Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Anti-sway Control for Multi-crane Cooperation

In this work, we have studied the distributed anti-sway control for the multi-crane cooperation. We have introduced our distributed material handling platform and derived dynamics of the multi-crane lifting and moving with a common load. To eliminate the sway motion of the common load, we have proposed a novel anti-sway control logic in the fully distributed manner. The control law has been drawn from the asymptotic consensus protocol and validated by numerical simulations. The simulation results show that the proposed distributed control law reduces the load sway motion at the destination and its performance comes close to the centralized control.
Periodic Time Series Data Analysis by Deep Learning Methodology

The detection of periodicity in a time series is considered a challenge in many research areas. The difficulty of period length extraction involves the varying noise levels among working environments. A system that performs well in one environment may not be accurate in another. Different methods, including deep neural networks, have been proposed across many applications to find suitable solutions to the period length extraction problem. This article proposes a convolutional neural network (CNN) based period classification algorithm, named PCA, to detect the dataset periods. In particular, assuming that a data stream contains periodical features, the PCA utilizes historical labeled data as training material and classifies new instances accordingly based on their periods. Its performance has been tested on both synthetic and real-world periodic time series data (PTSD) with very encouraging results. In particular, We have observed that the PCA is capable of achieving 100% accuracy in the case of low noise PTSD. Even the training of the PCA is not economical if the data do not contain much noise, it still demonstrates high performance on both synthetic and real-world datasets. Besides, we have shown that our new algorithm can capture the relationship between the shape of the waves and the target period, which is significantly different from the classical methods that mainly focus on the wave’s amplitude.
OMSAC - Ontology of Microservices Architecture Concepts

In recent years we have seen an increased interest in Microservices Architecture (MSA), both by industry and academia. It unveiled open challenges and issues in understanding, adoption and implementation of core principles of this recent architectural style. Different approaches have been proposed in literature to organize knowledge about Microservices, as taxonomies and classifications. However, the immaturity of MSA has not been a concern when modelling its knowledge. In this paper, we are proposing an evolutive representation of Microservices Architecture knowledge based on an ontological approach and able to address its immaturity issue.
Reliability Analysis for Commercial SiC MOSFETs in Single Pulse and Repetitive Surge Current Test

SiC MOSFET Body diodes are expected to compete against SiC schottky barrier diodes (SBD) in high power applications. To investigate the surge capability of the body diodes in commercial SiC MOSFETs, single pulse and repetitive surge current tests are processed on the DUTs. In single pulse surge current test, the DUTs are stressed to failure. Compared with the body diodes in SiC trench-gate MOSFETs, the body diodes in SiC planar-gate MOSFETs tend to show better surge capability at all rated current levels. On the other hand, in repetitive surge current test, the degradation of electrical parameters is observed after hundreds of pulse cycles. The degradation mechanism of bipolar degradation and oxide layer degradation has been analyzed.
Automatic Anchor Caliburation for UWB-based Indoor Positioning Systems

In order to navigate Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments localized indoor positioning systems is essential. One of the most common methods for such positioning systems uses high-bandwidth radio frequency called Ultra-wideband (UWB). The UWB radio has several characteristics suited for indoor communication such as low susceptibility to multipath and high immunity against wireless network interference. Although UWB-based positioning is widely used, most of the existing solutions require accurate coordinates of UWB nodes to be known in advance. This put limitation on deployment of positioning systems. In this study, we designed a method to automatically calibrate UWB nodes positions in order to make deployment of indoor positioning systems easier. The proposed method for auto-calibration are tested in real indoor environments to validate effectiveness of the strategy. The effect of node arrangement on accuracy of position estimation was examined as well. As the result, we could improve performance of auto-calibration by changing geometry of UWB nodes.
Comparative Evaluation of Assessment Activities in an Introductory Occupational Safety and Health Course

This paper emerged during the process of continuous quality improvement of an introductory course on occupational safety and health. Did the students correctly associate an assessment activity to the right course outcome? The paper compared the evaluation of the students and the facilitator on the different activities used to assess the demonstration of abilities/outcomes attributed with the course. There were two activities per quarter of the semester. Each activity had been scheduled to demonstrate specific outcome indicators by the facilitator and is described in detail. An evaluation questionnaire was provided at the end of the semester to ascertain whether the students assessed the same outcome indicator as what course the set for each activity. The contention was to harmonize what outcomes the students believed they are exhibiting and what outcomes the course had intended for such activity. The gathered data from the evaluation questionnaire were tabulated and then compared with the original intended outcomes by the facilitator. Interpretation of the data was also forwarded and the path for continuous quality improvement was drawn.
Development of ultrasmall chitosan/succinyl β‐cyclodextrin nanoparticles as a sustained protein‐delivery system

