Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture Design of Intelligent Diagnosis System for Airborne Electronic Products Based on Big Data

Intelligent diagnosis methods based on big data has been widely used in the field of machinery, which plays an important role. However, the application process of intelligent diagnostic methods in airborne electronic products is relatively slow. Aiming the requirements of fault diagnosis and the typical problems in engineering practice, a diagnosis system architecture of multi-intelligence model is defined, which is realized by constructing support vector machine (SVM), deep belief network (DBN), long short-term memory neural network (LSTM), unscented particle filter (UPF) and random process (RP) models with big data. The architecture can support condition based maintenance and meet the needs of online status analysis and remaining life prediction. According to the case of airborne electronic products, software development was implemented to prove the application effect of this framework in the field of airborne electronic product maintenance.
A 10-bit 60MHz-BW Continuous-Time Delta-Sigma ADC for wireless applications in 40nm CMOS

In this paper, a 10-bit 60MHz-BW continuous-time (CT) Delta-Sigma ADC is presented. A 3rd-order 3-bit ADC architecture with Cascaded Integrators Feed-Forward (CIFF) is adopted for the consideration of speed, performance, stability, power consumption and tolerance to non-idealities. The feedback digital signal is delayed by one clock period to absorb excess loop delay (ELD), and a zero-order feedback path is added to retain the noise transfer function. Both two-stage feedforward-compensated and single-stage OTAs are designed in the analog loop filter. The front-end feedback DAC is design with large area to ensure 10-bit intrinsic linearity. The ADC is designed and simulated in 40nm CMOS process with a sampling frequency of 1.92GHz. The simulations show that the ADC achieves 69.6dB SNR and 64.7dB SNDR for 60MHz BW, with a power consumption of 31.6mW, corresponding to a FoM of 188fJ/Step.
Safe and secure truck parking places in EMEA region

No business entities, except for quality staff, could operate without the transport of goods from the company to the final consumer. When we want to choose a transport company, references from well-known entrepreneurs as well as references on the Internet are decisive. Transport companies are overtaking and trying to attract potential clients, but sometimes they fail and clients remain dissatisfied. The reasons can be different: late delivery, damaged goods or shipment will not be arrived at all. This problem is often caused by theft of goods at unsecured parking areas. The article will focus on the number of secure and safe parking places for semi-trailers. We have focused on EMEA region (Europe, the Middle East and Africa), because TAPA EMEA is one of the organizations that issue certificates of safety parking areas.
Time-aware multi-resolutional approach to re-identifying location histories by using social networks

Identifying people from anonymous location histories is important for two purposes. i.e. to clarify privacy risks in using the location histories and to find evidence of who went where and when. Although linking with social network accounts is an excellent approach for such identification, previous methods need information about social relationships and have a limitation on the number of target data sets. Moreover, they make limited use of time information. We present models that overcome these problems by estimating the sameness and difference of people by using combinations of time and distance. Our proposed method uses these models along with multi-resolution models for both sides of linking, i.e. location histories and social network accounts. Evaluation using real data demonstrated the effectiveness of our method even when linking only one pseudonymized and obfuscated location history to 1 of 10,000 social network accounts without any information about social relationships.
A High Gain Flyback DC-DC Converter for PV Applications

This paper proposes a high voltage gain flyback DC-DC converter for PV applications. The flyback converter is composed of a voltage-doubler circuitry that is designed to reduce the turn ratio of the flyback converter. This enhance the gain of the converter and hence reduce the voltage and current stresses of the power semiconductor devices. Moreover, a resonant active-clamp circuit is employed in the primary that limits the voltage stress and provides zero voltage switching (ZVS) turn-on of the power switches. The resonant operation of the switches reduces the conduction loss and switching turnoff loss as well. The soft switching operation and low stresses in the switches make the overall efficiency high. The simulation result of the proposed converter has been analysed and the experiment of the prototype is done to verify the result.
NONADIABATIC ENERGIES OF THE GROUND STATE OF THE HYDROGEN MOLECULE

Possible sources of residual errors in the theoretical energies of the hydrogen molecule are investigated. Nonadiabatic corrections are computed for all bound, J≤10 X 1Σg+ ro‐vibrational states of the six isotopic hydrogen molecules. The new results improve significantly the overall agreement with accurate experimental transition frequencies. In order to estimate the convergence errors of the Born–Oppenheimer energies generalized James–Coolidge functions with powers of the interelectronic distance, r12, up to 6 are used and the precision of the computations is increased. Except for the equilibrium separation, R=1.4011 bohr, the obtained potential energy curve is lower by a few thousandths of a wave number than any other reported variational result. This lowers the v=0 vibrational levels by 0.009 cm−1 and results in a dissociation energy of H2, D0=36118.069 cm−1.
Gas Chromatographic Retention Index Prediction Using Multimodal Machine Learning

