Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust consensus for general second-order multi-agent systems with time-delay and randomly switching topologies

The article studies consensus problem with and without disturbance rejection for general second order multi-agent systems subject to random switching topology and constant time delay. The change of topologies is dominated by continuoustime Markov process. Then the consensus problem is transformed into a stability problem by making use of tree-type transformation. The sufficient conditions of consensus and consensus with H∞ performance are gained by establishing linear matrix inequation (LMI). Through solving LMI, we gain the upper bound of delay. Finally, the result were verified by numerical simulation.
Effect of Stemming Nazief & Adriani on the Ratcliff/Obershelp algorithm in identifying level of similarity between slang and formal words

The slang word is a word that is widely used everyday by social media users, especially in Indonesia. The language generally contains abbreviations or strange words that have absolutely no connection with the word in question, this often leads to ambiguity when analysis such as sentiment analysis and Information Retrieval (IR). But lately there has been a slang dictionary that keeps a list of slang words that are often used by internet users, the dictionary informs slang words into words in formal Indonesian. But there are still many studies, especially text mining that ignores or does not filter out the word slang so that it will likely affect the results in research that has been done. The Nazief & Adriani algorithm is an algorithm to transform basic forms using Indonesian morphology by eliminating prefixes and suffixes. The Ratcliff/Obershelp algorithm can determine the similarity between two strings by matching the two strings. The purpose of this study is to compare the word slang to the formal word Indonesian, it aims to find out the similarity between the word slang with the formal word so that it will be known the percentage level of slang similarity with the formal word to find out whether the formal basic word can indirectly representing the word slang with the degree of similarity. The dataset used by Kaggle and Github, the results of the study of the use of text preprocessing and stemming of the Nazief & Adriani algorithm affect the similarity of slang and formal words with the most frequent distribution in the range of values 0.8-0.89 (80% -89.99%) in three different tests with two types of datasets and affect the level of similarity of strings precisely on the dataset from Kaggle at the value of similarity 1 (100%) which previously amounted to 40 data to 927 data, the dataset from Github also increased to the value of similarity 1 (100%) which previously did not have data to 409 data.
Combined Fault Classification and Location Scheme for TCSC-compensated Transmission Lines using Machine Leaming Techniques

This article proposes the hybrid framework for detection, classification and location of transmission line fault. The proposed technique utilizes symmetrical component analysis, fast walsh-hadamard transform, bagged decision tree ensemble classifier and Gaussian process regression for fault diagnosis task. Fast Walsh-Hadamard Transform (FWHT) has been utilized to estimate the effective features from the faulty signals. A modified 3-machine 9-bus WSCC TCSC compensated line is considered for study and different types of fault and system scenarios is simulated. The simulated test result confirms that the proposed scheme is accurately classify the faults with $\lt 1\%$; of location error.
Fiber-loop ring-down spectroscopy

Pulsed, visible and near-infrared laser light is coupled into an optical fiber, which is wound into a loop using a fiber splice connector. The light pulses traveling through the fiber-loop are detected using a photomultiplier detector. It is found that once the light is coupled into the fiber it experiences very little loss and the light pulses do a large number of round trips before their intensity is below the detection threshold. Measurements of the loss-per-pass and of the ring-down time allow for characterization of the different loss mechanisms of the light pulses in the fiber and splice connector. This method resembles “cavity ring-down absorption spectroscopy” and is well suited to characterize low-loss processes in fiber optic transmission independent from power fluctuations of the light source. It is demonstrated that by measuring the ring-down times one can accurately determine the absolute transmission of an optical fiber and of the fiber connector. In addition it is demonstrated that the tech.
A Deep Reinforcement Learning Based Approach for AGVs Path Planning

