Miracle-dz/UniFAR-FTUs huggingface dataset:
Globular Cluster Classification in Galaxy M81 Using Machine Learning Techniques

Globular clusters are very important in astronomy since they can be used to study the process of galaxy formation and evolution. With the exponential growth of data in astronomy, it is currently inefficient to classify globular clusters from the other types of astronomical objects by humans. In this study, we explored the possibility of using machine learning in globular cluster classification to replace the classification by humans. We selected five standard classification methods including k-NN, Random Forest, SVM, Neural Network, and Decision Tree. All models were built and tested by using the Weka software with datasets from a galaxy M81. Our experiments showed that k-NN, Random Forest, and SVM are the best approaches for globular cluster classification, with 97.7% accuracy, 97.8% precision, 97.7% recall, and 97.7% F-measure. Finally, when we applied these models to an unseen dataset to predict new globular cluster candidates, we acquired 6.32% success rate compared to 30% success rate by humans. This suggests that machine learning techniques can be applied to globular clusters classification. However, our models need to be improved to achieve a higher success rate to replace the classification by humans.
2 x 2D Analytical Model of a Transverse Flux Magnetic Gear

This article presents a 2 x2D analytical computation of the magnetic field distribution of a magnetic gear with transverse flux in pole pieces. The underlying linear magnetostatic analytical model is based on the subdomain resolution of Laplace’s and Poisson’s equations in various 2D planes of the magnetic gear that require coupling. This analytical model serves to evaluate magnetic properties with an acceptable computation time compared to 3D finite element models. The advantage of this approach is even more pronounced for a magnetic gear with a high pole number. A comparison with the 3D finite element model is drawn in order to validate this model. Lastly, a computation time analysis on a high pole number magnetic gear leads to minimizing computation time without any loss of accuracy.
An Adaptive Mapping Strategy for Autonomous Underwater Vehicles

This paper specifically focuses on the control of autonomous underwater vehicles (AUVs) in mapping ocean processes. An adaptive control algorithm is proposed in this paper to control the heading of an AUV. The algorithm can be used to control a scout AUV typically used in multi-vehicle strategies to search for areas with potential rich information before secondary vehicles are called to further investigate the areas. Two control algorithms, proportional, integral and derivative (PID) and fuzzy logic, were compared based on their simplicity and practicality to be applied in the proposed adaptive sampling strategy. Simulation results indicated that the proposed strategy had the potential to be used for mapping oceanographic features in field trials. The results also showed that the fuzzy logic control was less sensitive to the change in its parameters compared to the PID when used in the proposed algorithm.
An Overview of PM Synchronous Machine Design Solutions for Enhanced Traction Performance

High power density, high efficiency, wide constant power speed range, lower torque ripple, and manufacturability are the major concerns of future electrical machines for electric vehicle (EV) propulsion applications. Towards this, permanent magnet synchronous machines (PMSMs) are the most relevant candidate for EVs mainly due to their high power/torque density, wide constant power speed range, compact size and higher efficiency than their counterpart induction machines. However, the focus of automakers on phasing out vehicles powered solely by internal combustion engines necessitates further improvement of the performance of EV traction machines. This paper highlights four critical design areas which significantly impact on the performance of PMSMs for EV propulsion application: i) new materials and their feasibility in manufacturing; ii) innovative topologies/structural design solutions; iii) design approaches for efficient thermal management; and iv) optimized design approaches, and provides insights to each area based on recent research and development recorded in the literature.
On the Modelling of Fuel Cell-Fed Power System in Electrified Vessels

Electrification of vessel power and propulsion system has been widely utilized in the shipbuilding industry in recent years. It is expected to be adopted in all marine vessel segments as the increasingly strict marine greenhouse gas (GHG) emissions regulations push the maritime fleet to decrease its reliance on conventional fuels such as marine diesel oil (MDO). With the development of sustainable energy sources and energy storage technologies, such as fuel cells, batteries, and super-capacitors, these renewable energy sources are integrated into the marine electric power system to reduce or even achieve zero-emission operation. A comprehensive shipboard power system model is the crucial foundation for the further research. In this paper, a hybrid shipboard power configuration will be studied and modeled using Matlab/Simulink. The detailed model using average converters’ models for the ship power system will be described. Besides, the simulation results shall be shown and analyzed considering a typical harbor tugboat load profile.
Evolution of the Performance of Pipelined FFT Architectures Through the Years

This paper explores the evolution of the three main figures of merit for pipeline fast Fourier transform (FFT) architecture through the years. These figures of merit are throughput, power consumption and area. By means of a careful analysis of more than 200 FFT architectures, this papers provides a big picture of the evolution of pipelined FFT architectures, it reveals hidden trends, and gives hints about the future of pipelined FFT architectures.
Urban Sound Classification Using Convolutional Neural Network and Long Short Term Memory Based on Multiple Features

