Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast algorithm to identify coevolutionary patterns from protein sequences based on tree-based data structure

Knowing how proteins interact with each other are crucial to for us to understand the functional mechanisms of proteins. It is for this reason that the CoFex has been developed in attempts to predict protein-protein interactions (PPIs) computationally. However, the procedure of obtaining coevolutionary patterns adopted by CoFex is inefficient especially for large-scale prediction of PPIs, as it needs to traverse the entire protein sequence dataset once when computing the number of co-occurrences for each candidate of coevolutionary patterns. Hence, to improve the efficiency of CoFex, we propose a novel tree-based data structure, namely CF-Tree, to integrate with CoFex so that the running time of CoFex can be reduced by only traversing the sequence dataset once. The experiment results show that CF-Tree is a promising tree-based data structure to identify the coevolutionary patterns more efficiently from the sequence information of proteins in different species.
A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization

The efficiency of traffic flows in urban areas largely depends on signal operation. The state-of-the-art traffic signal control strategies are not able to efficiently deal with varying or over-saturated conditions. To optimize the performance of existing traffic signal infrastructure, we present an end-to-end autonomous intersection control agent, based on Deep Reinforcement Learning (DRL). In the recent years, DRL has emerged as a powerful tool, solving control problems involving sequential decision making and demonstrating unprecedented success in complex settings. Our DRL traffic intersection control agent configures the traffic signal regimes based solely on live photo-realistic camera footage. We demonstrate that our agent consistently, significantly outperforms state-of-the-art fixed (pre-defined) and adaptive (induction loop-based) signal control methods under a wide range of ambient conditions, by increasing the traffic throughput and decreasing the intersection traversal time for individual vehicles.
Influence of Magnetic History and Accommodation on Hysteresis Loss for Arbitrary Core Excitations

When designing inductive components for switched-mode power supplies, the influence of magnetic history on hysteresis loss is customarily assumed to be negligible in steady-state. This, however, does not apply for excitations comprising major and minor loops. This paper demonstrates the significant effects of magnetic history and accommodation on hysteresis loss.
A Three-Dimensional Spatiotemporal Model Concepts and Its Application on Traffic Flow Analysis Using Trajectory Data

The characteristics of traffic flow at intersections are critical to traffic safety and efficiency analysis. In the context of big data, massive trajectory data becomes an important source for studying traffic flow. Currently, the two-dimensional time-space diagram is the primary visualization tool used to represent trajectory data. Most existing models mainly focus on the longitudinal behaviors while ignoring the lateral movement, which has limitations in representing realistic interactions among vehicles. This paper proposes a three-dimensional (3D) spatiotemporal analysis model using trajectory data. By establishing a complete space-time dimension, it is possible to simultaneously study traffic flow characteristics and vehicle conflicts at intersections in the visual 3D model. First, the basic definitions and assumptions of the 3D coordinate system are defined. The analysis function is then illustrated from different perspectives of the 3D model. Finally, two concepts and their calculation methods are introduced, which are the space-time utilization for efficiency analysis and the shortest space-time distance for traffic safety evaluation. The proposed model is applied with the simulated trajectory data of typical signalized intersections. In conclusion, the developed 3D analysis model provides a convenient and comprehensive visualization tool for traffic flow analysis, both in terms of safety evaluation and efficiency analysis.
Obstructive Sleep Apnea OSA detection through esophageal Pressure Pes

Obstructive Sleep Apnea OSA is a potentially common pathology characterized by partial or complete collapses of upper. If diagnosed late and untreated, it can lead to serious complications especially which are related to cardiovascular system. However, most of cases are undiagnosed because of expenses and discomfort of hospital diagnostic. Therefore, new techniques are being developed by investigators to detect OSA. In this paper, we present a new approach to control the occurrence of OSA based on esophageal pressure parameter. Then, we have developed an efficient algorithm to process the signal in real-time to detect apnea episodes.
DCLane for Real-time Understanding of Lane Markings

Lane detection has always been a critical task in the field of ADAS and autonomous driving, which determines the reliability of lane keeping, adaptive cruise control and path planning. However, the performance of most algorithms in the complex road traffic environment such as blurred lane markings, lane marking occlusion, shaded roads and laneless roads is not satisfactory. In this paper, we propose an end-to-end CNN network. The feature extraction part of the network adopts the most effective DenseNet. The dilated convolution and spatial pyramid pooling are used to expand the receptive field to maximize the context information of the lane marking. In addition, the combination of low-level features and advanced features is used to improve the smoothness of lane segmentation. Finally, a lightweight, fully connected network is used to suppress mis-segmentation in complex scenarios. According to its characteristics, we named the network DCLane. The proposed network is trained on the current largest lane detection dataset, called CULane. DCLane’s performance on the test sets exceeds the state-of-the-art model, while the model meets real-time requirements.
An Integrated PV-Battery Soft-switched Power Converter with MPPT and Voltage Regulation