Autonomous path planning of automated guided vehicles (AGVs) is an important part of the intelligent logistics systems (ILS), which can greatly improve the abilities of intelligence and automation. Traditional AGVs navigation, electromagnetic navigation, and tape navigation, only navigate on metal belts or magnetic tapes, and the freedom of navigation is greatly reduced. This paper models the AGVs path planning problem as a reinforcement learning model to improve the capabilities of autonomous path planning and freedom of navigation. The path planning method based on the Dueling Double Deep Q Network with Prioritized Experience Reply (Dueling DDQN-PER) is used to learn the AGVs control in a simulation environment of an ILS. Using multi-modal sensory environment information, the AGV’s ability to approach the target location and avoid obstacles has been significantly improved. Experimental results show that the AGVs can autonomously plan the path in the environment with obstacles, and has a high success rate and obstacle avoidance ability.
Long Distance Merge based on Vehicle-infrastructure Communication

The vehicles merge from long distance are promising application direction, such as platoon, multi-agents rounding etc. This paper uses vehicle-infrastructure as information intermediary applied in the scenario that vehicle interior sensors cannot get information from other vehicles. Artificial potential field is constructed to converge the position of vehicles. Furthermore, first-order velocity consensus algorithm is deployed to reach the same speed. Simulation shows that distant driving vehicles can reliably achieve the position and velocity consensus. The existing infrastructure such as communication base station can easily handle the massive vehicles information exchange with low latency and cost compared to vehicles to vehicles(V2V) directed communication. Also, vehicles can avoid fall into local minimum of artificial potential field.
An Improved FastSLAM Using Resampling Based on Particle Fission Propagation

FastSLAM is a SLAM algorithm based on particle filter. In order to solve the problem of particle degradation caused by resampling of FastSLAM algorithm in simultaneous localization and map construction of mobile robot, A method of attracting fission resampling is proposed in this paper . The heavy particles are fission at the time of resampling, and the parameters such as relative distance and attraction radius are introduced to screen the offspring particles produced by fission, and the weights of the filtered offspring particles and the parent particles are calculated uniformly. The results of simulation show that the filtering accuracy of this method is higher and the running time is optimizing compared with particle filter and fission bootstrap particle filter.
Promote detection of high frequency oscillation against the Gibbs phenomenon

High frequency oscillation is a potential biomarker that can be used to locate the onset or epileptogenic regions of epilepsy. The automatic detection method of high frequency oscillation can avoid the disadvantages of time consuming and easily influenced by the subjectivity of the analyst. At present. There are many reports on the optimization of high frequency oscillation automatic detection methods, mainly focusing on how to calculate the dynamic baseline. Because most of the detection algorithms based on fileted signal, the Gibbs phenomenon in the filtering process will lead to abnormal oscillation of the signal, bring false positive judgment, and reduce the detection accuracy. Therefore, this paper proposes an optimization method for Gibbs phenomenon based on the maximum distributed peak points detection algorithm. From the published iEEG data of intracranial epilepsy from the Mayo Clinic, 300 iEEG samples were intercepted, including 193 samples containing high frequency oscillations and 107 samples without high frequency oscillations, each of which was 10 s. First, the algorithm uses the detection algorithm based on the maximum distributed peak points to determine the dynamic baseline identifying the HFOs, To detect the false positive time period, use oscillation matching, differential the signal before and after filtering, to judge the consistency of the amplitude variation trends of them by a parameter, and the consistency of the filtering artifacts caused by Gibbs phenomenon is low, and to discard it. The results show that the new algorithm improves accuracy from 87% to 91.3%. Specificity is Improved reduced from 75.7% to 88.8 %. The study reduces the probability of false positive.
Suppressed Circulating Current of MMC with IPSO Optimized PR Controller*

In this paper, the circulating current is suppressed by improved particle swarm optimization (IPSO) is proposed. First of all, closed-loop control model of MMC with proportional-resonance (PR) controller is established. Next, the PSO algorithm is improved, and the IPSO algorithm is adopted to optimize the PR parameters. Finally, the simulations in MATLAB/Simulink environment are launched, by comparing IPSO with ant colony optimization and PSO, it is shown that the amplitude of internal circulation can be prominently reduced under fault, and the internal circulation of MMC can be effectively suppressed.
Evaluation of Orthodontic Treatment Based on the Registration of X-ray Image Features