There are many sounds all around us and our brain can easily and clearly identify them. Furthermore, our brain processes the received sound signals continuously and provides us with relevant environmental knowledge. Although not up to the level of accuracy of the brain, there are some smart devices which can extract necessary information from an audio signal with the help of different algorithms. Over the years several models like the Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Region-Convolutional Neural Network (R-CNN) and many machine learning techniques have been adopted to classify sound accurately and these have shown promising results in the recent years in distinguishing spectra-temporal pictures and different sound classes. The novelty of our research lies in showing that the long-short term memory (LSTM) shows a better result in classification accuracy compared to CNN for many features used. Moreover, we have tested the accuracy of the models based on different techniques such as augmentation and stacking of different spectral-features. In such a way it was possible with our LSTM model, to reach an accuracy of 98.81%, which is state-of-the-art performance on the UrbanSound8k dataset.
A 77 GHz Planar Antenna Array in Four Layer Printed Circuit Board for Automotive Radar Applications

This paper presents the design of a 77 GHz antenna array system in four-layer printed circuit boards for automotive radar applications. Different substrates are utilized for the antenna array design where the top layer in 0.203 mm of thickness is the core material of RO-4003C with dielectric constant (DK) 3.55 and dissipation factor (DF) 0.0027 to meet millimeter wave requirements. The feeding structure in microstrip with $50 \Omega$ of characteristic impedance is then miniaturized due to the thin substrate. Besides, the substrate-integrated-waveguide (SIW) is used for the power splitting function to reduce the parasitic radiation and the antenna array side-lobe level thereby can be suppressed. The second and third layer of substrates are in the prepreg and core materials of FR-4, respectively, with 0.203 mm for each layer to strengthen the mechanical support, where the worse DF in 0.021 does not affect the RF signal transmission because the copper foil as the ground plane isolates electromagnetic fields between the RO4003C and the FR-4 prepreg. The final design includes the launcher in grounded coplanar waveguide (GCPW), the SIW power divider, and a 4 by 4 microstrip antenna array. The simulation results show the operating frequency from 74.42 GHz to 79.28 GHz in lower than -10 dB reflection and the antenna gain of 14.6 dB, with enhanced sidelobe level reduction characteristics. Input impedance measurement using probe heads with the Thru-Reflect-Line (TRL) calibration is conducted to verify the simulation data. The end-launched connector is used for measurements of the radiation pattern and antenna gain, with discussions of possible error sources compared to simulations.
The Hidden Markov Model with Proximity Capacitive Sensor to Detect User Gesture

This paper presented the proximity capacitive sensor using Hidden Markov Model to detect user gesture. The human interactive gesture signal analyses have been a research topic smart home fields that algorithms build in local device to recognize real time. The Hidden Markov Model have been used in many fields that including identification and classification. The features of Hidden Markov Model used probability concept train and learn identification model that obtain correct input signal probability feedback to user. Moreover, Hidden Markov Model uses a transition between the observed value and the state to describe model which record dynamic random distribution to identify object. Therefore, the Hidden Markov Model methods to identify user gesture has satisfactory response.
Photometric Stereo Using CNN-based Feature-Merging Network

We propose a photometric stereo method using Convolutional Neural Network (CNN) based method, which is effective for deriving surface normal data from non-lambertian objects. Our method extracts feature maps from a set of images of object using shared feature extraction network, and merge the extracted feature maps using two pooling method: max-pooling and average-pooling. The merged feature maps are concatenated and passed to final CNN layers to derive the surface normal map. We tested our network on the most widely-used benchmark dataset and confirmed that our method performs better than existing deep learning based photometric stereo method.
Platoon Control of Connected Vehicles under Intermittent Communication Constraints*

This paper investigates the problem of platoon control for connected vehicles with intermittent communications among neighboring vehicles. A vehicle can receive the state information from its neighboring vehicle(s) only when the communication link between them is active. This paper proposes a platoon control law for each follower vehicle based on intermittently received relative state information from neighboring vehicles. A sufficient condition ensuring cooperative tracking of the leader vehicle is derived by using graph theory, non-smooth analysis and average system approach. Then, conditions for achieving string stability are derived. Based on these conditons, a platoon controller design algorithm is provided. Finally, the effectiveness of the platoon control methodology is illustrated by numeric simulations.
Research Progress in Speech Enhancement Technology

