Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Design of High Step-down Non-Isolated DC/DC Converter

This paper presents a high-gain DC/DC buck-derived converter topology, utilizing two active power semiconductors. Unlike many other multi-stage high step-down DC/DC converters, this topology uses relatively a smaller number of passive LC components. Due to the duty cycle limitation and deadtime constraints, high gain factor (more than 10) is very challenging to be achieved in a single-stage regular buck DC/DC converter. In the proposed converter, the net voltage gain is a nonlinear function of duty-cycles of the power devices, which produces step-down gain of 10 to 15 without pushing the duty cycles towards their extremums. For the concept verification of the converter operation, a voltage conversion from 48V to 1V DC at 200W using the proposed converter topology is simulated in MATLAB environment. An output voltage ripple of ±1% and a conversion efficiency of 94% are achieved according to the obtained results. Dynamic performance of the converter is also studied by implementing closed loop control using various control strategies. The converter is designed considering all the relevant conditions required for a typical Point of Load (PoL) application.
Efficient Tailoring of Universal Software Components for Conserving Resources: Evaluation in Practical ROS-based System

The method of development for robot systems is shifting toward rapid and efficient construction utilizing universal software components, such as various common libraries and operating systems, with tailored implementations rather than programming from scratch. The Robot Operating System (ROS), which is becoming widely used as a robotics infrastructure, is built on the Linux operating system, which has been commonly used in PC and server environments. However, many universal software components may contain functionalities and implementation codes that are unnecessary in certain cases, and these may have negative impacts, especially on resource-constrained platforms. Unfortunately, identifying and eliminating unnecessary code appropriately is a difficult process because it requires a deep understanding of the system behavior and implementation details of the component.This study proposes a dynamic analysis method and tools to help determine the necessity of each functionality of the Linux kernel, one of the largest universal software components in a ROS-based system, on the basis of energy and memory consumption. To evaluate the practicality of the proposal, we conduct an experimental application in a practical ROS-based robot system. The results show that even without understanding the details of the whole kernel, memory consumption can be reduced by about 40% by efficiently disabling unnecessary features.
FEM-Based Gain-Scheduling Control of a Soft Trunk Robot

Soft robotics has recently become an emergent research area due to its unique characteristics compared to conventional rigid robots. Its inherent properties, such as compliance and flexibility, provide some promising characteristics for the current robotic applications, including safe human machine interaction, great adaptability to unknown environments and so on. On the contrary, its intrinsic ‘soft’ characteristic would result in some complex non-linear behaviors, causing more difficulties in deducing kinematic or dynamic models of soft robots than what we often do for rigid robots. In this letter, using Finite Element Method (FEM), we demonstrate a gain-scheduling closed-loop method to control a soft trunk robot operating within its workspace. The main idea of this method is to divide the workspace into several sub-workspaces where the most suitable gains are applied correspondingly in each sub-workspace. As a result, it becomes feasible to control the trunk by gain scheduling when crossing from one sub-workspace to another as well as considering its dynamic characteristics. The derivation of the method is presented accordingly. In the end, the proposed method is validated by experimental testing with convincing results provided afterwards.
A Novel 4-level Converter with Inherent Voltage Boosting for 4-Phase SRM

The paper proposes a new 4-level converter configuration for a 4-phase, 8/6 switched reluctance motor (SRM) drive. The proposed converter is based on double bridge topology and shares a common leg for the quadrature phases in 4-phase SRM. The common leg allows the discharging quadrature-phase energy to charge the corresponding capacitor in the bridge. This, during the energization of the corresponding quadrature-phase, provides a maximum boost in voltage. Thus, an inherent voltage boosting is achieved. Analysis of different operating modes of the proposed converter is discussed. The proposed converter's effectiveness is verified through the simulation studies in the MATLAB/Simulink environment on a prototype 4-phase 8/6 SRM, designed in ANSYS/Maxwell. The proposed converter configuration uses the same number of switches and diodes as in a conventional three-level asymmetric half-bridge converter (TL-AHB). However, due to inherent boosting capability in the proposed converter configuration, the constant torque region, and the smooth torque output region are extended by 25%, compared with TL-AHB.
Influence Maximization in Signed Networks by Enhancing the Negative Influence

With the advancement of science and technology, research on influence maximization in networks has become a hot spot. In social networks, there is not only a positive relationship among nodes but also a negative impact, and the negative impact often plays a greater role than the positive impact, as observed for shopping websites or online votes. This paper proposes a method based on an independent cascade model by emphasizing the negative impact in symbolic networks to solve the problem of influence maximization. First, an algorithm that is based on an independent path and that emphasizes negative influence is designed to obtain the probability among nodes. Based on the activation probability, an algorithm is proposed to identify nodes that could have the greatest impact on the influence increment from the seeds. Finally, the seed set is confirmed based on the influence in the corresponding symbol network. In the experiment performed on real-world network data, the result indicate that the proposed algorithm causes more substantial influence propagation than do other algorithms.
Robotic Knee Exoskeleton Prototype to Assist Patients in Gait Rehabilitation

