Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-processing of tandem mass spectra using machine learning methods

Protein identification has been more helpful than before in the diagnosis and treatment of many diseases, such as cancer, heart disease and HIV. Tandem mass spectrometry is a powerful tool for protein identification. In a typical experiment, proteins are broken into small amino acid oligomers called peptides. By determining the amino acid sequence of several peptides of a protein, its whole amino acid sequence can be inferred. Therefore, peptide identification is the first step and a central issue for protein identification. Tandem mass spectrometers can produce a large number of tandem mass spectra which are used for peptide identification. Two issues should be addressed to improve the performance of current peptide identification algorithms. Firstly, nearly all spectra are noise-contaminated. As a result, the accuracy of peptide identification algorithms may suffer from the noise in spectra. Secondly, the majority of spectra are not identifiable because they are of too poor quality. Therefore, much time is wasted attempting to identify these unidentifiable spectra. The goal of this research is to design spectrum pre-processing algorithms to both speedup and improve the reliability of peptide identification from tandem mass spectra. Firstly, as a tandem mass spectrum is a one dimensional signal consisting of dozens to hundreds of peaks, and majority of peaks are noisy peaks, a spectrum denoising algorithm is proposed to remove most noisy peaks of spectra. Experimental results show that our denoising algorithm can remove about 69% of peaks which are potential noisy peaks among a spectrum. At the same time, the number of spectra that can be identified by Mascot algorithm increases by 31% and 14% for two tandem mass spectrum datasets. Next, a two-stage recursive feature elimination based on support vector machines (SV M -RFE) and a sparse logistic regression method are proposed to select the most relevant features to describe the quality of tandem mass spectra. Our methods can effectively select the most relevant features
An Improved Method Based on VGGNet for Refined Bathymetry 1 from Satellite Altimetry: Reducing the Errors Effectively 2

At present, only approximately 10% of the global seafloor topography has been finely modeled, and the rest are either lacking in data or not accurate enough to 15 meet practical requirements. On the one hand, satellite altimeter has the advantages of 16 large-scale and real-time observation, thus is widely used in the measurement of 17 bathymetry, the core of seafloor topography. However, there is often room for 18 improvement in its precision. On the other hand, multibeam echosounder bathymetric 19 data is highly precise but normally limited to a smaller coverage, which forms a 20 complementary relationship with the bathymetry derived from satellite altimetry. To 21 combine the advantages of satellite altimetry-derived and multibeam sonar-derived 22 bathymetry, we apply deep learning, which is powerful in the field of digital image 23 automation, to perform multibeam sonar-based bathymetry correction for satellite 24 altimetry bathymetry data. Specifically, we modify and improve a pretrained VGGNet 25 neural network model with a depth of 19 layers to train on three sets of bathymetry data 26 from the West Pacific, Southern Ocean, and East Pacific, respectively. Experiments 27 show that the correlation of bathymetry data before and after correction can reach a 28 high level, with the performance of R 2 being as high as 0.81 and the RMSE improved 29 over 19% compared with previous research. We then explore the relationship between 30 R 2 and water depth and conclude that it varies at different depths and thus the terrain 31 specificity was a factor that affects the precision of correction. Finally, we use the 32 difference of water depth before and after the correction to evaluate the correction results, and find that our method can improve by more than 17% compared with previous research. The results show that using the deep learning VGGNet model can better perform the correction of the bathymetry derived from satellite altimetry, thus 36 providing a method for accurate modeling of the seafloor topography.
A 24-GHz Wireless Locating System for Human–Robot Interaction

Due to technological advances in various fields and the rising demand for automation, the popularity of service robots is expected to grow rapidly in the upcoming years. One of the core functions of such robots is their interaction with people. One type of interaction, which is studied in this paper, is when the robot leads or follows a person. This presupposes that the position of the person is known relative to the mobile robot. In this paper, we present a 24-GHz wireless locating system to enable this kind of human–robot interaction. The system consists of a radar node attached to the robot and an active portable wireless beacon that is carried by the person. Using a secondary radar approach to measure the distance and angle to the beacon, the 2-D position of the person relative to the mobile robot can be accurately determined by a single radar node on the robot. This is unfeasible with standard multilateration localization approaches. An extended Kalman filter is used to increase the accuracy and reliability of the positioning system. The measurements presented in this paper, performed in a scenario that is both challenging and realistic, demonstrate the effectiveness of the proposed approach.
Identification and Counting of Soybean Aphids from Digital Images Using Shape Classification