In this paper, a front end integrated decentralized storage interface for photovoltaic (PV) battery systems is presented. Therefore, the battery charging power is directed to the battery from the front end, instead of the centralized battery system where the battery charging power processes twice. In the proposed approach, the battery charging circuit is integrated with the PV converter. Therefore, the proposed system can become more compact. The proposed converter is capable of tracking Maximum Power Point (MPP) and output voltage regulation while following the battery charging profile. In addition, all the switches are able to achieve soft-switching turn-on and turn-off for different load conditions. Simulation and experimental results for 100W are presented on the proposed integrated solar-battery system.
A Novel Model Predictive Current Control Strategy For Non-isolated Single-Phase Grid-Connected Inverter

In this paper, in order to solve the problem that the conventional MPC cannot obtain excellent performance for short of enough switching states. A novel model predictive current control strategy for single-phase grid-connected inverter without coordinate transformation is proposed. In order to get better control performance, the switching state set is extended by introducing virtual switching state in this study. To improve efficiency, a scheme to reduce the switching frequency is proposed. Subsequently, for facilitate the online modification and practical application of the algorithm, a universal algorithm model is proposed. Finally, the effectiveness of the proposed MPC strategy is verified by simulation using MATLAB/Simulink.
Recommender System Based on User Evaluations and Cosmetic Ingredients

This study considers the compatibility between users and basic skin care products based primarily on the products’ ingredients. We have developed a product recommender system that is expected to recommend products that provide the desired cosmetic effect for different user groups depending on age and skin type. From the cosmetics review site, based on an analysis of user evaluations, we extracted the names of ingredients that are thought to have the best effect and developed a method to recommend products that include these ingredients as their main ingredients. We propose the ingredient frequency-inverse product frequency (IF-IPF) method to derive ingredients characterizing strong-effect product group. We have defined the scale “the recommended product satisfaction level” to evaluate the effectiveness of our recommendation service. As a result, our system can recommend products with a high degree of serendipity and hidden attraction, among others.
Model Predictive Control of Energy Storage Systems for Power Regulation in Electricity Distribution Networks

This paper proposes a control strategy for an Energy Storage System (ESS) installed on a secondary substation of an electricity distribution line in order to mitigate power variations with respect to the day-ahead planning caused by renewable energy sources on the distribution line.In particular, the aim of the controller is to keep the power profile of at primary substations close to a reference profile foreseen on a day-ahead basis while guaranteeing the stable operation of its ESS, in terms of their state-of-charge dynamics. The inclusion of the ESS contribution to the network operation is attained by the integration of properly defined power flow constraints in a Model Predictive Control Framework. The proposed approach has been validated through numerical simulations, representative of real operative scenarios.
Design and Testing of MEMS Metal Bridge Solid State Switch

In this paper, a MEMS metal bridge solid-state switch with small size, good consistency, safety and controllability is developed. The MEMS micro-actuator designed a planar structure. There is a metal bridge structure in the device. Due to the characteristics of the metal bridge structure, the high current pulse drive signal tends to generate a large amount of Joule heat at the bridge structure of the device. In order to understand the possible failure conditions of the device, this paper analyzes the effect of such Joule heat inside the device, carries out related theoretical calculations, and uses COMSOL to complete the failure simulation analysis. At the same time, this paper has carried out reliability research on chip manufacturing, packaging and soldering process through wafer level test and device level test, and optimized the structure design. Finally, the processing and packaging of the device MEMS metal bridge solid state switch is realized.
A Heat Map Generation to Visualize Engagement in Classes Using Moodle Learning Logs

In this study, course materials, mainly in the form of PDF files, were uploaded to the Moodle learning management system and face-to-face lessons were conducted. Student clickstreams of the course materials and the results of quizzes were accumulated. The quizzes, which used a 5-alternative format, were prepared from the course materials. Data from the course material clickstreams and the results of the quizzes were analyzed from an educational data mining and learning analytics perspective. A scatter chart and heat map were created to classify students into four groups based on deviations from the average value of clickstreams and quiz scores. The groups were composed of (1) those with high clickstream and high quiz scores, (2) those with low clickstream but high quiz scores, (3) those with low clickstream and low quiz scores, and (4) those with high clickstream but low quiz scores. It is shown that creating a heat map based on an analysis of the results of 13 quizzes and student clickstreams can provide an early indication of student engagement and can be used to signal the need for a corrective intervention in the case of disengaged students.
Convolutional Trajectory Similarity Model: a faster method for trajectory similarity measurement