This paper presents the design and development of a low cost robotic knee exoskeleton with mobile interface for active assistance of gait rehabilitation of patients who suffer lower limb impairment. Interaction based on electromyography (EMG) is used for detecting motion intention to recognize muscular activity patterns by applying artificial neural network (ANN) algorithms. A comparison of muscular activity between the rectus femoris of each lower limb is made in order to find which offers better results. Once the system identifies a motion intention, it generates a predefined trajectory that mimics the gait cycle pattern of the knee joint. The actuator of the exoskeleton is required to accomplish this movement based on a position control strategy. The exoskeleton’s operation is supervised remotely through a mobile device, which is connected to a database that contains three rehabilitation routines previously set by medical staff. The robotic knee prototype is validated by monitoring its performance while being used, initially by healthy subjects.
CMP Process Optimization Engineering by Machine Learning

Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control to achieve better within wafer/within chip planarization performance, but also have capability to cover various topologies and layout densities patterned wafer and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density incoming pattern wafers to resolve the most challenging process issues at next generation.
ISDB-TB Single Frequency Network Transmitter Identification Proposal

A transmitter identification (TxID) signal into the Integrated Services Digital Broadcasting version B (ISDBT-B) Digital Terrestrial Television Broadcasting (DTTB) system is proposed in this paper. The TxID is a watermarking technique that attaches a Pseudo Random Noise (PN) sequence with excellent correlation properties to a Digital TV (DTV) signal before transmission. It is a helpful tool for troubleshooting issues related to a specific transmitter of interest. For this purpose, Gold Sequences (GS) are used as TxID. The TxID is periodically inserted in the first Orthogonal Frequency Division Multiplexing (OFDM) payload symbol, and it is detected by the correlation between the received signal and the transmitted TxID. This technique is mainly used in Single Frequency Network (SFN) for configuration time and power synchronization assistance, avoiding unwanted interference.
Study of Forsmark-Type Voltage Transient on Power UPS in Nuclear Power Plants

The uninterrupted power supply (UPS) system used in Indian Nuclear Power Plants (NPP) provides 415V three-phase four-wire supply to most critical loads. These loads are required for safe reactor operation, reactor shutdown as well as during accident conditions. Hence these UPS systems have very stringent qualification requirements. The newly proposed transient test as a part of qualification given in IEEE-650 is studied in this paper. This is based on the transient incident reported at the NPP of Forsmark, Sweden. The analysis presented here brings out the possible difficulties in carrying out this transient test. It also suggests possible design modifications that need to be addressed during the design of a power UPS.
High Voltage Flyback Converter for Pulsed Loaded Applications

High voltage pulses of the output power ranging from 5 W to 200 W are employed in food processing systems, water treatment process, electroporation, industrial and medical applications etc. The power converter configuration implemented to generate high voltage pulses is selected depending on the peak power rating requirement. In this work, a flyback converter configuration is selected to charge the energy storage capacitor meeting the desired voltage magnitude and charging time constraint. The energy storage capacitor is charged following peak current mode control with boundary conduction mode. In this proposed work, high voltage rectangular pulses of 1000 V magnitude with the desired pulse repetitive rate and pulse width is designed for various case studies and validated through corresponding simulation results.
On the Evaluation and Deployment of Machine Learning Approaches for Intrusion Detection

Machine learning for intrusion detection is an active research field. However, instances of real-world application of methods proposed in the literature are still rare. Although a plethora of papers applying machine learning methods to benchmark data sets report excellent results, these methods seem to be hard to deploy in practice.In this paper, we investigate this gap between research and practical application by focusing on two questions: Firstly, we ask whether the current evaluation methodology is able to adequately forecast the performance of machine learning methods in practice. Secondly, we ask what needs to be done to facilitate the deployment of these methods.As a consequence to our findings, we formulate requirements for future evaluation methodologies and data sets, aiming to help evaluations better reflect actual performance in the field. Additionally, we identify a research road map with respect to the application of machine learning models in network intrusion detection systems, in order to further close the gap.
Improving Accuracy of Low-Cost Vehicle Attitude Estimation by Denoising MIMU Based on SSA and ICA