Abstract. Soybean aphids are serious pests, causing negative yield impacts in the crop. Assessing their population is essential for making appropriate pesticide application decisions. Manual identification and counting, which is commonly performed to determine the economic threshold level, is time-consuming, laborious, and causes visual fatigue. In this study, an automatic image processing method was developed to identify and count aphids as well as exoskeletons and leaf spots on soybean leaves based on shape analysis. Aphid-infested soybean trifoliates were obtained at three infestation rates (low, medium, and high). Images of the front sides of the leaves were captured in the laboratory with three cameras (digital single-lens reflex or DSLR, consumer-grade digital, and smartphone) under two lighting conditions (direct and indirect). The shape parameters considered were area, perimeter, convex area, eccentricity, aspect ratio, solidity, hollowness, and roundness. Among the shape parameters tested, hollowness was the best in identifying aphids and was therefore used for developing the object classification algorithm. Of the three cameras tested, images from the consumer-grade digital camera produced the best identification accuracy (>82.4%), followed by the DSLR camera (>81.2%) and smartphone camera (>37.9%). Statistical analysis revealed that the accuracies did not differ significantly under different lighting conditions (p = 0.43), but the accuracies differed for the smartphone camera compared to the DSLR and consumer-grade digital cameras (p = 8.87 x 10-10). The results of automatic and manual counting were very well correlated (r = 0.92). The automatic image processing method achieved more rapid counting (
Fluid-film lubrication computing with many-core processors and graphics processing units

The advancement of modern processors with many-core and large-cache may have little computational advantages if only serial computing is employed. In this study, several parallel computing approaches, using devices with multiple or many processor cores, and graphics processing units are applied and compared to illustrate the potential applications in fluid-film lubrication study. Two Reynolds equations and an air bearing optimum design are solved using three parallel computing paradigms, OpenMP, Compute Unified Device Architecture, and OpenACC, on standalone shared-memory computers. The newly developed processors with many-integrated-core are also using OpenMP to release the computing potential. The results show that the OpenACC computing can have a better performance than the OpenMP computing for the discretized Reynolds equation with a large gridwork. This is mainly due to larger sizes of available cache in the tested graphics processing units. The bearing design can benefit most when the system with many-integrated-core processor is being used. This is due to the many-integrated-core system can perform computation in the optimization-algorithm-level and using the many processor cores effectively. A proper combination of parallel computing devices and programming models can complement efficient numerical methods or optimization algorithms to accelerate many tribological simulations or engineering designs.
Deployment of an autonomous mobile manipulator at MBZIRC

This paper introduces a custom mobile manipulator and presents the planning and control architecture to execute autonomous missions including navigation and manipulation. The developed system combines a commercially available ground vehicle with a custom‐designed robot arm. A typical mission requires the robot to find and navigate to its site of action, identify the suitable manipulation tools, and manipulate the correct objects according to the task specification. Our contribution includes a complete software pipeline for autonomous map building, localization, and navigation. We demonstrate stable localization on vast outdoor areas without GPS support. Our exploration strategy coupled with a path planner reliably scans the environment while avoiding obstacles until it finds the manipulation site. The position of the target location is estimated from laser data within a 5 cm radius, and the subsequent positioning procedure of the mobile base achieves a precision of ±4 cm. The developed manipulator uses series elastic actuation, making the system mechanically robust and compliant. Thanks to the precise joint torque controllability, a low end‐effector impedance can be realized to passively overcome positioning inaccuracies during manipulation. Our solution incorporates a support vector machine to detect the manipulation object as required for visual servoing. We show how a valve stem and standard wrenches can be identified with a detection rate higher than 90%. The paper finally discusses the performance and lessons learned while participating at the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where we showed the successful autonomous operation in a partially unknown environment.
Mechanism Design of a Bionic Robotic Fish