Similarity measurement between different trajectory sequences is one of the most fundamental problems in trajectory data mining. In this paper, we propose a parameter sharing supervised model, Convolutional Trajectory Similarity Model (CTSM), based on densely connected one-dimensional convolution layers to efficiently extract spatial-temporal features and to greatly keep accuracy and orderliness computed by Dynamic Time Wrapping (DTW) similarity. We use the downsampling GPS data of cars in Beijing to construct our trajectory dataset. In the training phase, we use a large amount of data to make the model fit the DTW similarity. In the testing phase, we enable the model to predict the similarity between any two trajectories. Our experiments show that CTSM exceedingly decreases the time consumption compared with DTW similarity acceleration algorithm on both fixed-length trajectory dataset and variable-length trajectory dataset. Besides, CTSM performs better than widely used Deep Neural Network (DNN) based models, and uses a much smaller amount of parameters.
Feasibility Study of a Novel Cross Assembled Multi-quadrotor Unmanned Aerial Vehicle

In this paper, a feasibility study for construction and control of a novel design of a Multi-quadrotor unmanned aerial vehicle (UAV) is presented. This novel concept tests four identical Quadrotors assembled in a bigger cross structure. And, it also tests the ability of using the same controller that is used to control a single quadrotor to fly the novel design of cross assembled four quadrotor UAV. The main objective here is to improve redundancy and robustness and load carrying capacity of quadrotor UAVs. Even though there are four quadrotors, there is only one controller involves. When each individual Quadrotor works on a certain flight maneuver, it is expected that the assembled UAV of four of these in a cross structure to also be able to demonstrate the same maneuver. The prototype of the novel UAV was designed and built, and has been experimentally tested for a proof of concept where it was able to stabilize the new UAV with the same controller that is used in typical quadrotors with minimal modifications. New UAV will provide the user with the ability to lift more weight while also allowing plug-and-play replacement of individual Quadrotors in the assembly.
Restoration of Upper Limb Voluntary Motor Function in Chronic Severe Hemiplegia Using a Brain-Computer Interface-Triggered Functional Electrical Stimulation Therapy*

Two individuals with chronic severe hemiplegia resulting from a stroke underwent a rehabilitation intervention for 40 and 80 hours to restore voluntary movement in their arms and hands. Both participants had a stroke six years prior to joining the study and had since tried multiple interventions all of which had failed to produce any meaningful improvement in their upper limb function. The intervention presented here used a brain-computer interface in combination with functional electrical stimulation therapy. During each session, functional tasks, such as lifting a water bottle, were practised. The performed motions engaged the muscles of the shoulder, arm, forearm, and hand. Both participants experienced significant and meaningful changes in their ability to use their affected arm and hand as measured by standard clinical measures.
Integrating State-of-the-Art CNNs for Multi-Sensor 3D Vehicle Detection in Real Autonomous Driving Environments

This paper presents two new approaches to detect surrounding vehicles in 3D urban driving scenes and their corresponding Bird’s Eye View (BEV). The proposals integrate two state-of-the-art Convolutional Neural Networks (CNNs), such as YOLOv3 and Mask-RCNN, in a framework presented by the authors in [1] for 3D vehicles detection fusing semantic image segmentation and LIDAR point cloud. Our proposals take advantage of multimodal fusion, geometrical constrains, and pre-trained modules inside our framework. The methods have been tested using the KITTI object detection benchmark and comparison is presented. Experiments show new approaches improve results with respect to the baseline and are on par with other competitive state-of-the-art proposals, being the only ones that do not apply an end-to-end learning process. In this way, they remove the need to train on a specific dataset and show a good capability of generalization to any domain, a key point for self-driving systems. Finally, we have tested our best proposal in KITTI in our driving environment, without any adaptation, obtaining results suitable for our autonomous driving application.
Analysis of an Impedance Modeling Approach for Droop-Controlled Inverters in System DQ Frame