In this article, we introduce a new method for preparing ultrasmall chitosan (CS)/succinyl β-cyclodextrin (SCD) nanoparticles (NPs) intended for loading bovine serum albumin (BSA) as a model protein. The proposed method is based on the complex coacervation technique followed by ionotropic gelation with tripolyphosphate. SCD, an anionic derivative of cyclodextrin, was synthesized and used in CS-based NPs to enhance the entrapment efficiency of BSA. The results show that with this approach, ultrasmall, compact, and neutralized NPs with a mean particle size near 30 nm were obtained. A high degree of protein entrapment in the NPs led to a significant improvement in the BSA release profile with a low initial burst release (ca. 3% w/v of the initially loaded BSA) and a sustained release over time. This enabled a suitable nanocarrier for long-term protein delivery (30% release over 120 h). © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 39648.
Reliability of Flexible LTPS TFTs under Dynamic Mechanical Stress

Reliability of p-type flexible low-temperature poly-Si (LTPS) thin-film transistors (TFTs) under dynamic mechanical stresses including dynamic stretch and bending is investigated. Under dynamic stretch, a positive $V_{\mathrm{G}}$ shift in the transfer curve is observed. Two types of crack induced failure model are proposed. Under dynamic bending, a similar positive $V_{G}$ shift of transfer curves is observed. Approximately the degradation follows a liner trend with the number of bending cycles and curvature. Moreover, an abnormal spontaneous degradation phenomenon after dynamic bending is observed. A model of “electrons transport and capture” with assist of O2 ambient is proposed to explain the spontaneous degradation.
Classification Method with CNN features and SVM for Computer-Aided Diagnosis System in Colorectal Magnified NBI Endoscopy

This paper presents a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. For the classification of a histologic findings, we consider an output result of a lesion endoscopic image from a pre-learned Convolutional Neural Network (CNN) as a feature vector and construct a set of Support Vector Machines (SVMs) by learning a set of the CNN feature vectors. In the video images, each frame has appearance changes such as blur, color shift, reflection of light and so on and it affects classification results. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image. We evaluated the proposed method on a customizable embedded DSP core implemented into a FPGA based prototyping system.
Multi-Scale Multi-Band Dilated DenseLSTM for Robust Recognition of Speech with Background Music

We propose a multi-scale multi-band dilated time- frequency DenseNet with LSTM for speech enhancement and speech recognition. In the convolutional neural network (CNN)- based architecture, it is important to increase the receptive field effectively in order to sufficiently consider the context information. In our previous study, we designed a dilated dense block that reflects acoustic characteristics by applying dilated convolutions to a densely connected convolutional network (DenseNet) in order to effectively increase the receptive field. In this study, for speech enhancement, we apply the dilated dense blocks to MMDenseLSTM based on a convolutional recurrent neural network (CRNN) which has shown good performance in recent studies using a deep learning architecture. We conduct a speech enhancement and speech recognition experiment using the proposed architecture and several existing deep learning architectures: Gated residual network (GRN), MMDenseLSTM, DilDenseNet. Overall, the proposed architecture shows the best performance compared to other deep learning architectures.
Review of Analog-Assisted-Digital and Digital-Assisted-Analog Low Dropout Regulators

The low dropout regulator (LDO) is a key component in power management, providing an accurate and stable output voltage against line/load variations and power supply ripple. From a literature review, we found that conventional analog and digital LDOs still exhibit difficulties to achieve good transient response or power supply rejection under certain scenarios. Therefore, it is straightforward to combine them as a hybrid LDO, enjoying the benefits of both topologies. Then, we can classify the hybrid LDO as analog-assisted-digital (AAD) and digital-assisted-analog (DAA). This brief reviews comparatively previous works on AAD and DAA LDOs, summarizes their pros and cons, and discusses possible application scenarios.
Improved Viseme Recognition using Generative Adversarial Networks

The proliferation of convolutional neural networks (CNN) has resulted in increased interest in the field of visual speech recognition (VSR). However, while VSR for word-level and sentence-level classification has received much of this attention, recognition of visemes has remained relatively unexplored. This paper focuses on the visemic approach for VSR as it can be used to build language-independent models. Our method employs generative adversarial networks (GANs) to create synthetic images that are used for data augmentation. VGG16 is used for classification both before and after augmentation. The results obtained prove that data augmentation using GANs is a viable technique for improving the performance of VSR models. Augmenting the dataset with images generated using the Progressive Growing Generative Adversarial Network (PGGAN) model led to an average increase in test accuracy of 3.695% across speakers. An average increase in test accuracy of 2.59% was achieved by augmenting the dataset using images generated by the conditional Deep Convolutional Generative Adversarial Network (DCGAN) model.
Deep Spiking Binary Neural Network for Digital Neuromorphic Hardware