In recent years, bionic robot fish have received more and more attention and have been applied in many fields, such as underwater detection, underwater archaeology, and military field. The main reason is that the propulsion and propulsion efficiency of the bionic fish are high, and the swimming performance is excellent, which is suitable for underwater operation. This paper studies the mechanism design of bionic fish which is suitable for underwater operation. The presented robotic fish consists of the power transmission mechanism of tail and the rising-falling mechanism of chest. The swing mechanism of tail uses DC motors as driver, and the transformation mechanism is added at the tail to turn the rotary motion of motor into swing movement. The pectoral fins wobble mechanism is used as the rising and falling mechanism, which is conducive to raising the swing frequency.
Coherent dual‐band radar system based on a unique antenna and a photonics‐based transceiver

This study reports the development of a coherent dual-band radar system consisting of a frequency selective surface (FSS)-based dual-band focal-point/Cassegrain parabolic antenna and a single photonics-based transceiver. The proposed antenna enables to simultaneously operate in the S and X bands due to the use of an FSS-based sub-reflector. Numerical and experimental results of the antenna reflection coefficient, radiation pattern and gain have been shown in excellent agreement for both frequency bands. Furthermore, the proposed dual-band antenna has been applied in conjunction with a photonics-based coherent dual-band radio-frequency transceiver operating in the same bands. The authors’ innovative radar system has been properly validated by the detection and ranging of a commercial aircraft and a helicopter on a building roof, using S and X bands concurrently.
ON THE SIZING OF BUILDING ENVELOPPE AND ENERGY SYSTEM INTEGRATING MANAGEMENT STRATEGY IN SKETCH PHASE

This paper presents a methodology of simultaneous sizing of building envelope and heating system integrating management strategies in sketch phase. This method uses a global optimization approach taking into consideration a lot of parameters and constraints. Our goal is to implement it in performance software tools, which allow architects and designers to analyze and quickly compare design solutions taking into account the cost over the building life cycle (including the envelope investment, heating system investment and operating cost) with respect to the need of comfort and budget. These tools are being developed and are valued as part of Vesta-System company (www.vesta-system.fr). Collaborations between academics are of course possible and established case by case. Our study in this paper is a building in Grenoble, France.
PREVALENCE OF APHTHOUS ULCER AMONG DENTAL STUDENTS: A QUESTIONNAIRE-BASED STUDY

: Aphthous Ulcer or Recurrent Aphthous Stomatitis is one of the most common mucosal diseases of the mouth. The precise etiology of aphthous ulcer is uncertain, however, triggered factors encompass strain, trauma, food sensitivity, and genetic predisposition. Previous research has advised that stress and anxiety have a role in the onset and recurrence of aphthous ulcers. This study aims to estimate the prevalence of Aphthous ulcers among Dental college students and find out its association with stress.
Intelligently Controlled Flywheel Storage for Enhanced Dynamic Performance

This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-induction-machine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear time-domain simulations and novel indices have been used for evaluation of controller performance against its predecessors. $\boldsymbol{OP}{\text{5600}}$ Real-Time-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme.
Fast and scalable interfacial convective assembly of nanoparticles for interconnects applications