Gas chromatography is a widely used method in analytical chemistry and metabolomics. Using gas chromatography, vaporizable compounds can be separated for their further identification. Retention indices are standardized values that depend only on a chemical structure of a compound and on a stationary phase and characterize the retention of a compound in a chromatographic system. Retention index prediction is an important task because databases contain experimental values for a small fraction of all possible molecules, while this information is usable for untargeted analysis. In this work, we consider four machine learning models for retention index prediction: 1D and 2D convolutional neural networks, deep residual multilayer perceptron, and gradient boosting. String representation of the molecule, 2D representation of the chemical structure, molecular descriptors and fingerprints, and molecular descriptors are used as inputs of these four models, respectively, along with information about the stationary phase. The first and third models show the best performance, while the other two perform slightly worse. The models predict retention index values for various standard and semi-standard non-polar stationary phases. Further improvement in performance was achieved using a linear model that uses the results of four previous models as inputs (model stacking). The models were tested using various diverse data sets: flavor compounds, essential oils, metabolomics-related compounds. Achieved accuracy: median absolute and percentage errors – 6–40 units and 0.8-2.2%. Accuracy depends on a test data set. The stacking model outperforms previously reported approaches for all test data sets. Parameters of a pre-trained model and some source code are provided.
Hierarchical Control of Electric Energy Router System Integrated with PV and Energy Storage

Electric energy routers play the vital role in micro-grid which is now becoming an integral part of the power system, the proper control strategies may make the grid more efficient and economic. This paper aims to explore the law of hierarchical control applied to electric energy routers. Starting from the classical hierarchical control, the control structures of the primary layer, the secondary layer and the tertiary layer are analyzed. The problem of bus voltage drop in the primary droop control and the influence of PV current on parameter adjustment are analyzed, and a correction method is given in the scenario of two EER cluster. For the tertiary control, this paper proposed the calculation method to minimize electricity cost of considering the transmission losses and varying electricity price. The SOC balance control is proposed to mitigate battery aging. In this paper, all proposed methods are verified in a four-port 70kVA system.
Sickle Cell Trait Modulates the Proteome and Phosphoproteome of Plasmodium falciparum-Infected Erythrocytes

The high prevalence of sickle cell disease in some human populations likely results from the protection afforded against severe Plasmodium falciparum malaria and death by heterozygous carriage of HbS. P. falciparum remodels the erythrocyte membrane and skeleton, displaying parasite proteins at the erythrocyte surface that interact with key human proteins in the Ankyrin R and 4.1R complexes. Oxidative stress generated by HbS, as well as by parasite invasion, disrupts the kinase/phosphatase balance, potentially interfering with the molecular interactions between human and parasite proteins. HbS is known to be associated with abnormal membrane display of parasite antigens. Studying the proteome and the phosphoproteome of red cell membrane extracts from P. falciparum infected and non-infected erythrocytes, we show here that HbS heterozygous carriage, combined with infection, modulates the phosphorylation of erythrocyte membrane transporters and skeletal proteins as well as of parasite proteins. Our results highlight modifications of Ser-/Thr- and/or Tyr- phosphorylation in key human proteins, such as ankyrin, β-adducin, β-spectrin and Band 3, and key parasite proteins, such as RESA or MESA. Altered phosphorylation patterns could disturb the interactions within membrane protein complexes, affect nutrient uptake and the infected erythrocyte cytoadherence phenomenon, thus lessening the severity of malaria symptoms.
Depth Estimation and Object Detection for Monocular Semantic SLAM Using Deep Convolutional Network

It is still challenging to efficiently construct semantic map with a monocular camera. In this paper, deep learning is introduced to combined with SLAM to realize semantic map production. We replace depth estimation module of SLAM with FCN which effectively solves the contradiction of triangulation. The Fc layers of FCN are modified to convolutional layers. Redundant calculation of Fc layers is avoided after optimization, and images can be input in any size. Besides, Faster RCNN, namely, a two-stage object detection network is utilized to obtain semantic information. We fine-tune RPN and Fc layers by transfer learning. The two algorithms are evaluated on official dataset. Results show that the average relative error of depth estimation is reduced by 12.6%, the accuracy of object detection is improved by 10.9%. The feasibility of the combination of deep learning and SLAM is verified.
Robotic Behavioral Cloning Through Task Building