In this article, we develop a novel method to improve the accuracy of the low-cost vehicle attitude estimation using singular spectrum analysis (SSA) and independent component analysis (ICA). The microelectromechanical system (MEMS) accelerometers and the MEMS gyros are fused by the Kalman filtering algorithm to provide a low-cost attitude estimation. The low-frequency vibration noise and the maneuvering acceleration disturbance in the measurements of the MEMS accelerometers introduce large errors to the attitude estimation. To mitigate the low-frequency vibration noise, we propose a method based on SSA to separate the quasi-periodic components in accelerometer measurements and keep the trend remained as the true attitude measurement. SSA is effective in decomposing original signal into trend, periodic or quasi-periodic components, and thus, the low-frequency vibration noise that behaves as a quasi-periodic signal can be well separated. However, the acceleration disturbance still exists in the trend. Thus, a method based on ICA is then proposed to remove the acceleration disturbance by the aid of the turning rate measurement. The proposed method is verified with driving tests under city road and off-road conditions, showing a significant improvement of the attitude accuracy.
A Lightweight Soft Gripper Driven by Self-Sensing Super-Coiled Polymer Actuator

This study proposes a soft gripper driven by a simple, lightweight, low-cost, self-sensing super-coiled polymer (SCP) actuator with a high power-to-weight ratio. SCP generates an untwisting motion when heated, and therefore, is suitable for designing the actuator. The actuator is fabricated with a multifilament sewing thread woven by the self-coiling method and yields a high output force. To enhance the accuracy of the actuator in measuring higher temperatures, we propose an improved temperature self-sensing method that uses a dedicated metal wire wound around the SCP. To provide a theoretical basis for the control of the actuator, we construct through experiments a thermoelectric model and a thermomechanical model for the SCP. The resulting kinematics of the soft gripper are analyzed, and a nested control system is established to realize a precisely control of the soft gripper. Finally, we build a prototype to test the grasping ability of the soft gripper.
Infrared studies of apatites. I. Vibrational assignments for calcium, strontium, and barium hydroxyapatites utilizing isotopic substitution

The infrared spectra of powdered calcium hydroxyapatite isotopically substituted with D, /sup 18/O, /sup 44/Ca, and /sup 48/Ca and infrared spectra of powdered strontium and barium hydroxyapatites and their deuterated analogs are reported at 48 and - 185 deg in the 4000-200cm-/sup 1/ region. Band assignments, based on isotopic frequency shifts, band intensity, band temperature dependency, and comparisons between corresponding bands of these structurally related apatites are presented for the above and other apatites. (auth)
Exploring the Tricks for Road Damage Detection with A One-Stage Detector

Fast and accurate road damage detection is essential for the automatization of road inspection. This paper describes our solution submitted to the Global Road Damage Detection Challenge of the 2020 IEEE International Conference on Big Data, for typical road damage detection in digital images based on deep learning. The recently proposed YOLOv4 is chosen as the baseline network, while the effects of data augmentation, transfer learning, Optimized Anchors, and their combination are evaluated. We propose a novel road damage data generation method based on a generative adversarial network, which can generate multi-class samples with a single model. The evaluation results demonstrate the effectiveness of different tricks and their combinations on the road damage detection task, which provides a reference for practical application. The code of our solution is available at https://github.com/ZhangXG001/RoadDamgeDetection.git.
An unsupervised auditory scene analysis system using incremental low-dimensional embedding

This paper addresses the low-dimensional embedding of sounds towards unsupervised auditory scene analysis. Summarizing long-time recordings by mapping them into lowdimensional space is an essential task in long-time environmental monitoring. In this paper, we propose a novel low-dimensional embedding system using an incremental embedding algorithm. To analyze long-time recordings, we design an incremental system consisting of recording, feature extraction, low-dimensional embedding, and visualization. Recently, many low-dimensional embedding methods for acoustic scenes have been studied; however, applicability of these methods to the long-time recording is not adequately evaluated. Thus, this paper describes the construction of the scene analysis system and evaluates the performance of this system. In this paper, we especially focus on two important viewpoints in long-time monitoring: incremental methods and effects of noisy data. To realize an incremental system, we use Self-Organizing Nebulous Growths (SONG), which can incrementally construct a low-dimensional embedding space. Also, in our experiments, we apply our system to bird song analysis under noise conditions. By the preliminary experiments using benchmark datasets, we discover noise sensitivity of our system and applicability to environmental monitoring.
Differential Power-processing with Built-in Energy Storage for Multi-String Grid Interactive PV System

