Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematics Analysis of Generalized SRU Manipulator Based on Screw Theory

Generalized SRU manipulator is commonly utilized in many robots as a functional module. By using POE formula based on screw theory, the kinematic analysis of generalized SRU manipulator was performed, and the verification paradigm was given. Firstly, the forward kinematics of the manipulator was analyzed by using POE formula, and the terminal pose corresponding to the given joint angles was derived. Secondly, the inverse kinematics of the manipulator was analyzed by combining three known P-K sub-problems and a novel sub-problem of 3 axes orthogonal to a point, and 8 groups of possible inverse solutions corresponding to the given terminal pose were derived. Finally, a humanoid arm with the configuration of generalized SRU was taken as the paradigm to validate the correctness of the analytical formula. The results showed that correct, complete and high precision solutions were obtained according to the formula given in this article. The conclusions provided a significant reference to the trajectory planning and real-time control of generalized SRU manipulator.
Back-Channel-Etched Thin-Film Transistors With Tunable Acid-Resistant Zr-Doped Indium Oxide Active Layer

In this paper, a tunable acid-resistant Zr-doped indium oxide (ZrInO) semiconductor material was developed. Detailed studies showed that the acid resistance of ZrInO thin films is tunable and increases with the increase in annealing temperature. Taking advantage of this special property, we successfully fabricated back-channel-etched (BCE) thin-film transistors (TFTs) based on the tunable acid-resistant ZrInO thin film. ZrInO-TFTs with BCE structure exhibited excellent electrical performance with a saturation mobility of 21.4 cm<inline-formula> <tex-math notation="LaTeX">$^{\textsf {2}}\textsf {V}^{-\textsf {1}}\textsf {s}^{-\textsf {1}}$ </tex-math></inline-formula>, a subthreshold swing of 0.28 V/decade, and an ON/OFF current ratio of <inline-formula> <tex-math notation="LaTeX">$1.0\times107$ </tex-math></inline-formula>. These results envision that the developed ZrInO semiconductor with tunable acid resistance has a good prospect for the channel layer of BCE-TFTs.
Performance of Service Function Chaining on the OpenStack Cloud Platform

Service Function Chaining (SFC) is considered as one of the most promising solutions to deploy future end-to-end network services, taking advantage of emerging network technologies such as Software Defined Networking and Network Function Virtualization that foster advanced communication infrastructure programmability. In this paper, we present an experimental study about the OpenStack SFC extension, which enables the well-known cloud platform as an open-source software solution for implementing SFC. We verify the correctness of the traffic steering operations put in place by the SFC extension and measure the performance in terms of network throughput and response time of the related REST API, considering an increasing length of the service chain. Reported results demonstrate that deploying a service chain takes a reasonable amount of time of a few seconds, whereas the throughput performance at the data plane can be heavily affected by packet processing overhead.
Robust Machine Learning Based Acoustic Classification of a Material Transport Process

This paper discusses the performance of machine learning classification algorithms based on psychoacoustic features for the monitoring of a material transport process. Reliable and robust classification strongly depends on the proper choice of the feature vector. The method of Principal Component Analysis (PCA) is applied in combination with a classification performance analysis of the individual psycho-acoustic feature types in order to select the best performing features and achieve a feature reduction. The resulting feature subsets are applied to a data set of a material transport process.
Missing Data Estimation for Traffic Volume by Searching an Optimum Closed Cut in Urban Networks

Traffic data imputation has drawn significant attention from both academia and industry because traffic data often suffer from data missing problems, caused by temporary deployment of sensors, detector malfunction, and lossy communication systems. To fully exploit the spatial-temporal correlation and road topological information in an urban traffic network, we propose an optimum closed cut (OCC)-based spatio-temporal imputation technique, which is implemented in two stages: a) employing graph theory to search the OCC in the road network, for which the traffic on roads intersected by the closed cut has the maximum correlation with that on the target road while minimizing the number of intersected roads; and b) estimating the missing data on the target road using OCC-based Kriging estimator, incorporating both the road topological information and flow conservation law to improve the estimation accuracy. Experimental results using traffic data collected on real roads indicate that the OCC search algorithm can effectively capture the optimum set of neighboring sensors. An OCC-based estimator can provide more accurate imputation results compared with nearest historical average and correlative ${k}$ -NN ( ${k}$ -nearest neighbors) methods. The road topological information and flow conservation law can be explored to further improve the estimation performance while reducing the number of sensors involved in the data imputation, hence improving the computational efficiency.
Research on the Detection Mechanism of Crack with Corrosion Using Eddy Current Pulsed Thermography