This paper proposes a method using feature-based registration, which quantitatively evaluates orthodontic treatment on X-ray lateral skull radiographs. Considering the problem of inconsistent scales of images before and after orthodontic, and to make the registration more robust to noise, this paper introduces a registration algorithm based on maximum correntropy criterion with scale constraints. By aligning the crania and mandibles separately and aligning teeth on this basis, the quantified movement of four key teeth can be acquired, which indicates the result of orthodontic treatment. Experimental results demonstrate that the proposed method makes evaluation of orthodontic treatment under the same reference and is not sensitive to the error of the mark points, which is accurate and robust.
Data-Driven Adaptive Optimal Control of UAV

This paper studies a data-driven adaptive optimal attitude control problem of a quadrotor unmanned aerial vehicle(UAV) via state feedback. A novel sampled-data-based adaptive dynamic programming (ADP) method is developed. We consider the uncertain system parameters and external disturbances. Then the optimal feedback gain is iteratively learned by input/state information. The stability of this method is ensured theoretically. Finally, simulation results are presented to verify the effectiveness of the method.
Real Time Power Scheduling incorporating Thermal Based Dynamic Line Ratings and Temporal Decomposition

Dynamic Line Ratings based on thermal limit increases utilization of existing transmission line network. The excess capacity can be used to alleviate congestion, which has positive ramification on system. The computational complexity of dispatch problem increases as temperature and current are related through set of differential equations. Hence, a temporal decomposition based multi-interval ACOPF incorporating dynamic line ratings is proposed to manage strict computational requirements in real time dispatch. The method is tested on 4 bus system and application based on congestion management during real time dispatch and improvement in utilization of wind power based system is proposed.
Design of a Quadrotor Navigation Platform Based on Dense Reconstruction

Considering that GPS based quadrotor can not fully realize positioning and navigation in an unknown environment and further environmental exploration. In this paper, we present our design for a system on a quadrotor, which is equipped with a 3D Lidar and camera, to explore and reconstruct the unknown scenes in real-time. Firstly, real-time positioning and sparse mapping are established to obtain quadrotor position information from Lidar points. Among them, for the successfully established sparse map, local map optimization is performed to complete the quadrotor pose estimation. Besides, through the fusion of Lidar and camera, the coloured point cloud information is obtained. Further, 3D surface reconstruction and updation are obtained based on TSDF by using real-time lidar odometer and colour point cloud information. Finally, the experiments verify that the platform can be used for SLAM and 3D reconstruction of unknown indoor and outdoor environments with better performance.
Second-Order Consensus for Heterogeneous Nonlinear Multi-agent Systems with Actuator Faults

In this paper, the consensus problem of heterogenous nonlinear multi-agent systems containing actuator faults is settled. The communication topology of the MAS is assumed to be a strongly connected digraph. A concept about the generalized algebraic connectivity is applied to illustrate the systems’ ability to achieve second-order consensus. On the basis of matrix theory, algebraic graph theory and Lyapunov stability criterion, sufficient conditions for fault-tolerant consensus of heterogeneous nonlinear multi-agent systems suffering from actuator faults are obtained. Ultimately, simple numerical simulation experiments are performed to prove the theoretical results.
Accent Recognition by Native Language Using Mel-Frequency Cepstral Coefficient and K-Nearest Neighbor