Fast and Scalable Interfacial Convective Assembly of Nanoparticles For Interconnects Applications by Adnan Korkmaz Doctor of Philosophy in Mechanical Engineering Northeastern University, April 2017 Dr. Ahmed Busnaina, Adviser Directed assembly of nanoelements has been used to fabricate 1, 2, and 3-dimensional or hybrid nanostructures with unique properties to be used in many applications including electronics, optics, and energy for enhanced performances. However, current directed assembly techniques have challenges in developing highly scalable and high-rate assembly for precisely placing nanoelements. Template-directed fluidic assembly and electric field induced assembly approaches are the most common assembly techniques. In the template-directed fluidic assembly, nanoelements are assembled either vertically or horizontally at the air-liquid interface with the help of a capillary force, which guides nanoelements to the desired surfaces. This process is slow since there is no external force applied on the nanoelements, hence it requires hours to assemble nanoelements less than an inch square. In addition, the resolution of nanoelements assembly is limited because of the wettability of solvent, which is water. On the other hand, electric field induced assembly techniques are fast and robust since the applied electric field guides the nanoelements to the desired surfaces. This process requires a conductive layer to attract the nanoelements. Here, we present an entirely new evaporation-driven assembly technique called interfacial convective assembly, in which colloidal particles are selectively and simultaneously integrated on the patterned surfaces up to two minutes over several square inches area. We have assembled organic and inorganic particles such as Polystyrene Latex (PSL), silica, gold, and silver into deep channels, holes, wells, and trenches, with sizes ranging from 25 nm to 20 μm on either conductive or insulating surfaces such as silicon, glass, gold, and ceramic. We demonstrate with in situ experiments that there is a turnover process because of the density, surface tension, and volatility difference between water and isopropyl alcohol (IPA). Particles migrate toward the surface, and once the solvent inside the feature starts evaporating, particles are driven together and change their position like a lake turnover. We have tested different solvents such as water, acetic acid, chloroform, and toluene to investigate the effects of evaporation, miscibility, and wettability on the interfacial convective assembly. Experimental results showed that the solvent needs to have low surface tension, be miscible, and volatile to perform successful assembly. We also examine the effect of changing parameters
Application of Spectral Element Method Combining Dilatation Theory to Sound Generated by a Co-rotating Vortex Pair

A spectral element method for the simulation of an acoustic field is applied to the case of a co-rotating vortex pair in both stationary medium and mean flow. Based on the dilatation theory, the second time derivative of the pseudopressure is used as the source of the inhomogeneous convected wave equation which is discretized by a spectral element method in space and the Newmark-β method in time marching. In addition, the nonreflecting boundary condition is adopted too. Then we compared the numerical results with the analytical solution. Numerical results are in good agreement with the analytical solution. Moreover, different grid spacings and time steps are investigated for evaluating the numerical accuracy. To study the frequency content of the sound, spectral analysis is also carried out. Finally, sound propagation in uniform flows and sheared mean flows are simulated and analyzed. This study shows the capabilities of the spectral element method combined with dilatation theory for the aeroacoustic problems.
Harmonic‐signature‐based islanding detection in grid‐connected distributed generation systems using Kalman filter

Islanding in power systems is a challenge that results in various uncertainties in the system parameters, degrades power quality, and may endanger the maintenance workers. This study presents a new passive islanding detection approach for grid-connected distributed generation (DG) units. The proposed method employs a Kalman filter (KF) to extract and filter the harmonic contents of the voltage signal measured at DG terminals. A three-phase voltage signal is measured at the point of common coupling and taken as a test signal for islanding detection. First, a residual signal is generated using the KF to detect different changes in the power system. In the second step, multiple harmonic contents are estimated by the KF to calculate a criterion named selected harmonic distortion (SHD). The variation of SHD classifies between islanding and normal conditions. The IEEE 13-bus test system simulated in Matlab/Simulink is used as a testbed to assess the performance of the proposed approach. The proposed method is extensively analysed under various islanding and non-islanding scenarios. The results demonstrate that the proposed method can successfully differentiate between islanding and non-islanding events. Moreover, it provides high reliability by eliminating the non-detection zone and stands robust against the false operation.
Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network

Automatic fall detection using radar aids in better assisted living and smarter health care. In this brief, a novel time series-based method for detecting fall incidents in human daily activities is proposed. A time series in the slow-time is obtained by summing all the range bins corresponding to fast-time of the ultra wideband radar return signals. This time series is used as input to the proposed deep convolutional neural network for automatic feature extraction. In contrast to other existing methods, the proposed fall detection method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep convolutional neural network for automating feature extraction as well as global maximum pooling technique for enhancing model discriminability. The performance of the proposed method is compared with that of the state-of-the-art, such as recurrent neural network, multi-layer perceptron, and dynamic time warping techniques. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of higher accuracy, precision, sensitivity, and specificity values.