The spiking neural network (SNN) converted from artificial neural network (ANN) usually contains many high-precision parameters. This will cause a lot of hardware resources to be consumed. A spiking binary neural network is proposed in this paper, whose weights are binary and it does not contain high-precision parameters. Experimental results show that the proposed network can be adapted to the neuromorphic chip and reduce memory consumption by 16 times without much loss of accuracy.
Rchecker: A CBMC-based Data Race Detector for Interrupt-driven Programs

Interrupt-driven programs are widely used in aerospace, medical equipment, and other embedded systems that require extreme safety and stability. However, uncertain interrupt interleaving executions may cause serious data race problems. Static analysis is an important technology to detect data race problems. Existing methods are either too conservative to have low accuracy, or bring lots of false alarms, there still need a more effective solution. The program verification tool CBMC has an excellent performance in the analysis and verification of C multi-threaded modeling, but it doesn't support interrupt-driven programs. In this paper, we proposed a method based on CBMC to detect data race in interrupt-driven programs. Our method achieved accurate analysis of interrupt-driven programs by performing analysis toward interrupt preemption and synchronization semantics, and further validation of the program can be supported. Experiments results on related benchmarks demonstrate our approach's usability and effectiveness.
VMCS: Elaborating APF-Based Swarm Intelligence for Mission-Oriented Multi-UV Control

This paper addresses a novel approach for multi-agent control systems including Unmanned Vehicles (UV). As UV technology advances, one or a group of UVs can be used in a wide range of industrial or military applications. Centralized monitoring has limitations on various resources (e.g. communication bandwidth, propagation delay, and computational power), but can lead to an optimal solution to the movement of the swarm. The artificial potential field (APF) method is well-known for modeling decentralized behavior, but most APF research only focuses on naive actions such as collision avoidance, flocking, or path planning. In our proposed design Versatile Multi-Vehicle Control System (VMCS), we defined high-level conditions as APF and let UVs perform swarm intelligence in various mission environments. Furthermore, we devised a novel algorithm that controls the UVs’ APF topology which can significantly enhance the mission efficiency. We simulated the VMCS in 3D space and showed our scheme can control the dynamic mission scenarios for multi-UV systems.
The evaluation of fitting functions for the representation of an O( 3P)+H2 potential energy surface. I

The DIM surface of Whitlock, Muckerman, and Fisher for the O(3P)+H2 system is used as a test case to evaluate the usefulness of a variety of fitting functions for the representation of potential energy surfaces. Fitting functions based on LEPS, BEBO, and rotated Morse oscillator (RMO) forms are examined. Fitting procedures are developed for combining information about a small portion of the surface and the fitting function to predict where on the surface more information must be obtained to improve the accuracy of the fit. Both unbiased procedures and procedures heavily biased toward the saddle point region of the surface are investigated. Collinear quasiclassical trajectory calculations of the reaction rate constant and one and three dimensional transition state theory rate constant calculations are performed and compared for selected fits and the exact DIM test surface. Fitting functions based on BEBO and RMO forms are found to give quite accurate results.
A Space-Air-Ground Integrated Network Assisted Maritime Communication Network Based on Mobile Edge Computing

In recent years, with the rapid development of maritime activities, the demand for high-speed, reliable, low-latency, and full-coverage marine communication network (MCN) has become increasingly urgent. At present, maritime communication services are mainly provided by satellite networks, but suffers from many limitations, such as surge of data volume, complex communication environment, uneven distribution of traffic and user density and different requirements for maritime services. In order to solve these problems, mobile edge computing (MEC), space-air-ground-sea integrated network (SAGSIN), and blockchain are considered to be promising technologies for MCN enhancement. In this paper, the challenges faced by MCN are discussed, and the above three technologies are applied to solve these challenges. Finally, a space-air-ground integrated network (SAGIN) assisted MCN architecture based on edge computing is proposed, and the future research directions are also put forward.
A Scenario for a Multi-UAV Mapping and Surveillance System in Emergency Response Applications

This work conceptualizes a system comprised of a swarm of cooperating Unmanned Aerial Vehicles (UAVs) and a mobile base station. The system is intended to support rescue units at major incidents, such as fires in industrial complexes. It shall autonomously map and then survey the mission site, aiding but not replacing the rescue units in navigation and detection tasks. After a short situation analysis of UAV use at the Dortmund Fire Department, the system concept is based on a requirements workshop conducted with domain professionals. It entails essential hard- and software components of the system, various roles for the human operators, and typical mission procedures. The paper closes with a brief venture into the state of the art of several relevant technologies.
A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame

