal number of components and ensures that most of the charging power is taken through BESS. The PPC design further, helps in achieving smaller rating devices in the converter due to reduced power processing for the conversion. The design of the converter is discussed in detail and dynamic performance is verified using MATLAB/Simulink based assessment. For a 17kW rating, the efficiency of the proposed TAHB converter for combined AC-DC charging is compared with the conventional FPP converter-based charger.
Multi-Class Image Generation from EEG Features with Conditional Generative Adversarial Networks

Exploring the visual coding mechanism of the human brain has been an important topic in brain-computer interface technology, holding the potential to provide new insights into the research of the Internet of Things (IoT). Especially, the use of brain signals for image generation has attracted people’s attention. In this paper, we propose a multi-class image generation method based on electroencephalography (EEG) features with conditional generative adversarial networks (CGAN). Specifically, we synchronously collect the EEG signals when subjects watch visual stimuli through EEG equipment and construct a multi-category image-EEG dataset. The obtained EEG dataset undergoes preprocessing to eliminate interference from electrooculographic signals and electromyographic signals. Based on this dataset, we extract EEG features related to visual stimuli by deep learning algorithms and integrate them with CGAN for image generation. We employ different EEG feature extraction methods for image generation and conduct comparative experiments. The experimental results demonstrate that the proposed method based on the CGAN model utilizing the joint EEG spatio-temporal and temporal-frequency features can achieve better performance in the image generation task.
Coordination of Protection Devices in Microgrids

In this paper, some challenges in microgrids $(\mu \text{Grid}$, which arise from the limitation of protection devices, such as the point of common coupling (PCC) switch and relays of Medium Voltage (MV) circuit breaker, are presented and discussed. These challenges directly influence the coordination of the electrical protection and the functionality of the $\mu \text{Grid}$ especially during transition between operation modes (on-grid to off-grid and vice-versa). Those limitations were observed in the $\mu \text{Grid}$ at the Alcântara Launch Center (ALC), Maranhão, Brazil. The solutions were based on guaranteeing flexibility to the protection coordination for different voltage transient levels, using single-phase, three-phase positive, negative and zero sequence voltage protections. In addition, to ensure black start functionality, a logic was implemented to inhibit tripping by undervoltage and by negative sequence overvoltage, both due the electrical transients that appear during that $\mu \text{Grid}$ starting. The applied solutions were validated and presented satisfactory results.
Biomimetic Real-Time Multimodal Tactile Perception and Haptics for Telepresence Humanoids

Touch is critical to human object manipulation, enabling rapid optimal material grasping. Contrarily, telepresence robots seldom display fine sensory information to their operators, resulting in awkward object interaction. This paper details the biomimetic real-time multimodal haptics system of telepresence humanoid Avatar-Hubo and its tactual perception techniques. Inspired by human sensing, the proposed system uses a custom robotic hand and dynamic sensor to measure finger compliance and vibrations during object contact. Then, the proposed Real-Time Haptics Method processes sensor signals into metrics to quantify and identify material compliance and texture. Finally, a commercial haptic interface provides operators with appropriate cutaneous force and texture feedback. Experimentation with the Real-Time Haptics Method reveals over 80 % accuracy in recognizing smooth and rough items without requiring the contact force/velocity control used in similar systems. Hence, the proposed method enables telepresence robots like Avatar-Hubo to identify tactile properties and provide operators with rich tactile feedback in real-time.
Research on poor contact fault of switch cabinet based on Fluent

The primary three variables that contribute to switch cabinet failure while electric systems for power are aging partial departure, and overloading of contacts. At the same time, due to the closed operation of switch cabinet, placing sensors inside for measurement is hard. The flow of model of the switch cabinet's heat is constructed in this work in order to deal with the matter of the switch cabinet's poor contact. Fluent simulation is used to look at the characteristics of heat flow inside switch cabinet. Combined with the law of heat flow inside switch cabinet, it is judged whether there is poor contact inside switch cabinet, to be able assess the switch cabinet's operational condition.
VAE-based Multi-domain Feature Alignment and Generative Network for Few-Shot Radar Models Identification

In the field of electronic countermeasures reconnaissance, it is generally difficult to obtain sufficient target radar pulse information to realize radar model identification, whether it is traditional machine learning or deep learning methods are prone to fall into the problem that the model is easy to be overfitted with the amount of data is too small, so the radar model identification based on the few-shot learning is proposed. Existing few-shot learning methods focus on extracting features with generalizability, while ignoring the key problem of overfitting. To solve the problem of declining classification accuracy caused by overfitting, this paper combines the training samples with the radar model expert's knowledge, and generates new samples to achieve the transformation of the radar model identification problem under few-shot conditions into a conventional classification problem by means of the dual-driven approach of knowledge and data. Specifically, to address the problem that the semantic features of expert knowledge and the category features of radar models cannot be used at the same time, we design a multidomain feature alignment model for radar models based on the probabilistic generative model and construct a feature library of radar models composed of expert knowledge and category features. In addition, for the problem of insufficient samples, we build a multi-branch intra-class feature distribution learning network to learn the intra-class feature distribution of the samples, and expand the samples by combining semantic and category features. Experimental results show that our approach achieves state-of-the-art results.
Deep Compression Based Intelligent Power Allocation Algorithm for Internet of Vehicles