The way to abstract original, pure speech signals from noisy speech signals has always been the research priority of the speech enhancement technology. Traditional approaches to handle speech signals face many restrictions and disadvantages, which lead to non-ideal speech enhancement effects. With the development of AI, DNN speech signal processing not only enhances speech signals better but also the hotspot of researching the speech enhancement technology. This paper introduces the research progress made in the speech enhancement technology through traditional methods and AI algorithms.
Framework for Combining Multiple Lightweight Underwater Vehicles into Super Underwater Vehicle

This paper investigates the potential of using multiple, rigidly interconnected lightweight underwater vehicles (LUVs) as a cheaper alternative to using a single vehicle for mapping and monitoring. The objective is to develop a prototype super underwater vehicle (SUV) demonstrating the proficiency of the multi-vehicle approach. This includes the definition of a framework for SUV control and a demonstration of a prototype SUV consisting of two remotely operated vehicles (ROVs) with supporting experimental results from using the SUV for underwater hyperspectral imaging of macroalgae in an Arctic habitat in Kongsfjorden, Svalbard.
A SLAM Loop Closure Algorithm of BoW Incorporating the Gray Level of Pixel

A loop closure algorithm of bag of words (BoW) incorporating the gray level of pixel is proposed, which aims at the loop mismatching easily occurring in the SLAM loop closure for current mobile robots. Firstly, K-means++clustering algorithm is used to construct a dictionary of k-tree model. Secondly, the loop closure algorithm based on BoW is applied for similarity detection and loop frame identification. Lastly, the loop frame is further verified by incorporating the gray value of pixel to eliminate mismatched loop frame and complete circuit loop. The experimental results show that this algorithm can effectively eliminate the mismatched loop frame, improve the accuracy of SLAM system and generate globally consistent map while meeting real-time requirements.
Critical Node Detection for Sequential Attacks in Complex Networks via Multi-objective Optimization

The critical node detection for sequential attacks based on cascading failure model is an important way for analyzing network vulnerability, which has attracted the attention of many researchers in the field of complex network recently. However, most of the existing cascading critical node detection algorithms focus on designing effective attack strategies leading to the maximal damage to the network (i.e. attack effect), while ignoring the cost of attacks. To this end, we transform the cascading critical node detection for sequential attacks as a bi-objective optimization problem (named BCVNDSeq), where the attack cost and the attack effect are simultaneously optimized. In order to solve the transformed problem, we propose a multiobjective cascading critical node detection algorithm (named MO-BCVNDSeq), which can provide decision makers with a holistic view for analyzing the network vulnerability. In MO-BCVNDSeq, a local search strategy based on sequential matrix is proposed to accelerate the population convergence and an individual repairing strategy is also suggested to further improve the search efficiency. Finally, the experimental results on 6 real-world complex networks demonstrate the effectiveness of the proposed algorithm compared with several representative baselines.
Initial Study of Low Cost Crepitus Joint Degradation Finding Using Acoustic Localization

Current methods of joint diagnosis depend on invasive, radiative methods, large set ups and requiring skilled operators, which are still not able to assess the joint during motion. Replacing these methods are acoustic methods, which are only able to diagnose joint health based on the characteristics sound emitted by a moving joint. This paper looks into informing the medical practitioner of the locations of joint damage instead, hence the feasibility of localizing upon the damaged part of the joint, which is represented by a playback of a recording of crepitus sounds emitted from a human joint in 2 dimensional settings. Three microphones, an earphone and a data acquisition device were used to implement the testing. Using the time-difference-of-arrival (TDOA) between the 3 sensors, the Angle-of-Arrival (AOA) method was used to attain the initial localization coordinates. These coordinates were then fed into a gradient descent algorithm to reduce the positioning error of the AOA method, yielding the final position results. Results show that the developed system is able to achieve localization errors of less than 1.0 cm.
Efficient Balancing Optimization of a Simplified Slider-Crank Mechanism

High-speed link motion produces reactions that are transmitted to the base of mechanisms causing undesired effects such as vibrations, wear, and fatigue. These problems can be reduced or even better eliminated if a balancing technique is performed. To this day, most of the papers in the literature dealing with the balancing of mechanisms obtain the balancing conditions with the use of Cartesian coordinates, which result in complex equations that involve trigonometric functions difficult to simplify. This work presents the balancing optimization of a simplified slider-crank mechanism by obtaining its balancing conditions with the use of natural coordinates. Numerical results show the effectiveness of this approach: the Shaking Moment and the Shaking Force dynamic reactions can be decreased in 97.76% and 94.58%, respectively.
Normal Distributions Transform is Enough: Real-time 3D Scan Matching for Pose correction of Mobile Robot Under Large Odometry Uncertainties