In the running process of locomotive, many tiny cracks emerge on the wheel tread due to the rolling contact between wheel and rail. On account of being exposed to complex environment, the crack flank will be corroded and filled with a third medium such as corrosion, then contact with each other under cyclic tensile or compressive stress, leading to local closure of the crack region, which affects the physical properties of the crack, showing low electrical conductivity for example. This is a new phenomenon that conventional electromagnetic detection method might not be capable of detecting such defects effectively. This paper aims to detect crack with corrosion on the basis of eddy current pulsed thermography (ECPT), establish a composite crack model to explore the performance of ECPT over low conductivity crack, and the influence of varying electromagnetic properties to ECPT will be studied as well. Crack detection principle was introduced first, and numerical simulations were conducted to detect crack with corrosion, transient and spatial temperature distribution characteristics were extracted during heating and cooling stage, experiments were designed and conducted to verify the accuracy and consistency.
REL-MOS—A Reliability-Aware MOS Transistor Model

This paper presents a new approach for compact modeling of aging and radiation effects for MOS transistors including process variation and environmental influences. This approach can be used in common design flows for analog integrated circuits. Moreover, the aging behavior of whole circuits can be analyzed without the need for abstractions or extrapolation. Only one common physically motivated transistor parameter is used in order to model radiation and degradation mechanisms.
Vibration Monitoring of On-load Tap-changer by Resonance-based Sparse Signal Decomposition

On-load Tap-changer (OLTC) is one of the significant components in power transformer. The breakdown of frequently switched OLTC may cause the cutoff of the corresponding in-service transformer. Hence, suitable indicators are needed for effectively monitoring the operation condition of OLTC. Although the vibration analysis method has been proven to be effective for the mechanical condition assessment of the OLTC, the diagnostic parameters are mostly submerged in the noises during the switch-over process of OLTC. To solve this problem, this paper presents a new methodology - resonance-based sparse signal decomposition (RSSD) - to extract the determining vibration signals’ features for the monitoring of OLTC. The vibration signal is decomposed into high-resonance components, low-resonance components, and redundant components. The obtained fault characteristics are mainly contained in the high-resonance components, which can be used for the fault detection.
Calibration of Event-based Camera and 3D LiDAR

Calibration is an important step before fusing the data of multi-sensors together. The methods which had been proposed to calibrate a regular camera and 3D LiDAR cannot be used to calibrate the event-based camera and 3D LiDAR. Because the event-based camera needs the changing light to trigger. Therefore, we proposed a novel 3D marker which consists of a blinking screen and a planar board with four circular holes. Using this 3D marker, the event-based camera will generate a stable pattern which also has a sharp difference in depth with the background, which means it can be detected easily and accurately in both 2D point set and 3D point cloud. The proposed approach is validated by using a novel calibration error and a cost function. Meanwhile, from the comparison with the manual calibration, our approach’s performance is satisfying.
Automated combination of bilateral energy contracts negotiation tactics

This paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach.
Cooperative Control of Robotic Swarm Based on Self-organized Method and Human Swarm Interaction*

Multi-platform cooperative system can achieve the whole task ability expansion through mutual ability complementation and action coordination. First, we identify the demands of multi-robot system application with the increasingly diverse complex tasks and highly dynamic environment. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates. Next, we present the control methods of swarm robotics in distributed control and decision systems. Two examples are simulated to illustrate formation obstacle avoidance and target seeking. Then, we discuss human-swarm interaction by gesture interaction with concern of the cognitive complexity. We develop a system that enables operators to control swarms effectively. Finally, we highlight remaining challenges, unanswered questions for robot swarm application, as well as how to address them in future works.
Improved Energy Efficiency of Indoor Positioning Systems by Adaptive Sampling and Smart Post-processing of Sensor Data

In this paper, a method for minimizing power consumption of indoor positioning and tracking systems is proposed. Most Dead-Reckoning (DR) systems nowadays available are based on an Inertial Measurement Unit (IMU) operating at a relatively high sampling frequency of more than 100 Hz in order to track the typical human motion with a sufficient resolution. In this work, a method is proposed which allows to reduce the sampling frequency for sensor data by a factor of more than 10. The inaccuracies and fluctuations associated with the low sampling rate are compensated by applying a correction algorithm that takes the geometry of the underlying environment as well as the previous motion direction into account. A similar concept for sampling rate reduction and post-processing will also be applied to the data of an air pressure sensor. This sensor type can be used for movement detection in vertical direction. The adjustment of the sampling frequency is done adaptively based on the movement of the person subject to track. It will be shown that by using the proposed methods the accuracy of position tracking is comparable to that of an Indoor Positioning System (IPS) operating at 200 Hz whereas the data rate and therewith the power consumption is reduced by a factor of more than 10. This is verified by measurements using an experimental setup based on low-cost Bluetooth Low Energy (BLE) modules.
Partial Grid Forming Concept for 100% Inverter-Based Transmission Systems