Robot learning by demonstration is a research paradigm that can play an important role in expanding areas where robots can be applied. An easy method to get a policy to reproduce the demonstrated behavior is to learn a model that maps directly from demonstrations to action. This is referred to as behavioral cloning (BC). In this paper, a novel robotic BC method for directly imitating a policy from trajectories generated by performing a task is proposed. The proposed method not only allows a user to obtain a demonstrated trajectory for performing a task by manipulating the robot, but also automatically generates a trajectory by having the robot perform the task on behalf of the user. Experiment results showed that the proposed method can effectively learn to make accurate behavior prediction for robot manipulators.
Analysis of forest loss by Sentinel-1 SAR time series

Synthetic Aperture Radar (SAR) has the advantage of operating regardless of the cloud cover or a lack of light. This makes SAR a potential substitution for optical sensors in various ways. SAR images have been effectively used for detecting the various terrain changes, particularly in comparing the terrain before and after natural disasters. In this paper, we compare SAR images to measure the forest loss rate before and after the fire. we analyze the Gangwon-do forest fire which occurred in 2019. SNAP (Sentinels Application Platform) is used in this paper to process Sentinel-1. Pre-processing, post-processing, color manipulation, cropping image was used in the program. Based on the calculated loss rate, we expect that it can be used for other researches such as flood or debris flow. It would be more accurate by using SAR images.
Iodine-mediated synthesis of indazolo-quinazolinones via a multi-component reaction

We report an expeditious synthesis of selected indazolo-quninazolinone derivatives via an iodine-mediated multi-component reaction (MCR). The MCR involved an in situ generation of the 1H-indazol-3-amine derivative in ethanol followed by its reaction with the diketone and aryl aldehyde in acetonitrile. A number of compounds have been synthesized using this methodology in good yields.
Online Prediction of Server Crash Based on Running Data

For web servers, the most typical and common failure is that the client's web page requests surge in a certain period of time, resulting in the server's collapse under extreme pressure. The early warning of server crash time provides the possibility to avoid crash loss. In this paper, we propose an early-warning method for Web service failure. Firstly, we obtain the key nodes on the running path of the server, collect and analyze the running status data of the path under various pressures through program instrumentation, and inform the occurrence of failure in advance through LSTM-SVM (the algorithm combining SVM and LSTM). We apply this method to Nginx, a widely used server, and the accuracy of crash warning is over 95%. Experiments show that the method of acquiring target data has little effect on the performance of server, achieves high degree of automation, and realizes high-precision fault warning. The innovation of this paper is that we realize the fault warning through the change of the parameters in the software.
Bandwidth Selection of Kernel Density Estimation for GIS-based Crime Occurrence Map Visualization

With the recent development of the Internet and satellites, the scope of utilization of the Geographic Information System (GIS)-based technology has been increasing. GIS refers to a system that integrates and manages spatial data and attribute data for objects with geographical locations to provide various forms of information, such as maps, diagrams and drawings In this paper, the Seoul Metropolitan Police Agency’s crime statistics data occurred over a specific month period have been linked to visualize three types of maps including point-based map, area-based map and density-based map, and then bandwidth selection of Kernel Density Estimation(KDE) is explored for density-based map. It is expected that such a crime map visualization research will identify the distribution of spatial characteristics of crime and further combine them with urban-engineered, socio-economic, and demographic characteristics to be used to establish policing policies that consider regional characteristics.
Power Flow Control using Modular Multilevel Converter with circulating current control for 3-phase AC/AC Conversion

A new AC/AC modular multilevel topology has introduced which is known as the modular multilevel converter (MMC). It reduces semiconductor losses because of its modular design with cascaded full-bridge cells. It is possible to realize lightweight and downsizing having its transformer-less configuration. The capacitor voltage is managed constantly for the stability of the power system. The operating principle is explained for the loop connection with the source to the load through the MMC and grid-connected three-phase MMC. The converter systems offer superior performance for the applications. The principle of the proposed control is explained and simulation results in the same typical situations for the grid-connected inverter and the loop power controller are presented.
Comparative Experiment of SPIN and SMT in Model Checking of Embedded Assembly Program

Embedded software, which has properties dependent on hardware, is important for Consumer electronics. Formal verifications of embedded software are useful techniques. Our study aims at comparative experimental study of SPIN and our Satisfiability Modulo Theories -Based Bounded Model Checking (SMT-Based BMC) of Embedded Assembly Program. The models are generated by our method include interrupts. The size of the models is reduced using Interrupt Handler Execution Reduction (IHER). We can verify assembly program using SPIN in less time than using our SMT-Based BMC, but our SMT-Based BMC is convenient because it can use background theories and its general purpose theorem provers.
Lowering the Detection Limit of Solvent Polymeric Ion-Selective Membrane Electrodes. 2. Influence of Composition of Sample and Internal Electrolyte Solution