Almost all the world use English to communicate. English accents appear in various parts of the world. Every Country that communicates in English has a different accent, for example, English, French, Spanish, Saudi Arabia, Korea, and China. The difference in accents influenced by environment, culture, and birthplace. Accent recognition is an important thing, by recognizing the speaker’s accent, it will be known the origin of the speaker. In the future, accented English is pivotal to know, both used in learning and other interesting Automatic Speech Recognition System (ASR), therefore, the research of recognizing an accent is interested. This paper is expected to contribute some methods for accent recognizing. This paper recognizes some Native language from English, French, Spanish, Arabic, Korean, and Mandarin using MFCC Feature extraction and KNN Method. This technique reaches 57 % accuracy with the best K=3, where there is an increase accuracy compared to previous studies.
Pre-processing Task for Classifying Satire in Indonesian News Headline

One of challenges in the topic of natural language processing is detecting satire sentences. In the news headlines, satire commonly used to criticize the goverment. This study aims to know the impact of preprocessing in the classification of satire sentences in Indonesian news headlines. Besides, feature extraction was conducted by using Term Frequency Inverse Document frequency (TF-IDF) employing Machine Learning: Naive Bayes to classify the satire in Indonesian news headline. In this study, there were six preprocessing combinations used which were; breaking down the word into tokens (tokenizing), changing word into base form (stemming), removing stopword, and removing punctuation. The result showed that preprocessing affected the accuracy of sarcastic sentence classification which are not on the dataset of Indonesian news headlines. The highest accuracy of 90.38% obtained by combination of standardized preprocessing which were tokenizing and lowercasing.
FPGA-based Architectures to Recover from Hardware Trojan Horses, Single Event Upsets and Hard Failures

Third-party IPs (3PIPs) may have a Hardware Trojan Horse (HTH) that escaped detection during the testing phase. This paper proposes two techniques to recover from a HTH during runtime. In the context of FPGA-based systems, it will be proven that these techniques can simultaneously recover from Single Event Upsets (SEUs), Hard Failures (HFs) as well as HTHs in permanent and temporary modules. The architectures can detect the type of fault and in some cases the identity of the 3PIP with the HTH. A DE10-Standard FPGA development board with Cyclone V devices was used to successfully test and simulate (using Modelsim) the proposed designs and compiled with Quartus v18.1.
Mechanical Analysis of Human DBS Electrodes

Deep brain stimulation (DBS) electrodes have been proved to be effective in treating neural related diseases in rodents. These devices were successfully extended to the field of human neuro therapy. Many different electrodes exist. However, no quantitative ranking criterion is available to allow meaningful comparison of the various DBS electrodes to aid the designer.This paper presents a novel Figure of Merit (FOM) dedicated to DBS electrodes. The proposed optimization performance takes into account safety factors of mechanical analysis and the estimated fabrication cost of some materials. The FOM is used to rank several DBS electrode designs.Finite Element Models (FEM) analysis for several electrode layouts are conducted. FEM shows the effects of different design parameters on the electrode mechanical performance. These parameters include electrode dimensions, geometry, and materials. The electrodes mechanical analysis is evaluated from different points of view including: linear buckling analysis, stationary analysis with axial and shear loading. The safety factors are calculated for several designs with different materials (brittle and ductile materials). The results obtained from FEM mechanical analysis for the various electrodes prototypes are presented, which provide guidelines for different electrode designs and material choice. A proposed fabrication process along with an estimated fabrication cost is also introduced.
Robust Non-rigid Point Set Registration Based on Global and Local Mixture Structural Feature

This paper proposes a mixture structural feature based non-rigid point set registration algorithm, this algorithm mainly contains correspondence estimation and space transformation. In the step of correspondence estimation, Euclidean distance and angle based global and local structural features are used to describe structural difference between two point sets. Then the global and local structural features are combined to construct a cost function, which provides the correspondence by minimizing structural difference using a linear assignment method. In the step of space transformation, a thin plate spline function is used to solves space transformation problem of the source point set. The performances of the proposed algorithm are verified by simulation data and real data, and compare with four classical algorithms where proposed algorithm achieves the best alignment in most cases.
Digital-LDO Switched Capacitors based for 0.5V applications