With the current trends in renewable energy integration, the concept of a 100% inverter-based power system is becoming more of a reality. However, the existing Voltage Source Converter (VSC) control schemes for such systems focus mostly on the operation of low-voltage microgrids, which have different requirements from the transmission system perspective. This paper proposes a new classification of VSC control strategies depending on their mode of operation. Then, the concept of partial grid forming VSC is introduced and it is shown that a system with zero rotational inertia can operate without a dedicated grid-forming VSC unit, but rather with partial forming of key system characteristics distributed across different VSC units. The performance of this approach is tested on detailed VSC models developed in both MATLAB Simulink and virtual Hardware-In-the-Loop (vHIL) platforms. Furthermore, an investigation towards necessary converter and network criteria for providing a stable system under the proposed control concepts is presented.
Drain Conductance Oscillations in Poly-Si Junctionless Nanowire Thin-Film Transistors

The drain conductance oscillations are simultaneously observed in the trigated and double-gated poly-Si junctionless (JL) nanowire thin-film transistors (TFTs). The strongest oscillations appear at <inline-formula> <tex-math notation="LaTeX">${V} _{\text {GS}}$ </tex-math></inline-formula> of 1 V and <inline-formula> <tex-math notation="LaTeX">$V_{\text {DS}}$ </tex-math></inline-formula> of 4–5 V/6–7 V in 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>/5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> poly-Si JL nanowire TFTs. To explain the drain conductance oscillations, in addition to the separated sub-bands and Coulomb blockade effect, the space-charge-potential barrier/grain boundary (GB) potential barrier in the channel and the kink effect in the output characteristics should be considered. The space-charge-potential barrier is affected by the unstable trapped electrons at GB and leads to the drain conductance oscillations. Due to the kink effect, an anomalous increase of the current in the output characteristics can enhance the conductance oscillations. Moreover, the channel of the double-gated device shows higher degree of crystallinity by solid-phase crystallization. Compared with the trigated device without solid-phase crystallization, less GB potential barriers in the channel of the double-gated device lead to a higher mobility in the channel, and these electrons can easily destroy the stability of the trapped electrons at the GBs, resulting in the stronger drain conductance oscillations. Therefore, the physical mechanisms are proposed to understand the drain conductance oscillations well in poly-Si JL nanowire TFTs.
Influence of Walking Speed and Smartphone Position on Gait Recognition

Nowadays, biometric recognition can be seen in almost every smartphone. Biometric continuous authentication, such as gait, or the manner of walking, could add to these systems as an ongoing recognition system. For the last decade, inertial gait recognition systems have been tested on ideal scenarios to prove viability. However, this study focuses on the variability of the sample by changing the phone position and walking speed. With this, we would like to focus on the future challenges that gait recognition will have to deal with. To test the system, a dataset with four different scenarios is evaluated. The scenarios are normal and fast walking, positioning the phone in left and right pocket for both speeds. The dataset consists of 10 different users, 7 males and 3 females, with 25 visits per scenario. The system under study has proven state-of-art accuracy in previous studies. The results obtained indicate that, independently, smartphone position is not of great influence however, when uncontrolled, error rates can quadruple. In the case of walking speed, the impact of the variability is clearer. Walking faster yields the accuracy of the system, more so when combining walking speeds. This indicates that for a robust system, it is needed to take into consideration the variability of both walking speed and smartphone position.
Service Placement for Hybrid Clouds Environments based on Realistic Network Measurements

The ever increasing sophistication in IT services demands to consider dynamic and large-scale deployments in distributed environments built on top of federated public and private Clouds. Unfortunately, in these complex hybrid Cloud environments, it is extremely difficult to estimate the impact of even simple reconfigurations of the IT service architecture before enacting them. There is the need for new service management tools that are capable of exploring alternative IT service architectures, of accurately evaluating them to find out the most convenient one, and of reconfiguring the IT service accordingly. This paper presents Business-Driven Management as a Service Plus (BDMaaS+), that significantly evolves our previous work to consider the business-driven evaluation of IT services in hybrid Cloud environment, advanced IT service models that complex multi-tier workflows, and realistic network models. The experimental evaluation of BDMaaS+ to optimize a realistic large-scale IT service demonstrates that our tool is capable of finding a more convenient configuration that significantly reduce the total daily costs while granting at the same time agreed service levels.
X-ray Screening of Hold Baggage: Are the Same Visual-Cognitive Abilities Needed for 2D and 3D Imaging?