Small-signal stability of droop-controlled inverters in islanded ac microgrids can be assessed by using impedance-based criterion. In order to have an accurate output impedance model for these systems, the effect of fundamental frequency must be taken into account. The existing works suggest to characterize each inverter by two transfer matrices: first, the transfer matrix between output current and output voltage (i.e., the dq-domain impedance matrix), and second, the transfer matrix between output current and fundamental angular frequency. Addressing the frequency contribution in a separate term, leads to a stability criterion, that requires an additional step, compared to the conventional impedance-based criterion in dq frame, that only needs the dq impedance matrix. This paper proposes to include the frequency contribution in the dq output impedance of droop-controlled inverters. This gives the possibility to use the dq- domain impedance-based stability criterion, even in off-grid systems. A laboratory prototype of droop-controlled inverter is implemented, and its output impedance is measured. The good matching between analytical models and experimentally-measured impedances, confirms the validity of the performed small-signal analysis.
Modeling of Stand-alone Power Supply System with Distributed Generation Sources

Increasing the efficiency and reliability of standalone power systems (SAPS) with distributed generation (DG) sources is an urgent task for the energy industry in many countries. Energy exchange between neighboring SAPS is a promising solution to this problem. The article is devoted to the optimal control of the operation modes of the microgrid, which consists of two SAPS. Energy sources include a gas piston unit (GPU) on biogas and a wind-diesel power station (WDPS). Mathematical models of the microgrid operation modes are presented. The analysis of the conditions for the possible energy exchange between SAPS has been performed. An algorithm to control the operating modes of the microgrid aimed at minimum consumption of diesel fuel and the minimum cost of electricity has been developed. A LabVIEW model of the studied microgrid has been worked out. A description of the LabVIEW-model blocks is illustrated and the research results on the blocks functioning are presented. The correctness of the LabVIEWmodel operation has been substantiated. The directions of further research based on the results described in the article are given.
Intern Training of Artificial Intelligence Systems

Thereis a significant problem in designing artificially intelligent driven systems to behave in a legally and morally acceptable fashion. Part of this problem arises from the fact that there is neither a universally accepted legal or moral framework for such systems. Our solution is to abandon the idea of constructing a common operating criterion and instead allow the system to adapt to a working hypothesis under the tutelage of a human master. While this will impact on the efficiency and the safety associated with the system it has advantages in making the system much more acceptable to a diverse number of people and societies.
SoH-Aware Charging of Supercapacitor with Lifetime Maximization

Supercapacitors have been widely used in both high-power applications and low-power applications due to their excellent power density. In practical applications, multiple su-percapacitor cells are typically connected in series as a stack to satisfy the specified voltage requirement. However, the lifetime of the supercapacitor stack is limited by the weakest cell, which degrades the performance of the supercapacitor stack. To address this challenge, in this paper, we propose a SoH-aware charging method for supercapacitors with the objective of maximizing the lifetime of the whole stack. The lifetime model of supercapacitors is analyzed and the state-of-health (SOH) of all cells is predicted by the model-predictive-control (MPC) algorithm. Then, an optimization problem is constructed with charging time and individual lifetime to balance the lifetime of all cells. By appropriately allocating the terminal voltages of cells, the lifetime of cells can be balanced and the lifetime of the supercapacitor stack is maximized. A laboratory testbed has been built to verify the effectiveness of the proposed method. Experiment results show the proposed method can reduce the lifetime differences among cells and prolong the lifetime of the stack by 10% when compared with the classical method.
A Novel Approach for Inter-Leg Balancing of a Three-Phase MMC in HDVC Applications

This paper proposes a harmonic balancing based approach to deal with the equalization process of the capacitors stored energies in the six legs of a three-phase Multi Modular Converter (MMC) serving as the active interface between an AC grid and a HVDC link. Indeed, the harmonic balancing control, applied to the converter energies status variables written in a special form by exploiting the Space Vector/Zero Component and Common/Differential Mode decompositions, allows to highlight all the viable controllable interactions able to constrain the MMC stored energies to the desired balanced status. The conceived voltage control strategy is validated by an extensive numerical simulation.
Learning Dynamic Graph Embedding for Traffic Flow Forecasting: A Graph Self-Attentive Method

The Traffic flow forecasting (TFF) problem is essential to modern intelligent transport systems (ITS). Massive flow data from contemporary transport systems have put forward the challenge of effectively capturing both the latent spatial patterns and the temporal dynamics of traffic flow data, when an ITS is doing forecasting. To cope with this challenge, we introduce a novel spatial-temporal graph self-attentive model (STGSA) for short-term traffic flow forecasting. Our model learns graph-level spatial embedding using graph self-attention layers with Gumbel-Softmax technique, and the temporal embedding leveraging RNN cells integrated with Gated Recurrent Units. We evaluate the effectiveness of our proposed method on the traffic flow data of Langfang, China throughout the year of 2014, and it outperforms most of the state-of-the-art baselines.