In this study, a 3D Normal Distributions Transform (NDT)-based pose correction framework for a mobile robot is proposed to prove its robustness under a considerable odometry uncertainty. Our proposed method consists of Extended Kalan Filter based on IMU and wheel odometry to estimate initial pose and NDT that corrects the initial guess via 3D scans. Then, the final pose correction is retrieved with Kalman gain using corrected poses from both EKF and NDT, followed by point cloud accumulation that generates the global map. Before examining our proposed method, quantitative comparisons of NDT over other 3D scan matching algorithms targeted for mobile robot pose correction were conducted. After that, the experiments are conducted for both indoor environments and outdoor environments. As verified experimentally, NDT is a) suitable for real-time pose correction over other 3D-scan matching algorithms b) robust even when loop closing is not available and c) shows better performance than EKF on pose correction under very imprecise raw odometry. Additionally, we analyzed the time-wise performance of NDT during registration over different voxel sizes of a global map.
A Comprehensive Solution for the Analysis, Validation and Optimization of SDN Data-Plane Configurations

Software Defined Networking (SDN), as an emerging paradigm, offers a centralized control platform by disassociating the forwarding process of network packets (data plane) from the routing process (control plane). However, the distributed state of the Openflow rules across various flow tables and the involvement of multiple independent rules writers may lead to problems of inconsistencies and conflicts within configurations at the infrastructure level. To tackle these issues, we propose, in this paper, an offline approach to fix violations at data plane side and a fine-grained control of SDN switches flow tables. Our solution considers Flow entries Decision Diagram (FeDD) as data structure and relies on formal techniques for analyzing the policy defects and resolving misconfigurations. It allows ensuring that the operator’s policies are correctly applied in an optimal way. The implemented prototype, on top of OpendayLight, of our solution and experimentations, based on a real network configurations topology, demonstrate the scalability and applicability of our approach.
Fluidspread: A New Method of Maximizing Positive Influence in Online Social Networks via Fluid Dynamics

Influence maximization aims at detecting the top-k influential users in online social networks. Almost all previous models of influence spread cannot simultaneously incorporate users’ attitudes, interactions between users, and dynamic influences. However, in this study, we established a new model of influence spread via fluid dynamics, which reveals the time-evolving process for influence spread. We modeled the spread of influence as the process of fluid update based on three dimensions: the difference of fluid height, the temperature of fluids, and the difference of temperature. Moreover, we formulated the problem of maximizing positive influence and devised a Fluid-spread greedy algorithm to solve it. We conducted extensive comparisons between our approach and several baselines, and experimental results illustrate the effectiveness and efficiency of the Fluidspread model and algorithm.
Control of tendon-driven(Twisted-string Actuator) robotic joint with adaptive variable-radius pulley

This paper introduces and controls a new variable radius pulley that passively changes the effective radius of the drive pulley in a belt driven transmission in response to changing load torque. The mechanism used in this paper is based on the principle that the effective radius of the pulley increases passively when torque is applied to the pulley by the cam element and the elastic element. A belt-driven transmission with a conventional pulley of constant radius has a fixed maximum torque and speed, but the variable radius pulley in this paper changes the maximum speed and torque to enable more efficient driving. This paper explains the principle of operation of the new variable radius pulley and introduces the experiments that performed the position control of the robot joint by applying the variable radius pulley.
Discrimination of Verbal/Visuospatial Memory Retrieval Processes by Measuring Prefrontal Lobe Blood Volume With Functional Near-Infrared Spectrometry

In cognitive task execution, retrieval processes transfer memories from long-term storage to working memory, comprising three short-term memories—phonological loop, visuospatial sketchpad, and episodic buffer—controlled by a short-term-memory central executive. The phonological loop and visuospatial sketchpad are prominent in verbal and visuospatial information processing, respectively. Individuals differ in how they retrieve the same information. Discrimination between verbal or visuospatial memory retrieval by monitoring central executive activation is useful. Functional near-infrared spectroscopy (fNIRS) was used to assess blood volume and prefrontal lobe brain activation. Using a novel approach, blood volume during memory retrieval was obtained using fNIRS. To eliminate other factors not related to the central executive activation, a base period was observed. Dynamic range was calculated for both memory retrieval and base periods, and differences obtained at ten positions within the prefrontal lobe were used as features to discriminate between verbal and visuospatial retrieval. For discrimination, k-nearest neighbor (kNN) and support vector machine (SVM) with different kernels were applied. Our method was tested on participants in verbal/visuospatial memory retrieval experiments. As the result, accuracy, positive predictive values, and negative predictive values of kNN and SVM were 1, which indicated that the proposed method successfully discriminated between verbal/visuospatial memory retrieval through prefrontal lobe blood volume observation.