2D multi-view X-ray imaging technology is widely used for security screening of hold baggage at airports. Newer technology is based on 3D CT imaging. Such systems offer the possibility to rotate a bag around 360 degrees. With the transition from 2D multi-view to advanced CT imaging, the question arises whether airport security officers (screeners) need the same visual-cognitive abilities when visually inspecting X-ray images of hold baggage. This study investigated the relationship between visual-cognitive abilities and visual inspection performance of screeners. Screeners conducted a computer-based visual cognitive test battery (VCTB) and a simulated hold baggage screening task with 2D and 3D imaging. We found that aspects of processing speed and visual processing correlated significantly with visual inspection performance of screeners using 2D imaging technology. In comparison, performance of screeners that visually inspected 3D images showed less correlations with the VCTB. These results indicate that with the expected change from 2D to 3D imaging technology in airport security, visual-cognitive requirements of the screeners might change. Therefore, further studies need to elucidate in more detail what visual-cognitive skills future 3D screeners need as it could affect personnel selection and development.
Deep Learning for Visual Navigation of Unmanned Ground Vehicles : A review

The capabilities that Artificial Intelligence and Computer Vision can provide to intelligent robotic systems is well recognized and as a result it is the subject of topical research in recent years. This paper will provide a broad review of the progress which has been made in applying deep learning and vision sensor data for the autonomous navigation of unmanned ground vehicles (UGVs). The current state-of-the-art techniques are compared in terms of their performance, implementation and deployment and performance. An outline of some of the most popular types of computer vision techniques is provided, as well as insights into how the recent availability of 3D vision systems can be exploited in the domain.
A Closed Normal Form Solution Under Near-Resonant Modal Interaction in Power Systems

Normal form (NF) is an effective tool to quantitatively analyze nonlinear modal interaction, which is believed to contribute to the complex nonlinear dynamics in power systems. However, in conventional NF analysis, the solution under resonance condition cannot be expressed in a closed form. Hence the NF analysis of power system higher order modal interaction was limited to nonresonant cases, or directly neglecting the resonant terms. In this paper, the NF solutions under modal resonance are derived, by means of the polynomial vector space decomposition. The obtained solution is in a simple closed form, including both the nonresonant part and the resonant part. Numerical simulations are performed to verify the effectiveness of the proposed approach. The results show that, under modal resonant conditions, neglecting the resonant terms may cause significant errors to the obtained numerical solutions, whereas including modal resonant terms in NF may increase the accuracy of the analysis. Finally, the approach proposed in this paper is compared with the modal series method, which, unlike NF method, is not limited by resonant condition. The results show that the proposed method in this paper has better performance in both accuracy and computing time.
Facet Cooling in High-Power InGaAs/AlGaAs Lasers

Several factors limit the reliable output power of a semiconductor laser under CW operation, such as carrier leakage, thermal effects, and catastrophic optical mirror damage (COMD). Ever higher operating powers may be possible if the COMD can be avoided. Despite exotic facet engineering and progress in non-absorbing mirrors, the temperature rise at the facets puts a strain on the long-term reliability of these diodes. Although thermoelectrically isolating the heat source away from the facets with non-injected windows helps lower the facet temperature, data suggests the farther the heat source is from the facets, the lower the temperature. In this letter, we show that longer non-injected sections lead to cooler windows and biasing this section to transparency eliminates the optical loss. We report on the facet temperature reduction that reaches below the bulk temperature in high power InGaAs/AlGaAs lasers under QCW operation with electrically isolated and biased windows. Acting as transparent optical interconnects, biased sections connect the active cavity to the facets. This approach can be applied to a wide range of semiconductor lasers to improve device reliability as well as enabling the monolithic integration of lasers in photonic integrated circuits.
Power Reduction in CNN Pooling Layers with a Preliminary Partial Computation Strategy

Convolutional neural networks (CNNs) are responsible for many recent successes in the computer vision field and are now the dominant approach for image classification. However, CNN-based methods perform many convolution operations and have high power consumption which makes them difficult to deploy on mobile devices. In this paper, we propose a new method to reduce CNN power consumption by simplifying computations before max-pooling layers. The proposed method estimates the output of the max-pooling layer by approximating the preceding convolutional layer with a preliminary partial computation. Then, the method performs a complementary computation to generate an exact convolution output only for the selected feature. We also present an analysis of the approximation parameters. Simulation results show that the proposed method reduces the power consumption by 21% and the silicon area by 19% with negligible degradation in classification accuracy for the CIFAR−10 dataset.