The influence of the composition of the internal electrolyte solution on the response of Pb2+- and Ca2+-selective membrane electrodes is investigated. It is shown that the lower detection limit is improved by generating, in the membrane, ionic gradients that lead to a flux of primary ions toward the inner reference electrolyte solution. If the ion flux is too strong, it may cause analyte depletion at the membrane surface and, as a consequence, apparent super-Nernstian response. Such electrodes are not adequate to measure low analyte activities but can be used to determine unbiased selectivity factors. The results are interpreted in terms of a steady-state model, introduced in the companion paper, that describes the influence of concentration gradients generated by ion-exchange and coextraction processes on both sides of the membrane.
Exponential Data Embedding Scheme for Display to Camera Communications

Digital displays and off-the-shelf smartphone cameras are the most common and promptly available electronic devices which have also been recently applied for data transmission and reception through optical medium in a wireless manner. In this paper, we propose a data embedding technique in display to camera communication (D2C) where the data is inserted into the mid-frequency region of a discrete cosine transform (DCT) domain image. The intended data in the form of modulated symbols are first multiplied with the predetermined embedding factor and then exponentially multiplied with the vector coefficients of the selected region. Extensive experiments considering receiver orientation, capture distance and angle were conducted in a real-world environment with the help of off-the-shelf smartphone camera and a digital display. We showed that the proposed technique offers fair peak signal to average ratio (PSNR) with robust bit error rate (BER) performance, while the data embedded on the image was not perceptible to human vision.
Predicting Valence and Arousal by Aggregating Acoustic Features for Acoustic-Linguistic Information Fusion

This paper presents an evaluation of acoustic feature aggregation and acoustic-linguistic features combination for valence and arousal prediction within a speech. First, acoustic features were aggregated from chunk-based processing for story-based processing. We evaluated mean and maximum aggregation methods for those acoustic features and compared the results with the baseline, which used majority voting aggregation. Second, the extracted acoustic features are combined with linguistic features for predicting valence and arousal categories: low, medium, or high. The unimodal result using acoustic features aggregation showed an improvement over the baseline majority voting on development partition for the same acoustic feature set. The bimodal results (by combining acoustic and linguistic information at the feature level) improved both development and test scores over the official baseline. This combination of acoustic-linguistic information targeted speech-based applications where acoustic and linguistic features can be extracted from the sole speech modality.
Recognizing Suicidal Intent in Depressed Population using NLP: A Pilot Study

Depression is a prevalent form of mental disorder that can affect productivity in daily activities and might lead to suicidal thoughts or attempts. Conventional diagnostic techniques performed by mental health professionals can help identify the level of depression present in a person. To facilitate such a diagnostic approach, in this paper, we present an automated conversational platform that was used as a preliminary method of identifying depression associated risks. The platform was developed to understand conversations using Natural Language Processing (NLP) via machine learning technique. In the proposed two-phased platform, the initial intent recognition phase would analyze conversation and identify associated sentiments into four categories of ‘happy’, ‘neutral’, ‘depressive’ and ‘suicidal’ states. In the final emotion nurturing phase, the platform continued with supportive conversations for the first three states while triggering a local call to a suicide prevention helpline for ‘suicidal’ state as a preventive measure. This multi-layer platform integrated Google Home mini, Google Dialogflow Machine Learning (ML) algorithm and Twilio API. Dialogflow ML obtained classification accuracy of 76% in recognizing user's mental state via NLP and was found efficient over the classic SVM classifier. As a pilot study, current focus of this paper was solely based on the usage of words and intent of the user and was found effective.
SoftKG: Building A Software Development Knowledge Graph through Wikipedia Taxonomy

At present, software development is an important way to make our life more convenient and intelligent. With the development of software programming, we have accumulated a lot of expert experience and common sense of domain knowledge. How to effectively organize and reuse these high-quality knowledge has become an urgent problem because reusing high-quality expert knowledge can greatly improve the efficiency of solving problems, especially for the novices of programming. When we encounter the programming problems, the usual solutions to get the answers is to query the search engines or consult the senior developers. However, these solutions have the following limitations: 1) the information in the field of software development is relatively scattered, for example, the demanded information is distributed on different websites. The developers need to query the search engine several times to get the information that they want, which is unfriendly, especially for the novices; 2) there is no such a unified organization form for the information we retrieve, and we need to process it further to get the answers, which is inefficient. To address the above limitations, we propose to build a software development knowledge graph (SoftKG) through Wikipedia taxonomy. Specifically, we propose a framework to build SoftKG based on open source knowledge communities.