This work presents the design of a 0.5V digital low dropout voltage regulator (DLDO) in 180nm CMOS technology for Dynamic Voltage Scaling applications. Dynamic and leakage power consumption in VLSI systems are effectively reduced by ultra-low voltage operation, being that the maximum energy efficiency is achieved at supply voltage below 0.5V. Feedback-controlled analog LDO based on an operational amplifier can fail if it operates at sub/near-threshold voltage. Digital LDOs have potential to replace the analog circuits in the feedback loop for a digital equivalent, which enables ultra-low voltage operation. An efficiency peak of 98%, an steady-state error lower than 7mVp was achieved by post-layout simulations for a load current range from 100μA to 1.25mA.
Stock Price Trend Prediction using MRCM-CNN

To tell if next day’s stock price will go up or down is always something fascinating. From econometrical approaches like ARIMA model to newly developed artificial neural networks, people have tried very hard to find a precise way of predicting the moving direction of stock price. In this paper, a new method, MRCM-CNN, which combines time series reconstruction and deep neural network is introduced. First, we use Spearman’s rank correlation as dependency measurement, and perform segmentation and clustering simultaneously. Then the time series was reconstructed based on the clustering result as well as daily trading data. At last, a CNN was utilized on this reconstructed time series to predict next day’s stock price change direction. In the test on 100 randomly selected stocks, our proposed method shows great robustness to different stocks, and it on average outperforms the competing algorithms for approximately 2.8%-7.6% in terms of accuracy, 14%-43% in terms of usable prediction rate and 10%-34% in terms of high-accuracy prediction rate.
Design of the millimeter-wave textile antenna loaded with AMC structures for 5G applications

In this work, we designed a millimeter wave textile antenna for the 28 GHz band. This antenna was tested using textile materials placed over an artificial magnetic conductor (AMC). The prototype (antenna and AMC) was fabricated on polyester substrate with a thickness equal to 0.35 mm and a size of 16.8*14.4*1.84 mm3 only. To isolate the antenna from the human body, an artificial magnetic conductor (AMC) plane was added on the reverse side of the ground plane. The simulation results obtained by simulator CST present the electrical and radiation characteristics of the proposed antenna. The comparison of the antenna without AMC in free space and that backed by AMC showed that the latter performance is better. The proposed structure can be used in smart garments with minimized risk to human health.
CWGAN: A Graph Vector Based Traffic Missing Data Adversarial Generation Approach

Traffic speed prediction is an important and basic application in intelligent transportation system. But due to the equipment failure, the sampled time-series data are often corrupted, which induces data missing problems and creates difficulties in traffic speed prediction. In this paper, To solve a special data missing case, successive long-term data missing, we propose a novel approach combining graph vectorization algorithm and conditional Wasserstein generative adversarial nets (CWGAN). Different from traditional generative adversarial nets, CWGAN introduces graph vectors generating from graph vectorization algorithm as its extra conditional input. Apart from this, we implement enough experiments based on various deep learning methods to compare performances. Finally, we find that CWGAN performs better than other comparative methods when data missing is serious.
Segment-based Cross-domain Localization between Aerial and Ground Robots

Cross-domain localization is the foundation and key link for collaborations of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). A major challenge of this problem is the extremely large difference in view-points of aerial and ground robots. In this work, we present a segment-based cross-domain localization solution that accurately calculates the pose of a UGV in the reference map generated by a UAV. Especially, in scenarios with few small-size structures, our method exploits the common information of large structures in aerial and ground view-points to match, which leads to a satisfactory recall of place recognition. Additionally, we build a large scale outdoor simulation urban environment at high fidelity and present three accurate synthetic benchmark datasets with easy, medium and difficult levels, to quantitatively evaluate the performance of our solution. Experimental results demonstrate that our method achieves notable better performance than the baseline method SegMap. Our implementation is publicly available at https://www.github. com/5john/AirGroundSegMap.