This paper introduces a novel configuration of a highly efficient self-sustained grid interactive photo voltaic (PV) system based on multi-string architecture. The goal is to achieve improved efficiency while performing Maximum Power Point Tracking (MPPT) operation during both uniform and nonuniform shading conditions. Differential Power-processing Converter (DPC) concept is adopted here for the dc-dc stage, targeting an improved Power Transfer Efficiency (PTE) for the system. The energy storage is connected at a common virtual bus instead of dc-link of the inverter, which eliminates the need of extra dc-dc converter for energy storage integration. A novel control algorithm is proposed for an effective power and energy management between generation, load, storage and grid all together, minimizing the grid interaction as much as possible to make the system self-sustainable and cost effective. Last but not the least, this paper also proposes a novel seamless Mode Transition Algorithm (MTA), which will ensure that the DPCs are completely bypassed during uniform insolation condition to improve all-day efficiency of the system. The proposed algorithms and associated control methods are implemented in MATLAB/Simulink environment for a given set of generation and load profile to verify the concept.
Exploring the Impact of Resampling Methods for Malware Detection

Malware detection is a well-known problem with severe consequences in terms of damages and financial losses. The typical imbalance of this domain causes serious problems to the learning algorithms that are not able to focus correctly in the scarce malware cases. Although resampling techniques have shown to be effective, their impact has not yet been studied for the particular domain of malware detection.This paper focus on the evaluation of resampling methods to tackle the malware detection problem under a realistic assumption of malware rarity. Several machine learning-based solutions proposed for this domain use non-public data sets and consider different malware prevalence in the data. This makes difficult the comparison of solutions. We use the freely available NSL-KDD data set which has a rarity associated with the malware cases that resembles a more realistic scenario. Our main goal is to assess the potential advantages of applying resampling techniques in this setting as well as their impact when using different standard learning algorithms. We also carry out an extensive analysis of resampling techniques using different parameters which allows to test the best configuration of these techniques. We explore not only a balanced class scenario but also other class configurations that may be more beneficial to improve the predictive performance of the algorithms.We present a systematic study of the effectiveness of existing resampling approaches for tackling the imbalance problem in malware domains. We show that resampling techniques present an advantage and explore their impact in the predictive accuracy of each class. Finally, we also show that different resampling strategies have a different impact in the importance of the features used by the learning algorithms.
Space Vector Modulation Technique with Improved Harmonic Performance for a Five Phase Induction Motor Drive in the Over-Modulation Region

This paper proposes a new space vector based pulse width modulation (PWM) technique to operate a five phase induction machine fed by two level inverter in the overmodulation region with significantly low harmonic distortion and switching loss. The proposed technique is simulated in MATLAB - SIMULINK and the results are compared with the existing PWM techniques. A comparative study of the performance of the five phase drive using stator flux ripple concept with the proposed PWM technique is presented in the over-modulation region. The simulation results are experimentally validated using TMS320F28379D micro-controller platform with a custom made five phase drive system. It is found that in the over-modulation region, the proposed PWM technique results in considerably low RMS current ripple and switching power loss as compared to those corresponding to the conventional PWM techniques.
Inductances Computation Using Finite Element Analysis for PMSM Sensorless Control

This paper investigates the nonlinear inductance model of a salient Permanent Magnet Synchronous Machine. In particular, the computation of the phase inductances as function of the rotor position under different load conditions is performed through Finite Element Analysis so to enable the numerical simulation of sensorless rotor position detection algorithms. Also, a sensitivity analysis is carried out in order to evaluate the effects of geometric variation on the inductances and on the accuracy of the estimated position.
Assessment of Data Augmentation Techniques for Firearm Detection in Surveillance Videos

In this paper, we propose a Faster R-CNN based model for detection of firearms in surveillance and Closed-Circuit Television (CCTV) video. Due to the lack of a publicly available benchmark surveillance video database containing firearms, we created our own firearm database composed of only real surveillance footage from scenes with people holding firearms. Portable firearms such as rifles and pistols, often referred to as small arms, have a high degree of variation in surveillance footage in terms of shape, illumination, scale, pose, and occlusion. To deploy a fast and accurate firearm detection system for real world scenarios, it is important to detect all types of common small arms. To accomplish this, we collected images for our database that contain both handguns (pistols and revolvers) and long guns (rifles and shotguns) to capture as much variation of commonly used small arms as possible. To train our firearm detector we first, assess 18 common geometric and photometric data augmentation techniques in order to identify which ones improve detection performance. Next, we identify deep learning based small arm detection techniques that improve detection performance. We reuse these techniques in another more comprehensive augmentation assessment in order to identify the most efficient combination in terms of detection performance. Finally, we optimize the hyper-parameters of all the tested firearm detection models and apply cross-validation. Our proposed model can accurately detect firearms in video frames taken from real surveillance footage close to real-time, yielding precision and recall scores of 93.9% and 96.4% for handguns, and 95.2% and 94.6% for long guns.1