The high-speed movement of vehicle brings about the rapid change of communication environment. This leads to a sharp increase in the exchange amount of vehicle environment status information (ESI), which results in the communication quality dissatisfaction for Internet of Vehicles (IoV) user. To achieve efficient resource allocation, how to reduce the ESI exchange imposes crucial challenge for the rate improvement of IoV. In this paper, we propose the ESI deep compression based intelligent power allocation (ECIA) algorithm for IoV. First, a deep neural network (DNN) is used to compress the complex ESI to reduce the amount of information exchange in IoV. Then, a decision network composed of the total power network and power allocation network is designed to maximize the sum rate of Vehicle-to-Vehicle (V2V) links. Finally, simulation results show that the proposed ECIA algorithm can effectively increase the sum rate of V2V links and guarantee the communication quality of IoV user.
High-Frequency Switching of State-of-the-Art Wide Band Gap Inverters and its Impact on Encoderless Control Schemes

The combination of wide band gap semiconductors and anisotropy-based encoderless control schemes is discussed in this paper. The steep voltage transients of the wide band gap semiconductors and the increasing noise level with higher switching frequencies impact the phase current measurement of an electrical machine. These effects on the measured current signal are analysed, and the influence on the encoderless control performance is illustrated. The phase current measurement and the position estimation of the encoderless control scheme are investigated based on experimental results.
Model Reference Adaptive Controller for a Distribution SSSC

The Distribution Static Synchronous Series Compensator (D-SSSC) offers a cost-effective approach to enhance distribution grid efficiency and resilience by rebalancing power between feeders and turning radial grids into meshed ones. However, distribution systems have a dynamic profile with many uncertainties caused by the penetration of distributed generation (DG), load variations, and contingencies. This paper presents a control approach for the D-SSSC applied to power flow control between distribution feeders. The proposed control is based on Model Reference Adaptive Control (MRAC), which has adaptive characteristics that make the equipment less exposed to uncertainties related to these systems. Simulation results presented in this paper show that these controllers have a good response to a wide range of variations in distribution feeders, such as line parameter and load variations.
Research on Hybrid Modal Control Nursing Bed System Based on BCI-VT

Aiming at the problems of low accuracy and low generalization performance of unimodal brain-computer interfaces, this paper fuses the line-of-sight tracking technology with brain-computer interfaces and proposes a nursing bed control system based on BCI-VT mixed modality (BV-hM). Firstly, personal template-based filter bank typical correlation analysis (it-FBCCA) is designed to recognize SSVEP signals to realize SSVEP-BCI unimodal control of nursing beds; secondly, by integrating efficient channel attention (ECA) and SE attention mechanism, improved CS-MobileNetV3 is proposed to recognize VT signals to realize VT unimodal control of nursing beds; finally BV -hM control of nursing beds is realized by using a voting strategy to adjust SSVEP and VT signal weights using adaptive weights. 5 subjects participated in the online experiment, and the results show that the average control accuracy of BV -hM is as high as 96.54 1.84% and the average ITR is as high as 126.94 1.98 bit/min under the 2s time window; compared to the SSVEP-BCI unimodal and VT unimodal, the average control accuracy is improved by 14.21 % and 5.69%, and the average ITR is improved by 13.72bit/s and 5.15bit/s; indicating that the performance of the BV -hM system is higher than that of the unimodal system in all cases, and the BV-hM system is more reliable.
Exoskeleton system in the right arm for people with atrophic muscle disorder

The percentage of the population in Mexico with physical limitation or disability in the motion of an arm is 58.3 of the total disabilities. The possibility to suffer one of these mobility loss increase with the age. This produce a decreasing in muscular strength causing a long-term permanent handicap. In this work we propose a preliminary design of a exoskeleton for the right arm for people suffering of muscular atrophy. The device is easy to use and maintain, portable and light. We test the prototype in 6 subjects and obtaining 100% of success in the desired motion.
Design and Analysis of Combined Control Strategy for Grid Integrated PV-Hybrid System with Sparrow Search Optimized Seamless Control

The energy storage system (ESS) makes it easier to handle sporadic renewable energy sources (RESs). However, maintaining the power equilibrium between RES, loads, and ESS is crucial and requires effective control. The work presents a combined control method for an AC grid system that is powered by photovoltaics (PV) and battery and supercapacitor (SC) as hybrid energy storage (HES) to balance the power. The control makes use of the battery uncompensated power to boost the performance of the HES system. The PV-HES system makes sure that the critical loads always get power. The proposed approach enables rapid recovery of the DC-link voltage, all the while guaranteeing efficient distribution of power between the battery and the SC. A proportional integral controller that has been fine-tuned using the sparrow search algorithm is employed to manage the DC bus voltage effectively, leading to reduced fluctuations in the dynamic state and enhanced overall system stability. A smooth voltage source converter control is obtained without any major disruption. A range of operational scenarios, encompassing factors like fluctuations in PV generation and load demands, demonstrate the effectiveness of the proposed control strategy compared to conventional control methods. Simulation findings demonstrate that the proposed control maintains system stability under dynamic conditions.