Simultaneous localization and mapping is an indispensable yet challenging direction for mobile robots. Attracted by 3D LiDAR with accurate depth information and robustness to illumination variations, many 3D LiDAR SLAM methods based on scan-to-map matching have been developed. However, there is a critical issue of existing approaches, where a large and dense map is generally required to achieve satisfactory localization accuracy, leading to low efficiency of scan-to-map matching. To solve this problem, in this letter, we propose a novel 3D LiDAR SLAM based on directed geometry point (DGP) and sparse frame. The former is used to provide a sparse distribution of points in the spatial dimension and the latter gives rise to a sparse distribution of frames in the temporal sequence. The sparsity of points and frames impove the efficiency of 3D LiDAR SLAM, and the strict data association based on directed geometric points also brings in good accuracy of pose estimation. To compensate the accuracy loss of the localization and mapping caused by frame sparsity, point propagation is proposed to improve the quality of directed geometric points in the map and the accuracy of scan-to-map matching. Also, loop detection and pose graph optimization are conducted for global consistency. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency.
Comparison of Various UEP Techniques for IRNSS Message Structure

This paper analyzes the message structure of Indian Regional Navigation Satellite System (IRNSS), which is independently constructed and operated in India. Based on this, we propose an IRNSS coded message structure that applies the Unequal Error Protection (UEP) scheme to be more error-free for specific data. We classify UEP techniques in a separated method and an integrated method depending on whether that specific data is processed in conjunction with other data or not. And we propose a separated UEP method and an integrated UEP method for IRNSS.
Policy Transfer from Simulation to Real World for Autonomous Control of an Omni Wheel Robot

We aim to develop an autonomous mobile robot which supports workers in warehouse to reduce their burden. The robot acquire state-action policy to avoid obstacles and reach a destination by reinforcement learning using LiDAR sensor. In case of real-world application of reinforcement learning, the policy learned previously under simulation environment are generally diverted to real robots because of uncertainties that is unexpected under simulation environment, for example, friction, sensor noise and so on. In this paper, we proposed a method to refine action control of an omni wheel robot by transfer learning on real environment to deal with this problem. We conduct the experiment of searching the route for reaching a goal on real environment using transfer learning’s results and verify the effectiveness of the policy acquired.
Degradation Reliability Evaluation of Mechanical Product Considering Individual Differences

In the process of evaluating the overall reliability of products, ignoring the impact of individual differences will lead to inaccurate reliability evaluation results. Based on the reliability modeling in Gamma process, the author introduced individual difference as a random factor into the overall reliability evaluation process of the product, and studied the realtime reliability evaluation of the individual product, and finally verified it with gear degradation data. It is concluded that, firstly, when evaluating the overall reliability, the reliability model considering individual differences is more accurate; Secondly. when evaluating the individual reliability, the overall prior information can accurately improve the accuracy of the individual reliability assessment; Thirdly, the longer the monitoring time of degradation data, the more accurate the reliability assessment results will be. It effectively improves the accuracy of overall and individual reliability assessment of mechanical products and provides guidance for monitoring degradation data.
Design and Development of 3D WLCSP for CMOS Image Sensor Using Vertical via Technology

Wafer-level chip scale packaging (WLCSP) using tapered through silicon via (TSV) has been widely applied in CMOS image sensor (CIS) in mass production. The mass production technology now is using laser drill and surface landing, which has been shown in Fig. 1. However, as the increase of IO numbers and reduction in chip size, there is no enough space for ball placement when using these two structures. Meanwhile, side wall delamination and function layer crack are often found in strict reliability conditions. To satisfy small chip size packaging requirement and pass higher reliability test, we propose a new design of WLCSP with vertical TSV which can effectively reduce the stress of pad in temperature cycle (TC) test. The finite element simulation evaluation and key processes including via etch, redistribution layer (RDL) lithography and solder mask formation are also presented.
Belief Propagation based Scheduling for Energy Efficient Multi-drone Monitoring System

A state-of-the-art commercial drones have typically battery limitations to support their operation. Effective battery management is one of the main enablers of practical drone system operations. This paper designs an energy-efficient monitoring drone-based scheduling algorithm to control the number of activation drones in a multi-drone system. This study focuses on reducing overlapping regions of the inter-drone monitoring area in terms of energy efficiency. We formulate this problem as finding a maximum weight independent set (MWIS) problem on a graph model and propose a real-time update of the message algorithm based on belief propagation. Our results showed that the proposed algorithm achieves significant improvements in energy consumption compared to the existing algorithm.